此内容没有您所选择的语言版本。

Chapter 2. Debezium Connector for PostgreSQL

Debezium’s PostgreSQL Connector can monitor and record row-level changes in the schemas of a PostgreSQL database.

The first time it connects to a PostgreSQL server/cluster, it reads a consistent snapshot of all of the schemas. When that snapshot is complete, the connector continuously streams the changes that were committed to PostgreSQL 9.6 or later and generates corresponding insert, update and delete events. All of the events for each table are recorded in a separate Kafka topic, where they can be easily consumed by applications and services.

2.1. Overview

PostgreSQL’s logical decoding feature was first introduced in version 9.4 and is a mechanism which allows the extraction of the changes which were committed to the transaction log and the processing of these changes in a user-friendly manner via the help of an output plugin. This output plugin must be installed prior to running the PostgreSQL server and enabled together with a replication slot in order for clients to be able to consume the changes.

PostgreSQL connector contains two different parts which work together in order to be able to read and process server changes:

A logical decoding output plugin, which has to be installed and configured in the PostgreSQL server.
Java code (the actual Kafka Connect connector) which reads the changes produced by the plugin, using PostgreSQL’s streaming replication protocol, via the PostgreSQL JDBC driver

The connector then produces a change event for every row-level insert, update, and delete operation that was received, recording all the change events for each table in a separate Kafka topic. Your client applications read the Kafka topics that correspond to the database tables they’re interested in following, and react to every row-level event it sees in those topics.

PostgreSQL normally purges WAL segments after some period of time. This means that the connector won’t have the complete history of all changes that have been made to the database. Therefore, when the PostgreSQL connector first connects to a particular PostgreSQL database, it starts by performing a consistent snapshot of each of the database schemas. After the connector completes the snapshot, it continues streaming changes from the exact point at which the snapshot was made. This way, we start with a consistent view of all of the data, yet continue reading without having lost any of the changes made while the snapshot was taking place.

The connector is also tolerant of failures. As the connector reads changes and produces events, it records the position in the write-ahead log with each event. If the connector stops for any reason (including communication failures, network problems, or crashes), upon restart it simply continues reading the WAL where it last left off. This includes snapshots: if the snapshot was not completed when the connector is stopped, upon restart it will begin a new snapshot.

2.1.1. Logical decoding output plugin

The pgoutput logical decoder is the only supported logical decoder in the Tecnhology Preview release of Debezium.

pgoutput, the standard logical decoding plug-in in PostgreSQL 10+, is maintained by the Postgres community, and is also used by Postgres for logical replication. The pgoutput plug-in is always present, meaning that no additional libraries must be installed, and the connector will interpret the raw replication event stream into change events directly.

Important

The connector’s functionality relies on PostgreSQL’s logical decoding feature. Please be aware of the following limitations which are also reflected by the connector:

Logical Decoding does not support DDL changes: this means that the connector is unable to report DDL change events back to consumers.
Logical Decoding replication slots are only supported on primary servers: this means that when there is a cluster of PostgreSQL servers, the connector can only run on the active primary server. It cannot run on hot or warm standby replicas. If the primary server fails or is demoted, the connector will stop. Once the primary has recovered the connector can simply be restarted. If a different PostgreSQL server has been promoted to primary, the connector configuration must be adjusted before the connector is restarted. Make sure you read more about how the connector behaves when things go wrong.

Important

Debezium currently supports only databases with UTF-8 character encoding. With a single byte character encoding it is not possible to correctly process strings containing extended ASCII code characters.

2.2. Setting up PostgreSQL

This release of Debezium only supports the native pgoutput logical replication stream. To set up PostgreSQL using pgoutput, you will need to enable a replication slot, and configure a user with sufficient privileges to perform the replication.

2.2.1. Configuring the replication slot

PostgreSQL’s logical decoding uses replication slots.

First, you configure the replication slot:

postgresql.conf

wal_level=logical
max_wal_senders=1
max_replication_slots=1

wal_level tells the server to use logical decoding with the write-ahead log
max_wal_senders tells the server to use a maximum of 1 separate processes for processing WAL changes
max_replication_slots tells the server to allow a maximum of 1 replication slots to be created for streaming WAL changes

Replication slots are guaranteed to retain all WAL required for Debezium even during Debezium outages. It is important for this reason to closely monitor replication slots to avoid too much disk consumption and other conditions that can happen such as catalog bloat if a replication slot stays unused for too long. For more information, refer to the the Postgres documentation.

Note

We recommend reading and understanding the WAL configuration documentation regarding the mechanics and configuration of the PostgreSQL write-ahead log.

2.2.2. Setting up Permissions

Next, configure a database user who can perform replications.

Replication can only be performed by a database user that has appropriate permissions and only for a configured number of hosts.

In order to give a user replication permissions, define a PostgreSQL role that has at least the REPLICATION and LOGIN permissions. For example:

CREATE ROLE name REPLICATION LOGIN;

Note

Superusers have by default both of the above roles.

Finally, configure the PostgreSQL server to allow replication to take place between the server machine and the host on which the PostgreSQL connector is running:

pg_hba.conf

local   replication     <youruser>                          trust   1
host    replication     <youruser>  127.0.0.1/32            trust   2
host    replication     <youruser>  ::1/128                 trust   3

1: Tells the server to allow replication for <youruser> locally (i.e. on the server machine)
2: Tells the server to allow <youruser> on localhost to receive replication changes using IPV4
3: Tells the server to allow <youruser> on localhost to receive replication changes using IPV6

Note

See the PostgreSQL documentation for more information on network masks.

2.2.3. WAL Disk Space Consumption

In certain cases, it is possible that PostgreSQL disk space consumed by WAL files either experiences spikes or increases out of usual proportions. There are three potential reasons that explain the situation:

Debezium regularly confirms LSN of processed events to the database. This is visible as confirmed_flush_lsn in the pg_replication_slots slots table. The database is responsible for reclaiming the disk space and the WAL size can be calculated from restart_lsn of the same table. So if the confirmed_flush_lsn is regularly increasing and restart_lsn lags then the database does need to reclaim the space. Disk space is usually reclaimed in batch blocks so this is expected behaviour and no action on a user’s side is necessary.
There are many updates in a monitored database but only a minuscule amount relates to the monitored table(s) and/or schema(s). This situation can be easily solved by enabling periodic heartbeat events using heartbeat.interval.ms configuration option.
The PostgreSQL instance contains multiple databases where one of them is a high-traffic database. Debezium monitors another database that is low-traffic in comparison to the other one. Debezium then cannot confirm the LSN as replication slots work per-database and Debezium is not invoked. As WAL is shared by all databases it tends to grow until an event is emitted by the database monitored by Debezium.

To overcome the third cause it is necessary to

enable periodic heartbeat record generation using the heartbeat.interval.ms configuration option
regularly emit change events from the database tracked by Debezium

A separate process would then periodically update the table (either inserting a new event or updating the same row all over). PostgreSQL then will invoke Debezium which will confirm the latest LSN and allow the database to reclaim the WAL space.

Tip

For users on AWS RDS with Postgres, a similar situation to the third cause may occur on an idle environment, since AWS RDS makes writes to its own system tables not visible to the useres on a frequent basis (5 minutes). Again regularly emitting events will solve the problem.

2.2.4. How the PostgreSQL connector works

2.2.4.1. Snapshots

Most PostgreSQL servers are configured to not retain the complete history of the database in the WAL segments, so the PostgreSQL connector would be unable to see the entire history of the database by simply reading the WAL. So, by default the connector will upon first startup perform an initial consistent snapshot of the database. Each snapshot consists of the following steps (when using the builtin snapshot modes, custom snapshot modes may override this):

Start a transaction with a SERIALIZABLE, READ ONLY, DEFERRABLE isolation level to ensure that all subsequent reads within this transaction are done against a single consistent version of the data. Any changes to the data due to subsequent INSERT, UPDATE, and DELETE operations by other clients will not be visible to this transaction.
Obtain a ACCESS SHARE MODE lock on each of the monitored tables to ensure that no structural changes can occur to any of the tables while the snapshot is taking place. Note that these locks do not prevent table INSERTS, UPDATES and DELETES from taking place during the operation. This step is omitted when using the exported snapshot mode to allow for a lock-free snapshots.
Read the current position in the server’s transaction log.
Scan all of the database tables and schemas, and generate a READ event for each row and write that event to the appropriate table-specific Kafka topic.
Commit the transaction.
Record the successful completion of the snapshot in the connector offsets.

