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Using AMQ Streams on RHEL

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Red Hat AMQ 7.5

For use with AMQ Streams 1.3 on Red Hat Enterprise Linux

Abstract

This guide describes how to install, configure, and manage Red Hat AMQ Streams to build a large-scale messaging network.

Chapter 1. Overview of AMQ Streams

Red Hat AMQ Streams is a massively-scalable, distributed, and high-performance data streaming platform based on the Apache Zookeeper and Apache Kafka projects. It consists of the following main components:

Zookeeper
Service for highly reliable distributed coordination.
Kafka Broker
Messaging broker responsible for delivering records from producing clients to consuming clients.
Kafka Connect
A toolkit for streaming data between Kafka brokers and other systems using Connector plugins.
Kafka Consumer and Producer APIs
Java based APIs for producing and consuming messages to and from Kafka brokers.
Kafka Streams API
API for writing stream processor applications.

A cluster of Kafka brokers is the hub connecting all these components. The broker uses Apache Zookeeper for storing configuration data and for cluster coordination. Before running Apache Kafka, an Apache Zookeeper cluster has to be ready.

Figure 1.1. Example Architecture diagram of AMQ Streams

AMQ Streams architecture

1.1. Key features

  • Scalability and performance

    • Designed for horizontal scalability

  • Message ordering guarantee

    • At partition level

  • Message rewind/replay

    • "Long term" storage
    • Allows to reconstruct application state by replaying the messages
    • Combined with compacted topics allows to use Kafka as key-value store

1.2. Supported Configurations

In order to be running in a supported configuration, AMQ Streams must be running in one of the following JVM versions and on one of the supported operating systems.

Table 1.1. List of supported Java Virtual Machines
Java Virtual MachineVersion

OpenJDK

1.8, 11 [a]

OracleJDK

1.8

IBM JDK

1.8

[a] For RHEL 8.x
Table 1.2. List of supported Operating Systems
Operating SystemArchitectureVersion

Red Hat Enterprise Linux

x86_64

7.x, 8.x

1.3. Document conventions

Replaceables

In this document, replaceable text is styled in monospace and surrounded by angle brackets.

For example, in the following code, you will want to replace <bootstrap-address> and <topic-name> with your own address and topic name: 

bin/kafka-console-consumer.sh --bootstrap-server <bootstrap-address> --topic <topic-name> --from-beginning

Chapter 2. Getting started

2.1. AMQ Streams distribution

AMQ Streams is distributed as single ZIP file. This ZIP file contains AMQ Streams components:

  • Apache Zookeeper
  • Apache Kafka
  • Apache Kafka Connect
  • Apache Kafka Mirror Maker
  • Kafka Exporter

2.2. Downloading an AMQ Streams Archive

An archived distribution of AMQ Streams is available for download from the Red Hat website. You can download a copy of the distribution by following the steps below.

Procedure

  • Download the latest version of the Red Hat AMQ Streams archive from the Customer Portal.

2.3. Installing AMQ Streams

Follow this procedure to install the latest version of AMQ Streams on Red Hat Enterprise Linux.

For instructions on upgrading an existing cluster to AMQ Streams 1.3, see AMQ Streams and Kafka upgrades.

Prerequisites

Procedure

  1. Add new kafka user and group. 

    sudo groupadd kafka
sudo useradd -g kafka kafka
sudo passwd kafka

  2. Create directory /opt/kafka. 

    sudo mkdir /opt/kafka

  3. Create a temporary directory and extract the contents of the AMQ Streams ZIP file. 

    mkdir /tmp/kafka
unzip amq-streams_y.y-x.x.x.zip -d /tmp/kafka

  4. Move the extracted contents into /opt/kafka directory and delete the temporary directory. 

    sudo mv /tmp/kafka/kafka_y.y-x.x.x/* /opt/kafka/
rm -r /tmp/kafka

  5. Change the ownership of the /opt/kafka directory to the kafka user. 

    sudo chown -R kafka:kafka /opt/kafka

  6. Create directory /var/lib/zookeeper for storing Zookeeper data and set its ownership to the kafka user. 

    sudo mkdir /var/lib/zookeeper
sudo chown -R kafka:kafka /var/lib/zookeeper

  7. Create directory /var/lib/kafka for storing Kafka data and set its ownership to the kafka user. 

    sudo mkdir /var/lib/kafka
sudo chown -R kafka:kafka /var/lib/kafka

2.4. Data storage considerations

An efficient data storage infrastructure is essential to the optimal performance of AMQ Streams.

AMQ Streams requires block storage and works well with cloud-based block storage solutions, such as Amazon Elastic Block Store (EBS). The use of file storage is not recommended.

Choose local storage when possible. If local storage is not available, you can use a Storage Area Network (SAN) accessed by a protocol such as Fibre Channel or iSCSI.

2.4.1. Apache Kafka and Zookeeper storage support

Use separate disks for Apache Kafka and Zookeeper.

Kafka supports JBOD (Just a Bunch of Disks) storage, a data storage configuration of multiple disks or volumes. JBOD provides increased data storage for Kafka brokers. It can also improve performance.

Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with Zookeeper, which requires fast, low latency data access.

Note

You do not need to provision replicated storage because Kafka and Zookeeper both have built-in data replication.

2.4.2. File systems

It is recommended that you configure your storage system to use the XFS file system. AMQ Streams is also compatible with the ext4 file system, but this might require additional configuration for best results.

Additional resources

2.5. Running single node AMQ Streams cluster

This procedure will show you how to run a basic AMQ Streams cluster consisting of single Zookeeper and single Apache Kafka node both running on the same host. It is using the default configuration files for both Zookeeper and Kafka.

Warning

A single node AMQ Streams cluster does not provide reliability and high availability and is suitable only for development purposes.

Prerequisites

  • AMQ Streams is installed on the host

Running the cluster

  1. Edit the Zookeeper configuration file /opt/kafka/config/zookeeper.properties. Set the dataDir option to /var/lib/zookeeper/. 

    dataDir=/var/lib/zookeeper/

  2. Start Zookeeper. 

    su - kafka
/opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

  3. Make sure that Apache Zookeeper is running. 

    jcmd | grep zookeeper

  4. Edit the Kafka configuration file /opt/kafka/config/server.properties. Set the log.dirs option to /var/lib/kafka/. 

    log.dirs=/var/lib/kafka/

  5. Start Kafka. 

    su - kafka
/opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

  6. Make sure that Kafka is running. 

    jcmd | grep kafka

Additional resources

2.6. Using the cluster

Prerequisites

  • AMQ Streams is installed on the host
  • Zookeeper and Kafka are up and running

Procedure

  1. Start the Kafka console producer. 

    bin/kafka-console-producer.sh --broker-list <bootstrap-address> --topic <topic-name>

    For example: 

    bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-topic
  2. Type your message into the console where the producer is running.
  3. Press Enter to send.
  4. Press Ctrl+C to exit the Kafka console producer.

  5. Start the message receiver. 

    bin/kafka-console-consumer.sh --bootstrap-server <bootstrap-address> --topic <topic-name> --from-beginning

    For example: 

    bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic my-topic --from-beginning
  6. Confirm that you see the incoming messages in the consumer console.
  7. Press Crtl+C to exit the Kafka console consumer.

2.7. Stopping the AMQ Streams services

You can stop the Kafka and Zookeeper services by running a script. All connections to the Kafka and Zookeeper services will be terminated.

Prerequisites

  • AMQ Streams is installed on the host
  • Zookeeper and Kafka are up and running

Procedure

  1. Stop the Kafka broker. 

    su - kafka
/opt/kafka/bin/kafka-server-stop.sh

  2. Confirm that the Kafka broker is stopped. 

    jcmd | grep kafka

  3. Stop Zookeeper. 

    su - kafka
/opt/kafka/bin/zookeeper-server-stop.sh

2.8. Configuring AMQ Streams

Prerequisites

  • AMQ Streams is downloaded and installed on the host

Procedure

  1. Open Zookeeper and Kafka broker configuration files in a text editor. The configuration files are located at :

    Zookeeper
    /opt/kafka/config/zookeeper.properties
    Kafka
    /opt/kafka/config/server.properties

  2. Edit the configuration options. The configuration files are in the Java properties format. Every configuration option should be on separate line in the following format: 

    <option> = <value>

    Lines starting with # or ! will be treated as comments and will be ignored by AMQ Streams components. 

    # This is a comment

    Values can be split into multiple lines by using \ directly before the newline / carriage return. 

    sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required \
    username="bob" \
    password="bobs-password";
  3. Save the changes
  4. Restart the Zookeeper or Kafka broker
  5. Repeat this procedure on all the nodes of the cluster.

Chapter 3. Configuring Zookeeper

Kafka uses Zookeeper to store configuration data and for cluster coordination. It is strongly recommended to run a cluster of replicated Zookeeper instances.

3.1. Basic configuration

The most important Zookeeper configuration options are:

tickTime
Zookeeper’s basic time unit in milliseconds. It is used for heartbeats and session timeouts. For example, minimum session timeout will be two ticks.
dataDir
The directory where Zookeeper stores its transaction log and snapshots of its in-memory database. This should be set to the /var/lib/zookeeper/ directory created during installation.
clientPort
Port number where clients can connect. Defaults to 2181.

An example zookeeper configuration file config/zookeeper.properties is located in the AMQ Streams installation directory. It is recommended to place the dataDir directory on a separate disk device to minimize the latency in Zookeeper.

Zookeeper configuration file should be located in /opt/kafka/config/zookeeper.properties. A basic example of the configuration file can be found below. The configuration file has to be readable by the kafka user. 

timeTick=2000
dataDir=/var/lib/zookeeper/
clientPort=2181

3.2. Zookeeper cluster configuration

For reliable ZooKeeper service, you should deploy ZooKeeper in a cluster. Hence, for production use cases, you must run a cluster of replicated Zookeeper instances. Zookeeper clusters are also referred as ensembles.

Zookeeper clusters usually consist of an odd number of nodes. Zookeeper requires a majority of the nodes in the cluster should be up and running. For example, a cluster with three nodes, at least two of them must be up and running. This means it can tolerate one node being down. A cluster consisting of five nodes, at least three nodes must be available. This means it can tolerate two nodes being down. A cluster consisting of seven nodes, at least four nodes must be available. This means it can tolerate three nodes being down. Having more nodes in the Zookeeper cluster delivers better resiliency and reliability of the whole cluster.

Zookeeper can run in clusters with an even number of nodes. The additional node, however, does not increase the resiliency of the cluster. A cluster with four nodes requires at least three nodes to be available and can tolerate only one node being down. Therefore it has exactly the same resiliency as a cluster with only three nodes.

The different Zookeeper nodes should be ideally placed into different data centers or network segments. Increasing the number of Zookeeper nodes increases the workload spent on cluster synchronization. For most Kafka use cases Zookeeper cluster with 3, 5 or 7 nodes should be fully sufficient.

Warning

Zookeeper cluster with 3 nodes can tolerate only 1 unavailable node. This means that when a cluster node crashes while you are doing maintenance on another node your Zookeeper cluster will be unavailable.

Replicated Zookeeper configuration supports all configuration options supported by the standalone configuration. Additional options are added for the clustering configuration:

initLimit
Amount of time to allow followers to connect and sync to the cluster leader. The time is specified as a number of ticks (see the timeTick option for more details).
syncLimit
Amount of time for which followers can be behind the leader. The time is specified as a number of ticks (see the timeTick option for more details).

In addition to the options above, every configuration file should contain a list of servers which should be members of the Zookeeper cluster. The server records should be specified in the format server.id=hostname:port1:port2, where:

id
The ID of the Zookeeper cluster node.
hostname
The hostname or IP address where the node listens for connections.
port1
The port number used for intra-cluster communication.
port2
The port number used for leader election.

The following is an example configuration file of a Zookeeper cluster with three nodes: 

timeTick=2000
dataDir=/var/lib/zookeeper/
clientPort=2181
initLimit=5
syncLimit=2

server.1=172.17.0.1:2888:3888
server.2=172.17.0.2:2888:3888
server.3=172.17.0.3:2888:3888

Each node in the Zookeeper cluster has to be assigned with a unique ID. Each node’s ID has to be configured in myid file and stored in the dataDir folder like /var/lib/zookeeper/. The myid files should contain only a single line with the written ID as text. The ID can be any integer from 1 to 255. You must manually create this file on each cluster node. Using this file, each Zookeeper instance will use the configuration from the corresponding server. line in the configuration file to configure its listeners. It will also use all other server. lines to identify other cluster members.

In the above example, there are three nodes, so each one will have a different myid with values 1, 2, and 3 respectively.

3.3. Running multi-node Zookeeper cluster

This procedure will show you how to configure and run Zookeeper as a multi-node cluster.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Zookeeper cluster nodes.

Running the cluster

  1. Create the myid file in /var/lib/zookeeper/. Enter ID 1 for the first Zookeeper node, 2 for the second Zookeeper node, and so on. 

    su - kafka
echo "_<NodeID>_" > /var/lib/zookeeper/myid

    For example: 

    su - kafka
echo "1" > /var/lib/zookeeper/myid

  2. Edit the Zookeeper /opt/kafka/config/zookeeper.properties configuration file for the following:

    • Set the option dataDir to /var/lib/zookeeper/. + Configure the initLimit and syncLimit options.

    • Add a list of all Zookeeper nodes. The list should include also the current node.

      Example configuration for a node of Zookeeper cluster with five members

      timeTick=2000
dataDir=/var/lib/zookeeper/
clientPort=2181
initLimit=5
syncLimit=2

server.1=172.17.0.1:2888:3888
server.2=172.17.0.2:2888:3888
server.3=172.17.0.3:2888:3888
server.4=172.17.0.4:2888:3888
server.5=172.17.0.5:2888:3888

  3. Start Zookeeper with the default configuration file. 

    su - kafka
/opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

  4. Verify that the Zookeeper is running. 

    jcmd | grep zookeeper
  5. Repeat this procedure on all the nodes of the cluster.

  6. Once all nodes of the clusters are up and running, verify that all nodes are members of the cluster by sending a stat command to each of the nodes using ncat utility.

    Use ncat stat to check the node status

    echo stat | ncat localhost 2181

    In the output you should see information that the node is either leader or follower.

    Example output from the ncat command

    Zookeeper version: 3.4.13-2d71af4dbe22557fda74f9a9b4309b15a7487f03, built on 06/29/2018 00:39 GMT
Clients:
 /0:0:0:0:0:0:0:1:59726[0](queued=0,recved=1,sent=0)

Latency min/avg/max: 0/0/0
Received: 2
Sent: 1
Connections: 1
Outstanding: 0
Zxid: 0x200000000
Mode: follower
Node count: 4

Additional resources

3.4. Authentication

By default, Zookeeper does not use any form of authentication and allows anonymous connections. However, it supports Java Authentication and Authorization Service (JAAS) which can be used to set up authentication using Simple Authentication and Security Layer (SASL). Zookeeper supports authentication using the DIGEST-MD5 SASL mechanism with locally stored credentials.

3.4.1. Authentication with SASL

JAAS is configured using a separate configuration file. It is recommended to place the JAAS configuration file in the same directory as the Zookeeper configuration (/opt/kafka/config/). The recommended file name is zookeeper-jaas.conf. When using a Zookeeper cluster with multiple nodes, the JAAS configuration file has to be created on all cluster nodes.

JAAS is configured using contexts. Separate parts such as the server and client are always configured with a separate context. The context is a configuration option and has the following format: 

ContextName {
       param1
       param2;
};

SASL Authentication is configured separately for server-to-server communication (communication between Zookeeper instances) and client-to-server communication (communication between Kafka and Zookeeper). Server-to-server authentication is relevant only for Zookeeper clusters with multiple nodes.

Server-to-Server authentication

For server-to-server authentication, the JAAS configuration file contains two parts:

  • The server configuration
  • The client configuration

When using DIGEST-MD5 SASL mechanism, the QuorumServer context is used to configure the authentication server. It must contain all the usernames to be allowed to connect together with their passwords in an unencrypted form. The second context, QuorumLearner, has to be configured for the client which is built into Zookeeper. It also contains the password in an unencrypted form. An example of the JAAS configuration file for DIGEST-MD5 mechanism can be found below: 

QuorumServer {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       user_zookeeper="123456";
};

QuorumLearner {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       username="zookeeper"
       password="123456";
};

In addition to the JAAS configuration file, you must enable the server-to-server authentication in the regular Zookeeper configuration file by specifying the following options: 

quorum.auth.enableSasl=true
quorum.auth.learnerRequireSasl=true
quorum.auth.serverRequireSasl=true
quorum.auth.learner.loginContext=QuorumLearner
quorum.auth.server.loginContext=QuorumServer
quorum.cnxn.threads.size=20

Use the KAFKA_OPTS environment variable to pass the JAAS configuration file to the Zookeeper server as a Java property: 

su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

For more information about server-to-server authentication, see Zookeeper wiki.

Client-to-Server authentication

Client-to-server authentication is configured in the same JAAS file as the server-to-server authentication. However, unlike the server-to-server authentication, it contains only the server configuration. The client part of the configuration has to be done in the client. For information on how to configure a Kafka broker to connect to Zookeeper using authentication, see the Kafka installation section.

Add the Server context to the JAAS configuration file to configure client-to-server authentication. For DIGEST-MD5 mechanism it configures all usernames and passwords: 

Server {
    org.apache.zookeeper.server.auth.DigestLoginModule required
    user_super="123456"
    user_kafka="123456"
    user_someoneelse="123456";
};

After configuring the JAAS context, enable the client-to-server authentication in the Zookeeper configuration file by adding the following line: 

requireClientAuthScheme=sasl
authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.2=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.3=org.apache.zookeeper.server.auth.SASLAuthenticationProvider

You must add the authProvider.<ID> property for every server that is part of the Zookeeper cluster.

Use the KAFKA_OPTS environment variable to pass the JAAS configuration file to the Zookeeper server as a Java property: 

su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

For more information about configuring Zookeeper authentication in Kafka brokers, see Section 4.6, “Zookeeper authentication”.

3.4.2. Enabling Server-to-server authentication using DIGEST-MD5

This procedure describes how to enable authentication using the SASL DIGEST-MD5 mechanism between the nodes of the Zookeeper cluster.

Prerequisites

  • AMQ Streams is installed on the host
  • Zookeeper cluster is configured with multiple nodes.

Enabling SASL DIGEST-MD5 authentication

  1. On all Zookeeper nodes, create or edit the /opt/kafka/config/zookeeper-jaas.conf JAAS configuration file and add the following contexts: 

    QuorumServer {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       user__<Username>_="<Password>";
};

QuorumLearner {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       username="<Username>"
       password="<Password>";
};

    The username and password must be the same in both JAAS contexts. For example: 

    QuorumServer {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       user_zookeeper="123456";
};

QuorumLearner {
       org.apache.zookeeper.server.auth.DigestLoginModule required
       username="zookeeper"
       password="123456";
};

  2. On all Zookeeper nodes, edit the /opt/kafka/config/zookeeper.properties Zookeeper configuration file and set the following options: 

    quorum.auth.enableSasl=true
quorum.auth.learnerRequireSasl=true
quorum.auth.serverRequireSasl=true
quorum.auth.learner.loginContext=QuorumLearner
quorum.auth.server.loginContext=QuorumServer
quorum.cnxn.threads.size=20

  3. Restart all Zookeeper nodes one by one. To pass the JAAS configuration to Zookeeper, use the KAFKA_OPTS environment variable. 

    su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

Additional resources

3.4.3. Enabling Client-to-server authentication using DIGEST-MD5

This procedure describes how to enable authentication using the SASL DIGEST-MD5 mechanism between Zookeeper clients and Zookeeper.

Prerequisites

Enabling SASL DIGEST-MD5 authentication

  1. On all Zookeeper nodes, create or edit the /opt/kafka/config/zookeeper-jaas.conf JAAS configuration file and add the following context: 

    Server {
    org.apache.zookeeper.server.auth.DigestLoginModule required
    user_super="<SuperUserPassword>"
    user<Username1>_="<Password1>" user<USername2>_="<Password2>";
};

    The super automatically has administrator priviledges. The file can contain multiple users, but only one additional user is required by the Kafka brokers. The recommended name for the Kafka user is kafka.

    The following example shows the Server context for client-to-server authentication: 

    Server {
    org.apache.zookeeper.server.auth.DigestLoginModule required
    user_super="123456"
    user_kafka="123456";
};

  2. On all Zookeeper nodes, edit the /opt/kafka/config/zookeeper.properties Zookeeper configuration file and set the following options: 

    requireClientAuthScheme=sasl
authProvider.<IdOfBroker1>=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.<IdOfBroker2>=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.<IdOfBroker3>=org.apache.zookeeper.server.auth.SASLAuthenticationProvider

    The authProvider.<ID> property has to be added for every node which is part of the Zookeeper cluster. An example three-node Zookeeper cluster configuration must look like the following: 

    requireClientAuthScheme=sasl
authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.2=org.apache.zookeeper.server.auth.SASLAuthenticationProvider
authProvider.3=org.apache.zookeeper.server.auth.SASLAuthenticationProvider

  3. Restart all Zookeeper nodes one by one. To pass the JAAS configuration to Zookeeper, use the KAFKA_OPTS environment variable. 

    su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/zookeeper-jaas.conf"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

Additional resources

3.5. Authorization

Zookeeper supports access control lists (ACLs) to protect data stored inside it. Kafka brokers can automatically configure the ACL rights for all Zookeeper records they create so no other Zookeeper user can modify them.

For more information about enabling Zookeeper ACLs in Kafka brokers, see Section 4.8, “Zookeeper authorization”.

3.6. TLS

The version of Zookeeper which is part of AMQ Streams currently does not support TLS for encryption or authentication.

3.7. Additional configuration options

You can set the following options based on your use case:

maxClientCnxns
The maximum number of concurrent client connections to a single member of the ZooKeeper cluster.
autopurge.snapRetainCount
Number of snapshots of Zookeeper’s in-memory database which will be retained. Default value is 3.
autopurge.purgeInterval
The time interval in hours for purging snapshots. The default value is 0 and this option is disabled.

All available configuration options can be found in Zookeeper documentation.

3.8. Logging

Zookeeper is using log4j as their logging infrastructure. Logging configuration is by default read from the log4j.properties configuration file which should be placed either in the /opt/kafka/config/ directory or in the classpath. The location and name of the configuration file can be changed using the Java property log4j.configuration which can be passed to Zookeeper using the KAFKA_LOG4J_OPTS environment variable: 

su - kafka
export KAFKA_LOG4J_OPTS="-Dlog4j.configuration=file:/my/path/to/log4j.properties"; /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

For more information about Log4j configurations, see Log4j documentation.

Chapter 4. Configuring Kafka

Kafka uses a properties file to store static configuration. The recommended location for the configuration file is /opt/kafka/config/server.properties. The configuration file has to be readable by the kafka user.

AMQ Streams ships an example configuration file that highlights various basic and advanced features of the product. It can be found under config/server.properties in the AMQ Streams installation directory.

This chapter explains the most important configuration options. For a complete list of supported Kafka broker configuration options, see Appendix A, Broker configuration parameters.

4.1. Zookeeper

Kafka brokers need Zookeeper to store some parts of their configuration as well as to coordinate the cluster (for example to decide which node is a leader for which partition). Connection details for the Zookeeper cluster are stored in the configuration file. The field zookeeper.connect contains a comma-separated list of hostnames and ports of members of the zookeeper cluster.

For example: 

zookeeper.connect=zoo1.my-domain.com:2181,zoo2.my-domain.com:2181,zoo3.my-domain.com:2181

Kafka will use these addresses to connect to the Zookeeper cluster. With this configuration, all Kafka znodes will be created directly in the root of Zookeeper database. Therefore, such a Zookeeper cluster could be used only for a single Kafka cluster. To configure multiple Kafka clusters to use single Zookeeper cluster, specify a base (prefix) path at the end of the Zookeeper connection string in the Kafka configuration file: 

zookeeper.connect=zoo1.my-domain.com:2181,zoo2.my-domain.com:2181,zoo3.my-domain.com:2181/my-cluster-1

4.2. Listeners

Kafka brokers can be configured to use multiple listeners. Each listener can be used to listen on a different port or network interface and can have different configuration. Listeners are configured in the listeners property in the configuration file. The listeners property contains a list of listeners with each listener configured as <listenerName>://<hostname>:_<port>_. When the hostname value is empty, Kafka will use java.net.InetAddress.getCanonicalHostName() as hostname. The following example shows how multiple listeners might be configured: 

listeners=INT1://:9092,INT2://:9093,REPLICATION://:9094

When a Kafka client wants to connect to a Kafka cluster, it first connects to a bootstrap server. The bootstrap server is one of the cluster nodes. It will provide the client with a list of all other brokers which are part of the cluster and the client will connect to them individually. By default the bootstrap server will provide the client with a list of nodes based on the listeners field.

Advertised listeners

It is possible to give the client a different set of addresses than given in the listeners property. It is useful in situations when additional network infrastructure, such as a proxy, is between the client and the broker, or when an external DNS name should be used instead of an IP address. Here, the broker allows defining the advertised addresses of the listeners in the advertised.listeners configuration property. This property has the same format as the listeners property. The following example shows how to configure advertised listeners: 

listeners=INT1://:9092,INT2://:9093
advertised.listeners=INT1://my-broker-1.my-domain.com:1234,INT2://my-broker-1.my-domain.com:1234:9093
Note

The names of the listeners have to match the names of the listeners from the listeners property.

Inter-broker listeners

When the cluster has replicated topics, the brokers responsible for such topics need to communicate with each other in order to replicate the messages in those topics. When multiple listeners are configured, the configuration field inter.broker.listener.name can be used to specify the name of the listener which should be used for replication between brokers. For example: 

inter.broker.listener.name=REPLICATION

4.3. Commit logs

Apache Kafka stores all records it receives from producers in commit logs. The commit logs contain the actual data, in the form of records, that Kafka needs to deliver. These are not the application log files which record what the broker is doing.

Log directories

You can configure log directories using the log.dirs property file to store commit logs in one or multiple log directories. It should be set to /var/lib/kafka directory created during installation: 

log.dirs=/var/lib/kafka

For performance reasons, you can configure log.dirs to multiple directories and place each of them on a different physical device to improve disk I/O performance. For example: 

log.dirs=/var/lib/kafka1,/var/lib/kafka2,/var/lib/kafka3

4.4. Broker ID

Broker ID is a unique identifier for each broker in the cluster. You can assign an integer greater than or equal to 0 as broker ID. The broker ID is used to identify the brokers after restarts or crashes and it is therefore important that the id is stable and does not change over time. The broker ID is configured in the broker properties file: 

broker.id=1

4.5. Running a multi-node Kafka cluster

This procedure describes how to configure and run Kafka as a multi-node cluster.

Prerequisites

Running the cluster

For each Kafka broker in your AMQ Streams cluster:

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file as follows:

    • Set the broker.id field to 0 for the first broker, 1 for the second broker, and so on.
    • Configure the details for connecting to Zookeeper in the zookeeper.connect option.
    • Configure the Kafka listeners.

    • Set the directories where the commit logs should be stored in the logs.dir directory.

      Here we see an example configuration for a Kafka broker: 

      broker.id=0
zookeeper.connect=zoo1.my-domain.com:2181,zoo2.my-domain.com:2181,zoo3.my-domain.com:2181
listeners=REPLICATION://:9091,PLAINTEXT://:9092
inter.broker.listener.name=REPLICATION
log.dirs=/var/lib/kafka

      In a typical installation where each Kafka broker is running on identical hardware, only the broker.id configuration property will differ between each broker config.

  2. Start the Kafka broker with the default configuration file. 

    su - kafka
/opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

  3. Verify that the Kafka broker is running. 

    jcmd | grep Kafka

Verifying the brokers

Once all nodes of the clusters are up and running, verify that all nodes are members of the Kafka cluster by sending a dump command to one of the Zookeeper nodes using the ncat utility. The command prints all Kafka brokers registered in Zookeeper.

  1. Use ncat stat to check the node status. 

    echo dump | ncat zoo1.my-domain.com 2181

    The output should contain all Kafka brokers you just configured and started.

    Example output from the ncat command for Kafka cluster with 3 nodes: 

    SessionTracker dump:
org.apache.zookeeper.server.quorum.LearnerSessionTracker@28848ab9
ephemeral nodes dump:
Sessions with Ephemerals (3):
0x20000015dd00000:
        /brokers/ids/1
0x10000015dc70000:
        /controller
        /brokers/ids/0
0x10000015dc70001:
        /brokers/ids/2

Additional resources

4.6. Zookeeper authentication

By default, connections between Zookeeper and Kafka are not authenticated. However, Kafka and Zookeeper support Java Authentication and Authorization Service (JAAS) which can be used to set up authentication using Simple Authentication and Security Layer (SASL). Zookeeper supports authentication using the DIGEST-MD5 SASL mechanism with locally stored credentials.

4.6.1. JAAS Configuration

SASL authentication for Zookeeper connections has to be configured in the JAAS configuration file. By default, Kafka will use the JAAS context named Client for connecting to Zookeeper. The Client context should be configured in the /opt/kafka/config/jass.conf file. The context has to enable the PLAIN SASL authentication, as in the following example: 

Client {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    username="kafka"
    password="123456";
};

4.6.2. Enabling Zookeeper authentication

This procedure describes how to enable authentication using the SASL DIGEST-MD5 mechanism when connecting to Zookeeper.

Prerequisites

  • Client-to-server authentication is enabled in Zookeeper

Enabling SASL DIGEST-MD5 authentication

  1. On all Kafka broker nodes, create or edit the /opt/kafka/config/jaas.conf JAAS configuration file and add the following context: 

    Client {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    username="<Username>"
    password="<Password>";
};

    The username and password should be the same as configured in Zookeeper.

    Following example shows the Client context: 

    Client {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    username="kafka"
    password="123456";
};

  2. Restart all Kafka broker nodes one by one. To pass the JAAS configuration to Kafka brokers, use the KAFKA_OPTS environment variable. 

    su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

Additional resources

4.7. Authorization

Authorization in Kafka brokers is implemented using authorizer plugins.

You can use your own authorization plugins or SimpleAclAuthorizer, the authorizer plugin provided with Kafka.

In this section we describe how to use SimpleAclAuthorizer.

4.7.1. Simple ACL authorizer

Authorizer plugins, including SimpleAclAuthorizer, are enabled through the authorizer.class.name property: 

authorizer.class.name=kafka.security.auth.SimpleAclAuthorizer

A fully-qualified name is required for the chosen authorizer. For SimpleAclAuthorizer, the fully-qualified name is kafka.security.auth.SimpleAclAuthorizer.

4.7.1.1. ACL rules

SimpleAclAuthorizer uses ACL rules to manage access to Kafka brokers.

ACL rules are defined in the format:

Principal P is allowed / denied operation O on Kafka resource R from host H

For example, a rule might be set so that user:

John can view the topic comments from host 127.0.0.1

Host is the IP address of the machine that John is connecting from.

In most cases, the user is a producer or consumer application:

Consumer01 can write to the consumer group accounts from host 127.0.0.1

If ACL rules are not present

If ACL rules are not present for a given resource, all actions are denied. This behavior can be changed by setting the property allow.everyone.if.no.acl.found to true in the Kafka configuration file /opt/kafka/config/server.properties.

4.7.1.2. Principals

A principal represents the identity of a user. The format of the ID depends on the authentication mechanism used by clients to connect to Kafka:

  • User:ANONYMOUS when connected without authentication.

  • User:<username> when connected using simple authentication mechanisms, such as PLAIN or SCRAM.

    For example User:admin or User:user1.

  • User:<DistinguishedName> when connected using TLS client authentication.

    For example User:CN=user1,O=MyCompany,L=Prague,C=CZ.

  • User:<Kerberos username> when connected using Kerberos.

The DistinguishedName is the distinguished name from the client certificate.

The Kerberos username is the primary part of the Kerberos principal, which is used by default when connecting using Kerberos. You can use the sasl.kerberos.principal.to.local.rules property to configure how the Kafka principal is built from the Kerberos principal.

4.7.1.3. Authentication of users

To use authorization, you need to have authentication enabled and used by your clients. Otherwise, all connections will have the principal User:ANONYMOUS.

For more information on methods of authentication, see Encryption and authentication.

Super users

Super users are allowed to take all actions regardless of the ACL rules.

Super users are defined in the Kafka configuration file using the property super.users.

For example: 

super.users=User:admin,User:operator

Replica broker authentication

When authorization is enabled, it is applied to all listeners and all connections. This includes the inter-broker connections used for replication of data between brokers. If enabling authorization, therefore, ensure that you use authentication for inter-broker connections and give the users used by the brokers sufficient rights. For example, if authentication between brokers uses the kafka-broker user, then super user configuration must include the username super.users=User:kafka-broker.

4.7.1.4. Supported resources

You can apply Kafka ACLs to these types of resource:

  • Topics
  • Consumer groups
  • The cluster
  • TransactionId
  • DelegationToken
4.7.1.5. Supported operations

SimpleAclAuthorizer authorizes operations on resources.

Fields with X in the following table mark the supported operations for each resource.

 TopicsConsumer GroupsCluster

Read

X

X

 

Write

X

  

Create

  

X

Delete

X

  

Alter

X

  

Describe

X

X

X

ClusterAction

  

X

All

X

X

X

4.7.1.6. ACL management options

ACL rules are managed using the bin/kafka-acls.sh utility, which is provided as part of the Kafka distribution package.

Use kafka-acls.sh parameter options to add, list and remove ACL rules, and perform other functions.

The parameters require a double-hyphen convention, such as --add.

OptionTypeDescriptionDefault

add

Action

Add ACL rule.

