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Chapter 16. Distributed tracing
Distributed tracing allows you to track the progress of transactions between applications in a distributed system. In a microservices architecture, tracing tracks the progress of transactions between services. Trace data is useful for monitoring application performance and investigating issues with target systems and end-user applications.
In AMQ Streams on Red Hat Enterprise Linux, tracing facilitates the end-to-end tracking of messages: from source systems to Kafka, and then from Kafka to target systems and applications. Tracing complements the available JMX metrics.
How AMQ Streams supports tracing
Support for tracing is provided for the following clients and components.
Kafka clients:
- Kafka producers and consumers
- Kafka Streams API applications
Kafka components:
- Kafka Connect
- Kafka Bridge
- MirrorMaker
- MirrorMaker 2.0
To enable tracing, you perform four high-level tasks:
- Enable a Jaeger tracer.
Enable the Interceptors:
- For Kafka clients, you instrument your application code using the OpenTracing Apache Kafka Client Instrumentation library (included with AMQ Streams).
- For Kafka components, you set configuration properties for each component.
- Set tracing environment variables.
- Deploy the client or component.
When instrumented, clients generate trace data. For example, when producing messages or writing offsets to the log.
Traces are sampled according to a sampling strategy and then visualized in the Jaeger user interface.
Tracing is not supported for Kafka brokers.
Setting up tracing for applications and systems beyond AMQ Streams is outside the scope of this chapter. To learn more about this subject, search for "inject and extract" in the OpenTracing documentation.
Outline of procedures
To set up tracing for AMQ Streams, follow these procedures in order:
Set up tracing for clients:
Set up tracing for MirrorMaker, MirrorMaker 2.0, and Kafka Connect:
- Enable tracing for the Kafka Bridge
Prerequisites
- The Jaeger backend components are deployed to the host operating system. For deployment instructions, see the Jaeger deployment documentation.
16.1. Overview of OpenTracing and Jaeger
AMQ Streams uses the OpenTracing and Jaeger projects.
OpenTracing is an API specification that is independent from the tracing or monitoring system.
- The OpenTracing APIs are used to instrument application code
- Instrumented applications generate traces for individual transactions across the distributed system
- Traces are composed of spans that define specific units of work over time
Jaeger is a tracing system for microservices-based distributed systems.
- Jaeger implements the OpenTracing APIs and provides client libraries for instrumentation
- The Jaeger user interface allows you to query, filter, and analyze trace data
Additional resources
16.2. Setting up tracing for Kafka clients
Initialize a Jaeger tracer to instrument your client applications for distributed tracing.
16.2.1. Initializing a Jaeger tracer for Kafka clients
Configure and initialize a Jaeger tracer using a set of tracing environment variables.
Procedure
In each client application:
Add Maven dependencies for Jaeger to the
pom.xml
file for the client application:<dependency> <groupId>io.jaegertracing</groupId> <artifactId>jaeger-client</artifactId> <version>1.1.0.redhat-00002</version> </dependency>
- Define the configuration of the Jaeger tracer using the tracing environment variables.
Create the Jaeger tracer from the environment variables that you defined in step two:
Tracer tracer = Configuration.fromEnv().getTracer();
NoteFor alternative ways to initialize a Jaeger tracer, see the Java OpenTracing library documentation.
Register the Jaeger tracer as a global tracer:
GlobalTracer.register(tracer);
A Jaeger tracer is now initialized for the client application to use.
16.2.2. Instrumenting producers and consumers for tracing
Use a Decorator pattern or Interceptors to instrument your Java producer and consumer application code for tracing.
Procedure
In the application code of each producer and consumer application:
Add a Maven dependency for OpenTracing to the producer or consumer’s
pom.xml
file.<dependency> <groupId>io.opentracing.contrib</groupId> <artifactId>opentracing-kafka-client</artifactId> <version>0.1.12.redhat-00001</version> </dependency>
Instrument your client application code using either a Decorator pattern or Interceptors.