If the connector fails, is rebalanced, or stops after Step 1 begins but before Step 6 completes, upon restart the connector will begin a new snapshot. Once the connector does complete its initial snapshot, the PostgreSQL connector then continues streaming from the position read during step 3, ensuring that it does not miss any updates. If the connector stops again for any reason, upon restart it will simply continue streaming changes from where it previously left off.

A second snapshot mode allows the connector to perform snapshots always. This behavior tells the connector to always perform a snapshot when it starts up, and after the snapshot completes to continue streaming changes from step 3 in the above sequence. This mode can be used in cases when it’s known that some WAL segments have been deleted and are no longer available, or in case of a cluster failure after a new primary has been promoted so that the connector doesn’t miss out on any potential changes that could’ve taken place after the new primary had been promoted but before the connector was restarted on the new primary.

The third snapshot mode instructs the connector to never performs snapshots. When a new connector is configured this way, if will either continue streaming changes from a previous stored offset or it will start from the point in time when the PostgreSQL logical replication slot was first created on the server. Note that this mode is useful only when you know all data of interest is still reflected in the WAL.

The fourth snapshot mode, initial only, will perform a database snapshot and then stop before streaming any other changes. If the connector had started but did not complete a snapshot before stopping, the connector will restart the snapshot process and stop once the snapshot completes.

The fifth snapshot mode, exported, will perform a database snapshot based on the point in time when the replication slot was created. This mode is an excellent way to perform a snapshot in a lock-free way.

2.2.4.2. Streaming Changes

The PostgreSQL connector will typically spend the vast majority of its time streaming changes from the PostgreSQL server to which it is connected. This mechanism relies on PostgreSQL’s replication protocol where the client can receive changes from the server as they are committed in the server’s transaction log at certain positions (also known as Log Sequence Numbers or in short LSNs).

Whenever the server commits a transaction, a separate server process invokes a callback function from the logical decoding plugin. This function processes the changes from the transaction, converts them to a specific format (Protobuf or JSON in the case of Debezium plugin) and writes them on an output stream which can then be consumed by clients.

The PostgreSQL connector acts as a PostgreSQL client, and when it receives these changes it transforms the events into Debezium create, update, or delete events that include the LSN position of the event. The PostgreSQL connector forwards these change events to the Kafka Connect framework (running in the same process), which then asynchronously writes them in the same order to the appropriate Kafka topic. Kafka Connect uses the term offset for the source-specific position information that Debezium includes with each event, and Kafka Connect periodically records the most recent offset in another Kafka topic.

When Kafka Connect gracefully shuts down, it stops the connectors, flushes all events to Kafka, and records the last offset received from each connector. Upon restart, Kafka Connect reads the last recorded offset for each connector, and starts the connector from that point. The PostgreSQL connector uses the LSN recorded in each change event as the offset, so that upon restart the connector requests the PostgreSQL server send it the events starting just after that position.

Note

The PostgreSQL connector retrieves the schema information as part of the events sent by the logical decoder plug-in. The only exception is the information about which columns compose the primary key, as this information is obtained from the JDBC metadata (side channel). If the primary key definition of a table changes (by adding, removing or renaming PK columns), then there exists a slight risk of an unfortunate timing when the primary key information from JDBC will not be synchronized with the change data in the logical decoding event and a small amount of messages will be created with an inconsistent key structure. If this happens then a restart of the connector and a reprocessing of the messages will fix the issue. To prevent the issue completely it is recommended to synchronize updates to the primary key structure with Debezium roughly using following sequence of operations:

Put the database or an application into a read-only mode
Let Debezium process all remaining events
Stop Debezium
Update the primary key definition
Put the database or the application into read/write state and start Debezium again

2.2.4.3. PostgreSQL 10+ Logical Decoding Support (pgoutput)

As of PostgreSQL 10+, a new logical replication stream mode was introduced, called pgoutput. This logical replication stream mode is natively supported by PostgreSQL, which means that this connector can consume that replication stream without the need for additional plug-ins being installed. This is particularly valuable for environments where installation of plug-ins isn’t supported or allowed.

See Setting up PostgreSQL for more details.

2.2.4.4. Topics Names

The PostgreSQL connector writes events for all insert, update, and delete operations on a single table to a single Kafka topic. By default, the Kafka topic name is serverName.schemaName.tableName where serverName is the logical name of the connector as specified with the database.server.name configuration property, schemaName is the name of the database schema where the operation occurred, and tableName is the name of the database table on which the operation occurred.

For example, consider a PostgreSQL installation with a postgres database and an inventory schema that contains four tables: products, products_on_hand, customers, and orders. If the connector monitoring this database were given a logical server name of fulfillment, then the connector would produce events on these four Kafka topics:

fulfillment.inventory.products
fulfillment.inventory.products_on_hand
fulfillment.inventory.customers
fulfillment.inventory.orders

If on the other hand the tables were not part of a specific schema but rather created in the default public PostgreSQL schema, then the name of the Kafka topics would be:

fulfillment.public.products
fulfillment.public.products_on_hand
fulfillment.public.customers
fulfillment.public.orders

2.2.4.5. Meta Information

Each record produced by the PostgreSQL connector has, in addition to the database event, some meta-information about where the event occurred on the server, the name of the source partition and the name of the Kafka topic and partition where the event should be placed:

"sourcePartition": {
     "server": "fulfillment"
 },
 "sourceOffset": {
     "lsn": "24023128",
     "txId": "555",
     "ts_ms": "1482918357011"
 },
 "kafkaPartition": null

The PostgreSQL connector uses only 1 Kafka Connect partition and it places the generated events into 1 Kafka partition. Therefore, the name of the sourcePartition will always default to the name of the database.server.name configuration property, while the kafkaPartition has the value null which means that the connector does not use a specific Kafka partition.

The sourceOffset portion of the message contains information about the location of the server where the event occurred:

lsn represents the PostgreSQL log sequence number or offset in the transaction log
txId represents the identifier of the server transaction which caused the event
ts_ms represents the number of microseconds since Unix Epoch as the server time at which the transaction was committed

2.2.4.6. Events

All data change events produced by the PostgreSQL connector have a key and a value, although the structure of the key and value depend on the table from which the change events originated (see Topic names).

Note

Starting with Kafka 0.10, Kafka can optionally record with the message key and value the timestamp at which the message was created (recorded by the producer) or written to the log by Kafka.

Warning

The PostgreSQL connector ensures that all Kafka Connect schema names are valid Avro schema names. This means that the logical server name must start with Latin letters or an underscore (e.g., [a-z,A-Z,_]), and the remaining characters in the logical server name and all characters in the schema and table names must be Latin letters, digits, or an underscore (e.g., [a-z,A-Z,0-9,\_]). If not, then all invalid characters will automatically be replaced with an underscore character.

This can lead to unexpected conflicts when the logical server name, schema names, and table names contain other characters, and the only distinguishing characters between table full names are invalid and thus replaced with underscores.

Debezium and Kafka Connect are designed around continuous streams of event messages, and the structure of these events may change over time. This could be difficult for consumers to deal with, so to make it easy Kafka Connect makes each event self-contained. Every message key and value has two parts: a schema and payload. The schema describes the structure of the payload, while the payload contains the actual data.

2.2.4.6.1. Change Event’s Key

For a given table, the change event’s key will have a structure that contains a field for each column in the primary key (or unique key constraint with REPLICA IDENTITY set to FULL or USING INDEX on the table) of the table at the time the event was created.

Consider a customers table defined in the public database schema:

CREATE TABLE customers (
  id SERIAL,
  first_name VARCHAR(255) NOT NULL,
  last_name VARCHAR(255) NOT NULL,
  email VARCHAR(255) NOT NULL,
  PRIMARY KEY(id)
);

If the database.server.name configuration property has the value PostgreSQL_server, every change event for the customers table while it has this definition will feature the same key structure, which in JSON looks like this:

{
  "schema": {
    "type": "struct",
    "name": "PostgreSQL_server.public.customers.Key",
    "optional": false,
    "fields": [
          {
              "name": "id",
              "index": "0",
              "schema": {
                  "type": "INT32",
                  "optional": "false"
              }
          }
      ]
  },
  "payload": {
      "id": "1"
  },
}

The schema portion of the key contains a Kafka Connect schema describing what is in the key portion, and in our case that means that the payload value is not optional, is a structure defined by a schema named PostgreSQL_server.public.customers.Key, and has one required field named id of type int32. If we look at the value of the key’s payload field, we’ll see that it is indeed a structure (which in JSON is just an object) with a single id field, whose value is 1.