 

remove

Action

Remove ACL rule.

 

list

Action

List ACL rules.

 

authorizer

Action

Fully-qualified class name of the authorizer.

kafka.security.auth.SimpleAclAuthorizer

authorizer-properties

Configuration

Key/value pairs passed to the authorizer for initialization.

For SimpleAclAuthorizer, the example values are: zookeeper.connect=zoo1.my-domain.com:2181.

 

bootstrap-server

Resource

Host/port pairs to connect to the Kafka cluster.

Use this option or the authorizer option, not both.

command-config

Resource

Configuration property file to pass to the Admin Client, which is used in conjunction with the bootstrap-server parameter.

 

cluster

Resource

Specifies a cluster as an ACL resource.

 

topic

Resource

Specifies a topic name as an ACL resource.

An asterisk (*) used as a wildcard translates to all topics.

A single command can specify multiple --topic options.

 

group

Resource

Specifies a consumer group name as an ACL resource.

A single command can specify multiple --group options.

 

transactional-id

Resource

Specifies a transactional ID as an ACL resource.

Transactional delivery means that all messages sent by a producer to multiple partitions must be successfully delivered or none of them.

An asterisk (*) used as a wildcard translates to all IDs.

 

delegation-token

Resource

Specifies a delegation token as an ACL resource.

An asterisk (*) used as a wildcard translates to all tokens.

 

resource-pattern-type

Configuration

Specifies a type of resource pattern for the add parameter or a resource pattern filter value for the list or remove parameters.

Use literal or prefixed as the resource pattern type for a resource name.

Use any or match as resource pattern filter values, or a specific pattern type filter.

literal

allow-principal

Principal

Principal added to an allow ACL rule.

A single command can specify multiple --allow-principal options.

 

deny-principal

Principal

Principal added to a deny ACL rule.

A single command can specify multiple --deny-principal options.

 

principal

Principal

Principal name used with the list parameter to return a list of ACLs for the principal.

A single command can specify multiple --principal options.

 

allow-host

Host

IP address that allows access to the principals listed in --allow-principal.

Hostnames or CIDR ranges are not supported.

If --allow-principal is specified, defaults to * meaning "all hosts".

deny-host

Host

IP address that denies access to the principals listed in --deny-principal.

Hostnames or CIDR ranges are not supported.

if --deny-principal is specified, defaults to * meaning "all hosts".

operation

Operation

Allows or denies an operation.

A single command can specify multipleMultiple --operation options can be specified in single command.

All

producer

Shortcut

A shortcut to allow or deny all operations needed by a message producer (WRITE and DESCRIBE on topic, CREATE on cluster).

 

consumer

Shortcut

A shortcut to allow or deny all operations needed by a message consumer (READ and DESCRIBE on topic, READ on consumer group).

 

idempotent

Shortcut

A shortcut to enable idempotence when used with the --producer parameter, so that messages are delivered exactly once to a partition.

Idepmotence is enabled automatically if the producer is authorized to send messages based on a specific transactional ID.

 

force

Shortcut

A shortcut to accept all queries and do not prompt.

 

4.7.2. Enabling authorization

This procedure describes how to enable the SimpleAclAuthorizer plugin for authorization in Kafka brokers.

Prerequisites

Procedure

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file to use the SimpleAclAuthorizer. 

    authorizer.class.name=kafka.security.auth.SimpleAclAuthorizer
  2. (Re)start the Kafka brokers.

Additional resources

4.7.3. Adding ACL rules

SimpleAclAuthorizer uses Access Control Lists (ACLs), which define a set of rules describing what users can and cannot do.

This procedure describes how to add ACL rules when using the SimpleAclAuthorizer plugin in Kafka brokers.

Rules are added using the kafka-acls.sh utility and stored in Zookeeper.

Prerequisites

Procedure

  1. Run kafka-acls.sh with the --add option.

    Examples:

    • Allow user1 and user2 access to read from myTopic using the MyConsumerGroup consumer group. 

      bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --add --operation Read --topic myTopic --allow-principal User:user1 --allow-principal User:user2

bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --add --operation Describe --topic myTopic --allow-principal User:user1 --allow-principal User:user2

bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --add --operation Read --operation Describe --group MyConsumerGroup --allow-principal User:user1 --allow-principal User:user2

    • Deny user1 access to read myTopic from IP address host 127.0.0.1. 

      bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --add --operation Describe --operation Read --topic myTopic --group MyConsumerGroup --deny-principal User:user1 --deny-host 127.0.0.1

    • Add user1 as the consumer of myTopic with MyConsumerGroup. 

      bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --add --consumer --topic myTopic --group MyConsumerGroup --allow-principal User:user1

Additional resources

4.7.4. Listing ACL rules

This procedure describes how to list existing ACL rules when using the SimpleAclAuthorizer plugin in Kafka brokers.

Rules are listed using the kafka-acls.sh utility.

Prerequisites

Procedure

  • Run kafka-acls.sh with the --list option.

    For example: 

    $ bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --list --topic myTopic

Current ACLs for resource `Topic:myTopic`:

User:user1 has Allow permission for operations: Read from hosts: *
User:user2 has Allow permission for operations: Read from hosts: *
User:user2 has Deny permission for operations: Read from hosts: 127.0.0.1
User:user1 has Allow permission for operations: Describe from hosts: *
User:user2 has Allow permission for operations: Describe from hosts: *
User:user2 has Deny permission for operations: Describe from hosts: 127.0.0.1

Additional resources

4.7.5. Removing ACL rules

This procedure describes how to remove ACL rules when using the SimpleAclAuthorizer plugin in Kafka brokers.

Rules are removed using the kafka-acls.sh utility.

Prerequisites

Procedure

  • Run kafka-acls.sh with the --remove option.

    Examples:

  • Remove the ACL allowing Allow user1 and user2 access to read from myTopic using the MyConsumerGroup consumer group. 

    bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --remove --operation Read --topic myTopic --allow-principal User:user1 --allow-principal User:user2

bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --remove --operation Describe --topic myTopic --allow-principal User:user1 --allow-principal User:user2

bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --remove --operation Read --operation Describe --group MyConsumerGroup --allow-principal User:user1 --allow-principal User:user2

  • Remove the ACL adding user1 as the consumer of myTopic with MyConsumerGroup. 

    bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --remove --consumer --topic myTopic --group MyConsumerGroup --allow-principal User:user1

  • Remove the ACL denying user1 access to read myTopic from IP address host 127.0.0.1. 

    bin/kafka-acls.sh --authorizer-properties zookeeper.connect=zoo1.my-domain.com:2181 --remove --operation Describe --operation Read --topic myTopic --group MyConsumerGroup --deny-principal User:user1 --deny-host 127.0.0.1

Additional resources

4.8. Zookeeper authorization

When authentication is enabled between Kafka and Zookeeper, you can use Zookeeper Access Control List (ACL) rules to automatically control access to Kafka’s metadata stored in Zookeeper.

4.8.1. ACL Configuration

Enforcement of Zookeeper ACL rules is controlled by the zookeeper.set.acl property in the config/server.properties Kafka configuration file.

The property is disabled by default and enabled by setting to true: 

zookeeper.set.acl=true

If ACL rules are enabled, when a znode is created in Zookeeper only the Kafka user who created it can modify or delete it. All other users have read-only access.

Kafka sets ACL rules only for newly created Zookeeper znodes. If the ACLs are only enabled after the first start of the cluster, the zookeeper-security-migration.sh tool can set ACLs on all existing znodes.

Confidentiality of data in Zookeeper

Data stored in Zookeeper includes:

  • Topic names and their configuration
  • Salted and hashed user credentials when SASL SCRAM authentication is used.

But Zookeeper does not store any records sent and received using Kafka. The data stored in Zookeeper is assumed to be non-confidential.

If the data is to be regarded as confidential (for example because topic names contain customer IDs), the only option available for protection is isolating Zookeeper on the network level and allowing access only to Kafka brokers.

4.8.2. Enabling Zookeeper ACLs for a new Kafka cluster

This procedure describes how to enable Zookeeper ACLs in Kafka configuration for a new Kafka cluster. Use this procedure only before the first start of the Kafka cluster. For enabling Zookeeper ACLs in a cluster that is already running, see Section 4.8.3, “Enabling Zookeeper ACLs in an existing Kafka cluster”.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • Zookeeper cluster is configured and running.
  • Client-to-server authentication is enabled in Zookeeper.
  • Zookeeper authentication is enabled in the Kafka brokers.
  • Kafka brokers have not yet been started.

Procedure

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file to set the zookeeper.set.acl field to true on all cluster nodes. 

    zookeeper.set.acl=true
  2. Start the Kafka brokers.

4.8.3. Enabling Zookeeper ACLs in an existing Kafka cluster

This procedure describes how to enable Zookeeper ACLs in Kafka configuration for a Kafka cluster that is running. Use the zookeeper-security-migration.sh tool to set Zookeeper ACLs on all existing znodes. The zookeeper-security-migration.sh is available as part of AMQ Streams, and can be found in the bin directory.

Prerequisites

Enabling the Zookeeper ACLs

  1. Edit the /opt/kafka/config/server.properties Kafka configuration file to set the zookeeper.set.acl field to true on all cluster nodes. 

    zookeeper.set.acl=true
  2. Restart all Kafka brokers one by one.

  3. Set the ACLs on all existing Zookeeper znodes using the zookeeper-security-migration.sh tool. 

    su - kafka
cd /opt/kafka
KAFKA_OPTS="-Djava.security.auth.login.config=./config/jaas.conf"; ./bin/zookeeper-security-migration.sh --zookeeper.acl=secure --zookeeper.connect=_<ZookeeperURL>_
exit

    For example: 

    su - kafka
cd /opt/kafka
KAFKA_OPTS="-Djava.security.auth.login.config=./config/jaas.conf"; ./bin/zookeeper-security-migration.sh --zookeeper.acl=secure --zookeeper.connect=zoo1.my-domain.com:2181
exit

4.9. Encryption and authentication

Kafka supports TLS for encrypting the communication with Kafka clients. Additionally, it supports two types of authentication:

  • TLS client authentication based on X.509 certificates
  • SASL Authentication based on a username and password

4.9.1. Listener configuration

Encryption and authentication in Kafka brokers is configured per listener. For more information about Kafka listener configuration, see Section 4.2, “Listeners”.

Each listener in the Kafka broker is configured with its own security protocol. The configuration property listener.security.protocol.map defines which listener uses which security protocol. It maps each listener name to its security protocol. Supported security protocols are:

PLAINTEXT
Listener without any encryption or authentication.
SSL
Listener using TLS encryption and, optionally, authentication using TLS client certificates.
SASL_PLAINTEXT
Listener without encryption but with SASL-based authentication.
SASL_SSL
Listener with TLS-based encryption and SASL-based authentication.

Given the following listeners configuration: 

listeners=INT1://:9092,INT2://:9093,REPLICATION://:9094

the listener.security.protocol.map might look like this: 

listener.security.protocol.map=INT1:SASL_PLAINTEXT,INT2:SASL_SSL,REPLICATION:SSL

This would configure the listener INT1 to use unencrypted connections with SASL authentication, the listener INT2 to use encrypted connections with SASL authentication and the REPLICATION interface to use TLS encryption (possibly with TLS client authentication). The same security protocol can be used multiple times. The following example is also a valid configuration: 

listener.security.protocol.map=INT1:SSL,INT2:SSL,REPLICATION:SSL

Such a configuration would use TLS encryption and TLS authentication for all interfaces. The following chapters will explain in more detail how to configure TLS and SASL.

4.9.2. TLS Encryption

In order to use TLS encryption and server authentication, a keystore containing private and public keys has to be provided. This is usually done using a file in the Java Keystore (JKS) format. A path to this file is set in the ssl.keystore.location property. The ssl.keystore.password property should be used to set the password protecting the keystore. For example: 

ssl.keystore.location=/path/to/keystore/server-1.jks
ssl.keystore.password=123456

In some cases, an additional password is used to protect the private key. Any such password can be set using the ssl.key.password property.

Kafka is able to use keys signed by certification authorities as well as self-signed keys. Using keys signed by certification authorities should always be the preferred method. In order to allow clients to verify the identity of the Kafka broker they are connecting to, the certificate should always contain the advertised hostname(s) as its Common Name (CN) or in the Subject Alternative Names (SAN).

It is possible to use different SSL configurations for different listeners. All options starting with ssl. can be prefixed with listener.name.<NameOfTheListener>., where the name of the listener has to be always in lower case. This will override the default SSL configuration for that specific listener. The following example shows how to use different SSL configurations for different listeners: 

listeners=INT1://:9092,INT2://:9093,REPLICATION://:9094
listener.security.protocol.map=INT1:SSL,INT2:SSL,REPLICATION:SSL

# Default configuration - will be used for listeners INT1 and INT2
ssl.keystore.location=/path/to/keystore/server-1.jks
ssl.keystore.password=123456

# Different configuration for listener REPLICATION
listener.name.replication.ssl.keystore.location=/path/to/keystore/server-1.jks
listener.name.replication.ssl.keystore.password=123456

Additional TLS configuration options

In addition to the main TLS configuration options described above, Kafka supports many options for fine-tuning the TLS configuration. For example, to enable or disable TLS / SSL protocols or cipher suites:

ssl.cipher.suites
List of enabled cipher suites. Each cipher suite is a combination of authentication, encryption, MAC and key exchange algorithms used for the TLS connection. By default, all available cipher suites are enabled.
ssl.enabled.protocols
List of enabled TLS / SSL protocols. Defaults to TLSv1.2,TLSv1.1,TLSv1.

For a complete list of supported Kafka broker configuration options, see Appendix A, Broker configuration parameters.

4.9.3. Enabling TLS encryption

This procedure describes how to enable encryption in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.

Procedure

  1. Generate TLS certificates for all Kafka brokers in your cluster. The certificates should have their advertised and bootstrap addresses in their Common Name or Subject Alternative Name.

  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Change the listener.security.protocol.map field to specify the SSL protocol for the listener where you want to use TLS encryption.
    • Set the ssl.keystore.location option to the path to the JKS keystore with the broker certificate.

    • Set the ssl.keystore.password option to the password you used to protect the keystore.

      For example: 

      listeners=UNENCRYPTED://:9092,ENCRYPTED://:9093,REPLICATION://:9094
listener.security.protocol.map=UNENCRYPTED:PLAINTEXT,ENCRYPTED:SSL,REPLICATION:PLAINTEXT
ssl.keystore.location=/path/to/keystore/server-1.jks
ssl.keystore.password=123456
  3. (Re)start the Kafka brokers

Additional resources

4.9.4. Authentication

Kafka supports two methods of authentication. On all connections, authentication using one of the supported SASL (Simple Authentication and Security Layer) mechanisms can be used. On encrypted connections, TLS client authentication based on X.509 certificates can be used.

4.9.4.1. TLS client authentication

TLS client authentication can be used only on connections which are already using TLS encryption. To use TLS client authentication, a truststore with public keys can be provided to the broker. These keys can be used to authenticate clients connecting to the broker. The truststore should be provided in Java Keystore (JKS) format and should contain public keys of the certification authorities. All clients with public and private keys signed by one of the certification authorities included in the truststore will be authenticated. The location of the truststore is set using field ssl.truststore.location. In case the truststore is password protected, the password should be set in the ssl.truststore.password property. For example: 

ssl.truststore.location=/path/to/keystore/server-1.jks
ssl.truststore.password=123456

Once the truststore is configured, TLS client authentication has to be enabled using the ssl.client.auth property. This property can be set to one of three different values:

none
TLS client authentication is switched off. (Default value)
requested
TLS client authentication is optional. Clients will be asked to authenticate using TLS client certificate but they can choose not to.
required
Clients are required to authenticate using TLS client certificate.

When a client authenticates using TLS client authentication, the authenticated principal name is the distinguished name from the authenticated client certificate. For example, a user with a certificate which has a distinguished name CN=someuser will be authenticated with the following principal CN=someuser,OU=Unknown,O=Unknown,L=Unknown,ST=Unknown,C=Unknown. When TLS client authentication is not used and SASL is disabled, the principal name will be ANONYMOUS.

4.9.4.2. SASL authentication

SASL authentication is configured using Java Authentication and Authorization Service (JAAS). JAAS is also used for authentication of connections between Kafka and Zookeeper. JAAS uses its own configuration file. The recommended location for this file is /opt/kafka/config/jaas.conf. The file has to be readable by the kafka user. When running Kafka, the location of this file is specified using Java system property java.security.auth.login.config. This property has to be passed to Kafka when starting the broker nodes: 

KAFKA_OPTS="-Djava.security.auth.login.config=/path/to/my/jaas.config"; bin/kafka-server-start.sh

SASL authentication is supported both through plain unencrypted connections as well as through TLS connections. SASL can be enabled individually for each listener. To enable it, the security protocol in listener.security.protocol.map has to be either SASL_PLAINTEXT or SASL_SSL.

SASL authentication in Kafka supports several different mechanisms:

PLAIN
Implements authentication based on username and passwords. Usernames and passwords are stored locally in Kafka configuration.
SCRAM-SHA-256 and SCRAM-SHA-512
Implements authentication using Salted Challenge Response Authentication Mechanism (SCRAM). SCRAM credentials are stored centrally in Zookeeper. SCRAM can be used in situations where Zookeeper cluster nodes are running isolated in a private network.
GSSAPI
Implements authentication against a Kerberos server.
Warning

The PLAIN mechanism sends the username and password over the network in an unencrypted format. It should be therefore only be used in combination with TLS encryption.

The SASL mechanisms are configured via the JAAS configuration file. Kafka uses the JAAS context named KafkaServer. After they are configured in JAAS, the SASL mechanisms have to be enabled in the Kafka configuration. This is done using the sasl.enabled.mechanisms property. This property contains a comma-separated list of enabled mechanisms: 

sasl.enabled.mechanisms=PLAIN,SCRAM-SHA-256,SCRAM-SHA-512

In case the listener used for inter-broker communication is using SASL, the property sasl.mechanism.inter.broker.protocol has to be used to specify the SASL mechanism which it should use. For example: 

sasl.mechanism.inter.broker.protocol=PLAIN

The username and password which will be used for the inter-broker communication has to be specified in the KafkaServer JAAS context using the field username and password.

SASL PLAIN

To use the PLAIN mechanism, the usernames and password which are allowed to connect are specified directly in the JAAS context. The following example shows the context configured for SASL PLAIN authentication. The example configures three different users:

  • admin
  • user1
  • user2 
KafkaServer {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    user_admin="123456"
    user_user1="123456"
    user_user2="123456";
};

The JAAS configuration file with the user database should be kept in sync on all Kafka brokers.

When SASL PLAIN is also used for inter-broker authentication, the username and password properties should be included in the JAAS context: 

KafkaServer {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    username="admin"
    password="123456"
    user_admin="123456"
    user_user1="123456"
    user_user2="123456";
};

SASL SCRAM

SCRAM authentication in Kafka consists of two mechanisms: SCRAM-SHA-256 and SCRAM-SHA-512. These mechanisms differ only in the hashing algorithm used - SHA-256 versus stronger SHA-512. To enable SCRAM authentication, the JAAS configuration file has to include the following configuration: 

KafkaServer {
    org.apache.kafka.common.security.scram.ScramLoginModule required;
};

When enabling SASL authentication in the Kafka configuration file, both SCRAM mechanisms can be listed. However, only one of them can be chosen for the inter-broker communication. For example: 

sasl.enabled.mechanisms=SCRAM-SHA-256,SCRAM-SHA-512
sasl.mechanism.inter.broker.protocol=SCRAM-SHA-512

User credentials for the SCRAM mechanism are stored in Zookeeper. The kafka-configs.sh tool can be used to manage them. For example, run the following command to add user user1 with password 123456: 

bin/kafka-configs.sh --zookeeper zoo1.my-domain.com:2181 --alter --add-config 'SCRAM-SHA-256=[password=123456],SCRAM-SHA-512=[password=123456]' --entity-type users --entity-name user1

To delete a user credential use: 

bin/kafka-configs.sh --zookeeper zoo1.my-domain.com:2181 --alter --delete-config 'SCRAM-SHA-512' --entity-type users --entity-name user1

SASL GSSAPI

The SASL mechanism used for authentication using Kerberos is called GSSAPI. To configure Kerberos SASL authentication, the following configuration should be added to the JAAS configuration file: 

KafkaServer {
    com.sun.security.auth.module.Krb5LoginModule required
    useKeyTab=true
    storeKey=true
    keyTab="/etc/security/keytabs/kafka_server.keytab"
    principal="kafka/kafka1.hostname.com@EXAMPLE.COM";
};

The domain name in the Kerberos principal has to be always in upper case.

In addition to the JAAS configuration, the Kerberos service name needs to be specified in the sasl.kerberos.service.name property in the Kafka configuration: 

sasl.enabled.mechanisms=GSSAPI
sasl.mechanism.inter.broker.protocol=GSSAPI
sasl.kerberos.service.name=kafka

Multiple SASL mechanisms

Kafka can use multiple SASL mechanisms at the same time. The different JAAS configurations can be all added to the same context: 

KafkaServer {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    user_admin="123456"
    user_user1="123456"
    user_user2="123456";

    com.sun.security.auth.module.Krb5LoginModule required
    useKeyTab=true
    storeKey=true
    keyTab="/etc/security/keytabs/kafka_server.keytab"
    principal="kafka/kafka1.hostname.com@EXAMPLE.COM";

    org.apache.kafka.common.security.scram.ScramLoginModule required;
};

When multiple mechanisms are enabled, clients will be able to choose the mechanism which they want to use.

4.9.5. Enabling TLS client authentication

This procedure describes how to enable TLS client authentication in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • TLS encryption is enabled.

Procedure

  1. Prepare a JKS truststore containing the public key of the certification authority used to sign the user certificates.

  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Set the ssl.truststore.location option to the path to the JKS truststore with the certification authority of the user certificates.
    • Set the ssl.truststore.password option to the password you used to protect the truststore.

    • Set the ssl.client.auth option to required.

      For example: 

      ssl.truststore.location=/path/to/truststore.jks
ssl.truststore.password=123456
ssl.client.auth=required
  3. (Re)start the Kafka brokers

Additional resources

4.9.6. Enabling SASL PLAIN authentication

This procedure describes how to enable SASL PLAIN authentication in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.

Procedure

  1. Edit or create the /opt/kafka/config/jaas.conf JAAS configuration file. This file should contain all your users and their passwords. Make sure this file is the same on all Kafka brokers.

    For example: 

    KafkaServer {
    org.apache.kafka.common.security.plain.PlainLoginModule required
    user_admin="123456"
    user_user1="123456"
    user_user2="123456";
};

  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Change the listener.security.protocol.map field to specify the SASL_PLAINTEXT or SASL_SSL protocol for the listener where you want to use SASL PLAIN authentication.

    • Set the sasl.enabled.mechanisms option to PLAIN.

      For example: 

      listeners=INSECURE://:9092,AUTHENTICATED://:9093,REPLICATION://:9094
listener.security.protocol.map=INSECURE:PLAINTEXT,AUTHENTICATED:SASL_PLAINTEXT,REPLICATION:PLAINTEXT
sasl.enabled.mechanisms=PLAIN

  3. (Re)start the Kafka brokers using the KAFKA_OPTS environment variable to pass the JAAS configuration to Kafka brokers. 

    su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

Additional resources

4.9.7. Enabling SASL SCRAM authentication

This procedure describes how to enable SASL SCRAM authentication in Kafka brokers.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.

Procedure

  1. Edit or create the /opt/kafka/config/jaas.conf JAAS configuration file. Enable the ScramLoginModule for the KafkaServer context. Make sure this file is the same on all Kafka brokers.

    For example: 

    KafkaServer {
    org.apache.kafka.common.security.scram.ScramLoginModule required;
};

  2. Edit the /opt/kafka/config/server.properties Kafka configuration file on all cluster nodes for the following:

    • Change the listener.security.protocol.map field to specify the SASL_PLAINTEXT or SASL_SSL protocol for the listener where you want to use SASL SCRAM authentication.

    • Set the sasl.enabled.mechanisms option to SCRAM-SHA-256 or SCRAM-SHA-512.

      For example: 

      listeners=INSECURE://:9092,AUTHENTICATED://:9093,REPLICATION://:9094
listener.security.protocol.map=INSECURE:PLAINTEXT,AUTHENTICATED:SASL_PLAINTEXT,REPLICATION:PLAINTEXT
sasl.enabled.mechanisms=SCRAM-SHA-512

  3. (Re)start the Kafka brokers using the KAFKA_OPTS environment variable to pass the JAAS configuration to Kafka brokers. 

    su - kafka
export KAFKA_OPTS="-Djava.security.auth.login.config=/opt/kafka/config/jaas.conf"; /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

Additional resources

4.9.8. Adding SASL SCRAM users

This procedure describes how to add new users for authentication using SASL SCRAM.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • SASL SCRAM authentication is enabled.

Procedure

  • Use the kafka-configs.sh tool to add new SASL SCRAM users. 

    bin/kafka-configs.sh --zookeeper <ZookeeperAddress> --alter --add-config 'SCRAM-SHA-512=[password=<Password>]' --entity-type users --entity-name <Username>

    For example: 

    bin/kafka-configs.sh --zookeeper zoo1.my-domain.com:2181 --alter --add-config 'SCRAM-SHA-512=[password=123456]' --entity-type users --entity-name user1

Additional resources

4.9.9. Deleting SASL SCRAM users

This procedure describes how to remove users when using SASL SCRAM authentication.

Prerequisites

  • AMQ Streams is installed on all hosts which will be used as Kafka brokers.
  • SASL SCRAM authentication is enabled.

Procedure

  • Use the kafka-configs.sh tool to delete SASL SCRAM users. 

    bin/kafka-configs.sh --zookeeper <ZookeeperAddress> --alter --delete-config 'SCRAM-SHA-512' --entity-type users --entity-name <Username>

    For example: 

    bin/kafka-configs.sh --zookeeper zoo1.my-domain.com:2181 --alter --delete-config 'SCRAM-SHA-512' --entity-type users --entity-name user1

Additional resources

4.10. Using OAuth 2.0 token based authentication

AMQ Streams supports the use of OAuth 2.0 authentication using the SASL OAUTHBEARER mechanism.

OAuth 2.0 enables standardized token based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources.

OAuth 2.0 is currently only supported for authentication, with no authorization support. However, OAuth 2.0 authentication can be used in conjunction with ACL-based Kafka authorization.

Using OAuth 2.0 token based authentication, application clients can access resources on application servers (called ‘resource servers’) without exposing account credentials. The client passes an access token as a means of authenticating, which application servers can also use to find more information about the level of access granted. The authorization server handles the granting of access and inquiries about access.

In the context of AMQ Streams:

  • Kafka brokers act as resource servers
  • Kafka clients act as resource clients

The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.

For a deployment of AMQ Streams, OAuth 2.0 integration provides:

  • Server-side OAuth 2.0 support for Kafka brokers
  • Client-side OAuth 2.0 support for Kafka Mirror Maker, Kafka Connect and the Kafka Bridge

Additional resources

4.10.1. OAuth 2.0 authentication mechanism

Support for OAuth 2.0 is based on the Kafka SASL OAUTHBEARER mechanism, which is used to establish authenticated sessions with a Kafka broker.

A Kafka client initiates a session with the Kafka broker using the SASL OAUTHBEARER mechanism for credentials exchange, where credentials take the form of an access token.

Kafka brokers and clients need to be configured to use OAuth 2.0.

Kafka broker configuration

The Kafka broker must be configured to validate the token received during session initiation. The recommended approach is to create a client definition in an authorization server with:

  • Client ID of kafka-broker
  • Client ID and secret as the authentication mechanism

Kafka client configuration

A Kafka client is configured with either:

  • Credentials required to obtain a valid access token from an authorization server
  • A valid long-lived access token, obtained using tools provided by an authorization server

Credentials are never sent to the Kafka broker. The only information ever sent to the Kafka broker is an access token. When a client obtains an access token, no further communication with the authorization server is needed.

The simplest mechanism, which requires no additional usage of authorization server tools, is authentication with a client ID and secret. Using a long-lived access token, or a long-lived refresh token, is more complex.

Note

If you are using long-lived access tokens, you can set policy in the authorization server to increase the maximum lifetime of the token.

If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client uses one of two mechanisms:

  • Client id and secret
  • Client id, refresh token, and (optionally) a secret

4.10.2. OAuth 2.0 client authentication flow

In this section, we explain and visualize the communication flow between Kafka client, Kafka broker, and authorization server during Kafka session initiation. The flow depends on the client and server configuration.

When a Kafka client sends an access token as credentials to a Kafka broker, the token needs to be validated.

Depending on the authorization server used, and the configuration options available, you may prefer to use:

  • Fast local token validation based on JWT signature checking and local token introspection, without contacting the authorization server
  • An OAuth 2.0 introspection endpoint provided by the authorization server

Using fast local token validation requires the authorization server to provide a JWKS endpoint with public certificates that are used to validate signatures on the tokens.

Note

An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible.

Another option is to use an OAuth 2.0 introspection endpoint on the authorization server. Each time a Kafka broker connection is established, the broker sends the access token it receives to the authorization server, and a response confirming whether or not the token is valid is returned.

Kafka client credentials can also be configured for:

  • Direct local access using a previously generated long-lived access token
  • Contact with the authorization server for a new access token to be issued and sent to the Kafka broker
4.10.2.1. Example client authentication flows

Here you can see the communication flows, for different configurations of Kafka clients and brokers, during Kafka session authentication.

Client using client ID and secret, with broker delegating validation to authorization server

Client using client ID and secret with broker delegating validation to authorization server

  1. Kafka client requests access token from authorization server, using client ID and secret, and optionally a refresh token.
  2. Authorization server generates a new access token.
  3. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.
  5. Kafka client session is established if the token is valid.

Client using client ID and secret, with broker performing fast local token validation

Client using client ID and secret with broker performing fast local token validation

  1. Kafka client authenticates with authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token.
  2. Authorization server generates a new access token.
  3. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
  4. Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.

Client using long-lived access token, with broker delegating validation to authorization server

Client using long-lived access token with broker delegating validation to authorization server

  1. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.
  3. Kafka client session is established if the token is valid.

Client using long-lived access token, with broker performing fast local validation

Client using long-lived access token with broker performing fast local validation

  1. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
  2. Kafka broker validates the access token locally using JWT token signature check, and local token introspection.
Warning

Fast local JWT token signature validation is suitable only for short-lived tokens as there is no check with the authorization server if a token has been revoked. Token expiration is written into the token, but revocation can happen at any time, so cannot be accounted for without contacting the authorization server. Any issued token would be considered valid until it expires.

4.10.3. Configuring OAuth 2.0 authentication

OAuth 2.0 is used for interaction between Kafka clients and AMQ Streams components.

In order to use OAuth 2.0 for AMQ Streams, you must:

  1. Deploy an authorization server and configure the deployment to integrate with AMQ Streams
  2. Deploy or update the Kafka cluster with Kafka broker listeners configured to use OAuth 2.0
  3. Update your Java-based Kafka clients to use OAuth 2.0
4.10.3.1. Configuring Red Hat Single Sign-On as an OAuth 2.0 authorization server

This procedure describes how to deploy Red Hat Single Sign-On as an authorization server and configure it for integration with AMQ Streams.

The authorization server provides a central point for authentication and authorization, and management of users, clients, and permissions. Red Hat Single Sign-On has a concept of realms where a realm represents a separate set of users, clients, permissions, and other configuration. You can use a default master realm, or create a new one. Each realm exposes its own OAuth 2.0 endpoints, which means that application clients and application servers all need to use the same realm.

To use OAuth 2.0 with AMQ Streams, you need a deployment of an authorization server to be able to create and manage authentication realms.

Note

If you already have Red Hat Single Sign-On deployed, you can skip the deployment step and use your current deployment.

Before you begin

You will need to be familiar with using Red Hat Single Sign-On.

For installation and administration instructions, see:

Prerequisites

  • AMQ Streams and Kafka are running

For the Red Hat Single Sign-On deployment:

Procedure

  1. Install Red Hat Single Sign-On.

    You can install from a ZIP file or by using an RPM.

  2. Log in to the Red Hat Single Sign-On Admin Console to create the OAuth 2.0 policies for AMQ Streams.

    Login details are provided when you deploy Red Hat Single Sign-On.

  3. Create and enable a realm.

    You can use an existing master realm.

  4. Adjust the session and token timeouts for the realm, if required.
  5. Create a client called kafka-broker.

  6. From the Settings tab, set:

    • Access Type to Confidential
    • Standard Flow Enabled to OFF to disable web login for this client
    • Service Accounts Enabled to ON to allow this client to authenticate in its own name
  7. Click Save before continuing.
  8. From the Credentials tab, take a note of the secret for using in your AMQ Streams Kafka cluster configuration.

  9. Repeat the client creation steps for any application client that will connect to your Kafka brokers.

    Create a definition for each new client.

    You will use the names as client IDs in your configuration.

What to do next

After deploying and configuring the authorization server, configure the Kafka brokers to use OAuth 2.0.

4.10.3.2. Configuring OAuth 2.0 support for Kafka brokers

This procedure describes how to configure Kafka brokers so that the broker listeners are enabled to use OAuth 2.0 authentication using an authorization server.

Before you start

For more information on the configuration and authentication of Kafka broker listeners, see:

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed

Procedure

  1. Check you have the following JAR files in your Kafka libs directory. 

    strimzi-kafka-oauth-common-*.jar
strimzi-kafka-oauth-server-*.jar
strimzi-kafka-oauth-client-*.jar
keycloak-common-*.jar
keycloak-core-*.jar
bcprov-*.jar

    If they are not already present, the files are available from the installation archive.