To use a Decorator pattern:
// Create an instance of the KafkaProducer: KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps); // Create an instance of the TracingKafkaProducer: TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer, tracer); // Send: tracingProducer.send(...); // Create an instance of the KafkaConsumer: KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps); // Create an instance of the TracingKafkaConsumer: TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer, tracer); // Subscribe: tracingConsumer.subscribe(Collections.singletonList("messages")); // Get messages: ConsumerRecords<Integer, String> records = tracingConsumer.poll(1000); // Retrieve SpanContext from polled record (consumer side): ConsumerRecord<Integer, String> record = ... SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
To use Interceptors:
// Register the tracer with GlobalTracer: GlobalTracer.register(tracer); // Add the TracingProducerInterceptor to the sender properties: senderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingProducerInterceptor.class.getName()); // Create an instance of the KafkaProducer: KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps); // Send: producer.send(...); // Add the TracingConsumerInterceptor to the consumer properties: consumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingConsumerInterceptor.class.getName()); // Create an instance of the KafkaConsumer: KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps); // Subscribe: consumer.subscribe(Collections.singletonList("messages")); // Get messages: ConsumerRecords<Integer, String> records = consumer.poll(1000); // Retrieve the SpanContext from a polled message (consumer side): ConsumerRecord<Integer, String> record = ... SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
Custom span names in a Decorator pattern
A span is a logical unit of work in Jaeger, with an operation name, start time, and duration.
To use a Decorator pattern to instrument your producer and consumer applications, define custom span names by passing a BiFunction
object as an additional argument when creating the TracingKafkaProducer
and TracingKafkaConsumer
objects. The OpenTracing Apache Kafka Client Instrumentation library includes several built-in span names.
Example: Using custom span names to instrument client application code in a Decorator pattern
// Create a BiFunction for the KafkaProducer that operates on (String operationName, ProducerRecord consumerRecord) and returns a String to be used as the name: BiFunction<String, ProducerRecord, String> producerSpanNameProvider = (operationName, producerRecord) -> "CUSTOM_PRODUCER_NAME"; // Create an instance of the KafkaProducer: KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps); // Create an instance of the TracingKafkaProducer TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer, tracer, producerSpanNameProvider); // Spans created by the tracingProducer will now have "CUSTOM_PRODUCER_NAME" as the span name. // Create a BiFunction for the KafkaConsumer that operates on (String operationName, ConsumerRecord consumerRecord) and returns a String to be used as the name: BiFunction<String, ConsumerRecord, String> consumerSpanNameProvider = (operationName, consumerRecord) -> operationName.toUpperCase(); // Create an instance of the KafkaConsumer: KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps); // Create an instance of the TracingKafkaConsumer, passing in the consumerSpanNameProvider BiFunction: TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer, tracer, consumerSpanNameProvider); // Spans created by the tracingConsumer will have the operation name as the span name, in upper-case. // "receive" -> "RECEIVE"
Built-in span names
When defining custom span names, you can use the following BiFunctions
in the ClientSpanNameProvider
class. If no spanNameProvider
is specified, CONSUMER_OPERATION_NAME
and PRODUCER_OPERATION_NAME
are used.
BiFunction | Description |
---|---|
|
Returns the |
|
Returns a String concatenation of |
|
Returns the name of the topic that the message was sent to or retrieved from in the format |
|
Returns a String concatenation of |
|
Returns the operation name and the topic name: |
|
Returns a String concatenation of |
16.2.3. Instrumenting Kafka Streams applications for tracing
Instrument Kafka Streams applications for distributed tracing using a supplier interface. This enables the Interceptors in the application.
Procedure
In each Kafka Streams application:
Add the
opentracing-kafka-streams
dependency to the Kafka Streams application’spom.xml
file.<dependency> <groupId>io.opentracing.contrib</groupId> <artifactId>opentracing-kafka-streams</artifactId> <version>0.1.12.redhat-00001</version> </dependency>
Create an instance of the
TracingKafkaClientSupplier
supplier interface:KafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer);
Provide the supplier interface to
KafkaStreams
:KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier); streams.start();
16.3. Setting up tracing for MirrorMaker and Kafka Connect
This section describes how to configure MirrorMaker, MirrorMaker 2.0, and Kafka Connect for distributed tracing.
You must enable a Jaeger tracer for each component.
16.3.1. Enabling tracing for MirrorMaker
Enable distributed tracing for MirrorMaker by passing the Interceptor properties as consumer and producer configuration parameters.
Messages are traced from the source cluster to the target cluster. The trace data records messages entering and leaving the MirrorMaker component.
Procedure
- Configure and enable a Jaeger tracer.