Therefore, we interpret this key as describing the row in the public.customers table (output from the connector named PostgreSQL_server) whose id primary key column had a value of 1.

Note

Although the column.blacklist configuration property allows you to remove columns from the event values, all columns in a primary or unique key are always included in the event’s key.

Warning

If the table does not have a primary or unique key, then the change event’s key will be null. This makes sense since the rows in a table without a primary or unique key constraint cannot be uniquely identified.

2.2.4.6.2. Change Event’s Value

The value of the change event message is a bit more complicated. Like the message key, it has a schema section and payload section. The payload section of every change event value produced by the PostgreSQL connector has an envelope structure with the following fields:

op is a mandatory field that contains a string value describing the type of operation. Values for the PostgreSQL connector are c for create (or insert), u for update, d for delete, and r for read (in the case of a snapshot).
before is an optional field that if present contains the state of the row before the event occurred. The structure will be described by the PostgreSQL_server.public.customers.Value Kafka Connect schema, which the PostgreSQL_server connector uses for all rows in the public.customers table.

Warning

Whether or not this field is available is highly dependent on the REPLICA IDENTITY setting for each table

after is an optional field that if present contains the state of the row after the event occurred. The structure is described by the same PostgreSQL_server.public.customers.Value Kafka Connect schema used in before.
source is a mandatory field that contains a structure describing the source metadata for the event, which in the case of PostgreSQL contains several fields: the Debezium version, the connector name, the name of the affected database, schema and table, whether the event is part of an ongoing snapshot or not and the same fields from the record’s meta information section
ts_ms is optional and if present contains the time (using the system clock in the JVM running the Kafka Connect task) at which the connector processed the event.

And of course, the schema portion of the event message’s value contains a schema that describes this envelope structure and the nested fields within it.

2.2.4.6.3. Replica Identity

REPLICA IDENTITY is a PostgreSQL specific table-level setting which determines the amount of information that is available to logical decoding in case of UPDATE and DELETE events. More specifically, this controls what (if any) information is available regarding the previous values of the table columns involved, whenever one of the aforementioned events occur.

There are 4 possible values for REPLICA IDENTITY:

DEFAULT - UPDATE and DELETE events will only contain the previous values for the primary key columns of a table, in case of UPDATE only the primary columns with changed values are present
NOTHING - UPDATE and DELETE events will not contain any information about the previous value on any of the table columns
FULL - UPDATE and DELETE events will contain the previous values of all the table’s columns
INDEX index name - UPDATE and DELETE events will contain the previous values of the columns contained in the index definition named index name, in case of UPDATE only the indexed columns with changed values are present

2.2.4.6.4. Create Events

Let’s look at what a create event value might look like for our customers table:

{
    "schema": {
        "type": "struct",
        "fields": [
            {
                "type": "struct",
                "fields": [
                    {
                        "type": "int32",
                        "optional": false,
                        "field": "id"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "first_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "last_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "email"
                    }
                ],
                "optional": true,
                "name": "PostgreSQL_server.inventory.customers.Value",
                "field": "before"
            },
            {
                "type": "struct",
                "fields": [
                    {
                        "type": "int32",
                        "optional": false,
                        "field": "id"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "first_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "last_name"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "email"
                    }
                ],
                "optional": true,
                "name": "PostgreSQL_server.inventory.customers.Value",
                "field": "after"
            },
            {
                "type": "struct",
                "fields": [
                    {
                        "type": "string",
                        "optional": false,
                        "field": "version"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "connector"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "name"
                    },
                    {
                        "type": "int64",
                        "optional": false,
                        "field": "ts_ms"
                    },
                    {
                        "type": "boolean",
                        "optional": true,
                        "default": false,
                        "field": "snapshot"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "db"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "schema"
                    },
                    {
                        "type": "string",
                        "optional": false,
                        "field": "table"
                    },
                    {
                        "type": "int64",
                        "optional": true,
                        "field": "txId"
                    },
                    {
                        "type": "int64",
                        "optional": true,
                        "field": "lsn"
                    },
                    {
                        "type": "int64",
                        "optional": true,
                        "field": "xmin"
                    }
                ],
                "optional": false,
                "name": "io.debezium.connector.postgresql.Source",
                "field": "source"
            },
            {
                "type": "string",
                "optional": false,
                "field": "op"
            },
            {
                "type": "int64",
                "optional": true,
                "field": "ts_ms"
            }
        ],
        "optional": false,
        "name": "PostgreSQL_server.inventory.customers.Envelope"
    },
    "payload": {
        "before": null,
        "after": {
            "id": 1,
            "first_name": "Anne",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": {
            "version": "1.0.3.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": true,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 555,
            "lsn": 24023128,
            "xmin": null
        },
        "op": "c",
        "ts_ms": 1559033904863
    }
}

If we look at the schema portion of this event’s value, we can see the schema for the envelope, the schema for the source structure (which is specific to the PostgreSQL connector and reused across all events), and the table-specific schemas for the before and after fields.

Note

The names of the schemas for the before and after fields are of the form logicalName.schemaName.tableName.Value, and thus are entirely independent from all other schemas for all other tables.

This means that when using the Avro Converter, the resulting Avro schemas for each table in each logical source have their own evolution and history.

If we look at the payload portion of this event’s value, we can see the information in the event, namely that it is describing that the row was created (since op=c), and that the after field value contains the values of the new inserted row’s' id, first_name, last_name, and email columns.

Note

It may appear that the JSON representations of the events are much larger than the rows they describe. This is true, because the JSON representation must include the schema and the payload portions of the message.

It is possible and even recommended to use the Avro Converter to dramatically decrease the size of the actual messages written to the Kafka topics.

2.2.4.6.5. Update Events

The value of an update change event on this table will actually have the exact same schema, and its payload will be structured the same but will hold different values. Here’s an example:

{
    "schema": { ... },
    "payload": {
        "before": {
            "id": 1
        },
        "after": {
            "id": 1,
            "first_name": "Anne Marie",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": {
            "version": "1.0.3.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": null,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 24023128,
            "xmin": null
        },
        "op": "u",
        "ts_ms": 1465584025523
    }
}

When we compare this to the value in the insert event, we see a couple of differences in the payload section:

The op field value is now u, signifying that this row changed because of an update
The before field now has the state of the row with the values before the database commit, but only for the primary key column id. This is because the REPLICA IDENTITY which is by default DEFAULT.

Note

Should we want to see the previous values of all the columns for the row, we would have to change the customers table first by running ALTER TABLE customers REPLICA IDENTITY FULL

The after field now has the updated state of the row, and here was can see that the first_name value is now Anne Marie.
The source field structure has the same fields as before, but the values are different since this event is from a different position in the WAL.
The ts_ms shows the timestamp that Debezium processed this event.

There are several things we can learn by just looking at this payload section. We can compare the before and after structures to determine what actually changed in this row because of the commit. The source structure tells us information about PostgreSQL’s record of this change (providing traceability), but more importantly this has information we can compare to other events in this and other topics to know whether this event occurred before, after, or as part of the same PostgreSQL commit as other events.

Note

When the columns for a row’s primary/unique key are updated, the value of the row’s key has changed so Debezium will output three events: a DELETE event and tombstone event with the old key for the row, followed by an INSERT event with the new key for the row.

2.2.4.6.6. Delete Events

So far we’ve seen samples of create and update events. Now, let’s look at the value of a delete event for the same table. Once again, the schema portion of the value will be exactly the same as with the create and update events:

{
    "schema": { ... },
    "payload": {
        "before": {
            "id": 1
        },
        "after": null,
        "source": {
            "version": "1.0.3.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": null,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 46523128,
            "xmin": null
        },
        "op": "d",
        "ts_ms": 1465581902461
    }
}

If we look at the payload portion, we see a number of differences compared with the create or update event payloads:

The op field value is now d, signifying that this row was deleted
The before field now has the state of the row that was deleted with the database commit. Again this only contains the primary key column due to the REPLICA IDENTITY setting
The after field is null, signifying that the row no longer exists
The source field structure has many of the same values as before, except the ts_ms, lsn and txId fields have changed
The ts_ms shows the timestamp that Debezium processed this event.