  2. Update the Kafka server.properties file. 

    sasl.enabled.mechanisms=OAUTHBEARER 1

listeners=CLIENTS://kafka:9092 2

listener.security.protocol.map=CLIENTS:SASL_PLAINTEXT 3

listener.name.clients.oauthbearer.sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required; 4
listener.name.clients.oauthbearer.sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
listener.name.clients.oauthbearer.sasl.server.callback.handler.class=io.strimzi.kafka.oauth.server.JaasServerOauthValidatorCallbackHandler
    1
    Enables the OAUTHBEARER as SASL mechanism for credentials exchange over SASL.
    2
    Configures a listener for client applications, replacing kafka with a valid hostname, resolvable by clients.
    3
    Specifies the channel protocol for the listener. SASL_PLAINTEXT means unencrypted connection (no TLS), so there is risk of eavesdropping and interception at the TCP connection layer.
    4
    The three listener properties enable OAuth 2.0 for AMQ Streams.

  3. To further configure OAuth 2.0 support, use one of the following connection options.

    • OPTION 1: Add environment variables to enable local token validation using a JWKS endpoint configuration. 

      export OAUTH_JWKS_ENDPOINT_URI=https://<authorization-server-address>/jwks 1
export OAUTH_JWKS_REFRESH_SECONDS=300 2
export OAUTH_JWKS_EXPIRY_SECONDS=360 3
      1
      The JWKS endpoint URL. For example, https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/certs.
      2
      The period between endpoint refreshes (default 300).
      3
      The certificate’s expiry time (default 360). If you specify a longer time, consider the risk of allowing access to revoked certificates.

    • OPTION 2: Add environment variables to enable delegating token validation to the authorization server through the OAuth 2.0 introspection endpoint. 

      export OAUTH_INTROSPECTION_ENDPOINT_URI=https://<authorization-server-address>/introspection 1
      1
      The OAuth 2.0 introspection endpoint URL. For example, https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/token/introspect.

  4. If required, configure access to the authorization server.

    This step is normally required for a production environment, unless a technology like service mesh is used to configure secure channels outside containers.

    1. Provide a custom truststore for connecting to the authorization server. SSL is always required for access to the authorization server.

      Set environment variables to configure a truststore for connecting to a secured authorization server:

      For example: 

      export OAUTH_SSL_TRUSTSTORE_LOCATION=</path/to/truststore.p12> 1
export OAUTH_SSL_TRUSTSTORE_PASSWORD=<my-password> 2
export OAUTH_SSL_TRUSTSTORE_TYPE=pkcs12 3
      1
      Location of the truststore file in PKCS format.
      2
      Password to access the truststore.
      3
      Truststore type in PKCS12 format.

    2. If the certificate hostname does not match the access URL hostname, you can turn off certificate hostname validation: 

      export OAUTH_SSL_ENDPOINT_IDENTIFICATION_ALGORITHM= ""

      The check ensures that client connection to the authorization server is authentic. You may wish to turn off the validation in a non-production environment.

  5. Configure the following environment variables according to your chosen authentication flow. 

    export OAUTH_TOKEN_ENDPOINT_URI=https://<authorization-server-address>/token 1
export OAUTH_VALID_ISSUER_URI=https://<authorization-server-address> 2
export OAUTH_CLIENT_ID=kafka-broker 3
export OAUTH_CLIENT_SECRET=kafka-broker-secret 4

export OAUTH_REFRESH_TOKEN=<my-refresh-token-for-kafka-brokers> 5
export OAUTH_ACCESS_TOKEN=<my-access-token-for-kafka-brokers> 6
    1
    The OAuth 2.0 token endpoint URL to your authorization server. For production, always use HTTPs.
    2
    A valid issuer URI. Only access tokens issued by this issuer will be accepted.
    3
    The configured client ID of the Kafka broker, which is the same for all brokers. This is the client registered with the authorization server as kafka-broker.
    4
    The configured secret for Kafka broker, which is the same for all brokers.
    5
    (Optional) A long-lived refresh token for Kafka brokers.
    6
    (Optional) A long-lived access token for Kafka brokers.

What to do next

4.10.3.3. Configuring Kafka Java clients to use OAuth 2.0

This procedure describes how to configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers.

Add a client callback plugin to your pom.xml file, and configure the system properties.

Prerequisites

  • AMQ Streams and Kafka are running
  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Add the client library with OAuth 2.0 support to the pom.xml file for the Kafka client: 

    <dependency>
 <groupId>io.strimzi</groupId>
 <artifactId>kafka-oauth-client</artifactId>
 <version>0.1.0.redhat-00002</version>
</dependency>

  2. Configure the system properties for the callback:

    For example: 

    System.setProperty(ClientConfig.OAUTH_TOKEN_ENDPOINT_URI, “https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/token”); 1
System.setProperty(ClientConfig.OAUTH_CLIENT_ID, "<client-name>"); 2
System.setProperty(ClientConfig.OAUTH_CLIENT_SECRET, "<client-secret>"); 3
    1
    URI of the authorization server token endpoint.
    2
    Client ID, which is the name used when creating the client in the authorization server.
    3
    Client secret created when creating the client in the authorization server.

  3. Enable the SASL OAUTHBEARER mechanism on a TLS encrypted connection in the Kafka client configuration:

    For example: 

    props.put("sasl.jaas.config", "org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required;");
props.put("security.protocol", "SASL_SSL"); 1
props.put("sasl.mechanism", "OAUTHBEARER");
props.put("sasl.login.callback.handler.class", "io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler");
    1
    Here we use SASL_SSL for use over TLS connections. Use SASL_PLAINTEXT over unencrypted connections.
  4. Verify that the Kafka client can access the Kafka brokers.

4.11. Logging

Kafka brokers use Log4j as their logging infrastructure. Logging configuration is by default read from the log4j.propeties configuration file which should be placed either in the /opt/kafka/config/ directory or on the classpath. The location and name of the configuration file can be changed using the Java property log4j.configuration which can be passed to Kafka using the KAFKA_LOG4J_OPTS environment variable: 

su - kafka
export KAFKA_LOG4J_OPTS="-Dlog4j.configuration=file:/my/path/to/log4j.config"; /opt/kafka/bin/kafka-server-start.sh /opt/kafka/config/server.properties

For more information about Log4j configurations, see Log4j manual.

Chapter 5. Topics

Messages in Kafka are always sent to or received from a topic. This chapter describes how to configure and manage Kafka topics.

5.1. Partitions and replicas

Messages in Kafka are always sent to or received from a topic. A topic is always split into one or more partitions. Partitions act as shards. That means that every message sent by a producer is always written only into a single partition. Thanks to the sharding of messages into different partitions, topics are easy to scale horizontally.

Each partition can have one or more replicas, which will be stored on different brokers in the cluster. When creating a topic you can configure the number of replicas using the replication factor. Replication factor defines the number of copies which will be held within the cluster. One of the replicas for given partition will be elected as a leader. The leader replica will be used by the producers to send new messages and by the consumers to consume messages. The other replicas will be follower replicas. The followers replicate the leader.

If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so the load is well balanced within the cluster.

Note

The replication factor determines the number of replicas including the leader and the followers. For example, if you set the replication factor to 3, then there will one leader and two follower replicas.

5.2. Message retention

The message retention policy defines how long the messages will be stored on the Kafka brokers. It can be defined based on time, partition size or both.

For example, you can define that the messages should be kept:

  • For 7 days
  • Until the parition has 1GB of messages. Once the limit is reached, the oldest messages will be removed.
  • For 7 days or until the 1GB limit has been reached. Whatever limit comes first will be used.
Warning

Kafka brokers store messages in log segments. The messages which are past their retention policy will be deleted only when a new log segment is created. New log segments are created when the previous log segment exceeds the configured log segment size. Additionally, users can request new segments to be created periodically.

Additionally, Kafka brokers support a compacting policy.

For a topic with the compacted policy, the broker will always keep only the last message for each key. The older messages with the same key will be removed from the partition. Because compacting is a periodically executed action, it does not happen immediately when the new message with the same key are sent to the partition. Instead it might take some time until the older messages are removed.

For more information about the message retention configuration options, see Section 5.5, “Topic configuration”.

5.3. Topic auto-creation

When a producer or consumer tries to sent to or received from from a topic which does not exist, Kafka will, by default, automatically create that topic. This behavior is controlled by the auto.create.topics.enable configuration property which is set to true by default.

To disable it, set auto.create.topics.enable to false in the Kafka broker configuration file: 

auto.create.topics.enable=false

5.4. Topic deletion

Kafka offers the possibility to disable deletion of topics. This is configured through the delete.topic.enable property, which is set to true by default (that is, deleting topics is possible). When this property is set to false it will be not possible to delete topics and all attempts to delete topic will return success but the topic will not be deleted. 

delete.topic.enable=false

5.5. Topic configuration

Auto-created topics will use the default topic configuration which can be specified in the broker properties file. However, when creating topics manually, their configuration can be specified at creation time. It is also possible to change a topic’s configuration after it has been created. The main topic configuration options for manually created topics are:

cleanup.policy
Configures the retention policy to delete or compact. The delete policy will delete old records. The compact policy will enable log compaction. The default value is delete. For more information about log compaction, see Kafka website.
compression.type
Specifies the compression which is used for stored messages. Valid values are gzip, snappy, lz4, uncompressed (no compression) and producer (retain the compression codec used by the producer). The default value is producer.
max.message.bytes
The maximum size of a batch of messages allowed by the Kafka broker, in bytes. The default value is 1000012.
min.insync.replicas
The minimum number of replicas which must be in sync for a write to be considered successful. The default value is 1.
retention.ms
Maximum number of milliseconds for which log segments will be retained. Log segments older than this value will be deleted. The default value is 604800000 (7 days).
retention.bytes
The maximum number of bytes a partition will retain. Once the partition size grows over this limit, the oldest log segments will be deleted. Value of -1 indicates no limit. The default value is -1.
segment.bytes
The maximum file size of a single commit log segment file in bytes. When the segment reaches its size, a new segment will be started. The default value is 1073741824 bytes (1 gibibyte).

For list of all supported topic configuration options, see Appendix B, Topic configuration parameters.

The defaults for auto-created topics can be specified in the Kafka broker configuration using similar options:

log.cleanup.policy
See cleanup.policy above.
compression.type
See compression.type above.
message.max.bytes
See max.message.bytes above.
min.insync.replicas
See min.insync.replicas above.
log.retention.ms
See retention.ms above.
log.retention.bytes
See retention.bytes above.
log.segment.bytes
See segment.bytes above.
default.replication.factor
Default replication factor for automatically created topics. Default value is 1.
num.partitions
Default number of partitions for automatically created topics. Default value is 1.

For list of all supported Kafka broker configuration options, see Appendix A, Broker configuration parameters.

5.6. Internal topics

Internal topics are created and used internally by the Kafka brokers and clients. Kafka has several internal topics. These are used to store consumer offsets (__consumer_offsets) or transaction state (__transaction_state). These topics can be configured using dedicated Kafka broker configuration options starting with prefix offsets.topic. and transaction.state.log.. The most important configuration options are:

offsets.topic.replication.factor
Number of replicas for __consumer_offsets topic. The default value is 3.
offsets.topic.num.partitions
Number of partitions for __consumer_offsets topic. The default value is 50.
transaction.state.log.replication.factor
Number of replicas for __transaction_state topic. The default value is 3.
transaction.state.log.num.partitions
Number of partitions for __transaction_state topic. The default value is 50.
transaction.state.log.min.isr
Minimum number of replicas that must acknowledge a write to __transaction_state topic to be considered successful. If this minimum cannot be met, then the producer will fail with an exception. The default value is 2.

5.7. Creating a topic

The kafka-topics.sh tool can be used to manage topics. kafka-topics.sh is part of the AMQ Streams distribution and can be found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running

Creating a topic

  1. Create a topic using the kafka-topics.sh utility and specify the following: Zookeeper URL in the --zookeeper option. The new topic to be created in the --create option. Topic name in the --topic option. The number of partitions in the --partitions option. Replication factor in the --replication-factor option.

    You can also override some of the default topic configuration options using the option --config. This option can be used multiple times to override different options. 

    bin/kafka-topics.sh --zookeeper <ZookeeperAddress> --create --topic <TopicName> --partitions <NumberOfPartitions> --replication-factor <ReplicationFactor> --config <Option1>=<Value1> --config <Option2>=<Value2>

    Example of the command to create a topic named mytopic

    bin/kafka-topics.sh --zookeeper zoo1.my-domain.com:2181 --create --topic mytopic --partitions 50 --replication-factor 3 --config cleanup.policy=compact --config min.insync.replicas=2

  2. Verify that the topic exists using kafka-topics.sh. 

    bin/kafka-topics.sh --zookeeper  <ZookeeperAddress> --describe --topic <TopicName>

    Example of the command to describe a topic named mytopic

    bin/kafka-topics.sh --zookeeper zoo1.my-domain.com:2181 --describe --topic mytopic

Additional resources

5.8. Listing and describing topics

The kafka-topics.sh tool can be used to list and describe topics. kafka-topics.sh is part of the AMQ Streams distribution and can be found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running
  • Topic mytopic exists

Describing a topic

  1. Describe a topic using the kafka-topics.sh utility.

    • Specify the Zookeeper URL in the --zookeeper option.
    • Use --describe option to specify that you want to describe a topic.
    • Topic name has to be specified in the --topic option.

    • When the --topic option is omitted, it will describe all available topics. 

      bin/kafka-topics.sh --zookeeper  <ZookeeperAddress> --describe --topic <TopicName>

      Example of the command to describe a topic named mytopic

      bin/kafka-topics.sh --zookeeper zoo1.my-domain.com:2181 --describe --topic mytopic

      The describe command will list all partitions and replicas which belong to this topic. It will also list all topic configuration options.

Additional resources

5.9. Modifying a topic configuration

The kafka-configs.sh tool can be used to modify topic configurations. kafka-configs.sh is part of the AMQ Streams distribution and can be found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running
  • Topic mytopic exists

Modify topic configuration

  1. Use the kafka-configs.sh tool to get the current configuration.

    • Specify the Zookeeper URL in the --zookeeper option.
    • Set the --entity-type as topic and --entity-name to the name of your topic.

    • Use --describe option to get the current configuration. 

      bin/kafka-configs.sh --zookeeper <ZookeeperAddress> --entity-type topics --entity-name <TopicName> --describe

      Example of the command to get configuration of a topic named mytopic

      bin/kafka-configs.sh --zookeeper zoo1.my-domain.com:2181 --entity-type topics --entity-name mytopic --describe

  2. Use the kafka-configs.sh tool to change the configuration.

    • Specify the Zookeeper URL in the --zookeeper options.
    • Set the --entity-type as topic and --entity-name to the name of your topic.
    • Use --alter option to modify the current configuration.

    • Specify the options you want to add or change in the option --add-config. 

      bin/kafka-configs.sh --zookeeper <ZookeeperAddress> --entity-type topics --entity-name <TopicName> --alter --add-config <Option>=<Value>

      Example of the command to change configuration of a topic named mytopic

      bin/kafka-configs.sh --zookeeper zoo1.my-domain.com:2181 --entity-type topics --entity-name mytopic --alter --add-config min.insync.replicas=1

  3. Use the kafka-configs.sh tool to delete an existing configuration option.

    • Specify the Zookeeper URL in the --zookeeper options.
    • Set the --entity-type as topic and --entity-name to the name of your topic.
    • Use --delete-config option to remove existing configuration option.

    • Specify the options you want to remove in the option --remove-config. 

      bin/kafka-configs.sh --zookeeper <ZookeeperAddress> --entity-type topics --entity-name <TopicName> --alter --delete-config <Option>

      Example of the command to change configuration of a topic named mytopic

      bin/kafka-configs.sh --zookeeper zoo1.my-domain.com:2181 --entity-type topics --entity-name mytopic --alter --delete-config min.insync.replicas

Additional resources

5.10. Deleting a topic

The kafka-topics.sh tool can be used to manage topics. kafka-topics.sh is part of the AMQ Streams distribution and can be found in the bin directory.

Prerequisites

  • AMQ Streams cluster is installed and running
  • Topic mytopic exists

Deleting a topic

  1. Delete a topic using the kafka-topics.sh utility.

    • Specify the Zookeeper URL in the --zookeeper option.
    • Use --delete option to specify that an existing topic should be deleted.

    • Topic name has to be specified in the --topic option. 

      bin/kafka-topics.sh --zookeeper  <ZookeeperAddress> --delete --topic <TopicName>

      Example of the command to create a topic named mytopic

      bin/kafka-topics.sh --zookeeper zoo1.my-domain.com:2181 --delete --topic mytopic

  2. Verify that the topic was deleted using kafka-topics.sh. 

    bin/kafka-topics.sh --zookeeper  <ZookeeperAddress> --list

    Example of the command to list all topics

    bin/kafka-topics.sh --zookeeper zoo1.my-domain.com:2181 --list

Additional resources

Chapter 6. Scaling Clusters

6.1. Scaling Kafka clusters

6.1.1. Adding brokers to a cluster

The primary way of increasing throughput for a topic is to increase the number of partitions for that topic. That works because the partitions allow the load for that topic to be shared between the brokers in the cluster. When the brokers are all constrained by some resource (typically I/O), then using more partitions will not yield an increase in throughput. Instead, you must add brokers to the cluster.

When you add an extra broker to the cluster, AMQ Streams does not assign any partitions to it automatically. You have to decide which partitions to move from the existing brokers to the new broker.

Once the partitions have been redistributed between all brokers, each broker should have a lower resource utilization.

6.1.2. Removing brokers from the cluster

Before you remove a broker from a cluster, you must ensure that it is not assigned to any partitions. You should decide which remaining brokers will be responsible for each of the partitions on the broker being decommissioned. Once the broker has no assigned partitions, you can stop it.

6.2. Reassignment of partitions

The kafka-reassign-partitions.sh utility is used to reassign partitions to different brokers.

It has three different modes:

--generate
Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. It is an easy way to generate a reassignment JSON file, but it operates on whole topics, so its use is not always appropriate.
--execute
Takes a reassignment JSON file and applies it to the partitions and brokers in the cluster. Brokers which are gaining partitions will become followers of the partition leader. For a given partition, once the new broker has caught up and joined the ISR the old broker will stop being a follower and will delete its replica.
--verify
Using the same reassignment JSON file as the --execute step, --verify checks whether all of the partitions in the file have been moved to their intended brokers. If the reassignment is complete it will also remove any throttles which are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.

It is only possible to have one reassignment running in the cluster at any given time, and it is not possible to cancel a running reassignment. If you need to cancel a reassignment you have to wait for it to complete and then perform another reassignment to revert the effects of the first one. The kafka-reassign-partitions.sh will print the reassignment JSON for this reversion as part of its output. Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.

6.2.1. Reassignment JSON file

The reassignment JSON file has a specific structure: 

{
  "version": 1,
  "partitions": [
    <PartitionObjects>
  ]
}

Where <PartitionObjects> is a comma-separated list of objects like: 

{
  "topic": <TopicName>,
  "partition": <Partition>,
  "replicas": [ <AssignedBrokerIds> ],
  "log_dirs": [<LogDirs>]
}

The "log_dirs" property is optional and is used to move the partition to a specific log directory.

The following is an example reassignment JSON file that assigns topic topic-a, partition 4 to brokers 2, 4 and 7, and topic topic-b partition 2 to brokers 1, 5 and 7: 

{
  "version": 1,
  "partitions": [
    {
      "topic": "topic-a",
      "partition": 4,
      "replicas": [2,4,7]
    },
    {
      "topic": "topic-b",
      "partition": 2,
      "replicas": [1,5,7]
    }
  ]
}

Partitions not included in the JSON are not changed.

6.2.2. Generating reassignment JSON files

The easiest way to assign all the partitions for a given set of topics to a given set of brokers is to generate a reassignment JSON file using the kafka-reassign-partitions.sh --generate, command. 

bin/kafka-reassign-partitions.sh --zookeeper <Zookeeper> --topics-to-move-json-file <TopicsFile> --broker-list <BrokerList> --generate

The <TopicsFile> is a JSON file which lists the topics to move. It has the following structure: 

{
  "version": 1,
  "topics": [
    <TopicObjects>
  ]
}

where <TopicObjects> is a comma-separated list of objects like: 

{
  "topic": <TopicName>
}

For example to move all the partitions of topic-a and topic-b to brokers 4 and 7 

bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 --topics-to-move-json-file topics-to-be-moved.json --broker-list 4,7 --generate

where topics-to-be-moved.json has contents: 

{
  "version": 1,
  "topics": [
    { "topic": "topic-a"},
    { "topic": "topic-b"}
  ]
}

6.2.3. Creating reassignment JSON files manually

You can manually create the reassignment JSON file if you want to move specific partitions.

6.3. Reassignment throttles

Reassigning partitions can be a slow process because it can require moving lots of data between brokers. To avoid this having a detrimental impact on clients it is possible to throttle the reassignment. Using a throttle can mean the reassignment takes longer. If the throttle is too low then the newly assigned brokers will not be able to keep up with records being published and the reassignment will never complete. If the throttle is too high then clients will be impacted. For example, for producers, this could manifest as higher than normal latency waiting for acknowledgement. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.

6.4. Scaling up a Kafka cluster

This procedure describes how to increase the number of brokers in a Kafka cluster.

Prerequisites

  • An existing Kafka cluster.
  • A new machine with the AMQ broker installed.
  • A reassignment JSON file of how partitions should be reassigned to brokers in the enlarged cluster.

Procedure

  1. Create a configuration file for the new broker using the same settings as for the other brokers in your cluster, except for broker.id which should be a number that is not already used by any of the other brokers.

  2. Start the new Kafka broker passing the configuration file you created in the previous step as the argument to the kafka-server-start.sh script: 

    su - kafka
/opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties

  3. Verify that the Kafka broker is running. 

    jcmd | grep Kafka
  4. Repeat the above steps for each new broker.

  5. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool. 

    kafka-reassign-partitions.sh --zookeeper <ZookeeperHostAndPort> --reassignment-json-file <ReassignmentJsonFile> --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example: 

    kafka-reassign-partitions.sh --zookeeper zookeeper1:2181 --reassignment-json-file reassignment.json --throttle 5000000 --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a file in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

  6. If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example: 

    kafka-reassign-partitions.sh --zookeeper zookeeper1:2181 --reassignment-json-file reassignment.json --throttle 10000000 --execute

  7. Periodically verify whether the reassignment has completed using the kafka-reassign-partitions.sh command line tool. This is the same command as the previous step but with the --verify option instead of the --execute option. 

    kafka-reassign-partitions.sh --zookeeper <ZookeeperHostAndPort> --reassignment-json-file <ReassignmentJsonFile> --verify

    For example: 

    kafka-reassign-partitions.sh --zookeeper zookeeper1:2181 --reassignment-json-file reassignment.json --verify
  8. The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.

6.5. Scaling down a Kafka cluster

Additional resources

This procedure describes how to decrease the number of brokers in a Kafka cluster.

Prerequisites

  • An existing Kafka cluster.
  • A reassignment JSON file of how partitions should be reassigned to brokers in the cluster once the broker(s) have been removed.

Procedure

  1. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool. 

    kafka-reassign-partitions.sh --zookeeper <ZookeeperHostAndPort> --reassignment-json-file <ReassignmentJsonFile> --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example: 

    kafka-reassign-partitions.sh --zookeeper zookeeper1:2181 --reassignment-json-file reassignment.json --throttle 5000000 --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a file in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

  2. If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example: 

    kafka-reassign-partitions.sh --zookeeper zookeeper1:2181 --reassignment-json-file reassignment.json --throttle 10000000 --execute

  3. Periodically verify whether the reassignment has completed using the kafka-reassign-partitions.sh command line tool. This is the same command as the previous step but with the --verify option instead of the --execute option. 

    kafka-reassign-partitions.sh --zookeeper <ZookeeperHostAndPort> --reassignment-json-file <ReassignmentJsonFile> --verify

    For example: 

    kafka-reassign-partitions.sh --zookeeper zookeeper1:2181 --reassignment-json-file reassignment.json --verify
  4. The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.

  5. Once all the partition reassignments have finished, the broker being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking each of the directories given in the broker’s log.dirs configuration parameters. If any of the log directories on the broker contains a directory that does not match the extended regular expression \.[a-z0-9]-delete$ then the broker still has live partitions and it should not be stopped.

    You can check this by executing the command: 

    ls -l <LogDir> | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'

    If the above command prints any output then the broker still has live partitions. In this case, either the reassignment has not finished, or the reassignment JSON file was incorrect.

  6. Once you have confirmed that the broker has no live partitions you can stop it. 

    su - kafka
/opt/kafka/bin/kafka-server-stop.sh

  7. Confirm that the Kafka broker is stopped. 

    jcmd | grep kafka

Chapter 7. Monitoring your cluster using JMX

Zookeeper, the Kafka broker, Kafka Connect, and the Kafka clients all expose management information using Java Management Extensions (JMX). Most management information is in the form of metrics that are useful for monitoring the condition and performance of your Kafka cluster. Like other Java applications, Kafka provides this management information through managed beans or MBeans.

JMX works at the level of the JVM (Java Virtual Machine). To obtain management information, external tools can connect to the JVM that is running Zookeeper, the Kafka broker, and so on. By default, only tools on the same machine and running as the same user as the JVM are able to connect.

Note

Management information for Zookeeper is not documented here. You can view Zookeeper metrics in JConsole. For more information, see Monitoring using JConsole.

7.1. JMX configuration options

You configure JMX using JVM system properties. The scripts provided with AMQ Streams (bin/kafka-server-start.sh and bin/connect-distributed.sh, and so on) use the KAFKA_JMX_OPTS environment variable to set these system properties. The system properties for configuring JMX are the same, even though Kafka producer, consumer, and streams applications typically start the JVM in different ways.

7.2. Disabling the JMX agent

You can prevent local JMX tools from connecting to the JVM (for example, for compliance reasons) by disabling the JMX agent for an AMQ Streams component. The following procedure explains how to disable the JMX agent for a Kafka broker.

Procedure

  1. Use the KAFKA_JMX_OPTS environment variable to set com.sun.management.jmxremote to false. 

    export KAFKA_JMX_OPTS=-Dcom.sun.management.jmxremote=false
bin/kafka-server-start.sh
  2. Start the JVM.

7.3. Connecting to the JVM from a different machine

You can connect to the JVM from a different machine by configuring the port that the JMX agent listens on. This is insecure because it allows JMX tools to connect from anywhere, with no authentication.

Procedure

  1. Use the KAFKA_JMX_OPTS environment variable to set -Dcom.sun.management.jmxremote.port=<port>. For <port>, enter the name of the port on which you want the Kafka broker to listen for JMX connections. 

    export KAFKA_JMX_OPTS="-Dcom.sun.management.jmxremote=true
  -Dcom.sun.management.jmxremote.port=<port>
  -Dcom.sun.management.jmxremote.authenticate=false
  -Dcom.sun.management.jmxremote.ssl=false"
bin/kafka-server-start.sh
  2. Start the JVM.
Important

It is recommended that you configure authentication and SSL to ensure that the remote JMX connection is secure. For more information about the system properties needed to do this, see the JMX documentation.

7.4. Monitoring using JConsole

The JConsole tool is distributed with the Java Development Kit (JDK). You can use JConsole to connect to a local or remote JVM and discover and display management information from Java applications. If using JConsole to connect to a local JVM, the names of the JVM processes corresponding to the different components of AMQ Streams are as follows:

AMQ Streams componentJVM process

Zookeeper

org.apache.zookeeper.server.quorum.QuorumPeerMain

Kafka broker

kafka.Kafka

Kafka Connect standalone

org.apache.kafka.connect.cli.ConnectStandalone

Kafka Connect distributed

org.apache.kafka.connect.cli.ConnectDistributed

A Kafka producer, consumer, or Streams application

The name of the class containing the main method for the application.

When using JConsole to connect to a remote JVM, use the appropriate host name and JMX port.

Many other tools and monitoring products can be used to fetch the metrics using JMX and provide monitoring and alerting based on those metrics. Refer to the product documentation for those tools.

7.5. Important Kafka broker metrics

Kafka provides many MBeans for monitoring the performance of the brokers in your Kafka cluster. These apply to an individual broker rather than the entire cluster.

The following tables present a selection of these broker-level MBeans organized into server, network, logging, and controller metrics.

7.5.1. Kafka server metrics

The following table shows a selection of metrics that report information about the Kafka server.

MetricMBeanDescriptionExpected value

Messages in per second

kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec

The rate at which individual messages are consumed by the broker.

Approximately the same as the other brokers in the cluster.

Bytes in per second

kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec

The rate at which data sent from producers is consumed by the broker.

Approximately the same as the other brokers in the cluster.

Replication bytes in per second

kafka.server:type=BrokerTopicMetrics,name=ReplicationBytesInPerSec

The rate at which data sent from other brokers is consumed by the follower broker.

N/A

Bytes out per second

kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec

The rate at which data is fetched and read from the broker by consumers.

N/A

Replication bytes out per second

kafka.server:type=BrokerTopicMetrics,name=ReplicationBytesOutPerSec

The rate at which data is sent from the broker to other brokers. This metric is useful to monitor if the broker is a leader for a group of partitions.

N/A

Under-replicated partitions

kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions

The number of partitions that have not been fully replicated in the follower replicas.

Zero

Under minimum ISR partition count

kafka.server:type=ReplicaManager,name=UnderMinIsrPartitionCount

The number of partitions under the minimum In-Sync Replica (ISR) count. The ISR count indicates the set of replicas that are up-to-date with the leader.

Zero

Partition count

kafka.server:type=ReplicaManager,name=PartitionCount

The number of partitions in the broker.

Approximately even when compared with the other brokers.

Leader count

kafka.server:type=ReplicaManager,name=LeaderCount

The number of replicas for which this broker is the leader.

Approximately the same as the other brokers in the cluster.

ISR shrinks per second

kafka.server:type=ReplicaManager,name=IsrShrinksPerSec

The rate at which the number of ISRs in the broker decreases

Zero

ISR expands per second

kafka.server:type=ReplicaManager,name=IsrExpandsPerSec

The rate at which the number of ISRs in the broker increases.

Zero

Maximum lag

kafka.server:type=ReplicaFetcherManager,name=MaxLag,clientId=Replica

The maximum lag between the time that messages are received by the leader replica and by the follower replicas.

Proportional to the maximum batch size of a produce request.

Requests in producer purgatory

kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Produce

The number of send requests in the producer purgatory.

N/A

Requests in fetch purgatory

kafka.server:type=DelayedOperationPurgatory,name=PurgatorySize,delayedOperation=Fetch

The number of fetch requests in the fetch purgatory.

N/A

Request handler average idle percent

kafka.server:type=KafkaRequestHandlerPool,name=RequestHandlerAvgIdlePercent

Indicates the percentage of time that the request handler (IO) threads are not in use.

A lower value indicates that the workload of the broker is high.

Request (Requests exempt from throttling)

kafka.server:type=Request

The number of requests that are exempt from throttling.

N/A

Zookeeper request latency in milliseconds

kafka.server:type=ZooKeeperClientMetrics,name=ZooKeeperRequestLatencyMs

The latency for ZooKeeper requests from the broker, in milliseconds.

N/A

Zookeeper session state

kafka.server:type=SessionExpireListener,name=SessionState

The status of the broker’s connection to Zookeeper.

CONNECTED

7.5.2. Kafka network metrics

The following table shows a selection of metrics that report information about requests.

MetricMBeanDescriptionExpected value

Requests per second

kafka.network:type=RequestMetrics,name=RequestsPerSec,request={Produce|FetchConsumer|FetchFollower}

The total number of requests made for the request type per second. The Produce, FetchConsumer, and FetchFollower request types each have their own MBeans.

N/A

Request bytes (request size in bytes)

kafka.network:type=RequestMetrics,name=RequestBytes,request=([-.\w]+)

The size of requests, in bytes, made for the request type identified by the request property of the MBean name. Separate MBeans for all available request types are listed under the RequestBytes node.

N/A

Temporary memory size in bytes

kafka.network:type=RequestMetrics,name=TemporaryMemoryBytes,request={Produce|Fetch}

The amount of temporary memory used for converting message formats and decompressing messages.

N/A

Message conversions time

kafka.network:type=RequestMetrics,name=MessageConversionsTimeMs,request={Produce|Fetch}

Time, in milliseconds, spent on converting message formats.

N/A

Total request time in milliseconds

kafka.network:type=RequestMetrics,name=TotalTimeMs,request={Produce|FetchConsumer|FetchFollower}

Total time, in milliseconds, spent processing requests.

N/A

Request queue time in milliseconds

kafka.network:type=RequestMetrics,name=RequestQueueTimeMs,request={Produce|FetchConsumer|FetchFollower}

The time, in milliseconds, that a request currently spends in the queue for the request type given in the request property.

N/A

Local time (leader local processing time) in milliseconds

kafka.network:type=RequestMetrics,name=LocalTimeMs,request={Produce|FetchConsumer|FetchFollower}

The time taken, in milliseconds, for the leader to process the request.

N/A

Remote time (leader remote processing time) in milliseconds

kafka.network:type=RequestMetrics,name=RemoteTimeMs,request={Produce|FetchConsumer|FetchFollower}

The length of time, in milliseconds, that the request waits for the follower. Separate MBeans for all available request types are listed under the RemoteTimeMs node.

N/A

Response queue time in milliseconds

kafka.network:type=RequestMetrics,name=ResponseQueueTimeMs,request={Produce|FetchConsumer|FetchFollower}

The length of time, in milliseconds, that the request waits in the response queue.