Edit the
/opt/kafka/config/consumer.properties
file.Add the following Interceptor property:
consumer.interceptor.classes=io.opentracing.contrib.kafka.TracingConsumerInterceptor
Edit the
/opt/kafka/config/producer.properties
file.Add the following Interceptor property:
producer.interceptor.classes=io.opentracing.contrib.kafka.TracingProducerInterceptor
Start MirrorMaker with the consumer and producer configuration files as parameters:
su - kafka /opt/kafka/bin/kafka-mirror-maker.sh --consumer.config /opt/kafka/config/consumer.properties --producer.config /opt/kafka/config/producer.properties --num.streams=2
16.3.2. Enabling tracing for MirrorMaker 2.0
Enable distributed tracing for MirrorMaker 2.0 by defining the Interceptor properties in the MirrorMaker 2.0 properties file.
Messages are traced between Kafka clusters. The trace data records messages entering and leaving the MirrorMaker 2.0 component.
Procedure
- Configure and enable a Jaeger tracer.
Edit the MirrorMaker 2.0 configuration properties file,
./config/connect-mirror-maker.properties
, and add the following properties:header.converter=org.apache.kafka.connect.converters.ByteArrayConverter 1 consumer.interceptor.classes=io.opentracing.contrib.kafka.TracingConsumerInterceptor 2 producer.interceptor.classes=io.opentracing.contrib.kafka.TracingProducerInterceptor
- Start MirrorMaker 2.0 using the instructions in Section 10.4, “Synchronizing data between Kafka clusters using MirrorMaker 2.0”.
Additional resources
16.3.3. Enabling tracing for Kafka Connect
Enable distributed tracing for Kafka Connect using configuration properties.
Only messages produced and consumed by Kafka Connect itself are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems.
Procedure
- Configure and enable a Jaeger tracer.
Edit the relevant Kafka Connect configuration file.
-
If you are running Kafka Connect in standalone mode, edit the
/opt/kafka/config/connect-standalone.properties
file. -
If you are running Kafka Connect in distributed mode, edit the
/opt/kafka/config/connect-distributed.properties
file.
-
If you are running Kafka Connect in standalone mode, edit the
Add the following properties to the configuration file:
producer.interceptor.classes=io.opentracing.contrib.kafka.TracingProducerInterceptor consumer.interceptor.classes=io.opentracing.contrib.kafka.TracingConsumerInterceptor
- Save the configuration file.
- Set tracing environment variables and then run Kafka Connect in standalone or distributed mode.
The Interceptors in Kafka Connect’s internal consumers and producers are now enabled.
16.4. Enabling tracing for the Kafka Bridge
Enable distributed tracing for the Kafka Bridge by editing the Kafka Bridge configuration file. You can then deploy a Kafka Bridge instance that is configured for distributed tracing to the host operating system.
Traces are generated when:
- The Kafka Bridge sends messages to HTTP clients and consumes messages from HTTP clients
- HTTP clients send HTTP requests to send and receive messages through the Kafka Bridge
To have end-to-end tracing, you must configure tracing in your HTTP clients.
Procedure
Edit the
config/application.properties
file in the Kafka Bridge installation directory.Remove the code comments from the following line:
bridge.tracing=jaeger
- Save the configuration file.
Run the
bin/kafka_bridge_run.sh
script using the configuration properties as a parameter:cd kafka-bridge-0.xy.x.redhat-0000x ./bin/kafka_bridge_run.sh --config-file=config/application.properties
The Interceptors in the Kafka Bridge’s internal consumers and producers are now enabled.
Additional resources
16.5. Environment variables for tracing
Use these environment variables when configuring a Jaeger tracer for Kafka clients and components.
The tracing environment variables are part of the Jaeger project and are subject to change. For the latest environment variables, see the Jaeger documentation.
Property | Required | Description |
---|---|---|
| Yes | The name of the Jaeger tracer service. |
| No |
The hostname for communicating with the |
| No |
The port used for communicating with the |
| No |
The |
| No | The authentication token to send to the endpoint as a bearer token. |
| No | The username to send to the endpoint if using basic authentication. |
| No | The password to send to the endpoint if using basic authentication. |
| No |
A comma-separated list of formats to use for propagating the trace context. Defaults to the standard Jaeger format. Valid values are |
| No | Indicates whether the reporter should also log the spans. |
| No | The reporter’s maximum queue size. |
| No | The reporter’s flush interval, in ms. Defines how frequently the Jaeger reporter flushes span batches. |
| No | The sampling strategy to use for client traces:
To sample all traces, use the Constant sampling strategy with a parameter of 1. For more information, see the Jaeger documentation. |
| No | The sampler parameter (number). |
| No | The hostname and port to use if a Remote sampling strategy is selected. |
| No | A comma-separated list of tracer-level tags that are added to all reported spans.
The value can also refer to an environment variable using the format |