This event gives a consumer all kinds of information that it can use to process the removal of this row.

Warning

Please pay attention to the tables without PK, any delete messages from such table with REPLICA IDENTITY DEFAULT will have no before part (because they have no PK which is the only field for the default identity level) and therefore will be skipped as totally empty. To be able to process messages from tables without PK set REPLICA IDENTITY to FULL level.

The PostgreSQL connector’s events are designed to work with Kafka log compaction, which allows for the removal of some older messages as long as at least the most recent message for every key is kept. This allows Kafka to reclaim storage space while ensuring the topic contains a complete dataset and can be used for reloading key-based state.

When a row is deleted, the delete event value listed above still works with log compaction, since Kafka can still remove all earlier messages with that same key. But only if the message value is null will Kafka know that it can remove all messages with that same key. To make this possible, the PostgreSQL connector always follows the delete event with a special tombstone event that has the same key but null value.

2.2.4.7. Data Types

As described above, the PostgreSQL connector represents the changes to rows with events that are structured like the table in which the row exist. The event contains a field for each column value, and how that value is represented in the event depends on the PostgreSQL data type of the column. This section describes this mapping.

The following table describes how the connector maps each of the PostgreSQL data types to a literal type and semantic type within the events' fields.

Here, the literal type describes how the value is literally represented using Kafka Connect schema types, namely INT8, INT16, INT32, INT64, FLOAT32, FLOAT64, BOOLEAN, STRING, BYTES, ARRAY, MAP, and STRUCT.

The semantic type describes how the Kafka Connect schema captures the meaning of the field using the name of the Kafka Connect schema for the field.

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`BOOLEAN`	`BOOLEAN`	n/a
`BIT(1)`	`BOOLEAN`	n/a
`BIT( > 1)`, `BIT VARYING[(M)]`	`BYTES`	`io.debezium.data.Bits`	The `length` schema parameter contains an integer representing the number of bits. The resulting `byte[]` will contain the bits in little-endian form and will be sized to contain at least the specified number of bits (e.g., `numBytes = n/8 + (n%8== 0 ? 0 : 1)` where `n` is the number of bits).
`SMALLINT`, `SMALLSERIAL`	`INT16`	n/a
`INTEGER`, `SERIAL`	`INT32`	n/a
`BIGINT`, `BIGSERIAL`	`INT64`	n/a
`REAL`	`FLOAT32`	n/a
`DOUBLE PRECISION`	`FLOAT64`	n/a
`CHAR[(M)]`	`STRING`	n/a
`VARCHAR[(M)]`	`STRING`	n/a
`CHARACTER[(M)]`	`STRING`	n/a
`CHARACTER VARYING[(M)]`	`STRING`	n/a
`TIMESTAMPTZ`, `TIMESTAMP WITH TIME ZONE`	`STRING`	`io.debezium.time.ZonedTimestamp`	A string representation of a timestamp with timezone information, where the timezone is GMT
`TIMETZ`, `TIME WITH TIME ZONE`	`STRING`	`io.debezium.time.ZonedTime`	A string representation of a time value with timezone information, where the timezone is GMT
`INTERVAL [P]`	`INT64`	`io.debezium.time.MicroDuration` (default)	The approximate number of microseconds for a time interval using the `365.25 / 12.0` formula for days per month average
`INTERVAL [P]`	`String`	`io.debezium.time.Interval` (when `interval.handling.mode` is set to `string`)	The string representation of the interval value that follows pattern `P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S`, e.g. `P1Y2M3DT4H5M6.78S`
`BYTEA`	`BYTES`	n/a
`JSON`, `JSONB`	`STRING`	`io.debezium.data.Json`	Contains the string representation of a JSON document, array, or scalar.
`XML`	`STRING`	`io.debezium.data.Xml`	Contains the string representation of an XML document
`UUID`	`STRING`	`io.debezium.data.Uuid`	Contains the string representation of a PostgreSQL UUID value
`POINT`	`STRUCT`	`io.debezium.data.geometry.Point`	Contains a structure with 2 `FLOAT64` fields - `(x,y)` - each representing the coordinates of a geometric point
`LTREE`	`STRING`	`io.debezium.data.Ltree`	Contains the string representation of a PostgreSQL LTREE value
`CITEXT`	`STRING`	n/a
`INET`	`STRING`	n/a
`INT4RANGE`	`STRING`	n/a	Range of integer
`INT8RANGE`	`STRING`	n/a	Range of bigint
`NUMRANGE`	`STRING`	n/a	Range of numeric
`TSRANGE`	`STRING`	n/a	Contains the string representation of timestamp range without time zone.
`TSTZRANGE`	`STRING`	n/a	Contains the string representation of a timestamp range with (local system) time zone.
`DATERANGE`	`STRING`	n/a	Contains the string representation of a date range. It always has an exclusive upper-bound.
`ENUM`	`STRING`	`io.debezium.data.Enum`	Contains the string representation of the PostgreSQL ENUM value. The set of allowed values are maintained in the schema parameter named `allowed`.

Other data type mappings are described in the following sections.

2.2.4.7.1. Temporal Values

Other than PostgreSQL’s TIMESTAMPTZ and TIMETZ data types (which contain time zone information), the other temporal types depend on the value of the time.precision.mode configuration property. When the time.precision.mode configuration property is set to adaptive (the default), then the connector will determine the literal type and semantic type for the temporal types based on the column’s data type definition so that events exactly represent the values in the database:

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`DATE`	`INT32`	`io.debezium.time.Date`	Represents the number of days since epoch.
`TIME(1)`, `TIME(2)`, `TIME(3)`	`INT32`	`io.debezium.time.Time`	Represents the number of milliseconds past midnight, and does not include timezone information.
`TIME(4)`, `TIME(5)`, `TIME(6)`	`INT64`	`io.debezium.time.MicroTime`	Represents the number of microseconds past midnight, and does not include timezone information.
`TIMESTAMP(1)`, `TIMESTAMP(2)`, `TIMESTAMP(3)`	`INT64`	`io.debezium.time.Timestamp`	Represents the number of milliseconds past epoch, and does not include timezone information.
`TIMESTAMP(4)`, `TIMESTAMP(5)`, `TIMESTAMP(6)`, `TIMESTAMP`	`INT64`	`io.debezium.time.MicroTimestamp`	Represents the number of microseconds past epoch, and does not include timezone information.

When the time.precision.mode configuration property is set to adaptive_time_microseconds, then the connector will determine the literal type and semantic type for the temporal types based on the column’s data type definition so that events exactly represent the values in the database, except that all TIME fields will be captured as microseconds:

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`DATE`	`INT32`	`io.debezium.time.Date`	Represents the number of days since epoch.
`TIME([P])`	`INT64`	`io.debezium.time.MicroTime`	Represents the time value in microseconds and does not include timezone information. PostgreSQL allows precision `P` to be in the range 0-6 to store up to microsecond precision.
`TIMESTAMP(1)` , `TIMESTAMP(2)`, `TIMESTAMP(3)`	`INT64`	`io.debezium.time.Timestamp`	Represents the number of milliseconds past epoch, and does not include timezone information.
`TIMESTAMP(4)` , `TIMESTAMP(5)`, `TIMESTAMP(6)`, `TIMESTAMP`	`INT64`	`io.debezium.time.MicroTimestamp`	Represents the number of microseconds past epoch, and does not include timezone information.

When the time.precision.mode configuration property is set to connect, then the connector will use the predefined Kafka Connect logical types. This may be useful when consumers only know about the built-in Kafka Connect logical types and are unable to handle variable-precision time values. On the other hand, since PostgreSQL supports microsecond precision, the events generated by a connector with the connect time precision mode will result in a loss of precision when the database column has a fractional second precision value greater than 3:

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`DATE`	`INT32`	`org.apache.kafka.connect.data.Date`	Represents the number of days since epoch.
`TIME([P])`	`INT64`	`org.apache.kafka.connect.data.Time`	Represents the number of milliseconds since midnight, and does not include timezone information. PostgreSQL allows `P` to be in the range 0-6 to store up to microsecond precision, though this mode results in a loss of precision when `P` > 3.
`TIMESTAMP([P])`	`INT64`	`org.apache.kafka.connect.data.Timestamp`	Represents the number of milliseconds since epoch, and does not include timezone information. PostgreSQL allows `P` to be in the range 0-6 to store up to microsecond precision, though this mode results in a loss of precision when `P` > 3.