N/A

Response send time in milliseconds

kafka.network:type=RequestMetrics,name=ResponseSendTimeMs,request={Produce|FetchConsumer|FetchFollower}

The time taken, in milliseconds, to send the response.

N/A

Network processor average idle percent

kafka.network:type=SocketServer,name=NetworkProcessorAvgIdlePercent

The average percentage of time that the network processors are idle.

Between zero and one.

7.5.3. Kafka log metrics

The following table shows a selection of metrics that report information about logging.

MetricMBeanDescriptionExpected Value

Log flush rate and time in milliseconds

kafka.log:type=LogFlushStats,name=LogFlushRateAndTimeMs

The rate at which log data is written to disk, in milliseconds.

N/A

Offline log directory count

kafka.log:type=LogManager,name=OfflineLogDirectoryCount

The number of offline log directories (for example, after a hardware failure).

Zero

7.5.4. Kafka controller metrics

The following table shows a selection of metrics that report information about the controller of the cluster.

MetricMBeanDescriptionExpected Value

Active controller count

kafka.controller:type=KafkaController,name=ActiveControllerCount

The number of brokers designated as controllers.

One indicates that the broker is the controller for the cluster.

Leader election rate and time in milliseconds

kafka.controller:type=ControllerStats,name=LeaderElectionRateAndTimeMs

The rate at which new leader replicas are elected.

Zero

7.5.5. Yammer metrics

Metrics that express a rate or unit of time are provided as Yammer metrics. The class name of an MBean that uses Yammer metrics is prefixed with com.yammer.metrics.

Yammer rate metrics have the following attributes for monitoring requests:

  • Count
  • EventType (Bytes)
  • FifteenMinuteRate
  • RateUnit (Seconds)
  • MeanRate
  • OneMinuteRate
  • FiveMinuteRate

Yammer time metrics have the following attributes for monitoring requests:

  • Max
  • Min
  • Mean
  • StdDev
  • 75/95/98/99/99.9th Percentile

7.6. Producer MBeans

The following MBeans will exist in Kafka producer applications, including Kafka Streams applications and Kafka Connect with source connectors.

7.6.1. MBeans matching kafka.producer:type=producer-metrics,client-id=*

These are metrics at the producer level.

AttributeDescription

batch-size-avg

The average number of bytes sent per partition per-request.

batch-size-max

The max number of bytes sent per partition per-request.

batch-split-rate

The average number of batch splits per second.

batch-split-total

The total number of batch splits.

buffer-available-bytes

The total amount of buffer memory that is not being used (either unallocated or in the free list).

buffer-total-bytes

The maximum amount of buffer memory the client can use (whether or not it is currently used).

bufferpool-wait-time

The fraction of time an appender waits for space allocation.

compression-rate-avg

The average compression rate of record batches.

connection-close-rate

Connections closed per second in the window.

connection-count

The current number of active connections.

connection-creation-rate

New connections established per second in the window.

failed-authentication-rate

Connections that failed authentication.

incoming-byte-rate

Bytes/second read off all sockets.

io-ratio

The fraction of time the I/O thread spent doing I/O.

io-time-ns-avg

The average length of time for I/O per select call in nanoseconds.

io-wait-ratio

The fraction of time the I/O thread spent waiting.

io-wait-time-ns-avg

The average length of time the I/O thread spent waiting for a socket ready for reads or writes in nanoseconds.

metadata-age

The age in seconds of the current producer metadata being used.

network-io-rate

The average number of network operations (reads or writes) on all connections per second.

outgoing-byte-rate

The average number of outgoing bytes sent per second to all servers.

produce-throttle-time-avg

The average time in ms a request was throttled by a broker.

produce-throttle-time-max

The maximum time in ms a request was throttled by a broker.

record-error-rate

The average per-second number of record sends that resulted in errors.

record-error-total

The total number of record sends that resulted in errors.

record-queue-time-avg

The average time in ms record batches spent in the send buffer.

record-queue-time-max

The maximum time in ms record batches spent in the send buffer.

record-retry-rate

The average per-second number of retried record sends.

record-retry-total

The total number of retried record sends.

record-send-rate

The average number of records sent per second.

record-send-total

The total number of records sent.

record-size-avg

The average record size.

record-size-max

The maximum record size.

records-per-request-avg

The average number of records per request.

request-latency-avg

The average request latency in ms.

request-latency-max

The maximum request latency in ms.

request-rate

The average number of requests sent per second.

request-size-avg

The average size of all requests in the window.

request-size-max

The maximum size of any request sent in the window.

requests-in-flight

The current number of in-flight requests awaiting a response.

response-rate

Responses received sent per second.

select-rate

Number of times the I/O layer checked for new I/O to perform per second.

successful-authentication-rate

Connections that were successfully authenticated using SASL or SSL.

waiting-threads

The number of user threads blocked waiting for buffer memory to enqueue their records.

7.6.2. MBeans matching kafka.producer:type=producer-metrics,client-id=*,node-id=*

These are metrics at the producer level about connection to each broker.

AttributeDescription

incoming-byte-rate

The average number of responses received per second for a node.

outgoing-byte-rate

The average number of outgoing bytes sent per second for a node.

request-latency-avg

The average request latency in ms for a node.

request-latency-max

The maximum request latency in ms for a node.

request-rate

The average number of requests sent per second for a node.

request-size-avg

The average size of all requests in the window for a node.

request-size-max

The maximum size of any request sent in the window for a node.

response-rate

Responses received sent per second for a node.

7.6.3. MBeans matching kafka.producer:type=producer-topic-metrics,client-id=*,topic=*

These are metrics at the topic level about topics the producer is sending messages to.

AttributeDescription

byte-rate

The average number of bytes sent per second for a topic.

byte-total

The total number of bytes sent for a topic.

compression-rate

The average compression rate of record batches for a topic.

record-error-rate

The average per-second number of record sends that resulted in errors for a topic.

record-error-total

The total number of record sends that resulted in errors for a topic.

record-retry-rate

The average per-second number of retried record sends for a topic.

record-retry-total

The total number of retried record sends for a topic.

record-send-rate

The average number of records sent per second for a topic.

record-send-total

The total number of records sent for a topic.

7.7. Consumer MBeans

The following MBeans will exist in Kafka consumer applications, including Kafka Streams applications and Kafka Connect with sink connectors.

7.7.1. MBeans matching kafka.consumer:type=consumer-metrics,client-id=*

These are metrics at the consumer level.

AttributeDescription

connection-close-rate

Connections closed per second in the window.

connection-count

The current number of active connections.

connection-creation-rate

New connections established per second in the window.

failed-authentication-rate

Connections that failed authentication.

incoming-byte-rate

Bytes/second read off all sockets.

io-ratio

The fraction of time the I/O thread spent doing I/O.

io-time-ns-avg

The average length of time for I/O per select call in nanoseconds.

io-wait-ratio

The fraction of time the I/O thread spent waiting.

io-wait-time-ns-avg

The average length of time the I/O thread spent waiting for a socket ready for reads or writes in nanoseconds.

network-io-rate

The average number of network operations (reads or writes) on all connections per second.

outgoing-byte-rate

The average number of outgoing bytes sent per second to all servers.

request-rate

The average number of requests sent per second.

request-size-avg

The average size of all requests in the window.

request-size-max

The maximum size of any request sent in the window.

response-rate

Responses received sent per second.

select-rate

Number of times the I/O layer checked for new I/O to perform per second.

successful-authentication-rate

Connections that were successfully authenticated using SASL or SSL.

7.7.2. MBeans matching kafka.consumer:type=consumer-metrics,client-id=*,node-id=*

These are metrics at the consumer level about connection to each broker.

AttributeDescription

incoming-byte-rate

The average number of responses received per second for a node.

outgoing-byte-rate

The average number of outgoing bytes sent per second for a node.

request-latency-avg

The average request latency in ms for a node.

request-latency-max

The maximum request latency in ms for a node.

request-rate

The average number of requests sent per second for a node.

request-size-avg

The average size of all requests in the window for a node.

request-size-max

The maximum size of any request sent in the window for a node.

response-rate

Responses received sent per second for a node.

7.7.3. MBeans matching kafka.consumer:type=consumer-coordinator-metrics,client-id=*

These are metrics at the consumer level about the consumer group.

AttributeDescription

assigned-partitions

The number of partitions currently assigned to this consumer.

commit-latency-avg

The average time taken for a commit request.

commit-latency-max

The max time taken for a commit request.

commit-rate

The number of commit calls per second.

heartbeat-rate

The average number of heartbeats per second.

heartbeat-response-time-max

The max time taken to receive a response to a heartbeat request.

join-rate

The number of group joins per second.

join-time-avg

The average time taken for a group rejoin.

join-time-max

The max time taken for a group rejoin.

last-heartbeat-seconds-ago

The number of seconds since the last controller heartbeat.

sync-rate

The number of group syncs per second.

sync-time-avg

The average time taken for a group sync.

sync-time-max

The max time taken for a group sync.

7.7.4. MBeans matching kafka.consumer:type=consumer-fetch-manager-metrics,client-id=*

These are metrics at the consumer level about the consumer's fetcher.

AttributeDescription

bytes-consumed-rate

The average number of bytes consumed per second.

bytes-consumed-total

The total number of bytes consumed.

fetch-latency-avg

The average time taken for a fetch request.

fetch-latency-max

The max time taken for any fetch request.

fetch-rate

The number of fetch requests per second.

fetch-size-avg

The average number of bytes fetched per request.

fetch-size-max

The maximum number of bytes fetched per request.

fetch-throttle-time-avg

The average throttle time in ms.

fetch-throttle-time-max

The maximum throttle time in ms.

fetch-total

The total number of fetch requests.

records-consumed-rate

The average number of records consumed per second.

records-consumed-total

The total number of records consumed.

records-lag-max

The maximum lag in terms of number of records for any partition in this window.

records-lead-min

The minimum lead in terms of number of records for any partition in this window.

records-per-request-avg

The average number of records in each request.

7.7.5. MBeans matching kafka.consumer:type=consumer-fetch-manager-metrics,client-id=*,topic=*

These are metrics at the topic level about the consumer's fetcher.

AttributeDescription

bytes-consumed-rate

The average number of bytes consumed per second for a topic.

bytes-consumed-total

The total number of bytes consumed for a topic.

fetch-size-avg

The average number of bytes fetched per request for a topic.

fetch-size-max

The maximum number of bytes fetched per request for a topic.

records-consumed-rate

The average number of records consumed per second for a topic.

records-consumed-total

The total number of records consumed for a topic.

records-per-request-avg

The average number of records in each request for a topic.

7.7.6. MBeans matching kafka.consumer:type=consumer-fetch-manager-metrics,client-id=*,topic=*,partition=*

These are metrics at the partition level about the consumer's fetcher.

AttributeDescription

records-lag

The latest lag of the partition.

records-lag-avg

The average lag of the partition.

records-lag-max

The max lag of the partition.

records-lead

The latest lead of the partition.

records-lead-avg

The average lead of the partition.

records-lead-min

The min lead of the partition.

7.8. Kafka Connect MBeans

Note

Kafka Connect will contain the producer MBeans for source connectors and consumer MBeans for sink connectors in addition to those documented here.

7.8.1. MBeans matching kafka.connect:type=connect-metrics,client-id=*

These are metrics at the connect level.

AttributeDescription

connection-close-rate

Connections closed per second in the window.

connection-count

The current number of active connections.

connection-creation-rate

New connections established per second in the window.

failed-authentication-rate

Connections that failed authentication.

incoming-byte-rate

Bytes/second read off all sockets.

io-ratio

The fraction of time the I/O thread spent doing I/O.

io-time-ns-avg

The average length of time for I/O per select call in nanoseconds.

io-wait-ratio

The fraction of time the I/O thread spent waiting.

io-wait-time-ns-avg

The average length of time the I/O thread spent waiting for a socket ready for reads or writes in nanoseconds.

network-io-rate

The average number of network operations (reads or writes) on all connections per second.

outgoing-byte-rate

The average number of outgoing bytes sent per second to all servers.

request-rate

The average number of requests sent per second.

request-size-avg

The average size of all requests in the window.

request-size-max

The maximum size of any request sent in the window.

response-rate

Responses received sent per second.

select-rate

Number of times the I/O layer checked for new I/O to perform per second.

successful-authentication-rate

Connections that were successfully authenticated using SASL or SSL.

7.8.2. MBeans matching kafka.connect:type=connect-metrics,client-id=*,node-id=*

These are metrics at the connect level about connection to each broker.

AttributeDescription

incoming-byte-rate

The average number of responses received per second for a node.

outgoing-byte-rate

The average number of outgoing bytes sent per second for a node.

request-latency-avg

The average request latency in ms for a node.

request-latency-max

The maximum request latency in ms for a node.

request-rate

The average number of requests sent per second for a node.

request-size-avg

The average size of all requests in the window for a node.

request-size-max

The maximum size of any request sent in the window for a node.

response-rate

Responses received sent per second for a node.

7.8.3. MBeans matching kafka.connect:type=connect-worker-metrics

These are metrics at the connect level.

AttributeDescription

connector-count

The number of connectors run in this worker.

connector-startup-attempts-total

The total number of connector startups that this worker has attempted.

connector-startup-failure-percentage

The average percentage of this worker’s connectors starts that failed.

connector-startup-failure-total

The total number of connector starts that failed.

connector-startup-success-percentage

The average percentage of this worker’s connectors starts that succeeded.

connector-startup-success-total

The total number of connector starts that succeeded.

task-count

The number of tasks run in this worker.

task-startup-attempts-total

The total number of task startups that this worker has attempted.

task-startup-failure-percentage

The average percentage of this worker’s tasks starts that failed.

task-startup-failure-total

The total number of task starts that failed.

task-startup-success-percentage

The average percentage of this worker’s tasks starts that succeeded.

task-startup-success-total

The total number of task starts that succeeded.

7.8.4. MBeans matching kafka.connect:type=connect-worker-rebalance-metrics

AttributeDescription

completed-rebalances-total

The total number of rebalances completed by this worker.

epoch

The epoch or generation number of this worker.

leader-name

The name of the group leader.

rebalance-avg-time-ms

The average time in milliseconds spent by this worker to rebalance.

rebalance-max-time-ms

The maximum time in milliseconds spent by this worker to rebalance.

rebalancing

Whether this worker is currently rebalancing.

time-since-last-rebalance-ms

The time in milliseconds since this worker completed the most recent rebalance.

7.8.5. MBeans matching kafka.connect:type=connector-metrics,connector=*

AttributeDescription

connector-class

The name of the connector class.

connector-type

The type of the connector. One of 'source' or 'sink'.

connector-version

The version of the connector class, as reported by the connector.

status

The status of the connector. One of 'unassigned', 'running', 'paused', 'failed', or 'destroyed'.

7.8.6. MBeans matching kafka.connect:type=connector-task-metrics,connector=*,task=*

AttributeDescription

batch-size-avg

The average size of the batches processed by the connector.

batch-size-max

The maximum size of the batches processed by the connector.

offset-commit-avg-time-ms

The average time in milliseconds taken by this task to commit offsets.

offset-commit-failure-percentage

The average percentage of this task’s offset commit attempts that failed.

offset-commit-max-time-ms

The maximum time in milliseconds taken by this task to commit offsets.

offset-commit-success-percentage

The average percentage of this task’s offset commit attempts that succeeded.

pause-ratio

The fraction of time this task has spent in the pause state.

running-ratio

The fraction of time this task has spent in the running state.

status

The status of the connector task. One of 'unassigned', 'running', 'paused', 'failed', or 'destroyed'.

7.8.7. MBeans matching kafka.connect:type=sink-task-metrics,connector=*,task=*

AttributeDescription

offset-commit-completion-rate

The average per-second number of offset commit completions that were completed successfully.

offset-commit-completion-total

The total number of offset commit completions that were completed successfully.

offset-commit-seq-no

The current sequence number for offset commits.

offset-commit-skip-rate

The average per-second number of offset commit completions that were received too late and skipped/ignored.

offset-commit-skip-total

The total number of offset commit completions that were received too late and skipped/ignored.

partition-count

The number of topic partitions assigned to this task belonging to the named sink connector in this worker.

put-batch-avg-time-ms

The average time taken by this task to put a batch of sinks records.

put-batch-max-time-ms

The maximum time taken by this task to put a batch of sinks records.

sink-record-active-count

The number of records that have been read from Kafka but not yet completely committed/flushed/acknowledged by the sink task.

sink-record-active-count-avg

The average number of records that have been read from Kafka but not yet completely committed/flushed/acknowledged by the sink task.

sink-record-active-count-max

The maximum number of records that have been read from Kafka but not yet completely committed/flushed/acknowledged by the sink task.

sink-record-lag-max

The maximum lag in terms of number of records that the sink task is behind the consumer’s position for any topic partitions.

sink-record-read-rate

The average per-second number of records read from Kafka for this task belonging to the named sink connector in this worker. This is before transformations are applied.

sink-record-read-total

The total number of records read from Kafka by this task belonging to the named sink connector in this worker, since the task was last restarted.

sink-record-send-rate

The average per-second number of records output from the transformations and sent/put to this task belonging to the named sink connector in this worker. This is after transformations are applied and excludes any records filtered out by the transformations.

sink-record-send-total

The total number of records output from the transformations and sent/put to this task belonging to the named sink connector in this worker, since the task was last restarted.

7.8.8. MBeans matching kafka.connect:type=source-task-metrics,connector=*,task=*

AttributeDescription

poll-batch-avg-time-ms

The average time in milliseconds taken by this task to poll for a batch of source records.

poll-batch-max-time-ms

The maximum time in milliseconds taken by this task to poll for a batch of source records.

source-record-active-count

The number of records that have been produced by this task but not yet completely written to Kafka.

source-record-active-count-avg

The average number of records that have been produced by this task but not yet completely written to Kafka.

source-record-active-count-max

The maximum number of records that have been produced by this task but not yet completely written to Kafka.

source-record-poll-rate

The average per-second number of records produced/polled (before transformation) by this task belonging to the named source connector in this worker.

source-record-poll-total

The total number of records produced/polled (before transformation) by this task belonging to the named source connector in this worker.

source-record-write-rate

The average per-second number of records output from the transformations and written to Kafka for this task belonging to the named source connector in this worker. This is after transformations are applied and excludes any records filtered out by the transformations.

source-record-write-total

The number of records output from the transformations and written to Kafka for this task belonging to the named source connector in this worker, since the task was last restarted.

7.8.9. MBeans matching kafka.connect:type=task-error-metrics,connector=*,task=*

AttributeDescription

deadletterqueue-produce-failures

The number of failed writes to the dead letter queue.

deadletterqueue-produce-requests

The number of attempted writes to the dead letter queue.

last-error-timestamp

The epoch timestamp when this task last encountered an error.

total-errors-logged

The number of errors that were logged.

total-record-errors

The number of record processing errors in this task.

total-record-failures

The number of record processing failures in this task.

total-records-skipped

The number of records skipped due to errors.

total-retries

The number of operations retried.

7.9. Kafka Streams MBeans

Note

A Streams application will contain the producer and consumer MBeans in addition to those documented here.

7.9.1. MBeans matching kafka.streams:type=stream-metrics,client-id=*

These metrics are collected when the metrics.recording.level configuration parameter is info or debug.

AttributeDescription

commit-latency-avg

The average execution time in ms for committing, across all running tasks of this thread.

commit-latency-max

The maximum execution time in ms for committing across all running tasks of this thread.

commit-rate

The average number of commits per second.

commit-total

The total number of commit calls across all tasks.

poll-latency-avg

The average execution time in ms for polling, across all running tasks of this thread.

poll-latency-max

The maximum execution time in ms for polling across all running tasks of this thread.

poll-rate

The average number of polls per second.

poll-total

The total number of poll calls across all tasks.

process-latency-avg

The average execution time in ms for processing, across all running tasks of this thread.

process-latency-max

The maximum execution time in ms for processing across all running tasks of this thread.

process-rate

The average number of process calls per second.

process-total

The total number of process calls across all tasks.

punctuate-latency-avg

The average execution time in ms for punctuating, across all running tasks of this thread.

punctuate-latency-max

The maximum execution time in ms for punctuating across all running tasks of this thread.

punctuate-rate

The average number of punctuates per second.

punctuate-total

The total number of punctuate calls across all tasks.

skipped-records-rate

The average number of skipped records per second.

skipped-records-total

The total number of skipped records.

task-closed-rate

The average number of tasks closed per second.

task-closed-total

The total number of tasks closed.

task-created-rate

The average number of newly created tasks per second.

task-created-total

The total number of tasks created.

7.9.2. MBeans matching kafka.streams:type=stream-task-metrics,client-id=*,task-id=*

Task metrics.

These metrics are collected when the metrics.recording.level configuration parameter is debug.

AttributeDescription

commit-latency-avg

The average commit time in ns for this task.

commit-latency-max

The maximum commit time in ns for this task.

commit-rate

The average number of commit calls per second.

commit-total

The total number of commit calls.

7.9.3. MBeans matching kafka.streams:type=stream-processor-node-metrics,client-id=*,task-id=*,processor-node-id=*

Processor node metrics.

These metrics are collected when the metrics.recording.level configuration parameter is debug.

AttributeDescription

create-latency-avg

The average create execution time in ns.

create-latency-max

The maximum create execution time in ns.

create-rate

The average number of create operations per second.

create-total

The total number of create operations called.

destroy-latency-avg

The average destroy execution time in ns.

destroy-latency-max

The maximum destroy execution time in ns.

destroy-rate

The average number of destroy operations per second.

destroy-total

The total number of destroy operations called.

forward-rate

The average rate of records being forwarded downstream, from source nodes only, per second.

forward-total

The total number of of records being forwarded downstream, from source nodes only.

process-latency-avg

The average process execution time in ns.

process-latency-max

The maximum process execution time in ns.

process-rate

The average number of process operations per second.

process-total

The total number of process operations called.

punctuate-latency-avg

The average punctuate execution time in ns.

punctuate-latency-max

The maximum punctuate execution time in ns.

punctuate-rate

The average number of punctuate operations per second.

punctuate-total

The total number of punctuate operations called.

7.9.4. MBeans matching kafka.streams:type=stream-[store-scope]-metrics,client-id=*,task-id=*,[store-scope]-id=*

State store metrics.

These metrics are collected when the metrics.recording.level configuration parameter is debug.

AttributeDescription

all-latency-avg

The average all operation execution time in ns.

all-latency-max

The maximum all operation execution time in ns.

all-rate

The average all operation rate for this store.

all-total

The total number of all operation calls for this store.

delete-latency-avg

The average delete execution time in ns.

delete-latency-max

The maximum delete execution time in ns.

delete-rate

The average delete rate for this store.

delete-total

The total number of delete calls for this store.

flush-latency-avg

The average flush execution time in ns.

flush-latency-max

The maximum flush execution time in ns.

flush-rate

The average flush rate for this store.

flush-total

The total number of flush calls for this store.

get-latency-avg

The average get execution time in ns.

get-latency-max

The maximum get execution time in ns.

get-rate

The average get rate for this store.

get-total

The total number of get calls for this store.

put-all-latency-avg

The average put-all execution time in ns.

put-all-latency-max

The maximum put-all execution time in ns.

put-all-rate

The average put-all rate for this store.

put-all-total

The total number of put-all calls for this store.

put-if-absent-latency-avg

The average put-if-absent execution time in ns.

put-if-absent-latency-max

The maximum put-if-absent execution time in ns.

put-if-absent-rate

The average put-if-absent rate for this store.

put-if-absent-total

The total number of put-if-absent calls for this store.

put-latency-avg

The average put execution time in ns.

put-latency-max

The maximum put execution time in ns.

put-rate

The average put rate for this store.

put-total

The total number of put calls for this store.

range-latency-avg

The average range execution time in ns.

range-latency-max

The maximum range execution time in ns.

range-rate

The average range rate for this store.

range-total

The total number of range calls for this store.

restore-latency-avg

The average restore execution time in ns.

restore-latency-max

The maximum restore execution time in ns.

restore-rate

The average restore rate for this store.

restore-total

The total number of restore calls for this store.

7.9.5. MBeans matching kafka.streams:type=stream-record-cache-metrics,client-id=*,task-id=*,record-cache-id=*

Record cache metrics.

These metrics are collected when the metrics.recording.level configuration parameter is debug.

AttributeDescription

hitRatio-avg

The average cache hit ratio defined as the ratio of cache read hits over the total cache read requests.

hitRatio-max

The maximum cache hit ratio.

hitRatio-min

The mininum cache hit ratio.

Chapter 8. Kafka Connect

Kafka Connect is a tool for streaming data between Apache Kafka and external systems. It provides a framework for moving large amounts of data while maintaining scalability and reliability. Kafka Connect is typically used to integrate Kafka with database, storage, and messaging systems that are external to your Kafka cluster.

Kafka Connect uses connector plug-ins that implement connectivity for different types of external systems. There are two types of connector plug-ins: sink and source. Sink connectors stream data from Kafka to external systems. Source connectors stream data from external systems into Kafka.

Kafka Connect can run in standalone or distributed modes.

Standalone mode
In standalone mode, Kafka Connect runs on a single node with user-defined configuration read from a properties file.
Distributed mode
In distributed mode, Kafka Connect runs across one or more worker nodes and the workloads are distributed among them. You manage connectors and their configuration using an HTTP REST interface.

8.1. Kafka Connect in standalone mode

In standalone mode, Kafka Connect runs as a single process, on a single node. You manage the configuration of standalone mode using properties files.

8.1.1. Configuring Kafka Connect in standalone mode

To configure Kafka Connect in standalone mode, edit the config/connect-standalone.properties configuration file. The following options are the most important.

bootstrap.servers
A list of Kafka broker addresses used as bootstrap connections to Kafka. For example, kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092.
key.converter
The class used to convert message keys to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
value.converter
The class used to convert message payloads to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
offset.storage.file.filename
Specifies the file in which the offset data is stored.

An example configuration file is provided in the installation directory at config/connect-standalone.properties. For a complete list of all supported Kafka Connect configuration options, see [kafka-connect-configuration-parameters-str].

Connector plug-ins open client connections to the Kafka brokers using the bootstrap address. To configure these connections, use the standard Kafka producer and consumer configuration options prefixed by producer. or consumer..

For more information on configuring Kafka producers and consumers, see:

8.1.2. Configuring connectors in Kafka Connect in standalone mode

You can configure connector plug-ins for Kafka Connect in standalone mode using properties files. Most configuration options are specific to each connector. The following options apply to all connectors:

name
The name of the connector, which must be unique within the current Kafka Connect instance.
connector.class
The class of the connector plug-in. For example, org.apache.kafka.connect.file.FileStreamSinkConnector.
tasks.max
The maximum number of tasks that the specified connector can use. Tasks enable the connector to perform work in parallel. The connector might create fewer tasks than specified.
key.converter
The class used to convert message keys to and from Kafka format. This overrides the default value set by the Kafka Connect configuration. For example, org.apache.kafka.connect.json.JsonConverter.
value.converter
The class used to convert message payloads to and from Kafka format. This overrides the default value set by the Kafka Connect configuration. For example, org.apache.kafka.connect.json.JsonConverter.

Additionally, you must set at least one of the following options for sink connectors:

topics
A comma-separated list of topics used as input.
topics.regex
A Java regular expression of topics used as input.

For all other options, see the documentation for the available connectors.

AMQ Streams includes example connector configuration files – see config/connect-file-sink.properties and config/connect-file-source.properties in the AMQ Streams installation directory.

8.1.3. Running Kafka Connect in standalone mode

This procedure describes how to configure and run Kafka Connect in standalone mode.

Prerequisites

  • An installed and running AMQ Streams} cluster.

Procedure

  1. Edit the /opt/kafka/config/connect-standalone.properties Kafka Connect configuration file and set bootstrap.server to point to your Kafka brokers. For example: 

    bootstrap.servers=kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092

  2. Start Kafka Connect with the configuration file and specify one or more connector configurations. 

    su - kafka
/opt/kafka/bin/connect-standalone.sh /opt/kafka/config/connect-standalone.properties connector1.properties
[connector2.properties ...]

  3. Verify that Kafka Connect is running. 

       jcmd | grep ConnectStandalone

Additional resources

8.2. Kafka Connect in distributed mode

In distributed mode, Kafka Connect runs across one or more worker nodes and the workloads are distributed among them. You manage connector plug-ins and their configuration using the HTTP REST interface.

8.2.1. Configuring Kafka Connect in distributed mode

To configure Kafka Connect in distributed mode, edit the config/connect-distributed.properties configuration file. The following options are the most important.

bootstrap.servers
A list of Kafka broker addresses used as bootstrap connections to Kafka. For example, kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092.
key.converter
The class used to convert message keys to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
value.converter
The class used to convert message payloads to and from Kafka format. For example, org.apache.kafka.connect.json.JsonConverter.
group.id
The name of the distributed Kafka Connect cluster. This must be unique and must not conflict with another consumer group ID. The default value is connect-cluster.
config.storage.topic
The Kafka topic used to store connector configurations. The default value is connect-configs.
offset.storage.topic
The Kafka topic used to store offsets. The default value is connect-offset.
status.storage.topic
The Kafka topic used for worker node statuses. The default value is connect-status.

AMQ Streams includes an example configuration file for Kafka Connect in distributed mode – see config/connect-distributed.properties in the AMQ Streams installation directory.

For a complete list of all supported Kafka Connect configuration options, see Appendix F, Kafka Connect configuration parameters.

Connector plug-ins open client connections to the Kafka brokers using the bootstrap address. To configure these connections, use the standard Kafka producer and consumer configuration options prefixed by producer. or consumer..

For more information on configuring Kafka producers and consumers, see:

8.2.2. Configuring connectors in distributed Kafka Connect

HTTP REST Interface

Connectors for distributed Kafka Connect are configured using HTTP REST interface. The REST interface listens on port 8083 by default. It supports following endpoints:

GET /connectors
Return a list of existing connectors.
POST /connectors
Create a connector. The request body has to be a JSON object with the connector configuration.
GET /connectors/<name>
Get information about a specific connector.
GET /connectors/<name>/config
Get configuration of a specific connector.
PUT /connectors/<name>/config
Update the configuration of a specific connector.
GET /connectors/<name>/status
Get the status of a specific connector.
PUT /connectors/<name>/pause
Pause the connector and all its tasks. The connector will stop processing any messages.
PUT /connectors/<name>/resume
Resume a paused connector.
POST /connectors/<name>/restart
Restart a connector in case it has failed.
DELETE /connectors/<name>
Delete a connector.
GET /connector-plugins
Get a list of all supported connector plugins.

Connector configuration

Most configuration options are connector specific and included in the documentation for the connectors. The following fields are common for all connectors.

name
Name of the connector. Must be unique within a given Kafka Connect instance.
connector.class
Class of the connector plugin. For example org.apache.kafka.connect.file.FileStreamSinkConnector.
tasks.max
The maximum number of tasks used by this connector. Tasks are used by the connector to parallelise its work. Connetors may create fewer tasks than specified.
key.converter
Class used to convert message keys to and from Kafka format. This overrides the default value set by the Kafka Connect configuration. For example, org.apache.kafka.connect.json.JsonConverter.
value.converter
Class used to convert message payloads to and from Kafka format. This overrides the default value set by the Kafka Connect configuration. For example, org.apache.kafka.connect.json.JsonConverter.

Additionally, one of the following options must be set for sink connectors:

topics
A comma-separated list of topics used as input.
topics.regex
A Java regular expression of topics used as input.

For all other options, see the documentation for the specific connector.

AMQ Streams includes example connector configuration files. They can be found in config/connect-file-sink.properties and config/connect-file-source.properties in the AMQ Streams installation directory.

8.2.3. Running distributed Kafka Connect

This procedure describes how to configure and run Kafka Connect in distributed mode.

Prerequisites

  • An installed and running AMQ Streams cluster.

Running the cluster

  1. Edit the /opt/kafka/config/connect-distributed.properties Kafka Connect configuration file on all Kafka Connect worker nodes.

    • Set the bootstrap.server option to point to your Kafka brokers.
    • Set the group.id option.
    • Set the config.storage.topic option.
    • Set the offset.storage.topic option.

    • Set the status.storage.topic option.

      For example: 

      bootstrap.servers=kafka0.my-domain.com:9092,kafka1.my-domain.com:9092,kafka2.my-domain.com:9092
group.id=my-group-id
config.storage.topic=my-group-id-configs
offset.storage.topic=my-group-id-offsets
status.storage.topic=my-group-id-status

  2. Start the Kafka Connect workers with the /opt/kafka/config/connect-distributed.properties configuration file on all Kafka Connect nodes. 

    su - kafka
/opt/kafka/bin/connect-distributed.sh /opt/kafka/config/connect-distributed.properties

  3. Verify that Kafka Connect is running. 

    jcmd | grep ConnectDistributed

Additional resources

8.2.4. Creating connectors

This procedure describes how to use the Kafka Connect REST API to create a connector plug-in for use with Kafka Connect in distributed mode.

Prerequisites

  • A Kafka Connect installation running in distributed mode.

Procedure

  1. Prepare a JSON payload with the connector configuration. For example: 

    {
  "name": "my-connector",
  "config": {
  "connector.class": "org.apache.kafka.connect.file.FileStreamSinkConnector",
    "tasks.max": "1",
    "topics": "my-topic-1,my-topic-2",
    "file": "/tmp/output-file.txt"
  }
}

  2. Send a POST request to <KafkaConnectAddress>:8083/connectors to create the connector. The following example uses curl: 

    curl -X POST -H "Content-Type: application/json" --data @sink-connector.json http://connect0.my-domain.com:8083/connectors

  3. Verify that the connector was deployed by sending a GET request to <KafkaConnectAddress>:8083/connectors. The following example uses curl: 

    curl http://connect0.my-domain.com:8083/connectors

8.2.5. Deleting connectors

This procedure describes how to use the Kafka Connect REST API to delete a connector plug-in from Kafka Connect in distributed mode.