2.2.4.7.2. `TIMESTAMP` values

The TIMESTAMP type represents a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. So for instance the TIMESTAMP value "2018-06-20 15:13:16.945104" will be represented by a io.debezium.time.MicroTimestamp with the value "1529507596945104" (assuming time.precision.mode is not set to connect).

Note that the timezone of the JVM running Kafka Connect and Debezium does not affect this conversion.

2.2.4.7.3. Decimal Values

When decimal.handling.mode configuration property is set to precise, then the connector will use the predefined Kafka Connect org.apache.kafka.connect.data.Decimal logical type for all DECIMAL and NUMERIC columns. This is the default mode.

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`NUMERIC[(M[,D])]`	`BYTES`	`org.apache.kafka.connect.data.Decimal`	The `scaled` schema parameter contains an integer representing how many digits the decimal point was shifted.
`DECIMAL[(M[,D])]`	`BYTES`	`org.apache.kafka.connect.data.Decimal`	The `scaled` schema parameter contains an integer representing how many digits the decimal point was shifted.

There is an exception to this rule. When the NUMERIC or DECIMAL types are used without any scale constraints then it means that the values coming from the database have a different (variable) scale for each value. In this case a type io.debezium.data.VariableScaleDecimal is used and it contains both value and scale of the transferred value.

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`NUMERIC`	`STRUCT`	`io.debezium.data.VariableScaleDecimal`	Contains a structure with two fields: `scale` of type `INT32` that contains the scale of the transferred value and `value` of type `BYTES` containing the original value in an unscaled form.
`DECIMAL`	`STRUCT`	`io.debezium.data.VariableScaleDecimal`	Contains a structure with two fields: `scale` of type `INT32` that contains the scale of the transferred value and `value` of type `BYTES` containing the original value in an unscaled form.

However, when decimal.handling.mode configuration property is set to double, then the connector will represent all DECIMAL and NUMERIC values as Java double values and encodes them as follows:

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`NUMERIC[(M[,D])]`	`FLOAT64`
`DECIMAL[(M[,D])]`	`FLOAT64`

The last option for decimal.handling.mode configuration property is string. In this case the connector will represent all DECIMAL and NUMERIC values as their formatted string representation and encodes them as follows:

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`NUMERIC[(M[,D])]`	`STRING`
`DECIMAL[(M[,D])]`	`STRING`

PostgreSQL supports NaN (not a number) special value to be stored in the DECIMAL/NUMERIC values. Only string and double modes are able to handle such values encoding them as either Double.NaN or string constant NAN.

2.2.4.7.4. HStore Values

When hstore.handling.mode configuration property is set to json (the default), the connector will represent all HSTORE values as string-ified JSON values and encode them as follows:

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`HSTORE`	`STRING`	`io.debezium.data.Json`	Example: output representation using the JSON converter is `{\"key\" : \"val\"}`

When hstore.handling.mode configuration property is set to map, then the connector will use the MAP schema type for all HSTORE columns.

PostgreSQL Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`HSTORE`	`MAP`		Example: output representation using the JSON converter is `{"key" : "val"}`

2.2.4.8. PostgreSQL Domain Types

PostgreSQL also supports the notion of user-defined types that are based upon other underlying types. When such column types are used, Debezium exposes the column’s representation based on the full type hierarchy.

Important

Special consideration should be taken when monitoring columns that use domain types.

When a column is defined using a domain type that extends one of the default database types and the domain type defines a custom length/scale, the generated schema will inherit that defined length/scale.

When a column is defined using a domain type that extends another domain type that defines a custom length/scale, the generated schema will not inherit the defined length/scale because the PostgreSQL driver’s column metadata implementation.

2.2.4.8.1. Network Address Types

PostgreSQL also have data types that can store IPv4, IPv6, and MAC addresses. It is better to use these instead of plain text types to store network addresses, because these types offer input error checking and specialized operators and functions.

PostgreSQL Data Type	Literal type (schema type)	Notes
`INET`	`STRING`	IPv4 and IPv6 networks
`CIDR`	`STRING`	IPv4 and IPv6 hosts and networks
`MACADDR`	`STRING`	MAC addresses
`MACADDR8`	`STRING`	MAC addresses in EUI-64 format

2.2.4.8.2. PostGIS Types

The PostgreSQL connector also has full support for all of the PostGIS data types

PostGIS Data Type	Literal type (schema type)	Semantic type (schema name)	Notes
`GEOMETRY` (planar)	`STRUCT`	`io.debezium.data.geometry.Geometry`	Contains a structure with 2 fields `srid (INT32)` - Spatial Reference System Identifier defining what type of geometry object is stored in the structure `wkb (BYTES)` - a binary representation of the geometry object encoded in the Well-Known-Binary format. Please see Open Geospatial Consortium Simple Features Access specification for the format details.
`GEOGRAPHY` (spherical)	`STRUCT`	`io.debezium.data.geometry.Geography`	Contains a structure with 2 fields `srid (INT32)` - Spatial Reference System Identifier defining what type of geography object is stored in the structure `wkb (BYTES)` - a binary representation of the geometry object encoded in the Well-Known-Binary format. Please see Open Geospatial Consortium Simple Features Access specification for the format details.

2.2.4.8.3. Toasted values

PostgreSQL has a hard limit on the page size. This means that values larger than ca. 8 KB need to be stored using TOAST storage. This impacts replication messages coming from database, as the values that were stored using the TOAST mechanism and have not been changed are not included in the message, unless they are part of the table’s replica identity. There is no safe way for Debezium to read the missing value out-of-bands directly from database, as this would lead into race conditions potentially. Debezium thus follows these rules to handle the toasted values:

tables with REPLICA IDENTITY FULL: TOAST column values are part of the before and after blocks of change events as any other column
tables with REPLICA IDENTITY DEFAULT: when receiving an UPDATE event from the database, any unchanged TOAST column value which is not part of the replica identity will not be part of that event; similarly, when receiving a DELETE event, any such TOAST column will not be part of the before block. As Debezium cannot safely provide the column value in this case, it returns a placeholder value defined in configuration option toasted.value.placeholder.

Important

There is a specific problem related to Amazon RDS instances. wal2json plugin has evolved over the time and there were releases that provided out-of-band toasted values. Amazon supports different versions of the plugin for different PostgreSQL versions. Please consult Amazon’s documentation to obtain version to version mapping. For consistent toasted values handling we recommend to

use pgoutput plugin for PostgreSQL 10+ instances
set include-unchanged-toast=0 for older versions of wal2json plugin using slot.stream.params configuration option

2.3. Deploying the PostgreSQL Connector

Installing the PostgreSQL connector is a simple process whereby you only need to download the JAR, extract it to your Kafka Connect environment, and ensure the plugin’s parent directory is specified in your Kafka Connect environment.

Prerequisites

You have Zookeeper, Kafka, and Kafka Connect installed.
You have PostgreSQL installed and setup.

Procedure

Download the Debezium PostgreSQL connector.
Extract the files into your Kafka Connect environment.
Add the plugin’s parent directory to your Kafka Connect plugin path:
```
plugin.path=/kafka/connect
```

Note

The above example assumes you have extracted the Debezium PostgreSQL connector to the /kafka/connect/Debezium-connector-postgresql path.

Restart your Kafka Connect process. This ensures the new JARs are picked up.

Additional resources

For more information on the deployment process, and deploying connectors with AMQ Streams, refer to the Debezium installation guides.

2.3.1. Example Configuration

To use the connector to produce change events for a particular PostgreSQL server or cluster:

Install the logical decoding plugin
Configure the PostgreSQL server to support logical replication
Create a configuration file for the PostgreSQL connector.

When the connector starts, it will grab a consistent snapshot of the databases in your PostgreSQL server and start streaming changes, producing events for every inserted, updated, and deleted row. You can also choose to produce events for a subset of the schemas and tables. Optionally ignore, mask, or truncate columns that are sensitive, too large, or not needed.

Following is an example of the configuration for a PostgreSQL connector that monitors a PostgreSQL server at port 5432 on 192.168.99.100, which we logically name fullfillment. Typically, you configure the Debezium PostgreSQL connector in a .yaml file using the configuration properties available for the connector.

apiVersion: kafka.strimzi.io/v1beta1
  kind: KafkaConnector
  metadata:
    name: inventory-connector  1
    labels: strimzi.io/cluster: my-connect-cluster
  spec:
    class: io.debezium.connector.postgresql.PostgresConnector
    tasksMax: 1  2
    config:  3
      database.hostname: postgresqldb   4
      database.port: 5432
      database.user: debezium
      database.password: dbz
      database.dbname: postgres
      database.server.name: fullfillment   5
      database.whitelist: public.inventory   6

1: The name of the connector.
2: Only one task should operate at any one time. Because the PostgreSQL connector reads the PostgreSQL server’s binlog, using a single connector task ensures proper order and event handling. The Kafka Connect service uses connectors to start one or more tasks that do the work, and it automatically distributes the running tasks across the cluster of Kafka Connect services. If any of the services stop or crash, those tasks will be redistributed to running services.
3: The connector’s configuration.
4: The database host, which is the name of the container running the PostgreSQL server (postgresqldb).
5: A unique server name. The server name is the logical identifier for the PostgreSQL server or cluster of servers. This name will be used as the prefix for all Kafka topics.
6: Only changes in the public.inventory database will be detected.

See the complete list of connector properties that can be specified in these configurations.

This configuration can be sent via POST to a running Kafka Connect service, which will then record the configuration and start up the one connector task that will connect to the PostgreSQL database and record events to Kafka topics.

2.3.2. Monitoring

Kafka, Zookeeper, and Kafka Connect all have built-in support for JMX metrics. The PostgreSQL connector also publishes a number of metrics about the connector’s activities that can be monitored through JMX. The connector has two types of metrics. Snapshot metrics help you monitor the snapshot activity and are available when the connector is performing a snapshot. Streaming metrics help you monitor the progress and activity while the connector processes the logical replication stream.

2.3.2.1. Snapshot Metrics

2.3.2.1.1. MBean: debezium.postgres:type=connector-metrics,context=snapshot,server=<database.server.name>

Attribute Name	Type	Description
`LastEvent`	`string`	The last snapshot event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since last started or reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by whitelist or blacklist filtering rules configured on the connector.
`MonitoredTables`	`string[]`	The list of tables that are monitored by the connector.
`QueueTotalCapcity`	`int`	The length of the queue used to pass events between the snapshotter and the main Kafka Connect loop.
`QueueRemainingCapcity`	`int`	The free capacity of the queue used to pass events between the snapshotter and the main Kafka Connect loop.
`TotalTableCount`	`int`	The total number of tables that are being included in the snapshot.
`RemainingTableCount`	`int`	The number of tables that the snapshot has yet to copy.
`SnapshotRunning`	`boolean`	Whether the snapshot was started.
`SnapshotAborted`	`boolean`	Whether the snapshot was aborted.
`SnapshotCompleted`	`boolean`	Whether the snapshot completed.
`SnapshotDurationInSeconds`	`long`	The total number of seconds that the snapshot has taken so far, even if not complete.
`RowsScanned`	`Map<String, Long>`	Map containing the number of rows scanned for each table in the snapshot. Tables are incrementally added to the Map during processing. Updates every 10,000 rows scanned and upon completing a table.

2.3.2.2. Streaming Metrics

2.3.2.2.1. MBean: debezium.postres:type=connector-metrics,context=streaming,server=<database.server.name>

Attribute Name	Type	Description
`LastEvent`	`string`	The last streaming event that the connector has read.
`MilliSecondsSinceLastEvent`	`long`	The number of milliseconds since the connector has read and processed the most recent event.
`TotalNumberOfEventsSeen`	`long`	The total number of events that this connector has seen since last started or reset.
`NumberOfEventsFiltered`	`long`	The number of events that have been filtered by whitelist or blacklist filtering rules configured on the connector.
`MonitoredTables`	`string[]`	The list of tables that are monitored by the connector.
`QueueTotalCapcity`	`int`	The length of the queue used to pass events between the streamer and the main Kafka Connect loop.
`QueueRemainingCapcity`	`int`	The free capacity of the queue used to pass events between the streamer and the main Kafka Connect loop.
`Connected`	`boolean`	Flag that denotes whether the connector is currently connected to the database server.
`MilliSecondsBehindSource`	`long`	The number of milliseconds between the last change event’s timestamp and the connector processing it. The values will incorporate any differences between the clocks on the machines where the database server and the connector are running.
`NumberOfCommittedTransactions`	`long`	The number of processed transactions that were committed.
`SourceEventPosition`	`map<string, string>`	The coordinates of the last received event.
`LastTransactionId`	`string`	Transaction identifier of the last processed transaction.

2.3.3. Connector Properties

The following configuration properties are required unless a default value is available.