Prerequisites

  • A Kafka Connect installation running in distributed mode.

Deleting connectors

  1. Verify that the connector exists by sending a GET request to <KafkaConnectAddress>:8083/connectors/<ConnectorName>. The following example uses curl: 

    curl http://connect0.my-domain.com:8083/connectors

  2. To delete the connector, send a DELETE request to <KafkaConnectAddress>:8083/connectors. The following example uses curl: 

    curl -X DELETE http://connect0.my-domain.com:8083/connectors/my-connector

  3. Verify that the connector was deleted by sending a GET request to <KafkaConnectAddress>:8083/connectors. The following example uses curl: 

    curl http://connect0.my-domain.com:8083/connectors

8.3. Connector plug-ins

The following connector plug-ins are included with AMQ Streams.

FileStreamSink Reads data from Kafka topics and writes the data to a file.

FileStreamSource Reads data from a file and sends the data to Kafka topics.

You can add more connector plug-ins if needed. Kafka Connect searches for and runs additional connector plug-ins at startup. To define the path that kafka Connect searches for plug-ins, set the plugin.path configuration option: 

plugin.path=/opt/kafka/connector-plugins,/opt/connectors

The plugin.path configuration option can contain a comma-separated list of paths.

When running Kafka Connect in distributed mode, plug-ins must be made available on all worker nodes.

8.4. Adding connector plugins

This procedure shows you how to add additional connector plug-ins.

Prerequisites

  • An installed and running AMQ Streams cluster.

Procedure

  1. Create the /opt/kafka/connector-plugins directory. 

    su - kafka
mkdir /opt/kafka/connector-plugins

  2. Edit the /opt/kafka/config/connect-standalone.properties or /opt/kafka/config/connect-distributed.properties Kafka Connect configuration file, and set the plugin.path option to /opt/kafka/connector-plugins. For example: 

    plugin.path=/opt/kafka/connector-plugins
  3. Copy your connector plug-ins to /opt/kafka/connector-plugins.
  4. Start or restart the Kafka Connect workers.

Additional resources

Chapter 9. Kafka clients

The kafka-clients JAR file contains the Kafka Producer and Consumer APIs together with the Kafka AdminClient API.

  • The Producer API allows applications to send data to a Kafka broker.
  • The Consumer API allows applications to consume data from a Kafka broker.
  • The AdminClient API provides functionality for managing Kafka clusters, including topics, brokers, and other components.

9.1. Adding Kafka clients as a dependency to your Maven project

This procedure shows you how to add the AMQ Streams Java clients as a dependency to your Maven project.

Prerequisites

  • A Maven project with an existing pom.xml.

Procedure

  1. Add the Red Hat Maven repository to the <repositories> section of your pom.xml file. 

    <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

    <!-- ... -->

    <repositories>
        <repository>
            <id>redhat-maven</id>
            <url>https://maven.repository.redhat.com/ga/</url>
        </repository>
    </repositories>

    <!-- ... -->

</project>

  2. Add the clients to the <dependencies> section of your pom.xml file. 

    <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

    <!-- ... -->

    <dependencies>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>2.3.0.redhat-00003</version>
        </dependency>
    </dependencies>

    <!-- ... -->
</project>
  3. Build your Maven project.

Chapter 10. Kafka Streams API overview

The Kafka Streams API allows applications to receive data from one or more input streams, execute complex operations like mapping, filtering or joining, and write the results into one or more output streams. It is part of the kafka-streams JAR package that is available in the Red Hat Maven repository.

10.1. Adding the Kafka Streams API as a dependency to your Maven project

This procedure shows you how to add the AMQ Streams Java clients as a dependency to your Maven project.

Prerequisites

  • A Maven project with an existing pom.xml.

Procedure

  1. Add the Red Hat Maven repository to the <repositories> section of your pom.xml file. 

    <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

    <!-- ... -->

    <repositories>
        <repository>
            <id>redhat-maven</id>
            <url>https://maven.repository.redhat.com/ga/</url>
        </repository>
    </repositories>

    <!-- ... -->

</project>

  2. Add kafka-streams to the <dependencies> section of your pom.xml file. 

    <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

    <!-- ... -->

    <dependencies>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-streams</artifactId>
            <version>2.3.0.redhat-00003</version>
        </dependency>
    </dependencies>

    <!-- ... -->
</project>
  3. Build your Maven project.

Chapter 11. Kafka Bridge

This chapter provides an overview of the AMQ Streams Kafka Bridge on Red Hat Enterprise Linux and helps you get started using its REST API to interact with AMQ Streams. To try out the Kafka Bridge in your local environment, see the Section 11.2, “Kafka Bridge quickstart” later in this chapter.

Additional resources

11.1. Kafka Bridge overview

The AMQ Streams Kafka Bridge provides an API for integrating HTTP-based clients with a Kafka cluster running on Red Hat Enterprise Linux. The API enables such clients to produce and consume messages without the requirement to use the native Kafka protocol.

The API has two main resources — consumers and topics — that are exposed and made accessible through endpoints to interact with consumers and producers in your Kafka cluster. The resources relate only to the Kafka Bridge, not the consumers and producers connected directly to Kafka.

You can:

  • Send messages to a topic.
  • Create and delete consumers.
  • Subscribe consumers to topics, so that they start receiving messages from those topics.
  • Retrieve a list of the topics to which a consumer is subscribed.
  • Unsubscribe consumers from topics.
  • Assign partitions to consumers.
  • Retrieve messages from topics.
  • Commit a list of consumer offsets.
  • Seek on a partition, so that a consumer starts receiving messages from the first or last offset position, or a given offset position.

Similar to an AMQ Streams installation, you can download the Kafka Bridge files for installation on Red Hat Enterprise Linux. See Section 11.1.4, “Downloading a Kafka Bridge archive”.

For more information on configuring the host and port for the KafkaBridge resource, see Section 11.1.5, “Configuring Kafka Bridge properties”.

11.1.1. Requests to the Kafka Bridge

11.1.1.1. Authentication and encryption

Authentication and encryption between HTTP clients and the Kafka Bridge is not yet supported. This means that requests sent from clients to the Kafka Bridge are:

  • Not encrypted, and must use HTTP rather than HTTPS
  • Sent without authentication

You can configure TLS or SASL-based authentication between the Kafka Bridge and your Kafka cluster.

You configure the Kafka Bridge for authentication through its properties file.

11.1.1.2. Data formats and headers

Specify data formats and HTTP headers to ensure valid requests are submitted to the Kafka Bridge.

API request and response bodies are always encoded as JSON.

11.1.1.2.1. Content Type headers

A Content-Type header must be submitted for all requests (apart from the exception described below).

Consumer operations (/consumers endpoints) and producer operations (/topics endpoints) require different Content-Type headers.

Content-Type headers for consumer operations

Regardless of the embedded data format, POST requests for consumer operations must provide the following Content-Type header if the request body contains data: 

Content-Type: application/vnd.kafka.v2+json

Do not send a Content-Type header if the POST request body is empty, or the request will fail.

Content-Type headers for producer operations

When performing producer operations, POST requests must provide the following Content-Type header specifying the desired embedded data format, either json or binary, as shown in the following table.

Embedded data formatContent-Type header

JSON

Content-Type: application/vnd.kafka.json.v2+json

Binary

Content-Type: application/vnd.kafka.binary.v2+json

You set the embedded data format when creating a consumer using the consumers/groupid endpoint—​for more information, see the next section.

The Content-Type must not be set if the POST request has an empty body. An empty body can be used to create a consumer with the default values.

11.1.1.2.2. Embedded data format

The embedded data format is the format of the Kafka messages that are transmitted, over HTTP, from a producer to a consumer using the Kafka Bridge. Two embedded data formats are supported: JSON or binary.

When creating a consumer using the /consumers/groupid endpoint, the POST request body must specify an embedded data format of either JSON or binary. This is specified in the format field in the request body, for example: 

{
  "name": "my-consumer",
  "format": "binary", 1
...
}
1
A binary embedded data format.

If an embedded data format for the consumer is not specified, then a binary format is set.

The embedded data format specified when creating a consumer must match the data format of the Kafka messages it will consume.

If you choose to specify a binary embedded data format, subsequent producer requests must provide the binary data in the request body as Base64-encoded strings. For example, when sending messages by making POST requests to the /topics/topicname endpoint, the value must be encoded in Base64: 

{
  "records": [
    {
      "key": "my-key",
      "value": "ZWR3YXJkdGhldGhyZWVsZWdnZWRjYXQ="
    },
  ]
}

Producer requests must also provide a Content-Type header that corresponds to the embedded data format, for example, Content-Type: application/vnd.kafka.binary.v2+json.

11.1.1.2.3. Accept headers

After creating a consumer, all subsequent GET requests must provide an Accept header in the following format: 

Accept: application/vnd.kafka.embedded-data-format.v2+json

The embedded-data-format is either json or binary.

For example, when retrieving records for a subscribed consumer using an embedded data format of JSON, include this Accept header: 

Accept: application/vnd.kafka.json.v2+json

11.1.2. Configuring loggers for the Kafka Bridge

The AMQ Streams Kafka bridge allows you to set a different log level for each operation that is defined by the related OpenAPI specification.

Each operation has a corresponding API endpoint through which the bridge receives requests from HTTP clients. You can change the log level on each endpoint to produce more or less fine-grained logging information about the incoming and outgoing HTTP requests.

Loggers are defined in the log4j.properties file, which has the following default configuration for healthy and ready endpoints: 

log4j.logger.http.openapi.operation.healthy=WARN, out
log4j.additivity.http.openapi.operation.healthy=false
log4j.logger.http.openapi.operation.ready=WARN, out
log4j.additivity.http.openapi.operation.ready=false

The log level of all other operations is set to INFO by default. Loggers are formatted as follows: 

log4j.logger.http.openapi.operation.<operation-id>

Where <operation-id> is the identifier of the specific operation. Following is the list of operations defined by the OpenAPI specification:

  • createConsumer
  • deleteConsumer
  • subscribe
  • unsubscribe
  • poll
  • assign
  • commit
  • send
  • sendToPartition
  • seekToBeginning
  • seekToEnd
  • seek
  • healthy
  • ready
  • openapi

11.1.3. Kafka Bridge API resources

For the full list of REST API endpoints and descriptions, including example requests and responses, see the Kafka Bridge API reference.

11.1.4. Downloading a Kafka Bridge archive

A zipped distribution of the AMQ Streams Kafka Bridge is available for download from the Red Hat website.

Procedure

  • Download the latest version of the Red Hat AMQ Streams Kafka Bridge archive from the Customer Portal.

11.1.5. Configuring Kafka Bridge properties

This procedure describes how to configure the properties used by the AMQ Streams Kafka Bridge.

You configure the Kafka Bridge, as any other Kafka client, using appropriate prefixes for Kafka-related properties.

  • kafka. for general configuration that applies to producers and consumers, such as server connection and security.
  • kafka.consumer. for consumer-specific configuration passed only to the consumer.
  • kafka.producer. for producer-specific configuration passed only to the producer.

Prerequisites

Procedure

  1. Edit the application.properties file provided with the AMQ Streams Kafka Bridge installation archive.

    Use the properties file to specify Kafka and HTTP-related properties.

    1. Configure standard Kafka-related properties, including properties specific to the Kafka consumers and producers.

      Use:

      • kafka.bootstrap.servers to define the host/port connections to the Kafka cluster
      • kafka.producer.acks to provide acknowledgments to the HTTP client

      • kafka.consumer.auto.offset.reset to determine how to manage reset of the offset in Kafka

        For more information on configuration of Kafka properties, see the Apache Kafka website

    2. Configure HTTP-related properties to enable HTTP access to the Kafka cluster. 

      http.enabled=true
http.host=0.0.0.0
http.port=8080

11.1.6. Installing the Kafka Bridge

Follow this procedure to install the AMQ Streams Kafka Bridge on Red Hat Enterprise Linux.

Prerequisites

Procedure

  1. If you have not already done so, unzip the AMQ Streams Kafka Bridge installation archive to any directory.

  2. Run the Kafka Bridge script using the configuration properties as a parameter:

    For example: 

    ./bin/kafka_bridge_run.sh --config-file=_path_/configfile.properties

  3. Check to see that the installation was successful in the log. 

    HTTP-Kafka Bridge started and listening on port 8080
HTTP-Kafka Bridge bootstrap servers localhost:9092

11.2. Kafka Bridge quickstart

Use this quickstart to try out the AMQ Streams Kafka Bridge on Red Hat Enterprise Linux. You will learn how to:

  • Install the Kafka Bridge
  • Produce messages to topics and partitions in your Kafka cluster
  • Create a Kafka Bridge consumer
  • Perform basic consumer operations, such as subscribing the consumer to topics and retrieving the messages that you produced

In this quickstart, HTTP requests are formatted as curl commands that you can copy and paste to your terminal.

Ensure you have the prerequisites and then follow the tasks in the order provided in this chapter.

About data formats

In this quickstart, you will produce and consume messages in JSON format, not binary. For more information on the data formats and HTTP headers used in the example requests, see Section 11.1.1, “Requests to the Kafka Bridge”.

Prerequisites for the quickstart

11.2.1. Deploying the Kafka Bridge locally

Deploy an instance of the AMQ Streams Kafka Bridge to the host. Use the application.properties file provided with the installation archive to apply the default configuration settings.

Procedure

  1. Open the application.properties file and check that the default HTTP related settings are defined: 

    http.enabled=true
http.host=0.0.0.0
http.port=8080

    This configures the Kafka Bridge to listen for requests on port 8080.

  2. Run the Kafka Bridge script using the configuration properties as a parameter: 

    ./bin/kafka_bridge_run.sh --config-file=<path>/application.properties

What to do next

11.2.2. Producing messages to topics and partitions

Produce messages to a topic in JSON format by using the topics endpoint.

You can specify destination partitions for messages in the request body, as shown below. The partitions endpoint provides an alternative method for specifying a single destination partition for all messages as a path parameter.

Procedure

  1. Create a Kafka topic using the kafka-topics.sh utility: 

    bin/kafka-topics.sh --zookeeper localhost:2181 --create --topic bridge-quickstart-topic --partitions 3 --replication-factor 1 --config retention.ms=7200000 --config segment.bytes=1073741824

    Specify three partitions.

  2. Verify that the topic was created: 

    bin/kafka-topics.sh --zookeeper localhost:2181 --describe --topic bridge-quickstart-topic

  3. Using the Kafka Bridge, produce three messages to the topic you created: 

    curl -X POST \
  http://localhost:8080/topics/bridge-quickstart-topic \
  -H 'content-type: application/vnd.kafka.json.v2+json' \
  -d '{
    "records": [
        {
            "key": "my-key",
            "value": "sales-lead-0001"
        },
        {
            "value": "sales-lead-0002",
            "partition": 2
        },
        {
            "value": "sales-lead-0003"
        }
    ]
}'
    • sales-lead-0001 is sent to a partition based on the hash of the key.
    • sales-lead-0002 is sent directly to partition 2.
    • sales-lead-0003 is sent to a partition in the bridge-quickstart-topic topic using a round-robin method.

  4. If the request is successful, the Kafka Bridge returns an offsets array, along with a 200 (OK) code and a content-type header of application/vnd.kafka.v2+json. For each message, the offsets array describes:

    • The partition that the message was sent to

    • The current message offset of the partition

      Example response

      #...
{
  "offsets":[
    {
      "partition":0,
      "offset":0
    },
    {
      "partition":2,
      "offset":0
    },
    {
      "partition":0,
      "offset":1
    }
  ]
}

What to do next

After producing messages to topics and partitions, create a Kafka Bridge consumer.

Additional resources

11.2.3. Creating a Kafka Bridge consumer

Before you can perform any consumer operations on the Kafka cluster, you must first create a consumer by using the consumers endpoint. The consumer is referred to as a Kafka Bridge consumer.

Procedure

  1. Create a Kafka Bridge consumer in a new consumer group named bridge-quickstart-consumer-group: 

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "name": "bridge-quickstart-consumer",
    "auto.offset.reset": "earliest",
    "format": "json",
    "enable.auto.commit": false,
    "fetch.min.bytes": 512,
    "consumer.request.timeout.ms": 30000
  }'
    • The consumer is named bridge-quickstart-consumer and the embedded data format is set as json.

    • The consumer will not commit offsets to the log automatically because the enable.auto.commit setting is false. You will commit the offsets manually later in this quickstart.

      Note

      The Kafka Bridge generates a random consumer name if you do not specify a consumer name in the request body.

      If the request is successful, the Kafka Bridge returns the consumer ID (instance_id) and base URL (base_uri) in the response body, along with a 200 (OK) code.

      Example response

      #...
{
  "instance_id": "bridge-quickstart-consumer",
  "base_uri":"http://<bridge-name>-bridge-service:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer"
}

  2. Copy the base URL (base_uri) to use in the other consumer operations in this quickstart.

What to do next

Now that you have created a Kafka Bridge consumer, you can subscribe it to topics.

Additional resources

11.2.4. Subscribing a Kafka Bridge consumer to topics

Subscribe the Kafka Bridge consumer to one or more topics by using the subscription endpoint. Once subscribed, the consumer starts receiving all messages that are produced to the topic.

Procedure

  • Subscribe the consumer to the bridge-quickstart-topic topic that you created earlier, in Producing messages to topics and partitions: 

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/subscription \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "topics": [
        "bridge-quickstart-topic"
    ]
}'

    The topics array can contain a single topic (as shown above) or multiple topics. If you want to subscribe the consumer to multiple topics that match a regular expression, you can use the topic_pattern string instead of the topics array.

    If the request is successful, the Kafka Bridge returns a 204 No Content code only.

What to do next

After subscribing a Kafka Bridge consumer to topics, you can retrieve messages from the consumer.

Additional resources

11.2.5. Retrieving the latest messages from a Kafka Bridge consumer

Retrieve the latest messages from the Kafka Bridge consumer by requesting data from the records endpoint. In production, HTTP clients can call this endpoint repeatedly (in a loop).

Procedure

  1. Produce additional messages to the Kafka Bridge consumer, as described in Producing messages to topics and partitions.

  2. Submit a GET request to the records endpoint: 

    curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'

    After creating and subscribing to a Kafka Bridge consumer, a first GET request will return an empty response because the poll operation triggers a rebalancing process to assign partitions.

  3. Repeat step two to retrieve messages from the Kafka Bridge consumer.

    The Kafka Bridge returns an array of messages — describing the topic name, key, value, partition, and offset — in the response body, along with a 200 (OK) code. Messages are retrieved from the latest offset by default. 

    HTTP/1.1 200 OK
content-type: application/vnd.kafka.json.v2+json
#...
[
  {
    "topic":"bridge-quickstart-topic",
    "key":"my-key",
    "value":"sales-lead-0001",
    "partition":0,
    "offset":0
  },
  {
    "topic":"bridge-quickstart-topic",
    "key":null,
    "value":"sales-lead-0003",
    "partition":0,
    "offset":1
  },
#...
    Note

    If an empty response is returned, produce more records to the consumer as described in Producing messages to topics and partitions, and then try retrieving messages again.

What to do next

After retrieving messages from a Kafka Bridge consumer, try committing offsets to the log.

Additional resources

11.2.6. Commiting offsets to the log

Use the offsets endpoint to manually commit offsets to the log for all messages received by the Kafka Bridge consumer. This is required because the Kafka Bridge consumer that you created earlier, in Creating a Kafka Bridge consumer, was configured with the enable.auto.commit setting as false.

Procedure

  • Commit offsets to the log for the bridge-quickstart-consumer: 

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/offsets

    Because no request body is submitted, offsets are committed for all the records that have been received by the consumer. Alternatively, the request body can contain an array (OffsetCommitSeekList) that specifies the topics and partitions that you want to commit offsets for.

    If the request is successful, the Kafka Bridge returns a 204 No Content code only.

What to do next

After committing offsets to the log, try out the endpoints for seeking to offsets.

Additional resources

11.2.7. Seeking to offsets for a partition

Use the positions endpoints to configure the Kafka Bridge consumer to retrieve messages for a partition from a specific offset, and then from the latest offset. This is referred to in Apache Kafka as a seek operation.

Procedure

  1. Seek to a specific offset for partition 0 of the quickstart-bridge-topic topic: 

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "offsets": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0,
            "offset": 2
        }
    ]
}'

    If the request is successful, the Kafka Bridge returns a 204 No Content code only.

  2. Submit a GET request to the records endpoint: 

    curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'

    The Kafka Bridge returns messages from the offset that you seeked to.

  3. Restore the default message retrieval behavior by seeking to the last offset for the same partition. This time, use the positions/end endpoint. 

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions/end \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "partitions": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0
        }
    ]
}'

    If the request is successful, the Kafka Bridge returns another 204 No Content code.

Note

You can also use the positions/beginning endpoint to seek to the first offset for one or more partitions.

What to do next

In this quickstart, you have used the AMQ Streams Kafka Bridge to perform several common operations on a Kafka cluster. You can now delete the Kafka Bridge consumer that you created earlier.

Additional resources

11.2.8. Deleting a Kafka Bridge consumer

Finally, delete the Kafa Bridge consumer that you used throughout this quickstart.

Procedure

  • Delete the Kafka Bridge consumer by sending a DELETE request to the instances endpoint. 

    curl -X DELETE http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer

    If the request is successful, the Kafka Bridge returns a 204 No Content code only.

Additional resources

Chapter 12. Kafka Exporter

Kafka Exporter is an open source project to enhance monitoring of Apache Kafka brokers and clients.

Kafka Exporter is provided with AMQ Streams for deployment with a Kafka cluster to extract additional metrics data from Kafka brokers related to offsets, consumer groups, consumer lag, and topics.

The metrics data is used, for example, to help identify slow consumers.

Lag data is exposed as Prometheus metrics, which can then be presented in Grafana for analysis.

If you are already using Prometheus and Grafana for monitoring of built-in Kafka metrics, you can configure Prometheus to also scrape the Kafka Exporter Prometheus endpoint.

Additional resources

Kafka exposes metrics through JMX, which can then be exported as Prometheus metrics.

12.1. Consumer lag

Consumer lag indicates the difference in the rate of production and consumption of messages. Specifically, consumer lag for a given consumer group indicates the delay between the last message in the partition and the message being currently picked up by that consumer. The lag reflects the position of the consumer offset in relation to the end of the partition log.

This difference is sometimes referred to as the delta between the producer offset and consumer offset, the read and write positions in the Kafka broker topic partitions.

Suppose a topic streams 100 messages a second. A lag of 1000 messages between the producer offset (the topic partition head) and the last offset the consumer has read means a 10-second delay.

The importance of monitoring consumer lag

For applications that rely on the processing of (near) real-time data, it is critical to monitor consumer lag to check that it does not become too big. The greater the lag becomes, the further the process moves from the real-time processing objective.

Consumer lag, for example, might be a result of consuming too much old data that has not been purged, or through unplanned shutdowns.

Reducing consumer lag

Typical actions to reduce lag include:

  • Scaling-up consumer groups by adding new consumers
  • Increasing the retention time for a message to remain in a topic
  • Adding more disk capacity to increase the message buffer

Actions to reduce consumer lag depend on the underlying infrastructure and the use cases AMQ Streams is supporting. For instance, a lagging consumer is less likely to benefit from the broker being able to service a fetch request from its disk cache. And in certain cases, it might be acceptable to automatically drop messages until a consumer has caught up.

12.2. Kafka Exporter alerting rule examples

The sample alert notification rules specific to Kafka Exporter are as follows:

UnderReplicatedPartition
An alert to warn that a topic is under-replicated and the broker is not replicating enough partitions. The default configuration is for an alert if there are one or more under-replicated partitions for a topic. The alert might signify that a Kafka instance is down or the Kafka cluster is overloaded. A planned restart of the Kafka broker may be required to restart the replication process.
TooLargeConsumerGroupLag
An alert to warn that the lag on a consumer group is too large for a specific topic partition. The default configuration is 1000 records. A large lag might indicate that consumers are too slow and are falling behind the producers.
NoMessageForTooLong
An alert to warn that a topic has not received messages for a period of time. The default configuration for the time period is 10 minutes. The delay might be a result of a configuration issue preventing a producer from publishing messages to the topic.

You can adapt alerting rules according to your specific needs.

Additional resources

For more information about setting up alerting rules, see Configuration in the Prometheus documentation.

12.3. Kafka Exporter metrics

Lag information is exposed by Kafka Exporter as Prometheus metrics for presentation in Grafana.

Kafka Exporter exposes metrics data for brokers, topics, and consumer groups.

The data extracted is described here.

Table 12.1. Broker metrics output
NameInformation

kafka_brokers

Number of brokers in the Kafka cluster

Table 12.2. Topic metrics output
NameInformation

kafka_topic_partitions

Number of partitions for a topic

kafka_topic_partition_current_offset

Current topic partition offset for a broker

kafka_topic_partition_oldest_offset

Oldest topic partition offset for a broker

kafka_topic_partition_in_sync_replica

Number of in-sync replicas for a topic partition

kafka_topic_partition_leader

Leader broker ID of a topic partition

kafka_topic_partition_leader_is_preferred

Shows 1 if a topic partition is using the preferred broker

kafka_topic_partition_replicas

Number of replicas for this topic partition

kafka_topic_partition_under_replicated_partition

Shows 1 if a topic partition is under-replicated

Table 12.3. Consumer group metrics output
NameInformation

kafka_consumergroup_current_offset

Current topic partition offset for a consumer group

kafka_consumergroup_lag

Current approximate lag for a consumer group at a topic partition

12.4. Running Kafka Exporter

Kafka Exporter is provided with the download archive used for Installing AMQ Streams.

You can run it to expose Prometheus metrics for presentation in a Grafana dashboard.

Prerequisites

This procedure assumes you already have access to a Grafana user interface and Prometheus is deployed and added as a data source.

Procedure

  1. Run the Kafka Exporter script using appropriate configuration parameter values. 

    ./bin/kafka_exporter --kafka.server=<kafka-bootstrap-address>:9092 --kafka.version=2.3.0  --<my-other-parameters>

    The parameters require a double-hyphen convention, such as --kafka.server.

    OptionDescriptionDefault

    kafka.server

    Host/post address of the Kafka server.

    kafka:9092

    kafka.version

    Kafka broker version.

    1.0.0

    group.filter

    A regular expression to specify the consumer groups to include in the metrics.

    .* (all)

    topic.filter

    A regular expression to specify the topics to include in the metrics.

    .* (all)

    sasl.<parameter>

    Parameters to enable and connect to the Kafka cluster using SASL/PLAIN authentication, with user name and password.

    false

    tls.<parameter>

    Parameters to enable connect to the Kafka cluster using TLS authentication, with optional certificate and key.

    false

    web.listen-address

    Port address to expose the metrics.

    :9308

    web.telemetry-path

    Path for the exposed metrics.

    /metrics

    log.level

    Logging configuration, to log messages with a given severity (debug, info, warn, error, fatal) or above.

    info

    log.enable-sarama

    Boolean to enable Sarama logging, a Go client library used by the Kafka Exporter.

    false

    You can use kafka_exporter --help for information on the properties.

  2. Configure Prometheus to monitor the Kafka Exporter metrics.

    For more information on configuring Prometheus, see the Prometheus documentation.

  3. Enable Grafana to present the Kafka Exporter metrics data exposed by Prometheus.

    For more information, see Presenting Kafka Exporter metrics in Grafana.

12.5. Presenting Kafka Exporter metrics in Grafana

Using Kafka Exporter Prometheus metrics as a data source, you can create a dashboard of Grafana charts.

For example, from the metrics you can create the following Grafana charts:

  • Message in per second (from topics)
  • Message in per minute (from topics)
  • Lag by consumer group
  • Messages consumed per minute (by consumer groups)

When metrics data has been collected for some time, the Kafka Exporter charts are populated.

Use the Grafana charts to analyze lag and to check if actions to reduce lag are having an impact on an affected consumer group. If, for example, Kafka brokers are adjusted to reduce lag, the dashboard will show the Lag by consumer group chart going down and the Messages consumed per minute chart going up.

Additional resources

Chapter 13. AMQ Streams and Kafka upgrades

AMQ Streams can be upgraded with no cluster downtime. Each version of AMQ Streams supports one or more versions of Apache Kafka: you can upgrade to a higher Kafka version as long as it is supported by your version of AMQ Streams. In some cases, you can also downgrade to a lower supported Kafka version.

Newer versions of AMQ Streams may support newer versions of Kafka, but you need to upgrade AMQ Streams before you can upgrade to a higher supported Kafka version.

13.1. Upgrade prerequisites

Before you begin the upgrade process, make sure that:

13.2. Upgrade process

Upgrading AMQ Streams is a two-stage process. To upgrade brokers and clients without downtime, you must complete the upgrade procedures in the following order:

  1. Upgrade to the latest AMQ Streams version.

  2. Upgrade all Kafka brokers and client applications to the latest Kafka version

13.3. Kafka versions

AMQ Streams is based on a specific version of Apache Kafka.

AMQ Streams versionKafka version

1.3

2.3.0

Kafka’s log message format version and inter-broker protocol version specify the version of the format of messages appended to broker logs and the version of protocol used between brokers in a cluster. As a result, the upgrade process involves making configuration changes to existing Kafka brokers and code changes to client applications (consumers and producers) to ensure the correct versions are used.

The following table shows the differences between Kafka versions:

Kafka versionInterbroker protocol versionLog message format versionZookeeper version

2.2.1

2.2

2.2

3.4.14

2.3.0

2.3

2.3

3.4.14

Although Kafka versions may use the same version of Zookeeper, it is recommended that you update your Zookeeper cluster to use the newest Zookeeper binaries before proceeding with the main AMQ Streams upgrade.

Message format version

When a producer sends a message to a Kafka broker, the message is encoded using a specific format. The format can change between Kafka releases, so messages include a version identifying which version of the format they were encoded with. You can configure a Kafka broker to convert messages from newer format versions to a given older format version before the broker appends the message to the log.

In Kafka, there are two different methods for setting the message format version:

  • The message.format.version property is set on topics.
  • The log.message.format.version property is set on Kafka brokers.

The default value of message.format.version for a topic is defined by the log.message.format.version that is set on the Kafka broker. You can manually set the message.format.version of a topic by modifying its topic configuration.

The upgrade tasks in this section assume that the message format version is defined at the broker level by the log.message.format.version.

13.4. Upgrading to AMQ Streams 1.3

The steps to upgrade your deployment to use AMQ Streams 1.3 are outlined in this section.

The availability of Kafka clusters managed by AMQ Streams is not affected by the upgrade operation.

Note

Refer to the documentation supporting a specific version of AMQ Streams for information on how to upgrade to that version.

13.4.1. Upgrading Zookeeper

This procedure describes how to upgrade Zookeeper on a host machine.

Prerequisites

  • You are logged in to Red Hat Enterprise Linux as the kafka user.

Procedure

For each Kafka broker in your AMQ Streams cluster and one at a time:

  1. Download the AMQ Streams archive for this release from the Customer Portal.

    Note

    If prompted, log in to your Red Hat account.

  2. On the command line, create a temporary directory and extract the contents of the amq-streams-x.y.z-bin.zip file: 

    mkdir /tmp/kafka
unzip amq-streams-x.y.z-bin.zip -d /tmp/kafka

  3. Delete the libs, bin, and docs directories from your existing installation: 

    rm -rf /opt/kafka/libs /opt/kafka/bin /opt/kafka/docs

  4. Copy the libs, bin, and docs directories from the temporary directory: 

    cp -r /tmp/kafka/kafka_y.y-x.x.x/libs /opt/kafka/
cp -r /tmp/kafka/kafka_y.y-x.x.x/bin /opt/kafka/
cp -r /tmp/kafka/kafka_y.y-x.x.x/docs /opt/kafka/

  5. Delete the temporary directory: 

    rm -rf /tmp/kafka

  6. Restart Zookeeper: 

    /opt/kafka/bin/zookeeper-server-start.sh -daemon /opt/kafka/config/zookeeper.properties

13.4.2. Upgrading Kafka brokers

This procedure describes how to upgrade Kafka brokers on a host machine.

Prerequisites

  • You are logged in to Red Hat Enterprise Linux as the kafka user.

Procedure

For each Kafka broker in your AMQ Streams cluster and one at a time:

  1. Download the AMQ Streams archive from the Customer Portal.

    Note

    If prompted, log in to your Red Hat account.

  2. On the command line, create a temporary directory and extract the contents of the amq-streams-x.y.z-bin.zip file. 

    mkdir /tmp/kafka
unzip amq-streams-x.y.z-bin.zip -d /tmp/kafka

  3. Delete the libs, bin, and docs directories from your existing installation: 

    rm -rf /opt/kafka/libs /opt/kafka/bin /opt/kafka/docs

  4. Copy the libs, bin, and docs directories from the temporary directory: 

    cp -r /tmp/kafka/kafka_y.y-x.x.x/libs /opt/kafka/
cp -r /tmp/kafka/kafka_y.y-x.x.x/bin /opt/kafka/
cp -r /tmp/kafka/kafka_y.y-x.x.x/docs /opt/kafka/

  5. Delete the temporary directory. 

    rm -r /tmp/kafka
  6. In a text editor, open the broker properties file, commonly stored in the /opt/kafka/config/ directory.