Property	Default	Description
`name`		Unique name for the connector. Attempting to register again with the same name will fail. (This property is required by all Kafka Connect connectors.)
`connector.class`		The name of the Java class for the connector. Always use a value of `io.debezium.connector.postgresql.PostgresConnector` for the PostgreSQL connector.
`tasks.max`	`1`	The maximum number of tasks that should be created for this connector. The PostgreSQL connector always uses a single task and therefore does not use this value, so the default is always acceptable.
`plugin.name`	`decoderbufs`	The name of the Postgres logical decoding plugin installed on the server. The only supported value is `pgoutput`. When the processed transactions are very large it is possible that the `JSON` batch event with all changes in the transaction will not fit into the hard-coded memory buffer of size 1 GB. In such cases it is possible to switch to so-called streaming mode when every change in transactions is sent as a separate message from PostgreSQL into Debezium.
`slot.name`	`debezium`	The name of the Postgres logical decoding slot created for streaming changes from a plugin and database instance. Values must conform to Postgres replication slot naming rules which state: "Each replication slot has a name, which can contain lower-case letters, numbers, and the underscore character."
`slot.drop.on.stop`	`false`	Whether or not to drop the logical replication slot when the connector finishes orderly. Should only be set to `true` in testing or development environments. Dropping the slot allows WAL segments to be discarded by the database, so it may happen that after a restart the connector cannot resume from the WAL position where it left off before.
`publication.name`	`dbz_publication`	The name of the PostgreSQL publication created created for streaming changes when using `pgoutput`. This publication is created at start-up if it does not already exist to include all tables. Debezium will then use its own white-/blacklist filtering capabilities to limit change events to the specific tables of interest if configured. Note the connector user must have superuser permissions in order to create this publication, so it is usually preferable to create the publication upfront. If the publication already exists (either for all tables or configured with a subset of tables), Debezium will instead use the publication as defined.
`database.hostname`		IP address or hostname of the PostgreSQL database server.
`database.port`	`5432`	Integer port number of the PostgreSQL database server.
`database.user`		Name of the PostgreSQL database to use when connecting to the PostgreSQL database server.
`database.password`		Password to use when connecting to the PostgreSQL database server.
`database.dbname`		The name of the PostgreSQL database from which to stream the changes
`database.server.name`		Logical name that identifies and provides a namespace for the particular PostgreSQL database server/cluster being monitored. The logical name should be unique across all other connectors, since it is used as a prefix for all Kafka topic names coming from this connector. Only alphanumeric characters and underscores should be used.
`schema.whitelist`		An optional comma-separated list of regular expressions that match schema names to be monitored; any schema name not included in the whitelist will be excluded from monitoring. By default all non-system schemas will be monitored. May not be used with `schema.blacklist`.
`schema.blacklist`		An optional comma-separated list of regular expressions that match schema names to be excluded from monitoring; any schema name not included in the blacklist will be monitored, with the exception of system schemas. May not be used with `schema.whitelist`.
`table.whitelist`		An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be monitored; any table not included in the whitelist will be excluded from monitoring. Each identifier is of the form schemaName.tableName. By default the connector will monitor every non-system table in each monitored schema. May not be used with `table.blacklist`.
`table.blacklist`		An optional comma-separated list of regular expressions that match fully-qualified table identifiers for tables to be excluded from monitoring; any table not included in the blacklist will be monitored. Each identifier is of the form schemaName.tableName. May not be used with `table.whitelist`.
`column.blacklist`		An optional comma-separated list of regular expressions that match the fully-qualified names of columns that should be excluded from change event message values. Fully-qualified names for columns are of the form schemaName.tableName.columnName
`time.precision.mode`	`adaptive`	Time, date, and timestamps can be represented with different kinds of precision, including: `adaptive` (the default) captures the time and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type; `adaptive_time_microseconds` captures the date, datetime and timestamp values exactly as in the database using either millisecond, microsecond, or nanosecond precision values based on the database column’s type, with the exception of TIME type fields, which are always captured as microseconds; or `connect` always represents time and timestamp values using Kafka Connect’s built-in representations for Time, Date, and Timestamp, which uses millisecond precision regardless of the database columns' precision. See temporal values.
`decimal.handling.mode`	`precise`	Specifies how the connector should handle values for `DECIMAL` and `NUMERIC` columns: `precise` (the default) represents them precisely using `java.math.BigDecimal` values represented in change events in a binary form; or `double` represents them using `double` values, which may result in a loss of precision but will be far easier to use. `string` option encodes values as formatted string which is easy to consume but a semantic information about the real type is lost. See Section 2.2.4.7.3, “Decimal Values”.
`hstore.handling.mode`	`map`	Specifies how the connector should handle values for `hstore` columns: `map` (the default) represents using `MAP`; or `json` represents them using `json string`.`json` option encodes values as formatted string such as `{"key" : "val"}`. See Section 2.2.4.7.4, “HStore Values”.
`interval.handling.mode`	`numeric`	Specifies how the connector should handle values for `interval` columns: `numeric` (the default) represents interval using approximate number of microseconds; `string` represents them exactly, using the string pattern representation `P<years>Y<months>M<days>DT<hours>H<minutes>M<seconds>S`, e.g. `P1Y2M3DT4H5M6.78S`. See Section 2.2.4.7, “Data Types”.
`database.sslmode`	`disable`	Whether to use an encrypted connection to the PostgreSQL server. Options include: disable (the default) to use an unencrypted connection ; require to use a secure (encrypted) connection, and fail if one cannot be established; verify-ca like `require` but additionally verify the server TLS certificate against the configured Certificate Authority (CA) certificates, or fail if no valid matching CA certificates are found; verify-full like `verify-ca` but additionally verify that the server certificate matches the host to which the connection is attempted. See the PostgreSQL documentation for more information.
`database.sslcert`		The path to the file containing the SSL Certificate for the client. See the PostgreSQL documentation for more information.
`database.sslkey`		The path to the file containing the SSL private key of the client. See the PostgreSQL documentation for more information.
`database.sslpassword`		The password to access the client private key from the file specified by `database.sslkey`. See the PostgreSQL documentation for more information.
`database.sslrootcert`		The path to the file containing the root certificate(s) against which the server is validated. See the PostgreSQL documentation for more information.
`database.tcpKeepAlive`		Enable TCP keep-alive probe to verify that database connection is still alive. (enabled by default). See the PostgreSQL documentation for more information.
`tombstones.on.delete`	`true`	Controls whether a tombstone event should be generated after a delete event. When `true` the delete operations are represented by a delete event and a subsequent tombstone event. When `false` only a delete event is sent. Emitting the tombstone event (the default behavior) allows Kafka to completely delete all events pertaining to the given key once the source record got deleted.
`column.truncate.to.length.chars`	n/a	An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be truncated in the change event message values if the field values are longer than the specified number of characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer. Fully-qualified names for columns are of the form databaseName.tableName.columnName, or databaseName.schemaName.tableName.columnName.
`column.mask.with.length.chars`	n/a	An optional comma-separated list of regular expressions that match the fully-qualified names of character-based columns whose values should be replaced in the change event message values with a field value consisting of the specified number of asterisk (``) characters. Multiple properties with different lengths can be used in a single configuration, although in each the length must be a positive integer or zero. Fully-qualified names for columns are of the form databaseName.tableName.columnName, or databaseName*.schemaName.tableName.columnName.
`column.propagate.source.type`	n/a	An optional comma-separated list of regular expressions that match the fully-qualified names of columns whose original type and length should be added as a parameter to the corresponding field schemas in the emitted change messages. The schema parameters `__debezium.source.column.type`, `__debezium.source.column.length` and `__debezium.source.column.scale` will be used to propagate the original type name and length (for variable-width types), respectively. Useful to properly size corresponding columns in sink databases. Fully-qualified names for columns are of the form databaseName.tableName.columnName, or databaseName.schemaName.tableName.columnName.
`message.key.columns`	empty string	A semi-colon list of regular expressions that match fully-qualified tables and columns to map a primary key. Each item (regular expression) must match the fully-qualified `<fully-qualified table>:<a comma-separated list of columns>` representing the custom key. Fully-qualified tables could be defined as `DB_NAME.TABLE_NAME` or `SCHEMA_NAME.TABLE_NAME`, depending on the specific connector.

The following advanced configuration properties have good defaults that will work in most situations and therefore rarely need to be specified in the connector’s configuration.

Property	Default	Description
`snapshot.mode`	`initial`	Specifies the criteria for running a snapshot upon startup of the connector. The default is initial, and specifies the connector can run a snapshot only when no offsets have been recorded for the logical server name. The always option specifies that the connector run a snapshot each time on startup. The never option specifies that the connect should never use snapshots and that upon first startup with a logical server name the connector should read from either from where it last left off (last LSN position) or start from the beginning from the point of the view of the logical replication slot. The initial_only option specifies that the connector should only take an initial snapshot and then stop, without processing any subsequent changes. The exported option specifies that the database snapshot will be based on the point in time when the replication slot was created and is an excellent way to perform the snapshot in a lock-free way.
`snapshot.lock.timeout.ms`	`10000`	Positive integer value that specifies the maximum amount of time (in milliseconds) to wait to obtain table locks when performing a snapshot. If table locks cannot be acquired in this time interval, the snapshot will fail. See snapshots
`snapshot.select.statement.overrides`		Controls which rows from tables will be included in snapshot. This property contains a comma-separated list of fully-qualified tables (DB_NAME.TABLE_NAME). Select statements for the individual tables are specified in further configuration properties, one for each table, identified by the id `snapshot.select.statement.overrides.[DB_NAME].[TABLE_NAME]`. The value of those properties is the SELECT statement to use when retrieving data from the specific table during snapshotting. A possible use case for large append-only tables is setting a specific point where to start (resume) snapshotting, in case a previous snapshotting was interrupted. NOTE: This setting has impact on snapshots only. Events generated by logical decoder are not affected by it at all.
`event.processing.failure.handling.mode`	`fail`	Specifies how the connector should react to exceptions during processing of events. `fail` will propagate the exception (indicating the offset of the problematic event), causing the connector to stop. `warn` will cause the problematic event to be skipped and the offset of the problematic event to be logged. `skip` will cause the problematic event to be skipped.
`max.queue.size`	`20240`	Positive integer value that specifies the maximum size of the blocking queue into which change events received via streaming replication are placed before they are written to Kafka. This queue can provide backpressure when, for example, writes to Kafka are slower or if Kafka is not available.
`max.batch.size`	`10240`	Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector.
`poll.interval.ms`	`1000`	Positive integer value that specifies the number of milliseconds the connector should wait during each iteration for new change events to appear. Defaults to 1000 milliseconds, or 1 second.
`include.unknown.datatypes`	`false`	When Debezium meets a field whose data type is unknown, then by default the field is omitted from the change event and a warning is logged. In some cases it may be preferable though to include the field and send it downstream to clients in the opaque binary representation so the clients will decode it themselves. Set to `false` to filter unknown data out of events and `true` to keep them in binary format. Note The clients risk backward compatibility issues. Not only may the database specific binary representation change between releases, but also when the datatype is supported by Debezium eventually, it will be sent downstream in a logical type, requiring adjustments by consumers. In general, when encountering unsupported data types, please file a feature request so that support can be added.
`database.initial.statements`		A semicolon separated list of SQL statements to be executed when a JDBC connection (not the transaction log reading connection) to the database is established. Use doubled semicolon (';;') to use a semicolon as a character and not as a delimiter. Note The connector may establish JDBC connections at its own discretion, so this should typically be used for configuration of session parameters only, but not for executing DML statements.
`heartbeat.interval.ms`	`0`	Controls how frequently heartbeat messages are sent. This property contains an interval in milli-seconds that defines how frequently the connector sends messages into a heartbeat topic. This can be used to monitor whether the connector is still receiving change events from the database. You also should leverage heartbeat messages in cases where only records in non-captured tables are changed for a longer period of time. In such situation the connector would proceed to read the log from the database but never emit any change messages into Kafka, which in turn means that no offset updates will be committed to Kafka. This will cause the WAL files to be retained by the database longer than needed (as the connector actually has processed them already but never got a chance to flush the latest retrieved LSN to the database) and also may result in more change events to be re-sent after a connector restart. Set this parameter to `0` to not send heartbeat messages at all. Disabled by default.
`heartbeat.topics.prefix`	`__debezium-heartbeat`	Controls the naming of the topic to which heartbeat messages are sent. The topic is named according to the pattern `<heartbeat.topics.prefix>.<server.name>`.
`schema.refresh.mode`	`columns_diff`	Specify the conditions that trigger a refresh of the in-memory schema for a table. `columns_diff` (the default) is the safest mode, ensuring the in-memory schema stays in-sync with the database table’s schema at all times. `columns_diff_exclude_unchanged_toast` instructs the connector to refresh the in-memory schema cache if there is a discrepancy between it and the schema derived from the incoming message, unless unchanged TOASTable data fully accounts for the discrepancy. This setting can improve connector performance significantly if there are frequently-updated tables that have TOASTed data that are rarely part of these updates. However, it is possible for the in-memory schema to become outdated if TOASTable columns are dropped from the table.
`snapshot.delay.ms`		An interval in milli-seconds that the connector should wait before taking a snapshot after starting up; Can be used to avoid snapshot interruptions when starting multiple connectors in a cluster, which may cause re-balancing of connectors.
`snapshot.fetch.size`	`10240`	Specifies the maximum number of rows that should be read in one go from each table while taking a snapshot. The connector will read the table contents in multiple batches of this size. Defaults to 10240.
`slot.stream.params`		Optional list of parameters to be passed to the configured logical decoding plug-in. For example, `add-tables=public.table,public.table2;include-lsn=true`.
`sanitize.field.names`	`true` when connector configuration explicitly specifies the `key.converter` or `value.converter` parameters to use Avro, otherwise defaults to `false`.	Whether field names will be sanitized to adhere to Avro naming requirements. See Avro naming for more details.
`slot.max.retries`	6	How many times to retry connecting to a replication slot when an attempt fails.
`slot.retry.delay.ms`	10000 (10 seconds)	The number of milli-seconds to wait between retry attempts when the connector fails to connect to a replication slot.
`toasted.value.placeholder`	`__debezium_unavailable_value`	Specify the constant that will be provided by Debezium to indicate that the original value is a toasted value not provided by the database. If starts with `hex:` prefix it is expected that the rest of the string repesents hexadecimally encoded octets. See section with additional details.