  7. Temporarily override the default inter-broker protocol and message format versions for Kafka 2.3.0 by adding or updating the following properties in the file: 

    inter.broker.protocol.version=2.2
log.message.format.version=2.2

    This configures the Kafka broker to process data using the previous inter-broker protocol (2.2) and message format versions.

  8. On the command line, stop the Kafka broker that you modified: 

    /opt/kafka/bin/kafka-server-stop.sh
jcmd | grep kafka

  9. Restart the Kafka broker that you modified: 

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
    Note

    The Kafka broker will start using the binaries for the latest Kafka version.

  10. Verify that the restarted Kafka broker has caught up with the partition replicas it is following. Use the kafka-topics.sh tool to ensure that all replicas contained in the broker are back in sync. For instructions, see Listing and describing topics.

13.4.3. Upgrading Kafka Connect

This procedure describes how to upgrade a Kafka Connect cluster on a host machine.

Kafka Connect is a client application and should be included in your chosen strategy for upgrading clients. For more information, see Strategies for upgrading clients.

Prerequisites

  • You are logged in to Red Hat Enterprise Linux as the kafka user.

Procedure

For each Kafka broker in your AMQ Streams cluster and one at a time:

  1. Download the AMQ Streams archive from the Customer Portal.

    Note

    If prompted, log in to your Red Hat account.

  2. On the command line, create a temporary directory and extract the contents of the amq-streams-x.y.z-bin.zip file. 

    mkdir /tmp/kafka
unzip amq-streams-x.y.z-bin.zip -d /tmp/kafka

  3. Delete the libs, bin, and docs directories from your existing installation: 

    rm -rf /opt/kafka/libs /opt/kafka/bin /opt/kafka/docs

  4. Copy the libs, bin, and docs directories from the temporary directory: 

    cp -r /tmp/kafka/kafka_y.y-x.x.x/libs /opt/kafka/
cp -r /tmp/kafka/kafka_y.y-x.x.x/bin /opt/kafka/
cp -r /tmp/kafka/kafka_y.y-x.x.x/docs /opt/kafka/

  5. Delete the temporary directory. 

    rm -r /tmp/kafka

  6. Start Kafka Connect in either standalone or distributed mode.

    • To start in standalone mode, run the connect-standalone.sh script. Specify the Kafka Connect standalone configuration file and the configuration files of your Kafka Connect connectors. 

      su - kafka
/opt/kafka/bin/connect-standalone.sh /opt/kafka/config/connect-standalone.properties connector1.properties
[connector2.properties ...]

    • To start in distributed mode, start the Kafka Connect workers with the /opt/kafka/config/connect-distributed.properties configuration file on all Kafka Connect nodes: 

      su - kafka
/opt/kafka/bin/connect-distributed.sh /opt/kafka/config/connect-distributed.properties

  7. Verify that Kafka Connect is running:

    • In standalone mode: 

      jcmd | grep ConnectStandalone

    • In distributed mode: 

      jcmd | grep ConnectDistributed
  8. Verify that Kafka Connect is producing and consuming data as expected.

Additional resources

13.5. Upgrading Kafka

After you have upgraded your binaries to use the latest version of AMQ Streams, you can upgrade your brokers and clients to use a higher supported version of Kafka.

Take care to follow the steps in the correct order:

  1. Section 13.5.1, “Upgrading Kafka brokers to use the new inter-broker protocol version”
  2. Section 13.5.3, “Upgrading client applications to the new Kafka version”
  3. Section 13.5.4, “Upgrading Kafka brokers to use the new message format version”

Additional resources

13.5.1. Upgrading Kafka brokers to use the new inter-broker protocol version

Manually configure and restart all Kafka brokers to use the new inter-broker protocol version. After performing these steps, data is transmitted between the Kafka brokers using the new inter-broker protocol version.

Messages received are still appended to the message logs in the earlier message format version.

Warning

Downgrading AMQ Streams is not possible after completing this procedure.

Prerequisites

Procedure

For each Kafka broker in your AMQ Streams cluster and one at a time:

  1. In a text editor, open the broker properties file for the Kafka broker you want to update. Broker properties files are commonly stored in the /opt/kafka/config/ directory.

  2. Set the inter.broker.protocol.version to 2.3. 

    inter.broker.protocol.version=2.3

  3. On the command line, stop the Kafka broker that you modified: 

    /opt/kafka/bin/kafka-server-stop.sh
jcmd | grep kafka

  4. Restart the Kafka broker that you modified: 

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  5. Verify that the restarted Kafka broker has caught up with the partition replicas it is following. Use the kafka-topics.sh tool to ensure that all replicas contained in the broker are back in sync. For instructions, see Listing and describing topics.

13.5.2. Strategies for upgrading clients

The best approach to upgrading your client applications depends on your particular circumstances. Client applications might include producers, consumers, Kafka Connect, and Kafka MirrorMaker.

Consuming applications need to receive messages in a message format that they understand. You can ensure that this is the case in one of two ways:

  • By upgrading all the consumers for a topic before upgrading any of the producers.
  • By having the brokers down-convert messages to an older format.

Using broker down-conversion puts extra load on the brokers, so it is not ideal to rely on down-conversion for all topics for a prolonged period of time. For brokers to perform optimally they should not be down converting messages at all.

Broker down-conversion is configured in two ways:

  • The topic-level message.format.version configures it for a single topic.
  • The broker-level log.message.format.version is the default for topics that do not have the topic-level message.format.version configured.

Another aspect to consider is that once new-version messages have been published to a topic then they will be visible to consumers. This is because brokers perform down-conversion when they receive messages from producers, not when they are sent to consumers.

There are a number of strategies you can use to upgrade your clients:

Consumers first
  1. Upgrade all the consuming applications.
  2. Change the broker-level log.message.format.version to the new version.

  3. Upgrade all the producing applications.

    This strategy is straightforward, and avoids any broker down-conversion. However, it assumes that all consumers in your organization can be upgraded in a coordinated way, and it does not work for applications that are both consumers and producers. There is also a risk that, if there is a problem with the upgraded clients, new-format messages might get added to the message log so that you cannot revert to the previous consumer version.

Per-topic consumers first

For each topic:

  1. Upgrade all the consuming applications.
  2. Change the topic-level message.format.version to the new version.

  3. Upgrade all the producing applications.

    This strategy avoids any broker down-conversion, and means you can proceed on a topic-by-topic basis. It does not work for applications that are both consumers and producers of the same topic. Again, it has the risk that, if there is a problem with the upgraded clients, new-format messages might get added to the message log.

Per-topic consumers first, with down conversion

For each topic:

  1. Change the topic-level message.format.version to the old version (or rely on the topic defaulting to the broker-level log.message.format.version).
  2. Upgrade all the consuming and producing applications.
  3. Verify that the upgraded applications function correctly.

  4. Change the topic-level message.format.version to the new version.

    This strategy requires broker down-conversion, but the load on the brokers is minimized because it is only required for a single topic (or small group of topics) at a time. It also works for applications that are both consumers and producers of the same topic. This approach ensures that the upgraded producers and consumers are working correctly before you commit to using the new message format version.

    The main drawback of this approach is that it can be complicated to manage in a cluster with many topics and applications.

Other strategies for upgrading client applications are also possible.

Note

It is also possible to apply multiple strategies. For example, for the first few applications and topics the "per-topic consumers first, with down conversion" strategy can be used. When this has proved successful another, more efficient strategy can be considered acceptable to use instead.

13.5.3. Upgrading client applications to the new Kafka version

This procedure describes one possible approach to upgrading your client applications to the Kafka version used for AMQ Streams 1.3.

The procedure is based on the "per-topic consumers first, with down conversion" approach outlined in Strategies for upgrading clients.

Client applications might include producers, consumers, Kafka Connect, and MirrorMaker.

Prerequisites

Procedure

For each topic:

  1. On the command line, set the message.format.version configuration option to 2.2. 

    bin/kafka-configs.sh --zookeeper <ZookeeperAddress> --entity-type topics --entity-name <TopicName> --alter --add-config message.format.version=2.2
  2. Upgrade all the consuming and producing applications for the topic.
  3. Verify that the upgraded applications function correctly.

  4. Change the topic’s message.format.version configuration option to 2.3. 

    bin/kafka-configs.sh --zookeeper <ZookeeperAddress> --entity-type topics --entity-name <TopicName> --alter --add-config message.format.version=2.3

Additional resources

13.5.4. Upgrading Kafka brokers to use the new message format version

When client applications have been upgraded, you can update the Kafka brokers to use the new message format version.

If you did not modify topic configurations when you upgraded your client applications to use the Kafka version required for AMQ Streams 1.3, the Kafka brokers are now converting messages down to the previous message format version, which can cause a reduction in performance. Therefore, it is important that you update all Kafka brokers to use the new message format version as soon as possible.

Note

Update and restart the Kafka brokers one-by-one. Before you restart a modified broker, stop the broker you configured and restarted previously.

Prerequisites

Procedure

For each Kafka broker in your AMQ Streams cluster and one at a time:

  1. In a text editor, open the broker properties file for the Kafka broker you want to update. Broker properties files are commonly stored in the /opt/kafka/config/ directory.

  2. Set the log.message.format.version to 2.3. 

    log.message.format.version=2.3

  3. On the command line, stop the Kafka broker that you most recently modified and restarted as part of this procedure. If you are modifying the first Kafka broker in this procedure, go to step four. 

    /opt/kafka/bin/kafka-server-stop.sh
jcmd | grep kafka

  4. Restart the Kafka broker whose configuration you modified in step two: 

    /opt/kafka/bin/kafka-server-start.sh -daemon /opt/kafka/config/server.properties
  5. Verify that the restarted Kafka broker has caught up with the partition replicas it is following. Use the kafka-topics.sh tool to ensure that all replicas contained in the broker are back in sync. For instructions, see Listing and describing topics.

Appendix A. Broker configuration parameters

zookeeper.connect

Type: string
Importance: high
Dynamic update: read-only

Specifies the ZooKeeper connection string in the form hostname:port where host and port are the host and port of a ZooKeeper server. To allow connecting through other ZooKeeper nodes when that ZooKeeper machine is down you can also specify multiple hosts in the form hostname1:port1,hostname2:port2,hostname3:port3. The server can also have a ZooKeeper chroot path as part of its ZooKeeper connection string which puts its data under some path in the global ZooKeeper namespace. For example to give a chroot path of /chroot/path you would give the connection string as hostname1:port1,hostname2:port2,hostname3:port3/chroot/path.

advertised.host.name

Type: string
Default: null
Importance: high
Dynamic update: read-only

DEPRECATED: only used when advertised.listeners or listeners are not set. Use advertised.listeners instead. Hostname to publish to ZooKeeper for clients to use. In IaaS environments, this may need to be different from the interface to which the broker binds. If this is not set, it will use the value for host.name if configured. Otherwise it will use the value returned from java.net.InetAddress.getCanonicalHostName().

advertised.listeners

Type: string
Default: null
Importance: high
Dynamic update: per-broker

Listeners to publish to ZooKeeper for clients to use, if different than the listeners config property. In IaaS environments, this may need to be different from the interface to which the broker binds. If this is not set, the value for listeners will be used. Unlike listeners it is not valid to advertise the 0.0.0.0 meta-address.

advertised.port

Type: int
Default: null
Importance: high
Dynamic update: read-only

DEPRECATED: only used when advertised.listeners or listeners are not set. Use advertised.listeners instead. The port to publish to ZooKeeper for clients to use. In IaaS environments, this may need to be different from the port to which the broker binds. If this is not set, it will publish the same port that the broker binds to.

auto.create.topics.enable

Type: boolean
Default: true
Importance: high
Dynamic update: read-only

Enable auto creation of topic on the server.

auto.leader.rebalance.enable

Type: boolean
Default: true
Importance: high
Dynamic update: read-only

Enables auto leader balancing. A background thread checks and triggers leader balance if required at regular intervals.

background.threads

Type: int
Default: 10
Valid Values: [1,…​]
Importance: high
Dynamic update: cluster-wide

The number of threads to use for various background processing tasks.

broker.id

Type: int
Default: -1
Importance: high
Dynamic update: read-only

The broker id for this server. If unset, a unique broker id will be generated.To avoid conflicts between zookeeper generated broker id’s and user configured broker id’s, generated broker ids start from reserved.broker.max.id + 1.

compression.type

Type: string
Default: producer
Importance: high
Dynamic update: cluster-wide

Specify the final compression type for a given topic. This configuration accepts the standard compression codecs ('gzip', 'snappy', 'lz4', 'zstd'). It additionally accepts 'uncompressed' which is equivalent to no compression; and 'producer' which means retain the original compression codec set by the producer.

control.plane.listener.name

Type: string
Default: null
Importance: high
Dynamic update: read-only

Name of listener used for communication between controller and brokers. Broker will use the control.plane.listener.name to locate the endpoint in listeners list, to listen for connections from the controller. For example, if a broker’s config is : listeners = INTERNAL://192.1.1.8:9092, EXTERNAL://10.1.1.5:9093, CONTROLLER://192.1.1.8:9094 listener.security.protocol.map = INTERNAL:PLAINTEXT, EXTERNAL:SSL, CONTROLLER:SSL control.plane.listener.name = CONTROLLER On startup, the broker will start listening on "192.1.1.8:9094" with security protocol "SSL". On controller side, when it discovers a broker’s published endpoints through zookeeper, it will use the control.plane.listener.name to find the endpoint, which it will use to establish connection to the broker. For example, if the broker’s published endpoints on zookeeper are : "endpoints" : ["INTERNAL://broker1.example.com:9092","EXTERNAL://broker1.example.com:9093","CONTROLLER://broker1.example.com:9094"] and the controller’s config is : listener.security.protocol.map = INTERNAL:PLAINTEXT, EXTERNAL:SSL, CONTROLLER:SSL control.plane.listener.name = CONTROLLER then controller will use "broker1.example.com:9094" with security protocol "SSL" to connect to the broker. If not explicitly configured, the default value will be null and there will be no dedicated endpoints for controller connections.

delete.topic.enable

Type: boolean
Default: true
Importance: high
Dynamic update: read-only

Enables delete topic. Delete topic through the admin tool will have no effect if this config is turned off.

host.name

Type: string
Default: ""
Importance: high
Dynamic update: read-only

DEPRECATED: only used when listeners is not set. Use listeners instead. hostname of broker. If this is set, it will only bind to this address. If this is not set, it will bind to all interfaces.

leader.imbalance.check.interval.seconds

Type: long
Default: 300
Importance: high
Dynamic update: read-only

The frequency with which the partition rebalance check is triggered by the controller.

leader.imbalance.per.broker.percentage

Type: int
Default: 10
Importance: high
Dynamic update: read-only

The ratio of leader imbalance allowed per broker. The controller would trigger a leader balance if it goes above this value per broker. The value is specified in percentage.

listeners

Type: string
Default: null
Importance: high
Dynamic update: per-broker

Listener List - Comma-separated list of URIs we will listen on and the listener names. If the listener name is not a security protocol, listener.security.protocol.map must also be set. Specify hostname as 0.0.0.0 to bind to all interfaces. Leave hostname empty to bind to default interface. Examples of legal listener lists: PLAINTEXT://myhost:9092,SSL://:9091 CLIENT://0.0.0.0:9092,REPLICATION://localhost:9093.

log.dir

Type: string
Default: /tmp/kafka-logs
Importance: high
Dynamic update: read-only

The directory in which the log data is kept (supplemental for log.dirs property).

log.dirs

Type: string
Default: null
Importance: high
Dynamic update: read-only

The directories in which the log data is kept. If not set, the value in log.dir is used.

log.flush.interval.messages

Type: long
Default: 9223372036854775807
Valid Values: [1,…​]
Importance: high
Dynamic update: cluster-wide

The number of messages accumulated on a log partition before messages are flushed to disk.

log.flush.interval.ms

Type: long
Default: null
Importance: high
Dynamic update: cluster-wide

The maximum time in ms that a message in any topic is kept in memory before flushed to disk. If not set, the value in log.flush.scheduler.interval.ms is used.

log.flush.offset.checkpoint.interval.ms

Type: int
Default: 60000
Valid Values: [0,…​]
Importance: high
Dynamic update: read-only

The frequency with which we update the persistent record of the last flush which acts as the log recovery point.

log.flush.scheduler.interval.ms

Type: long
Default: 9223372036854775807
Importance: high
Dynamic update: read-only

The frequency in ms that the log flusher checks whether any log needs to be flushed to disk.

log.flush.start.offset.checkpoint.interval.ms

Type: int
Default: 60000
Valid Values: [0,…​]
Importance: high
Dynamic update: read-only

The frequency with which we update the persistent record of log start offset.

log.retention.bytes

Type: long
Default: -1
Importance: high
Dynamic update: cluster-wide

The maximum size of the log before deleting it.

log.retention.hours

Type: int
Default: 168
Importance: high
Dynamic update: read-only

The number of hours to keep a log file before deleting it (in hours), tertiary to log.retention.ms property.

log.retention.minutes

Type: int
Default: null
Importance: high
Dynamic update: read-only

The number of minutes to keep a log file before deleting it (in minutes), secondary to log.retention.ms property. If not set, the value in log.retention.hours is used.

log.retention.ms

Type: long
Default: null
Importance: high
Dynamic update: cluster-wide

The number of milliseconds to keep a log file before deleting it (in milliseconds), If not set, the value in log.retention.minutes is used. If set to -1, no time limit is applied.

log.roll.hours

Type: int
Default: 168
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The maximum time before a new log segment is rolled out (in hours), secondary to log.roll.ms property.

log.roll.jitter.hours

Type: int
Default: 0
Valid Values: [0,…​]
Importance: high
Dynamic update: read-only

The maximum jitter to subtract from logRollTimeMillis (in hours), secondary to log.roll.jitter.ms property.

log.roll.jitter.ms

Type: long
Default: null
Importance: high
Dynamic update: cluster-wide

The maximum jitter to subtract from logRollTimeMillis (in milliseconds). If not set, the value in log.roll.jitter.hours is used.

log.roll.ms

Type: long
Default: null
Importance: high
Dynamic update: cluster-wide

The maximum time before a new log segment is rolled out (in milliseconds). If not set, the value in log.roll.hours is used.

log.segment.bytes

Type: int
Default: 1073741824
Valid Values: [14,…​]
Importance: high
Dynamic update: cluster-wide

The maximum size of a single log file.

log.segment.delete.delay.ms

Type: long
Default: 60000
Valid Values: [0,…​]
Importance: high
Dynamic update: cluster-wide

The amount of time to wait before deleting a file from the filesystem.

message.max.bytes

Type: int
Default: 1000012
Valid Values: [0,…​]
Importance: high
Dynamic update: cluster-wide

The largest record batch size allowed by Kafka. If this is increased and there are consumers older than 0.10.2, the consumers' fetch size must also be increased so that the they can fetch record batches this large.

In the latest message format version, records are always grouped into batches for efficiency. In previous message format versions, uncompressed records are not grouped into batches and this limit only applies to a single record in that case.

This can be set per topic with the topic level max.message.bytes config.

min.insync.replicas

Type: int
Default: 1
Valid Values: [1,…​]
Importance: high
Dynamic update: cluster-wide

When a producer sets acks to "all" (or "-1"), min.insync.replicas specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. If this minimum cannot be met, then the producer will raise an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend). When used together, min.insync.replicas and acks allow you to enforce greater durability guarantees. A typical scenario would be to create a topic with a replication factor of 3, set min.insync.replicas to 2, and produce with acks of "all". This will ensure that the producer raises an exception if a majority of replicas do not receive a write.

num.io.threads

Type: int
Default: 8
Valid Values: [1,…​]
Importance: high
Dynamic update: cluster-wide

The number of threads that the server uses for processing requests, which may include disk I/O.

num.network.threads

Type: int
Default: 3
Valid Values: [1,…​]
Importance: high
Dynamic update: cluster-wide

The number of threads that the server uses for receiving requests from the network and sending responses to the network.

num.recovery.threads.per.data.dir

Type: int
Default: 1
Valid Values: [1,…​]
Importance: high
Dynamic update: cluster-wide

The number of threads per data directory to be used for log recovery at startup and flushing at shutdown.

num.replica.alter.log.dirs.threads

Type: int
Default: null
Importance: high
Dynamic update: read-only

The number of threads that can move replicas between log directories, which may include disk I/O.

num.replica.fetchers

Type: int
Default: 1
Importance: high
Dynamic update: cluster-wide

Number of fetcher threads used to replicate messages from a source broker. Increasing this value can increase the degree of I/O parallelism in the follower broker.

offset.metadata.max.bytes

Type: int
Default: 4096
Importance: high
Dynamic update: read-only

The maximum size for a metadata entry associated with an offset commit.

offsets.commit.required.acks

Type: short
Default: -1
Importance: high
Dynamic update: read-only

The required acks before the commit can be accepted. In general, the default (-1) should not be overridden.

offsets.commit.timeout.ms

Type: int
Default: 5000
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

Offset commit will be delayed until all replicas for the offsets topic receive the commit or this timeout is reached. This is similar to the producer request timeout.

offsets.load.buffer.size

Type: int
Default: 5242880
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

Batch size for reading from the offsets segments when loading offsets into the cache (soft-limit, overridden if records are too large).

offsets.retention.check.interval.ms

Type: long
Default: 600000
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

Frequency at which to check for stale offsets.

offsets.retention.minutes

Type: int
Default: 10080
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

After a consumer group loses all its consumers (i.e. becomes empty) its offsets will be kept for this retention period before getting discarded. For standalone consumers (using manual assignment), offsets will be expired after the time of last commit plus this retention period.

offsets.topic.compression.codec

Type: int
Default: 0
Importance: high
Dynamic update: read-only

Compression codec for the offsets topic - compression may be used to achieve "atomic" commits.

offsets.topic.num.partitions

Type: int
Default: 50
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The number of partitions for the offset commit topic (should not change after deployment).

offsets.topic.replication.factor

Type: short
Default: 3
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The replication factor for the offsets topic (set higher to ensure availability). Internal topic creation will fail until the cluster size meets this replication factor requirement.

offsets.topic.segment.bytes

Type: int
Default: 104857600
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The offsets topic segment bytes should be kept relatively small in order to facilitate faster log compaction and cache loads.

port

Type: int
Default: 9092
Importance: high
Dynamic update: read-only

DEPRECATED: only used when listeners is not set. Use listeners instead. the port to listen and accept connections on.

queued.max.requests

Type: int
Default: 500
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The number of queued requests allowed for data-plane, before blocking the network threads.

quota.consumer.default

Type: long
Default: 9223372036854775807
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

DEPRECATED: Used only when dynamic default quotas are not configured for <user, <client-id> or <user, client-id> in Zookeeper. Any consumer distinguished by clientId/consumer group will get throttled if it fetches more bytes than this value per-second.

quota.producer.default

Type: long
Default: 9223372036854775807
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

DEPRECATED: Used only when dynamic default quotas are not configured for <user>, <client-id> or <user, client-id> in Zookeeper. Any producer distinguished by clientId will get throttled if it produces more bytes than this value per-second.

replica.fetch.min.bytes

Type: int
Default: 1
Importance: high
Dynamic update: read-only

Minimum bytes expected for each fetch response. If not enough bytes, wait up to replicaMaxWaitTimeMs.

replica.fetch.wait.max.ms

Type: int
Default: 500
Importance: high
Dynamic update: read-only

max wait time for each fetcher request issued by follower replicas. This value should always be less than the replica.lag.time.max.ms at all times to prevent frequent shrinking of ISR for low throughput topics.

replica.high.watermark.checkpoint.interval.ms

Type: long
Default: 5000
Importance: high
Dynamic update: read-only

The frequency with which the high watermark is saved out to disk.

replica.lag.time.max.ms

Type: long
Default: 10000
Importance: high
Dynamic update: read-only

If a follower hasn’t sent any fetch requests or hasn’t consumed up to the leaders log end offset for at least this time, the leader will remove the follower from isr.

replica.socket.receive.buffer.bytes

Type: int
Default: 65536
Importance: high
Dynamic update: read-only

The socket receive buffer for network requests.

replica.socket.timeout.ms

Type: int
Default: 30000
Importance: high
Dynamic update: read-only

The socket timeout for network requests. Its value should be at least replica.fetch.wait.max.ms.

request.timeout.ms

Type: int
Default: 30000
Importance: high
Dynamic update: read-only

The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.

socket.receive.buffer.bytes

Type: int
Default: 102400
Importance: high
Dynamic update: read-only

The SO_RCVBUF buffer of the socket server sockets. If the value is -1, the OS default will be used.

socket.request.max.bytes

Type: int
Default: 104857600
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The maximum number of bytes in a socket request.

socket.send.buffer.bytes

Type: int
Default: 102400
Importance: high
Dynamic update: read-only

The SO_SNDBUF buffer of the socket server sockets. If the value is -1, the OS default will be used.

transaction.max.timeout.ms

Type: int
Default: 900000
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The maximum allowed timeout for transactions. If a client’s requested transaction time exceed this, then the broker will return an error in InitProducerIdRequest. This prevents a client from too large of a timeout, which can stall consumers reading from topics included in the transaction.

transaction.state.log.load.buffer.size

Type: int
Default: 5242880
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

Batch size for reading from the transaction log segments when loading producer ids and transactions into the cache (soft-limit, overridden if records are too large).

transaction.state.log.min.isr

Type: int
Default: 2
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

Overridden min.insync.replicas config for the transaction topic.

transaction.state.log.num.partitions

Type: int
Default: 50
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The number of partitions for the transaction topic (should not change after deployment).

transaction.state.log.replication.factor

Type: short
Default: 3
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The replication factor for the transaction topic (set higher to ensure availability). Internal topic creation will fail until the cluster size meets this replication factor requirement.

transaction.state.log.segment.bytes

Type: int
Default: 104857600
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The transaction topic segment bytes should be kept relatively small in order to facilitate faster log compaction and cache loads.

transactional.id.expiration.ms

Type: int
Default: 604800000
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The maximum amount of time in ms that the transaction coordinator will wait before proactively expire a producer’s transactional id without receiving any transaction status updates from it.

unclean.leader.election.enable

Type: boolean
Default: false
Importance: high
Dynamic update: cluster-wide

Indicates whether to enable replicas not in the ISR set to be elected as leader as a last resort, even though doing so may result in data loss.

zookeeper.connection.timeout.ms

Type: int
Default: null
Importance: high
Dynamic update: read-only

The max time that the client waits to establish a connection to zookeeper. If not set, the value in zookeeper.session.timeout.ms is used.

zookeeper.max.in.flight.requests

Type: int
Default: 10
Valid Values: [1,…​]
Importance: high
Dynamic update: read-only

The maximum number of unacknowledged requests the client will send to Zookeeper before blocking.

zookeeper.session.timeout.ms

Type: int
Default: 6000
Importance: high
Dynamic update: read-only

Zookeeper session timeout.

zookeeper.set.acl

Type: boolean
Default: false
Importance: high
Dynamic update: read-only

Set client to use secure ACLs.

broker.id.generation.enable

Type: boolean
Default: true
Importance: medium
Dynamic update: read-only

Enable automatic broker id generation on the server. When enabled the value configured for reserved.broker.max.id should be reviewed.

broker.rack

Type: string
Default: null
Importance: medium
Dynamic update: read-only

Rack of the broker. This will be used in rack aware replication assignment for fault tolerance. Examples: RACK1, us-east-1d.

connections.max.idle.ms

Type: long
Default: 600000
Importance: medium
Dynamic update: read-only

Idle connections timeout: the server socket processor threads close the connections that idle more than this.

connections.max.reauth.ms

Type: long
Default: 0
Importance: medium
Dynamic update: read-only

When explicitly set to a positive number (the default is 0, not a positive number), a session lifetime that will not exceed the configured value will be communicated to v2.2.0 or later clients when they authenticate. The broker will disconnect any such connection that is not re-authenticated within the session lifetime and that is then subsequently used for any purpose other than re-authentication. Configuration names can optionally be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.oauthbearer.connections.max.reauth.ms=3600000.

controlled.shutdown.enable

Type: boolean
Default: true
Importance: medium
Dynamic update: read-only

Enable controlled shutdown of the server.

controlled.shutdown.max.retries

Type: int
Default: 3
Importance: medium
Dynamic update: read-only

Controlled shutdown can fail for multiple reasons. This determines the number of retries when such failure happens.

controlled.shutdown.retry.backoff.ms

Type: long
Default: 5000
Importance: medium
Dynamic update: read-only

Before each retry, the system needs time to recover from the state that caused the previous failure (Controller fail over, replica lag etc). This config determines the amount of time to wait before retrying.

controller.socket.timeout.ms

Type: int
Default: 30000
Importance: medium
Dynamic update: read-only

The socket timeout for controller-to-broker channels.

default.replication.factor

Type: int
Default: 1
Importance: medium
Dynamic update: read-only

default replication factors for automatically created topics.

delegation.token.expiry.time.ms

Type: long
Default: 86400000
Valid Values: [1,…​]
Importance: medium
Dynamic update: read-only

The token validity time in miliseconds before the token needs to be renewed. Default value 1 day.

delegation.token.master.key

Type: password
Default: null
Importance: medium
Dynamic update: read-only

Master/secret key to generate and verify delegation tokens. Same key must be configured across all the brokers. If the key is not set or set to empty string, brokers will disable the delegation token support.

delegation.token.max.lifetime.ms

Type: long
Default: 604800000
Valid Values: [1,…​]
Importance: medium
Dynamic update: read-only

The token has a maximum lifetime beyond which it cannot be renewed anymore. Default value 7 days.

delete.records.purgatory.purge.interval.requests

Type: int
Default: 1
Importance: medium
Dynamic update: read-only

The purge interval (in number of requests) of the delete records request purgatory.

fetch.purgatory.purge.interval.requests

Type: int
Default: 1000
Importance: medium
Dynamic update: read-only

The purge interval (in number of requests) of the fetch request purgatory.

group.initial.rebalance.delay.ms

Type: int
Default: 3000
Importance: medium
Dynamic update: read-only

The amount of time the group coordinator will wait for more consumers to join a new group before performing the first rebalance. A longer delay means potentially fewer rebalances, but increases the time until processing begins.

group.max.session.timeout.ms

Type: int
Default: 1800000
Importance: medium
Dynamic update: read-only

The maximum allowed session timeout for registered consumers. Longer timeouts give consumers more time to process messages in between heartbeats at the cost of a longer time to detect failures.

group.max.size

Type: int
Default: 2147483647
Valid Values: [1,…​]
Importance: medium
Dynamic update: read-only

The maximum number of consumers that a single consumer group can accommodate.

group.min.session.timeout.ms

Type: int
Default: 6000
Importance: medium
Dynamic update: read-only

The minimum allowed session timeout for registered consumers. Shorter timeouts result in quicker failure detection at the cost of more frequent consumer heartbeating, which can overwhelm broker resources.

inter.broker.listener.name

Type: string
Default: null
Importance: medium
Dynamic update: read-only

Name of listener used for communication between brokers. If this is unset, the listener name is defined by security.inter.broker.protocol. It is an error to set this and security.inter.broker.protocol properties at the same time.

inter.broker.protocol.version

Type: string
Default: 2.3-IV1
Valid Values: [0.8.0, 0.8.1, 0.8.2, 0.9.0, 0.10.0-IV0, 0.10.0-IV1, 0.10.1-IV0, 0.10.1-IV1, 0.10.1-IV2, 0.10.2-IV0, 0.11.0-IV0, 0.11.0-IV1, 0.11.0-IV2, 1.0-IV0, 1.1-IV0, 2.0-IV0, 2.0-IV1, 2.1-IV0, 2.1-IV1, 2.1-IV2, 2.2-IV0, 2.2-IV1, 2.3-IV0, 2.3-IV1]
Importance: medium
Dynamic update: read-only

Specify which version of the inter-broker protocol will be used. This is typically bumped after all brokers were upgraded to a new version. Example of some valid values are: 0.8.0, 0.8.1, 0.8.1.1, 0.8.2, 0.8.2.0, 0.8.2.1, 0.9.0.0, 0.9.0.1 Check ApiVersion for the full list.

log.cleaner.backoff.ms

Type: long
Default: 15000
Valid Values: [0,…​]
Importance: medium
Dynamic update: cluster-wide

The amount of time to sleep when there are no logs to clean.

log.cleaner.dedupe.buffer.size

Type: long
Default: 134217728
Importance: medium
Dynamic update: cluster-wide

The total memory used for log deduplication across all cleaner threads.

log.cleaner.delete.retention.ms

Type: long
Default: 86400000
Importance: medium
Dynamic update: cluster-wide

How long are delete records retained?

log.cleaner.enable

Type: boolean
Default: true
Importance: medium
Dynamic update: read-only

Enable the log cleaner process to run on the server. Should be enabled if using any topics with a cleanup.policy=compact including the internal offsets topic. If disabled those topics will not be compacted and continually grow in size.

log.cleaner.io.buffer.load.factor

Type: double
Default: 0.9
Importance: medium
Dynamic update: cluster-wide

Log cleaner dedupe buffer load factor. The percentage full the dedupe buffer can become. A higher value will allow more log to be cleaned at once but will lead to more hash collisions.

log.cleaner.io.buffer.size

Type: int
Default: 524288
Valid Values: [0,…​]
Importance: medium
Dynamic update: cluster-wide

The total memory used for log cleaner I/O buffers across all cleaner threads.

log.cleaner.io.max.bytes.per.second

Type: double
Default: 1.7976931348623157E308
Importance: medium
Dynamic update: cluster-wide

The log cleaner will be throttled so that the sum of its read and write i/o will be less than this value on average.

log.cleaner.max.compaction.lag.ms

Type: long
Default: 9223372036854775807
Importance: medium
Dynamic update: cluster-wide

The maximum time a message will remain ineligible for compaction in the log. Only applicable for logs that are being compacted.

log.cleaner.min.cleanable.ratio

Type: double
Default: 0.5
Importance: medium
Dynamic update: cluster-wide

The minimum ratio of dirty log to total log for a log to eligible for cleaning. If the log.cleaner.max.compaction.lag.ms or the log.cleaner.min.compaction.lag.ms configurations are also specified, then the log compactor considers the log eligible for compaction as soon as either: (i) the dirty ratio threshold has been met and the log has had dirty (uncompacted) records for at least the log.cleaner.min.compaction.lag.ms duration, or (ii) if the log has had dirty (uncompacted) records for at most the log.cleaner.max.compaction.lag.ms period.

log.cleaner.min.compaction.lag.ms

Type: long
Default: 0
Importance: medium
Dynamic update: cluster-wide

The minimum time a message will remain uncompacted in the log. Only applicable for logs that are being compacted.

log.cleaner.threads

Type: int
Default: 1
Valid Values: [0,…​]
Importance: medium
Dynamic update: cluster-wide

The number of background threads to use for log cleaning.

log.cleanup.policy

Type: list
Default: delete
Valid Values: [compact, delete]
Importance: medium
Dynamic update: cluster-wide

The default cleanup policy for segments beyond the retention window. A comma separated list of valid policies. Valid policies are: "delete" and "compact".

log.index.interval.bytes

Type: int
Default: 4096
Valid Values: [0,…​]
Importance: medium
Dynamic update: cluster-wide

The interval with which we add an entry to the offset index.

log.index.size.max.bytes

Type: int
Default: 10485760
Valid Values: [4,…​]
Importance: medium
Dynamic update: cluster-wide

The maximum size in bytes of the offset index.

log.message.format.version

Type: string
Default: 2.3-IV1
Valid Values: [0.8.0, 0.8.1, 0.8.2, 0.9.0, 0.10.0-IV0, 0.10.0-IV1, 0.10.1-IV0, 0.10.1-IV1, 0.10.1-IV2, 0.10.2-IV0, 0.11.0-IV0, 0.11.0-IV1, 0.11.0-IV2, 1.0-IV0, 1.1-IV0, 2.0-IV0, 2.0-IV1, 2.1-IV0, 2.1-IV1, 2.1-IV2, 2.2-IV0, 2.2-IV1, 2.3-IV0, 2.3-IV1]
Importance: medium
Dynamic update: read-only

Specify the message format version the broker will use to append messages to the logs. The value should be a valid ApiVersion. Some examples are: 0.8.2, 0.9.0.0, 0.10.0, check ApiVersion for more details. By setting a particular message format version, the user is certifying that all the existing messages on disk are smaller or equal than the specified version. Setting this value incorrectly will cause consumers with older versions to break as they will receive messages with a format that they don’t understand.

log.message.timestamp.difference.max.ms

Type: long
Default: 9223372036854775807
Importance: medium
Dynamic update: cluster-wide

The maximum difference allowed between the timestamp when a broker receives a message and the timestamp specified in the message. If log.message.timestamp.type=CreateTime, a message will be rejected if the difference in timestamp exceeds this threshold. This configuration is ignored if log.message.timestamp.type=LogAppendTime.The maximum timestamp difference allowed should be no greater than log.retention.ms to avoid unnecessarily frequent log rolling.

log.message.timestamp.type

Type: string
Default: CreateTime
Valid Values: [CreateTime, LogAppendTime]
Importance: medium
Dynamic update: cluster-wide

Define whether the timestamp in the message is message create time or log append time. The value should be either CreateTime or LogAppendTime.

log.preallocate

Type: boolean
Default: false
Importance: medium
Dynamic update: cluster-wide

Should pre allocate file when create new segment? If you are using Kafka on Windows, you probably need to set it to true.

log.retention.check.interval.ms

Type: long
Default: 300000
Valid Values: [1,…​]
Importance: medium
Dynamic update: read-only

The frequency in milliseconds that the log cleaner checks whether any log is eligible for deletion.

max.connections

Type: int
Default: 2147483647
Valid Values: [0,…​]
Importance: medium
Dynamic update: cluster-wide

The maximum number of connections we allow in the broker at any time. This limit is applied in addition to any per-ip limits configured using max.connections.per.ip. Listener-level limits may also be configured by prefixing the config name with the listener prefix, for example, listener.name.internal.max.connections. Broker-wide limit should be configured based on broker capacity while listener limits should be configured based on application requirements. New connections are blocked if either the listener or broker limit is reached. Connections on the inter-broker listener are permitted even if broker-wide limit is reached. The least recently used connection on another listener will be closed in this case.

max.connections.per.ip

Type: int
Default: 2147483647
Valid Values: [0,…​]
Importance: medium
Dynamic update: cluster-wide

The maximum number of connections we allow from each ip address. This can be set to 0 if there are overrides configured using max.connections.per.ip.overrides property. New connections from the ip address are dropped if the limit is reached.

max.connections.per.ip.overrides

Type: string
Default: ""
Importance: medium
Dynamic update: cluster-wide

A comma-separated list of per-ip or hostname overrides to the default maximum number of connections. An example value is "hostName:100,127.0.0.1:200".

max.incremental.fetch.session.cache.slots

Type: int
Default: 1000
Valid Values: [0,…​]
Importance: medium
Dynamic update: read-only

The maximum number of incremental fetch sessions that we will maintain.

num.partitions

Type: int
Default: 1
Valid Values: [1,…​]
Importance: medium
Dynamic update: read-only

The default number of log partitions per topic.

password.encoder.old.secret

Type: password
Default: null
Importance: medium
Dynamic update: read-only

The old secret that was used for encoding dynamically configured passwords. This is required only when the secret is updated. If specified, all dynamically encoded passwords are decoded using this old secret and re-encoded using password.encoder.secret when broker starts up.

password.encoder.secret

Type: password
Default: null
Importance: medium
Dynamic update: read-only

The secret used for encoding dynamically configured passwords for this broker.

principal.builder.class

Type: class
Default: null
Importance: medium
Dynamic update: per-broker

The fully qualified name of a class that implements the KafkaPrincipalBuilder interface, which is used to build the KafkaPrincipal object used during authorization. This config also supports the deprecated PrincipalBuilder interface which was previously used for client authentication over SSL. If no principal builder is defined, the default behavior depends on the security protocol in use. For SSL authentication, the principal will be derived using the rules defined by ssl.principal.mapping.rules applied on the distinguished name from the client certificate if one is provided; otherwise, if client authentication is not required, the principal name will be ANONYMOUS. For SASL authentication, the principal will be derived using the rules defined by sasl.kerberos.principal.to.local.rules if GSSAPI is in use, and the SASL authentication ID for other mechanisms. For PLAINTEXT, the principal will be ANONYMOUS.

producer.purgatory.purge.interval.requests

Type: int
Default: 1000
Importance: medium
Dynamic update: read-only

The purge interval (in number of requests) of the producer request purgatory.

queued.max.request.bytes

Type: long
Default: -1
Importance: medium
Dynamic update: read-only

The number of queued bytes allowed before no more requests are read.

replica.fetch.backoff.ms

Type: int
Default: 1000
Valid Values: [0,…​]
Importance: medium
Dynamic update: read-only

The amount of time to sleep when fetch partition error occurs.

replica.fetch.max.bytes

Type: int
Default: 1048576
Valid Values: [0,…​]
Importance: medium
Dynamic update: read-only

The number of bytes of messages to attempt to fetch for each partition. This is not an absolute maximum, if the first record batch in the first non-empty partition of the fetch is larger than this value, the record batch will still be returned to ensure that progress can be made. The maximum record batch size accepted by the broker is defined via message.max.bytes (broker config) or max.message.bytes (topic config).

replica.fetch.response.max.bytes

Type: int
Default: 10485760
Valid Values: [0,…​]
Importance: medium
Dynamic update: read-only

Maximum bytes expected for the entire fetch response. Records are fetched in batches, and if the first record batch in the first non-empty partition of the fetch is larger than this value, the record batch will still be returned to ensure that progress can be made. As such, this is not an absolute maximum. The maximum record batch size accepted by the broker is defined via message.max.bytes (broker config) or max.message.bytes (topic config).

reserved.broker.max.id

Type: int
Default: 1000
Valid Values: [0,…​]
Importance: medium
Dynamic update: read-only

Max number that can be used for a broker.id.

sasl.client.callback.handler.class

Type: class
Default: null
Importance: medium
Dynamic update: read-only

The fully qualified name of a SASL client callback handler class that implements the AuthenticateCallbackHandler interface.

sasl.enabled.mechanisms

Type: list
Default: GSSAPI
Importance: medium
Dynamic update: per-broker

The list of SASL mechanisms enabled in the Kafka server. The list may contain any mechanism for which a security provider is available. Only GSSAPI is enabled by default.

sasl.jaas.config

Type: password
Default: null
Importance: medium
Dynamic update: per-broker

JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described here. The format for the value is: ‘loginModuleClass controlFlag (optionName=optionValue)*;’. For brokers, the config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.jaas.config=com.example.ScramLoginModule required;.

sasl.kerberos.kinit.cmd

Type: string
Default: /usr/bin/kinit
Importance: medium
Dynamic update: per-broker

Kerberos kinit command path.

sasl.kerberos.min.time.before.relogin

Type: long
Default: 60000
Importance: medium
Dynamic update: per-broker

Login thread sleep time between refresh attempts.

sasl.kerberos.principal.to.local.rules

Type: list
Default: DEFAULT
Importance: medium
Dynamic update: per-broker

A list of rules for mapping from principal names to short names (typically operating system usernames). The rules are evaluated in order and the first rule that matches a principal name is used to map it to a short name. Any later rules in the list are ignored. By default, principal names of the form {username}/{hostname}@{REALM} are mapped to {username}. For more details on the format please see security authorization and acls. Note that this configuration is ignored if an extension of KafkaPrincipalBuilder is provided by the principal.builder.class configuration.

sasl.kerberos.service.name

Type: string
Default: null
Importance: medium
Dynamic update: per-broker

The Kerberos principal name that Kafka runs as. This can be defined either in Kafka’s JAAS config or in Kafka’s config.

sasl.kerberos.ticket.renew.jitter

Type: double
Default: 0.05
Importance: medium
Dynamic update: per-broker

Percentage of random jitter added to the renewal time.

sasl.kerberos.ticket.renew.window.factor

Type: double
Default: 0.8
Importance: medium
Dynamic update: per-broker

Login thread will sleep until the specified window factor of time from last refresh to ticket’s expiry has been reached, at which time it will try to renew the ticket.

sasl.login.callback.handler.class

Type: class
Default: null
Importance: medium
Dynamic update: read-only

The fully qualified name of a SASL login callback handler class that implements the AuthenticateCallbackHandler interface. For brokers, login callback handler config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.callback.handler.class=com.example.CustomScramLoginCallbackHandler.

sasl.login.class

Type: class
Default: null
Importance: medium
Dynamic update: read-only

The fully qualified name of a class that implements the Login interface. For brokers, login config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.class=com.example.CustomScramLogin.

sasl.login.refresh.buffer.seconds

Type: short
Default: 300
Importance: medium
Dynamic update: per-broker

The amount of buffer time before credential expiration to maintain when refreshing a credential, in seconds. If a refresh would otherwise occur closer to expiration than the number of buffer seconds then the refresh will be moved up to maintain as much of the buffer time as possible. Legal values are between 0 and 3600 (1 hour); a default value of 300 (5 minutes) is used if no value is specified. This value and sasl.login.refresh.min.period.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.

sasl.login.refresh.min.period.seconds

Type: short
Default: 60
Importance: medium
Dynamic update: per-broker

The desired minimum time for the login refresh thread to wait before refreshing a credential, in seconds. Legal values are between 0 and 900 (15 minutes); a default value of 60 (1 minute) is used if no value is specified. This value and sasl.login.refresh.buffer.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.

sasl.login.refresh.window.factor

Type: double
Default: 0.8
Importance: medium
Dynamic update: per-broker

Login refresh thread will sleep until the specified window factor relative to the credential’s lifetime has been reached, at which time it will try to refresh the credential. Legal values are between 0.5 (50%) and 1.0 (100%) inclusive; a default value of 0.8 (80%) is used if no value is specified. Currently applies only to OAUTHBEARER.

sasl.login.refresh.window.jitter

Type: double
Default: 0.05
Importance: medium
Dynamic update: per-broker

The maximum amount of random jitter relative to the credential’s lifetime that is added to the login refresh thread’s sleep time. Legal values are between 0 and 0.25 (25%) inclusive; a default value of 0.05 (5%) is used if no value is specified. Currently applies only to OAUTHBEARER.

sasl.mechanism.inter.broker.protocol

Type: string
Default: GSSAPI
Importance: medium
Dynamic update: per-broker

SASL mechanism used for inter-broker communication. Default is GSSAPI.

sasl.server.callback.handler.class

Type: class
Default: null
Importance: medium
Dynamic update: read-only

The fully qualified name of a SASL server callback handler class that implements the AuthenticateCallbackHandler interface. Server callback handlers must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.plain.sasl.server.callback.handler.class=com.example.CustomPlainCallbackHandler.

security.inter.broker.protocol

Type: string
Default: PLAINTEXT
Importance: medium
Dynamic update: read-only

Security protocol used to communicate between brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL. It is an error to set this and inter.broker.listener.name properties at the same time.

ssl.cipher.suites

Type: list
Default: ""
Importance: medium
Dynamic update: per-broker

A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.

ssl.client.auth

Type: string
Default: none
Valid Values: [required, requested, none]
Importance: medium
Dynamic update: per-broker

Configures kafka broker to request client authentication. The following settings are common:

  • ssl.client.auth=required If set to required client authentication is required.
  • ssl.client.auth=requested This means client authentication is optional. unlike requested , if this option is set client can choose not to provide authentication information about itself
  • ssl.client.auth=none This means client authentication is not needed.
ssl.enabled.protocols

Type: list
Default: TLSv1.2,TLSv1.1,TLSv1
Importance: medium
Dynamic update: per-broker

The list of protocols enabled for SSL connections.

ssl.key.password

Type: password
Default: null
Importance: medium
Dynamic update: per-broker

The password of the private key in the key store file. This is optional for client.

ssl.keymanager.algorithm

Type: string
Default: SunX509
Importance: medium
Dynamic update: per-broker

The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.

ssl.keystore.location

Type: string
Default: null
Importance: medium
Dynamic update: per-broker

The location of the key store file. This is optional for client and can be used for two-way authentication for client.

ssl.keystore.password

Type: password
Default: null
Importance: medium
Dynamic update: per-broker

The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.

ssl.keystore.type

Type: string
Default: JKS
Importance: medium
Dynamic update: per-broker

The file format of the key store file. This is optional for client.

ssl.protocol

Type: string
Default: TLS
Importance: medium
Dynamic update: per-broker

The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.

ssl.provider

Type: string
Default: null
Importance: medium
Dynamic update: per-broker

The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.

ssl.trustmanager.algorithm

Type: string
Default: PKIX
Importance: medium
Dynamic update: per-broker

The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.

ssl.truststore.location

Type: string
Default: null
Importance: medium
Dynamic update: per-broker

The location of the trust store file.

ssl.truststore.password

Type: password
Default: null
Importance: medium
Dynamic update: per-broker

The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.

ssl.truststore.type

Type: string
Default: JKS
Importance: medium
Dynamic update: per-broker

The file format of the trust store file.

alter.config.policy.class.name

Type: class
Default: null
Importance: low
Dynamic update: read-only

The alter configs policy class that should be used for validation. The class should implement the org.apache.kafka.server.policy.AlterConfigPolicy interface.

alter.log.dirs.replication.quota.window.num

Type: int
Default: 11
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The number of samples to retain in memory for alter log dirs replication quotas.

alter.log.dirs.replication.quota.window.size.seconds

Type: int
Default: 1
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The time span of each sample for alter log dirs replication quotas.

authorizer.class.name

Type: string
Default: ""
Importance: low
Dynamic update: read-only

The authorizer class that should be used for authorization.

client.quota.callback.class

Type: class
Default: null
Importance: low
Dynamic update: read-only

The fully qualified name of a class that implements the ClientQuotaCallback interface, which is used to determine quota limits applied to client requests. By default, <user, client-id>, <user> or <client-id> quotas stored in ZooKeeper are applied. For any given request, the most specific quota that matches the user principal of the session and the client-id of the request is applied.

connection.failed.authentication.delay.ms

Type: int
Default: 100
Valid Values: [0,…​]
Importance: low
Dynamic update: read-only

Connection close delay on failed authentication: this is the time (in milliseconds) by which connection close will be delayed on authentication failure. This must be configured to be less than connections.max.idle.ms to prevent connection timeout.

create.topic.policy.class.name

Type: class
Default: null
Importance: low
Dynamic update: read-only

The create topic policy class that should be used for validation. The class should implement the org.apache.kafka.server.policy.CreateTopicPolicy interface.

delegation.token.expiry.check.interval.ms

Type: long
Default: 3600000
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

Scan interval to remove expired delegation tokens.

kafka.metrics.polling.interval.secs

Type: int
Default: 10
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The metrics polling interval (in seconds) which can be used in kafka.metrics.reporters implementations.

kafka.metrics.reporters

Type: list
Default: ""
Importance: low
Dynamic update: read-only

A list of classes to use as Yammer metrics custom reporters. The reporters should implement kafka.metrics.KafkaMetricsReporter trait. If a client wants to expose JMX operations on a custom reporter, the custom reporter needs to additionally implement an MBean trait that extends kafka.metrics.KafkaMetricsReporterMBean trait so that the registered MBean is compliant with the standard MBean convention.

listener.security.protocol.map

Type: string
Default: PLAINTEXT:PLAINTEXT,SSL:SSL,SASL_PLAINTEXT:SASL_PLAINTEXT,SASL_SSL:SASL_SSL
Importance: low
Dynamic update: per-broker

Map between listener names and security protocols. This must be defined for the same security protocol to be usable in more than one port or IP. For example, internal and external traffic can be separated even if SSL is required for both. Concretely, the user could define listeners with names INTERNAL and EXTERNAL and this property as: INTERNAL:SSL,EXTERNAL:SSL. As shown, key and value are separated by a colon and map entries are separated by commas. Each listener name should only appear once in the map. Different security (SSL and SASL) settings can be configured for each listener by adding a normalised prefix (the listener name is lowercased) to the config name. For example, to set a different keystore for the INTERNAL listener, a config with name listener.name.internal.ssl.keystore.location would be set. If the config for the listener name is not set, the config will fallback to the generic config (i.e. ssl.keystore.location).

log.message.downconversion.enable

Type: boolean
Default: true
Importance: low
Dynamic update: cluster-wide

This configuration controls whether down-conversion of message formats is enabled to satisfy consume requests. When set to false, broker will not perform down-conversion for consumers expecting an older message format. The broker responds with UNSUPPORTED_VERSION error for consume requests from such older clients. This configurationdoes not apply to any message format conversion that might be required for replication to followers.

metric.reporters

Type: list
Default: ""
Importance: low
Dynamic update: cluster-wide

A list of classes to use as metrics reporters. Implementing the org.apache.kafka.common.metrics.MetricsReporter interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.

metrics.num.samples

Type: int
Default: 2
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The number of samples maintained to compute metrics.

metrics.recording.level

Type: string
Default: INFO
Importance: low
Dynamic update: read-only

The highest recording level for metrics.

metrics.sample.window.ms

Type: long
Default: 30000
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The window of time a metrics sample is computed over.

password.encoder.cipher.algorithm

Type: string
Default: AES/CBC/PKCS5Padding
Importance: low
Dynamic update: read-only

The Cipher algorithm used for encoding dynamically configured passwords.

password.encoder.iterations

Type: int
Default: 4096
Valid Values: [1024,…​]
Importance: low
Dynamic update: read-only

The iteration count used for encoding dynamically configured passwords.

password.encoder.key.length

Type: int
Default: 128
Valid Values: [8,…​]
Importance: low
Dynamic update: read-only

The key length used for encoding dynamically configured passwords.

password.encoder.keyfactory.algorithm

Type: string
Default: null
Importance: low
Dynamic update: read-only

The SecretKeyFactory algorithm used for encoding dynamically configured passwords. Default is PBKDF2WithHmacSHA512 if available and PBKDF2WithHmacSHA1 otherwise.

quota.window.num

Type: int
Default: 11
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The number of samples to retain in memory for client quotas.

quota.window.size.seconds

Type: int
Default: 1
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The time span of each sample for client quotas.

replication.quota.window.num

Type: int
Default: 11
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The number of samples to retain in memory for replication quotas.

replication.quota.window.size.seconds

Type: int
Default: 1
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The time span of each sample for replication quotas.

ssl.endpoint.identification.algorithm

Type: string
Default: https
Importance: low
Dynamic update: per-broker

The endpoint identification algorithm to validate server hostname using server certificate.

ssl.principal.mapping.rules

Type: list
Default: DEFAULT
Importance: low
Dynamic update: read-only

A list of rules for mapping from distinguished name from the client certificate to short name. The rules are evaluated in order and the first rule that matches a principal name is used to map it to a short name. Any later rules in the list are ignored. By default, distinguished name of the X.500 certificate will be the principal. For more details on the format please see security authorization and acls. Note that this configuration is ignored if an extension of KafkaPrincipalBuilder is provided by the principal.builder.class configuration.

ssl.secure.random.implementation

Type: string
Default: null
Importance: low
Dynamic update: per-broker

The SecureRandom PRNG implementation to use for SSL cryptography operations.

transaction.abort.timed.out.transaction.cleanup.interval.ms

Type: int
Default: 60000
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The interval at which to rollback transactions that have timed out.

transaction.remove.expired.transaction.cleanup.interval.ms

Type: int
Default: 3600000
Valid Values: [1,…​]
Importance: low
Dynamic update: read-only

The interval at which to remove transactions that have expired due to transactional.id.expiration.ms passing.

zookeeper.sync.time.ms

Type: int
Default: 2000
Importance: low
Dynamic update: read-only

How far a ZK follower can be behind a ZK leader.

Appendix B. Topic configuration parameters

cleanup.policy

Type: list
Default: delete
Valid Values: [compact, delete]
Server Default Property: log.cleanup.policy
Importance: medium

A string that is either "delete" or "compact" or both. This string designates the retention policy to use on old log segments. The default policy ("delete") will discard old segments when their retention time or size limit has been reached. The "compact" setting will enable log compaction on the topic.

compression.type

Type: string
Default: producer
Valid Values: [uncompressed, zstd, lz4, snappy, gzip, producer]
Server Default Property: compression.type
Importance: medium

Specify the final compression type for a given topic. This configuration accepts the standard compression codecs ('gzip', 'snappy', 'lz4', 'zstd'). It additionally accepts 'uncompressed' which is equivalent to no compression; and 'producer' which means retain the original compression codec set by the producer.

delete.retention.ms

Type: long
Default: 86400000
Valid Values: [0,…​]
Server Default Property: log.cleaner.delete.retention.ms
Importance: medium

The amount of time to retain delete tombstone markers for log compacted topics. This setting also gives a bound on the time in which a consumer must complete a read if they begin from offset 0 to ensure that they get a valid snapshot of the final stage (otherwise delete tombstones may be collected before they complete their scan).

file.delete.delay.ms

Type: long
Default: 60000
Valid Values: [0,…​]
Server Default Property: log.segment.delete.delay.ms
Importance: medium

The time to wait before deleting a file from the filesystem.

flush.messages

Type: long
Default: 9223372036854775807
Valid Values: [0,…​]
Server Default Property: log.flush.interval.messages
Importance: medium

This setting allows specifying an interval at which we will force an fsync of data written to the log. For example if this was set to 1 we would fsync after every message; if it were 5 we would fsync after every five messages. In general we recommend you not set this and use replication for durability and allow the operating system’s background flush capabilities as it is more efficient. This setting can be overridden on a per-topic basis (see the per-topic configuration section).

flush.ms

Type: long
Default: 9223372036854775807
Valid Values: [0,…​]
Server Default Property: log.flush.interval.ms
Importance: medium

This setting allows specifying a time interval at which we will force an fsync of data written to the log. For example if this was set to 1000 we would fsync after 1000 ms had passed. In general we recommend you not set this and use replication for durability and allow the operating system’s background flush capabilities as it is more efficient.

follower.replication.throttled.replicas

Type: list
Default: ""
Valid Values: [partitionId]:[brokerId],[partitionId]:[brokerId],…​
Server Default Property: follower.replication.throttled.replicas
Importance: medium

A list of replicas for which log replication should be throttled on the follower side. The list should describe a set of replicas in the form [PartitionId]:[BrokerId],[PartitionId]:[BrokerId]:…​ or alternatively the wildcard '*' can be used to throttle all replicas for this topic.

index.interval.bytes

Type: int
Default: 4096
Valid Values: [0,…​]
Server Default Property: log.index.interval.bytes
Importance: medium

This setting controls how frequently Kafka adds an index entry to its offset index. The default setting ensures that we index a message roughly every 4096 bytes. More indexing allows reads to jump closer to the exact position in the log but makes the index larger. You probably don’t need to change this.

leader.replication.throttled.replicas

Type: list
Default: ""
Valid Values: [partitionId]:[brokerId],[partitionId]:[brokerId],…​
Server Default Property: leader.replication.throttled.replicas
Importance: medium

A list of replicas for which log replication should be throttled on the leader side. The list should describe a set of replicas in the form [PartitionId]:[BrokerId],[PartitionId]:[BrokerId]:…​ or alternatively the wildcard '*' can be used to throttle all replicas for this topic.

max.compaction.lag.ms

Type: long
Default: 9223372036854775807
Valid Values: [1,…​]
Server Default Property: log.cleaner.max.compaction.lag.ms
Importance: medium

The maximum time a message will remain ineligible for compaction in the log. Only applicable for logs that are being compacted.

max.message.bytes

Type: int
Default: 1000012
Valid Values: [0,…​]
Server Default Property: message.max.bytes
Importance: medium

The largest record batch size allowed by Kafka. If this is increased and there are consumers older than 0.10.2, the consumers' fetch size must also be increased so that the they can fetch record batches this large.

In the latest message format version, records are always grouped into batches for efficiency. In previous message format versions, uncompressed records are not grouped into batches and this limit only applies to a single record in that case.

message.format.version

Type: string
Default: 2.3-IV1
Valid Values: [0.8.0, 0.8.1, 0.8.2, 0.9.0, 0.10.0-IV0, 0.10.0-IV1, 0.10.1-IV0, 0.10.1-IV1, 0.10.1-IV2, 0.10.2-IV0, 0.11.0-IV0, 0.11.0-IV1, 0.11.0-IV2, 1.0-IV0, 1.1-IV0, 2.0-IV0, 2.0-IV1, 2.1-IV0, 2.1-IV1, 2.1-IV2, 2.2-IV0, 2.2-IV1, 2.3-IV0, 2.3-IV1]
Server Default Property: log.message.format.version
Importance: medium

Specify the message format version the broker will use to append messages to the logs. The value should be a valid ApiVersion. Some examples are: 0.8.2, 0.9.0.0, 0.10.0, check ApiVersion for more details. By setting a particular message format version, the user is certifying that all the existing messages on disk are smaller or equal than the specified version. Setting this value incorrectly will cause consumers with older versions to break as they will receive messages with a format that they don’t understand.

message.timestamp.difference.max.ms

Type: long
Default: 9223372036854775807
Valid Values: [0,…​]
Server Default Property: log.message.timestamp.difference.max.ms
Importance: medium

The maximum difference allowed between the timestamp when a broker receives a message and the timestamp specified in the message. If message.timestamp.type=CreateTime, a message will be rejected if the difference in timestamp exceeds this threshold. This configuration is ignored if message.timestamp.type=LogAppendTime.

message.timestamp.type

Type: string
Default: CreateTime
Valid Values: [CreateTime, LogAppendTime]
Server Default Property: log.message.timestamp.type
Importance: medium

Define whether the timestamp in the message is message create time or log append time. The value should be either CreateTime or LogAppendTime.

min.cleanable.dirty.ratio

Type: double
Default: 0.5
Valid Values: [0,…​,1]
Server Default Property: log.cleaner.min.cleanable.ratio
Importance: medium

This configuration controls how frequently the log compactor will attempt to clean the log (assuming log compaction is enabled). By default we will avoid cleaning a log where more than 50% of the log has been compacted. This ratio bounds the maximum space wasted in the log by duplicates (at 50% at most 50% of the log could be duplicates). A higher ratio will mean fewer, more efficient cleanings but will mean more wasted space in the log. If the max.compaction.lag.ms or the min.compaction.lag.ms configurations are also specified, then the log compactor considers the log eligible for compaction as soon as either: (i) the dirty ratio threshold has been met and the log has had dirty (uncompacted) records for at least the min.compaction.lag.ms duration, or (ii) if the log has had dirty (uncompacted) records for at most the max.compaction.lag.ms period.

min.compaction.lag.ms

Type: long
Default: 0
Valid Values: [0,…​]
Server Default Property: log.cleaner.min.compaction.lag.ms
Importance: medium

The minimum time a message will remain uncompacted in the log. Only applicable for logs that are being compacted.

min.insync.replicas

Type: int
Default: 1
Valid Values: [1,…​]
Server Default Property: min.insync.replicas
Importance: medium

When a producer sets acks to "all" (or "-1"), this configuration specifies the minimum number of replicas that must acknowledge a write for the write to be considered successful. If this minimum cannot be met, then the producer will raise an exception (either NotEnoughReplicas or NotEnoughReplicasAfterAppend). When used together, min.insync.replicas and acks allow you to enforce greater durability guarantees. A typical scenario would be to create a topic with a replication factor of 3, set min.insync.replicas to 2, and produce with acks of "all". This will ensure that the producer raises an exception if a majority of replicas do not receive a write.

preallocate

Type: boolean
Default: false
Server Default Property: log.preallocate
Importance: medium

True if we should preallocate the file on disk when creating a new log segment.

retention.bytes

Type: long
Default: -1
Server Default Property: log.retention.bytes
Importance: medium

This configuration controls the maximum size a partition (which consists of log segments) can grow to before we will discard old log segments to free up space if we are using the "delete" retention policy. By default there is no size limit only a time limit. Since this limit is enforced at the partition level, multiply it by the number of partitions to compute the topic retention in bytes.

retention.ms

Type: long
Default: 604800000
Valid Values: [-1,…​]
Server Default Property: log.retention.ms
Importance: medium

This configuration controls the maximum time we will retain a log before we will discard old log segments to free up space if we are using the "delete" retention policy. This represents an SLA on how soon consumers must read their data. If set to -1, no time limit is applied.

segment.bytes

Type: int
Default: 1073741824
Valid Values: [14,…​]
Server Default Property: log.segment.bytes
Importance: medium

This configuration controls the segment file size for the log. Retention and cleaning is always done a file at a time so a larger segment size means fewer files but less granular control over retention.

segment.index.bytes

Type: int
Default: 10485760
Valid Values: [0,…​]
Server Default Property: log.index.size.max.bytes
Importance: medium

This configuration controls the size of the index that maps offsets to file positions. We preallocate this index file and shrink it only after log rolls. You generally should not need to change this setting.

segment.jitter.ms

Type: long
Default: 0
Valid Values: [0,…​]
Server Default Property: log.roll.jitter.ms
Importance: medium

The maximum random jitter subtracted from the scheduled segment roll time to avoid thundering herds of segment rolling.

segment.ms

Type: long
Default: 604800000
Valid Values: [1,…​]
Server Default Property: log.roll.ms
Importance: medium

This configuration controls the period of time after which Kafka will force the log to roll even if the segment file isn’t full to ensure that retention can delete or compact old data.

unclean.leader.election.enable

Type: boolean
Default: false
Server Default Property: unclean.leader.election.enable
Importance: medium

Indicates whether to enable replicas not in the ISR set to be elected as leader as a last resort, even though doing so may result in data loss.

message.downconversion.enable

Type: boolean
Default: true
Server Default Property: log.message.downconversion.enable
Importance: low

This configuration controls whether down-conversion of message formats is enabled to satisfy consume requests. When set to false, broker will not perform down-conversion for consumers expecting an older message format. The broker responds with UNSUPPORTED_VERSION error for consume requests from such older clients. This configurationdoes not apply to any message format conversion that might be required for replication to followers.

Appendix C. Consumer configuration parameters

key.deserializer

Type: class
Importance: high

Deserializer class for key that implements the org.apache.kafka.common.serialization.Deserializer interface.

value.deserializer

Type: class
Importance: high

Deserializer class for value that implements the org.apache.kafka.common.serialization.Deserializer interface.

bootstrap.servers

Type: list
Default: ""
Valid Values: non-null string
Importance: high

A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form host1:port1,host2:port2,…​. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).

fetch.min.bytes

Type: int
Default: 1
Valid Values: [0,…​]
Importance: high

The minimum amount of data the server should return for a fetch request. If insufficient data is available the request will wait for that much data to accumulate before answering the request. The default setting of 1 byte means that fetch requests are answered as soon as a single byte of data is available or the fetch request times out waiting for data to arrive. Setting this to something greater than 1 will cause the server to wait for larger amounts of data to accumulate which can improve server throughput a bit at the cost of some additional latency.

group.id

Type: string
Default: null
Importance: high

A unique string that identifies the consumer group this consumer belongs to. This property is required if the consumer uses either the group management functionality by using subscribe(topic) or the Kafka-based offset management strategy.

heartbeat.interval.ms

Type: int
Default: 3000
Importance: high

The expected time between heartbeats to the consumer coordinator when using Kafka’s group management facilities. Heartbeats are used to ensure that the consumer’s session stays active and to facilitate rebalancing when new consumers join or leave the group. The value must be set lower than session.timeout.ms, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances.

max.partition.fetch.bytes

Type: int
Default: 1048576
Valid Values: [0,…​]
Importance: high

The maximum amount of data per-partition the server will return. Records are fetched in batches by the consumer. If the first record batch in the first non-empty partition of the fetch is larger than this limit, the batch will still be returned to ensure that the consumer can make progress. The maximum record batch size accepted by the broker is defined via message.max.bytes (broker config) or max.message.bytes (topic config). See fetch.max.bytes for limiting the consumer request size.

session.timeout.ms

Type: int
Default: 10000
Importance: high

The timeout used to detect consumer failures when using Kafka’s group management facility. The consumer sends periodic heartbeats to indicate its liveness to the broker. If no heartbeats are received by the broker before the expiration of this session timeout, then the broker will remove this consumer from the group and initiate a rebalance. Note that the value must be in the allowable range as configured in the broker configuration by group.min.session.timeout.ms and group.max.session.timeout.ms.

ssl.key.password

Type: password
Default: null
Importance: high

The password of the private key in the key store file. This is optional for client.

ssl.keystore.location

Type: string
Default: null
Importance: high

The location of the key store file. This is optional for client and can be used for two-way authentication for client.

ssl.keystore.password

Type: password
Default: null
Importance: high

The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.

ssl.truststore.location

Type: string
Default: null
Importance: high

The location of the trust store file.

ssl.truststore.password

Type: password
Default: null
Importance: high

The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.

allow.auto.create.topics

Type: boolean
Default: true
Importance: medium

Allow automatic topic creation on the broker when subscribing to or assigning a topic. A topic being subscribed to will be automatically created only if the broker allows for it using auto.create.topics.enable broker configuration. This configuration must be set to false when using brokers older than 0.11.0.

auto.offset.reset

Type: string
Default: latest
Valid Values: [latest, earliest, none]
Importance: medium

What to do when there is no initial offset in Kafka or if the current offset does not exist any more on the server (e.g. because that data has been deleted):

  • earliest: automatically reset the offset to the earliest offset
  • latest: automatically reset the offset to the latest offset
  • none: throw exception to the consumer if no previous offset is found for the consumer’s group
  • anything else: throw exception to the consumer.
client.dns.lookup

Type: string
Default: default
Valid Values: [default, use_all_dns_ips, resolve_canonical_bootstrap_servers_only]
Importance: medium

Controls how the client uses DNS lookups.

If set to use_all_dns_ips then, when the lookup returns multiple IP addresses for a hostname, they will all be attempted to connect to before failing the connection. Applies to both bootstrap and advertised servers.

If the value is resolve_canonical_bootstrap_servers_only each entry will be resolved and expanded into a list of canonical names.

connections.max.idle.ms

Type: long
Default: 540000
Importance: medium

Close idle connections after the number of milliseconds specified by this config.

default.api.timeout.ms

Type: int
Default: 60000
Valid Values: [0,…​]
Importance: medium

Specifies the timeout (in milliseconds) for consumer APIs that could block. This configuration is used as the default timeout for all consumer operations that do not explicitly accept a timeout parameter.

enable.auto.commit

Type: boolean
Default: true
Importance: medium

If true the consumer’s offset will be periodically committed in the background.

exclude.internal.topics

Type: boolean
Default: true
Importance: medium

Whether internal topics matching a subscribed pattern should be excluded from the subscription. It is always possible to explicitly subscribe to an internal topic.

fetch.max.bytes

Type: int
Default: 52428800
Valid Values: [0,…​]
Importance: medium

The maximum amount of data the server should return for a fetch request. Records are fetched in batches by the consumer, and if the first record batch in the first non-empty partition of the fetch is larger than this value, the record batch will still be returned to ensure that the consumer can make progress. As such, this is not a absolute maximum. The maximum record batch size accepted by the broker is defined via message.max.bytes (broker config) or max.message.bytes (topic config). Note that the consumer performs multiple fetches in parallel.

group.instance.id

Type: string
Default: null
Importance: medium

A unique identifier of the consumer instance provided by end user. Only non-empty strings are permitted. If set, the consumer is treated as a static member, which means that only one instance with this ID is allowed in the consumer group at any time. This can be used in combination with a larger session timeout to avoid group rebalances caused by transient unavailability (e.g. process restarts). If not set, the consumer will join the group as a dynamic member, which is the traditional behavior.

isolation.level

Type: string
Default: read_uncommitted
Valid Values: [read_committed, read_uncommitted]
Importance: medium

Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to `read_uncommitted’ (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode.

Messages will always be returned in offset order. Hence, in read_committed mode, consumer.poll() will only return messages up to the last stable offset (LSO), which is the one less than the offset of the first open transaction. In particular any messages appearing after messages belonging to ongoing transactions will be withheld until the relevant transaction has been completed. As a result, read_committed consumers will not be able to read up to the high watermark when there are in flight transactions. 

Further, when in `read_committed` the seekToEnd method will return the LSO.
max.poll.interval.ms

Type: int
Default: 300000
Valid Values: [1,…​]
Importance: medium

The maximum delay between invocations of poll() when using consumer group management. This places an upper bound on the amount of time that the consumer can be idle before fetching more records. If poll() is not called before expiration of this timeout, then the consumer is considered failed and the group will rebalance in order to reassign the partitions to another member.

max.poll.records

Type: int
Default: 500
Valid Values: [1,…​]
Importance: medium

The maximum number of records returned in a single call to poll().

partition.assignment.strategy

Type: list
Default: class org.apache.kafka.clients.consumer.RangeAssignor
Valid Values: non-null string
Importance: medium

The class name of the partition assignment strategy that the client will use to distribute partition ownership amongst consumer instances when group management is used.

receive.buffer.bytes

Type: int
Default: 65536
Valid Values: [-1,…​]
Importance: medium

The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.

request.timeout.ms

Type: int
Default: 30000
Valid Values: [0,…​]
Importance: medium

The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.

sasl.client.callback.handler.class

Type: class
Default: null
Importance: medium

The fully qualified name of a SASL client callback handler class that implements the AuthenticateCallbackHandler interface.

sasl.jaas.config

Type: password
Default: null
Importance: medium

JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described here. The format for the value is: ‘loginModuleClass controlFlag (optionName=optionValue)*;’. For brokers, the config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.jaas.config=com.example.ScramLoginModule required;.

sasl.kerberos.service.name

Type: string
Default: null
Importance: medium

The Kerberos principal name that Kafka runs as. This can be defined either in Kafka’s JAAS config or in Kafka’s config.

sasl.login.callback.handler.class

Type: class
Default: null
Importance: medium

The fully qualified name of a SASL login callback handler class that implements the AuthenticateCallbackHandler interface. For brokers, login callback handler config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.callback.handler.class=com.example.CustomScramLoginCallbackHandler.

sasl.login.class

Type: class
Default: null
Importance: medium

The fully qualified name of a class that implements the Login interface. For brokers, login config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.class=com.example.CustomScramLogin.

sasl.mechanism

Type: string
Default: GSSAPI
Importance: medium

SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.

security.protocol

Type: string
Default: PLAINTEXT
Importance: medium

Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.

send.buffer.bytes

Type: int
Default: 131072
Valid Values: [-1,…​]
Importance: medium

The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.

ssl.enabled.protocols

Type: list
Default: TLSv1.2,TLSv1.1,TLSv1
Importance: medium

The list of protocols enabled for SSL connections.

ssl.keystore.type

Type: string
Default: JKS
Importance: medium

The file format of the key store file. This is optional for client.

ssl.protocol

Type: string
Default: TLS
Importance: medium

The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.

ssl.provider

Type: string
Default: null
Importance: medium

The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.

ssl.truststore.type

Type: string
Default: JKS
Importance: medium

The file format of the trust store file.

auto.commit.interval.ms

Type: int
Default: 5000
Valid Values: [0,…​]
Importance: low

The frequency in milliseconds that the consumer offsets are auto-committed to Kafka if enable.auto.commit is set to true.

check.crcs

Type: boolean
Default: true
Importance: low

Automatically check the CRC32 of the records consumed. This ensures no on-the-wire or on-disk corruption to the messages occurred. This check adds some overhead, so it may be disabled in cases seeking extreme performance.

client.id

Type: string
Default: ""
Importance: low

An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.

client.rack

Type: string
Default: ""
Importance: low

A rack identifier for this client. This can be any string value which indicates where this client is physically located. It corresponds with the broker config 'broker.rack'.

fetch.max.wait.ms

Type: int
Default: 500
Valid Values: [0,…​]
Importance: low

The maximum amount of time the server will block before answering the fetch request if there isn’t sufficient data to immediately satisfy the requirement given by fetch.min.bytes.

interceptor.classes

Type: list
Default: ""
Valid Values: non-null string
Importance: low

A list of classes to use as interceptors. Implementing the org.apache.kafka.clients.consumer.ConsumerInterceptor interface allows you to intercept (and possibly mutate) records received by the consumer. By default, there are no interceptors.

metadata.max.age.ms

Type: long
Default: 300000
Valid Values: [0,…​]
Importance: low

The period of time in milliseconds after which we force a refresh of metadata even if we haven’t seen any partition leadership changes to proactively discover any new brokers or partitions.

metric.reporters

Type: list
Default: ""
Valid Values: non-null string
Importance: low

A list of classes to use as metrics reporters. Implementing the org.apache.kafka.common.metrics.MetricsReporter interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.

metrics.num.samples

Type: int
Default: 2
Valid Values: [1,…​]
Importance: low

The number of samples maintained to compute metrics.

metrics.recording.level

Type: string
Default: INFO
Valid Values: [INFO, DEBUG]
Importance: low

The highest recording level for metrics.

metrics.sample.window.ms

Type: long
Default: 30000
Valid Values: [0,…​]
Importance: low

The window of time a metrics sample is computed over.

reconnect.backoff.max.ms

Type: long
Default: 1000
Valid Values: [0,…​]
Importance: low

The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.

reconnect.backoff.ms

Type: long
Default: 50
Valid Values: [0,…​]
Importance: low

The base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.

retry.backoff.ms

Type: long
Default: 100
Valid Values: [0,…​]
Importance: low

The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.

sasl.kerberos.kinit.cmd

Type: string
Default: /usr/bin/kinit
Importance: low

Kerberos kinit command path.

sasl.kerberos.min.time.before.relogin

Type: long
Default: 60000
Importance: low

Login thread sleep time between refresh attempts.

sasl.kerberos.ticket.renew.jitter

Type: double
Default: 0.05
Importance: low

Percentage of random jitter added to the renewal time.

sasl.kerberos.ticket.renew.window.factor

Type: double
Default: 0.8
Importance: low

Login thread will sleep until the specified window factor of time from last refresh to ticket’s expiry has been reached, at which time it will try to renew the ticket.

sasl.login.refresh.buffer.seconds

Type: short
Default: 300
Valid Values: [0,…​,3600]
Importance: low

The amount of buffer time before credential expiration to maintain when refreshing a credential, in seconds. If a refresh would otherwise occur closer to expiration than the number of buffer seconds then the refresh will be moved up to maintain as much of the buffer time as possible. Legal values are between 0 and 3600 (1 hour); a default value of 300 (5 minutes) is used if no value is specified. This value and sasl.login.refresh.min.period.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.

sasl.login.refresh.min.period.seconds

Type: short
Default: 60
Valid Values: [0,…​,900]
Importance: low

The desired minimum time for the login refresh thread to wait before refreshing a credential, in seconds. Legal values are between 0 and 900 (15 minutes); a default value of 60 (1 minute) is used if no value is specified. This value and sasl.login.refresh.buffer.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.

sasl.login.refresh.window.factor

Type: double
Default: 0.8
Valid Values: [0.5,…​,1.0]
Importance: low

Login refresh thread will sleep until the specified window factor relative to the credential’s lifetime has been reached, at which time it will try to refresh the credential. Legal values are between 0.5 (50%) and 1.0 (100%) inclusive; a default value of 0.8 (80%) is used if no value is specified. Currently applies only to OAUTHBEARER.

sasl.login.refresh.window.jitter

Type: double
Default: 0.05
Valid Values: [0.0,…​,0.25]
Importance: low

The maximum amount of random jitter relative to the credential’s lifetime that is added to the login refresh thread’s sleep time. Legal values are between 0 and 0.25 (25%) inclusive; a default value of 0.05 (5%) is used if no value is specified. Currently applies only to OAUTHBEARER.

ssl.cipher.suites

Type: list
Default: null
Importance: low

A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.

ssl.endpoint.identification.algorithm

Type: string
Default: https
Importance: low

The endpoint identification algorithm to validate server hostname using server certificate.

ssl.keymanager.algorithm

Type: string
Default: SunX509
Importance: low

The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.

ssl.secure.random.implementation

Type: string
Default: null
Importance: low

The SecureRandom PRNG implementation to use for SSL cryptography operations.

ssl.trustmanager.algorithm

Type: string
Default: PKIX
Importance: low

The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.

Appendix D. Producer configuration parameters

key.serializer

Type: class
Importance: high

Serializer class for key that implements the org.apache.kafka.common.serialization.Serializer interface.

value.serializer

Type: class
Importance: high

Serializer class for value that implements the org.apache.kafka.common.serialization.Serializer interface.

acks

Type: string
Default: 1
Valid Values: [all, -1, 0, 1]
Importance: high

The number of acknowledgments the producer requires the leader to have received before considering a request complete. This controls the durability of records that are sent. The following settings are allowed:

  • acks=0 If set to zero then the producer will not wait for any acknowledgment from the server at all. The record will be immediately added to the socket buffer and considered sent. No guarantee can be made that the server has received the record in this case, and the retries configuration will not take effect (as the client won’t generally know of any failures). The offset given back for each record will always be set to -1.
  • acks=1 This will mean the leader will write the record to its local log but will respond without awaiting full acknowledgement from all followers. In this case should the leader fail immediately after acknowledging the record but before the followers have replicated it then the record will be lost.
  • acks=all This means the leader will wait for the full set of in-sync replicas to acknowledge the record. This guarantees that the record will not be lost as long as at least one in-sync replica remains alive. This is the strongest available guarantee. This is equivalent to the acks=-1 setting.
bootstrap.servers

Type: list
Default: ""
Valid Values: non-null string
Importance: high

A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form host1:port1,host2:port2,…​. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).

buffer.memory

Type: long
Default: 33554432
Valid Values: [0,…​]
Importance: high

The total bytes of memory the producer can use to buffer records waiting to be sent to the server. If records are sent faster than they can be delivered to the server the producer will block for max.block.ms after which it will throw an exception.

This setting should correspond roughly to the total memory the producer will use, but is not a hard bound since not all memory the producer uses is used for buffering. Some additional memory will be used for compression (if compression is enabled) as well as for maintaining in-flight requests.

compression.type

Type: string
Default: none
Importance: high

The compression type for all data generated by the producer. The default is none (i.e. no compression). Valid values are none, gzip, snappy, lz4, or zstd. Compression is of full batches of data, so the efficacy of batching will also impact the compression ratio (more batching means better compression).

retries

Type: int
Default: 2147483647
Valid Values: [0,…​,2147483647]
Importance: high

Setting a value greater than zero will cause the client to resend any record whose send fails with a potentially transient error. Note that this retry is no different than if the client resent the record upon receiving the error. Allowing retries without setting max.in.flight.requests.per.connection to 1 will potentially change the ordering of records because if two batches are sent to a single partition, and the first fails and is retried but the second succeeds, then the records in the second batch may appear first. Note additionally that produce requests will be failed before the number of retries has been exhausted if the timeout configured by delivery.timeout.ms expires first before successful acknowledgement. Users should generally prefer to leave this config unset and instead use delivery.timeout.ms to control retry behavior.

ssl.key.password

Type: password
Default: null
Importance: high

The password of the private key in the key store file. This is optional for client.

ssl.keystore.location

Type: string
Default: null
Importance: high

The location of the key store file. This is optional for client and can be used for two-way authentication for client.

ssl.keystore.password

Type: password
Default: null
Importance: high

The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.

ssl.truststore.location

Type: string
Default: null
Importance: high

The location of the trust store file.

ssl.truststore.password

Type: password
Default: null
Importance: high

The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.

batch.size

Type: int
Default: 16384
Valid Values: [0,…​]
Importance: medium

The producer will attempt to batch records together into fewer requests whenever multiple records are being sent to the same partition. This helps performance on both the client and the server. This configuration controls the default batch size in bytes.

No attempt will be made to batch records larger than this size.

Requests sent to brokers will contain multiple batches, one for each partition with data available to be sent.

A small batch size will make batching less common and may reduce throughput (a batch size of zero will disable batching entirely). A very large batch size may use memory a bit more wastefully as we will always allocate a buffer of the specified batch size in anticipation of additional records.

client.dns.lookup

Type: string
Default: default
Valid Values: [default, use_all_dns_ips, resolve_canonical_bootstrap_servers_only]
Importance: medium

Controls how the client uses DNS lookups.

If set to use_all_dns_ips then, when the lookup returns multiple IP addresses for a hostname, they will all be attempted to connect to before failing the connection. Applies to both bootstrap and advertised servers.

If the value is resolve_canonical_bootstrap_servers_only each entry will be resolved and expanded into a list of canonical names.

client.id

Type: string
Default: ""
Importance: medium

An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.

connections.max.idle.ms

Type: long
Default: 540000
Importance: medium

Close idle connections after the number of milliseconds specified by this config.

delivery.timeout.ms

Type: int
Default: 120000
Valid Values: [0,…​]
Importance: medium

An upper bound on the time to report success or failure after a call to send() returns. This limits the total time that a record will be delayed prior to sending, the time to await acknowledgement from the broker (if expected), and the time allowed for retriable send failures. The producer may report failure to send a record earlier than this config if either an unrecoverable error is encountered, the retries have been exhausted, or the record is added to a batch which reached an earlier delivery expiration deadline. The value of this config should be greater than or equal to the sum of request.timeout.ms and linger.ms.

linger.ms

Type: long
Default: 0
Valid Values: [0,…​]
Importance: medium

The producer groups together any records that arrive in between request transmissions into a single batched request. Normally this occurs only under load when records arrive faster than they can be sent out. However in some circumstances the client may want to reduce the number of requests even under moderate load. This setting accomplishes this by adding a small amount of artificial delay—that is, rather than immediately sending out a record the producer will wait for up to the given delay to allow other records to be sent so that the sends can be batched together. This can be thought of as analogous to Nagle’s algorithm in TCP. This setting gives the upper bound on the delay for batching: once we get batch.size worth of records for a partition it will be sent immediately regardless of this setting, however if we have fewer than this many bytes accumulated for this partition we will 'linger' for the specified time waiting for more records to show up. This setting defaults to 0 (i.e. no delay). Setting linger.ms=5, for example, would have the effect of reducing the number of requests sent but would add up to 5ms of latency to records sent in the absence of load.

max.block.ms

Type: long
Default: 60000
Valid Values: [0,…​]
Importance: medium

The configuration controls how long KafkaProducer.send() and KafkaProducer.partitionsFor() will block.These methods can be blocked either because the buffer is full or metadata unavailable.Blocking in the user-supplied serializers or partitioner will not be counted against this timeout.

max.request.size

Type: int
Default: 1048576
Valid Values: [0,…​]
Importance: medium

The maximum size of a request in bytes. This setting will limit the number of record batches the producer will send in a single request to avoid sending huge requests. This is also effectively a cap on the maximum record batch size. Note that the server has its own cap on record batch size which may be different from this.

partitioner.class

Type: class
Default: org.apache.kafka.clients.producer.internals.DefaultPartitioner
Importance: medium

Partitioner class that implements the org.apache.kafka.clients.producer.Partitioner interface.

receive.buffer.bytes

Type: int
Default: 32768
Valid Values: [-1,…​]
Importance: medium

The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.

request.timeout.ms

Type: int
Default: 30000
Valid Values: [0,…​]
Importance: medium

The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted. This should be larger than replica.lag.time.max.ms (a broker configuration) to reduce the possibility of message duplication due to unnecessary producer retries.

sasl.client.callback.handler.class

Type: class
Default: null
Importance: medium

The fully qualified name of a SASL client callback handler class that implements the AuthenticateCallbackHandler interface.

sasl.jaas.config

Type: password
Default: null
Importance: medium

JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described here. The format for the value is: ‘loginModuleClass controlFlag (optionName=optionValue)*;’. For brokers, the config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.jaas.config=com.example.ScramLoginModule required;.

sasl.kerberos.service.name

Type: string
Default: null
Importance: medium

The Kerberos principal name that Kafka runs as. This can be defined either in Kafka’s JAAS config or in Kafka’s config.

sasl.login.callback.handler.class

Type: class
Default: null
Importance: medium

The fully qualified name of a SASL login callback handler class that implements the AuthenticateCallbackHandler interface. For brokers, login callback handler config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.callback.handler.class=com.example.CustomScramLoginCallbackHandler.

sasl.login.class

Type: class
Default: null
Importance: medium

The fully qualified name of a class that implements the Login interface. For brokers, login config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.class=com.example.CustomScramLogin.

sasl.mechanism

Type: string
Default: GSSAPI
Importance: medium

SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.

security.protocol

Type: string
Default: PLAINTEXT
Importance: medium

Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.

send.buffer.bytes

Type: int
Default: 131072
Valid Values: [-1,…​]
Importance: medium

The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.

ssl.enabled.protocols

Type: list
Default: TLSv1.2,TLSv1.1,TLSv1
Importance: medium

The list of protocols enabled for SSL connections.

ssl.keystore.type

Type: string
Default: JKS
Importance: medium

The file format of the key store file. This is optional for client.

ssl.protocol

Type: string
Default: TLS
Importance: medium

The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.

ssl.provider

Type: string
Default: null
Importance: medium

The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.

ssl.truststore.type

Type: string
Default: JKS
Importance: medium

The file format of the trust store file.

enable.idempotence

Type: boolean
Default: false
Importance: low

When set to 'true', the producer will ensure that exactly one copy of each message is written in the stream. If 'false', producer retries due to broker failures, etc., may write duplicates of the retried message in the stream. Note that enabling idempotence requires max.in.flight.requests.per.connection to be less than or equal to 5, retries to be greater than 0 and acks must be 'all'. If these values are not explicitly set by the user, suitable values will be chosen. If incompatible values are set, a ConfigException will be thrown.

interceptor.classes

Type: list
Default: ""
Valid Values: non-null string
Importance: low

A list of classes to use as interceptors. Implementing the org.apache.kafka.clients.producer.ProducerInterceptor interface allows you to intercept (and possibly mutate) the records received by the producer before they are published to the Kafka cluster. By default, there are no interceptors.

max.in.flight.requests.per.connection

Type: int
Default: 5
Valid Values: [1,…​]
Importance: low

The maximum number of unacknowledged requests the client will send on a single connection before blocking. Note that if this setting is set to be greater than 1 and there are failed sends, there is a risk of message re-ordering due to retries (i.e., if retries are enabled).

metadata.max.age.ms

Type: long
Default: 300000
Valid Values: [0,…​]
Importance: low

The period of time in milliseconds after which we force a refresh of metadata even if we haven’t seen any partition leadership changes to proactively discover any new brokers or partitions.

metric.reporters

Type: list
Default: ""
Valid Values: non-null string
Importance: low

A list of classes to use as metrics reporters. Implementing the org.apache.kafka.common.metrics.MetricsReporter interface allows plugging in classes that will be notified of new metric creation. The JmxReporter is always included to register JMX statistics.

metrics.num.samples

Type: int
Default: 2
Valid Values: [1,…​]
Importance: low

The number of samples maintained to compute metrics.

metrics.recording.level

Type: string
Default: INFO
Valid Values: [INFO, DEBUG]
Importance: low

The highest recording level for metrics.

metrics.sample.window.ms

Type: long
Default: 30000
Valid Values: [0,…​]
Importance: low

The window of time a metrics sample is computed over.

reconnect.backoff.max.ms

Type: long
Default: 1000
Valid Values: [0,…​]
Importance: low

The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. After calculating the backoff increase, 20% random jitter is added to avoid connection storms.

reconnect.backoff.ms

Type: long
Default: 50
Valid Values: [0,…​]
Importance: low

The base amount of time to wait before attempting to reconnect to a given host. This avoids repeatedly connecting to a host in a tight loop. This backoff applies to all connection attempts by the client to a broker.

retry.backoff.ms

Type: long
Default: 100
Valid Values: [0,…​]
Importance: low

The amount of time to wait before attempting to retry a failed request to a given topic partition. This avoids repeatedly sending requests in a tight loop under some failure scenarios.

sasl.kerberos.kinit.cmd

Type: string
Default: /usr/bin/kinit
Importance: low

Kerberos kinit command path.

sasl.kerberos.min.time.before.relogin

Type: long
Default: 60000
Importance: low

Login thread sleep time between refresh attempts.

sasl.kerberos.ticket.renew.jitter

Type: double
Default: 0.05
Importance: low

Percentage of random jitter added to the renewal time.

sasl.kerberos.ticket.renew.window.factor

Type: double
Default: 0.8
Importance: low

Login thread will sleep until the specified window factor of time from last refresh to ticket’s expiry has been reached, at which time it will try to renew the ticket.

sasl.login.refresh.buffer.seconds

Type: short
Default: 300
Valid Values: [0,…​,3600]
Importance: low

The amount of buffer time before credential expiration to maintain when refreshing a credential, in seconds. If a refresh would otherwise occur closer to expiration than the number of buffer seconds then the refresh will be moved up to maintain as much of the buffer time as possible. Legal values are between 0 and 3600 (1 hour); a default value of 300 (5 minutes) is used if no value is specified. This value and sasl.login.refresh.min.period.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.

sasl.login.refresh.min.period.seconds

Type: short
Default: 60
Valid Values: [0,…​,900]
Importance: low

The desired minimum time for the login refresh thread to wait before refreshing a credential, in seconds. Legal values are between 0 and 900 (15 minutes); a default value of 60 (1 minute) is used if no value is specified. This value and sasl.login.refresh.buffer.seconds are both ignored if their sum exceeds the remaining lifetime of a credential. Currently applies only to OAUTHBEARER.

sasl.login.refresh.window.factor

Type: double
Default: 0.8
Valid Values: [0.5,…​,1.0]
Importance: low

Login refresh thread will sleep until the specified window factor relative to the credential’s lifetime has been reached, at which time it will try to refresh the credential. Legal values are between 0.5 (50%) and 1.0 (100%) inclusive; a default value of 0.8 (80%) is used if no value is specified. Currently applies only to OAUTHBEARER.

sasl.login.refresh.window.jitter

Type: double
Default: 0.05
Valid Values: [0.0,…​,0.25]
Importance: low

The maximum amount of random jitter relative to the credential’s lifetime that is added to the login refresh thread’s sleep time. Legal values are between 0 and 0.25 (25%) inclusive; a default value of 0.05 (5%) is used if no value is specified. Currently applies only to OAUTHBEARER.

ssl.cipher.suites

Type: list
Default: null
Importance: low

A list of cipher suites. This is a named combination of authentication, encryption, MAC and key exchange algorithm used to negotiate the security settings for a network connection using TLS or SSL network protocol. By default all the available cipher suites are supported.

ssl.endpoint.identification.algorithm

Type: string
Default: https
Importance: low

The endpoint identification algorithm to validate server hostname using server certificate.

ssl.keymanager.algorithm

Type: string
Default: SunX509
Importance: low

The algorithm used by key manager factory for SSL connections. Default value is the key manager factory algorithm configured for the Java Virtual Machine.

ssl.secure.random.implementation

Type: string
Default: null
Importance: low

The SecureRandom PRNG implementation to use for SSL cryptography operations.

ssl.trustmanager.algorithm

Type: string
Default: PKIX
Importance: low

The algorithm used by trust manager factory for SSL connections. Default value is the trust manager factory algorithm configured for the Java Virtual Machine.

transaction.timeout.ms

Type: int
Default: 60000
Importance: low

The maximum amount of time in ms that the transaction coordinator will wait for a transaction status update from the producer before proactively aborting the ongoing transaction.If this value is larger than the transaction.max.timeout.ms setting in the broker, the request will fail with a InvalidTransactionTimeout error.

transactional.id

Type: string
Default: null
Valid Values: non-empty string
Importance: low

The TransactionalId to use for transactional delivery. This enables reliability semantics which span multiple producer sessions since it allows the client to guarantee that transactions using the same TransactionalId have been completed prior to starting any new transactions. If no TransactionalId is provided, then the producer is limited to idempotent delivery. Note that enable.idempotence must be enabled if a TransactionalId is configured. The default is null, which means transactions cannot be used. Note that, by default, transactions require a cluster of at least three brokers which is the recommended setting for production; for development you can change this, by adjusting broker setting transaction.state.log.replication.factor.

Appendix E. Admin Client configuration parameters

bootstrap.servers

Type: list
Importance: high

A list of host/port pairs to use for establishing the initial connection to the Kafka cluster. The client will make use of all servers irrespective of which servers are specified here for bootstrapping—this list only impacts the initial hosts used to discover the full set of servers. This list should be in the form host1:port1,host2:port2,…​. Since these servers are just used for the initial connection to discover the full cluster membership (which may change dynamically), this list need not contain the full set of servers (you may want more than one, though, in case a server is down).

ssl.key.password

Type: password
Default: null
Importance: high

The password of the private key in the key store file. This is optional for client.

ssl.keystore.location

Type: string
Default: null
Importance: high

The location of the key store file. This is optional for client and can be used for two-way authentication for client.

ssl.keystore.password

Type: password
Default: null
Importance: high

The store password for the key store file. This is optional for client and only needed if ssl.keystore.location is configured.

ssl.truststore.location

Type: string
Default: null
Importance: high

The location of the trust store file.

ssl.truststore.password

Type: password
Default: null
Importance: high

The password for the trust store file. If a password is not set access to the truststore is still available, but integrity checking is disabled.

client.dns.lookup

Type: string
Default: default
Valid Values: [default, use_all_dns_ips, resolve_canonical_bootstrap_servers_only]
Importance: medium

Controls how the client uses DNS lookups.

If set to use_all_dns_ips then, when the lookup returns multiple IP addresses for a hostname, they will all be attempted to connect to before failing the connection. Applies to both bootstrap and advertised servers.

If the value is resolve_canonical_bootstrap_servers_only each entry will be resolved and expanded into a list of canonical names.

client.id

Type: string
Default: ""
Importance: medium

An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.

connections.max.idle.ms

Type: long
Default: 300000
Importance: medium

Close idle connections after the number of milliseconds specified by this config.

receive.buffer.bytes

Type: int
Default: 65536
Valid Values: [-1,…​]
Importance: medium

The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. If the value is -1, the OS default will be used.

request.timeout.ms

Type: int
Default: 120000
Valid Values: [0,…​]
Importance: medium

The configuration controls the maximum amount of time the client will wait for the response of a request. If the response is not received before the timeout elapses the client will resend the request if necessary or fail the request if retries are exhausted.

sasl.client.callback.handler.class

Type: class
Default: null
Importance: medium

The fully qualified name of a SASL client callback handler class that implements the AuthenticateCallbackHandler interface.

sasl.jaas.config

Type: password
Default: null
Importance: medium

JAAS login context parameters for SASL connections in the format used by JAAS configuration files. JAAS configuration file format is described here. The format for the value is: ‘loginModuleClass controlFlag (optionName=optionValue)*;’. For brokers, the config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.jaas.config=com.example.ScramLoginModule required;.

sasl.kerberos.service.name

Type: string
Default: null
Importance: medium

The Kerberos principal name that Kafka runs as. This can be defined either in Kafka’s JAAS config or in Kafka’s config.

sasl.login.callback.handler.class

Type: class
Default: null
Importance: medium

The fully qualified name of a SASL login callback handler class that implements the AuthenticateCallbackHandler interface. For brokers, login callback handler config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.callback.handler.class=com.example.CustomScramLoginCallbackHandler.

sasl.login.class

Type: class
Default: null
Importance: medium

The fully qualified name of a class that implements the Login interface. For brokers, login config must be prefixed with listener prefix and SASL mechanism name in lower-case. For example, listener.name.sasl_ssl.scram-sha-256.sasl.login.class=com.example.CustomScramLogin.

sasl.mechanism

Type: string
Default: GSSAPI
Importance: medium

SASL mechanism used for client connections. This may be any mechanism for which a security provider is available. GSSAPI is the default mechanism.

security.protocol

Type: string
Default: PLAINTEXT
Importance: medium

Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.

send.buffer.bytes

Type: int
Default: 131072
Valid Values: [-1,…​]
Importance: medium

The size of the TCP send buffer (SO_SNDBUF) to use when sending data. If the value is -1, the OS default will be used.

ssl.enabled.protocols

Type: list
Default: TLSv1.2,TLSv1.1,TLSv1
Importance: medium

The list of protocols enabled for SSL connections.

ssl.keystore.type

Type: string
Default: JKS
Importance: medium

The file format of the key store file. This is optional for client.

ssl.protocol

Type: string
Default: TLS
Importance: medium

The SSL protocol used to generate the SSLContext. Default setting is TLS, which is fine for most cases. Allowed values in recent JVMs are TLS, TLSv1.1 and TLSv1.2. SSL, SSLv2 and SSLv3 may be supported in older JVMs, but their usage is discouraged due to known security vulnerabilities.

ssl.provider

Type: string
Default: null
Importance: medium

The name of the security provider used for SSL connections. Default value is the default security provider of the JVM.

ssl.truststore.type

Type: string
Default: JKS
Importance: medium

The file format of the trust store file.

metadata.max.age.ms

Type: long
Default: 300000
Valid Values: [0,…​]
Importance:<