The connector also supports pass-through configuration properties that are used when creating the Kafka producer and consumer.

Be sure to consult the Kafka documentation for all of the configuration properties for Kafka producers and consumers. (The PostgreSQL connector does use the new consumer.)

2.4. PostgreSQL common issues

Debezium is a distributed system that captures all changes in multiple upstream databases, and will never miss or lose an event. Of course, when the system is operating nominally or being administered carefully, then Debezium provides exactly once delivery of every change event. However, if a fault does happen then the system will still not lose any events, although while it is recovering from the fault it may repeat some change events. Thus, in these abnormal situations Debezium, like Kafka, provides at least once delivery of change events.

The rest of this section describes how Debezium handles various kinds of faults and problems.

2.4.1. Configuration and Startup Errors

The connector will fail upon startup, report an error/exception in the log, and stop running when the connector’s configuration is invalid, when the connector cannot successfully connect to PostgreSQL using the specified connectivity parameters, or when the connector is restarting from a previously-recorded position in the PostgreSQL WAL (via the LSN value) and PostgreSQL no longer has that history available.

In these cases, the error will have more details about the problem and possibly a suggested work around. The connector can be restarted when the configuration has been corrected or the PostgreSQL problem has been addressed.

2.4.2. PostgreSQL Becomes Unavailable

Once the connector is running, if the PostgreSQL server it has been connected to becomes unavailable for any reason, the connector will fail with an error and the connector will stop. Simply restart the connector when the server is available.

The PostgreSQL connector stores externally the last processed offset (in the form of a PostgreSQL log sequence number value). Once a connector is restarted and connects to a server instance, it will ask the server to continue streaming from that particular offset. This offset will always remain available so long as the Debezium replication slot remains intact. Never drop a replication slot on the primary or you will lose data. See the next section for failure cases when a slot has been removed.

2.4.3. Cluster Failures

As of 12, PostgreSQL allows logical replication slots only on primary servers, which means that a PostgreSQL connector can only be pointed to the active primary of a database cluster. Also replication slots themselves are not propagated to replicas. If the primary node goes down, only after a new primary has been promoted (with the logical decoding plugin installed) and a replication slot has been created there, the connector can be restarted and pointed to the new server.

There are some really important caveats to failovers, and you should pause Debezium until you can verify that you have a replication slot intact which has not lost data. After a failover, you will miss change events unless your administration of failovers includes a process to recreate the Debezium replication slot before the application is allowed to write to the new primary. You also may need to verify in a failover situation that Debezium was able to read all changes in the slot before the old primary failed.

One reliable method of recovering and verifying any lost changes (yet administratively difficult) is to recover a backup of your failed primary to the point immediately before it failed, which would allow you to inspect the replication slot for any unconsumed changes. In any case, it is crucial that you recreate the replication slot on the new primary prior to allowing writes to it.

2.4.4. Kafka Connect Process Stops Gracefully

If Kafka Connect is being run in distributed mode, and a Kafka Connect process is stopped gracefully, then prior to shutdown of that processes Kafka Connect will migrate all of the process' connector tasks to another Kafka Connect process in that group, and the new connector tasks will pick up exactly where the prior tasks left off. There will be a short delay in processing while the connector tasks are stopped gracefully and restarted on the new processes.

2.4.5. Kafka Connect Process Crashes

If the Kafka Connector process stops unexpectedly, then any connector tasks it was running will obviously terminate without recording their most recently-processed offsets. When Kafka Connect is being run in distributed mode, it will restart those connector tasks on other processes. However, the PostgreSQL connectors will resume from the last offset recorded by the earlier processes, which means that the new replacement tasks may generate some of the same change events that were processed just prior to the crash. The number of duplicate events will depend on the offset flush period and the volume of data changes just before the crash.

Note

Because there is a chance that some events may be duplicated during a recovery from failure, consumers should always anticipate some events may be duplicated. Debezium changes are idempotent, so a sequence of events always results in the same state.

Debezium also includes with each change event message the source-specific information about the origin of the event, including the PostgreSQL server’s time of the event, the id of the server transaction and the position in the write-ahead log where the transaction changes were written. Consumers can keep track of this information (especially the LSN position) to know whether they have already seen a particular event.

2.4.6. Kafka Becomes Unavailable

As the connector generates change events, the Kafka Connect framework records those events in Kafka using the Kafka producer API. Kafka Connect will also periodically record the latest offset that appears in those change events, at a frequency you’ve specified in the Kafka Connect worker configuration. If the Kafka brokers become unavailable, the Kafka Connect worker process running the connectors will simply repeatedly attempt to reconnect to the Kafka brokers. In other words, the connector tasks will simply pause until a connection can be re-established, at which point the connectors will resume exactly where they left off.

2.4.7. Connector Is Stopped for a Duration

If the connector is gracefully stopped, the database can continue to be used and any new changes will be recorded in the PostgreSQL WAL. When the connector is restarted, it will resume streaming changes where it last left off, recording change events for all of the changes that were made while the connector was stopped.

A properly configured Kafka cluster is able to handle massive throughput. Kafka Connect is written with Kafka best practices, and given enough resources will also be able to handle very large numbers of database change events. Because of this, when a connector has been restarted after a while, it is very likely to catch up with the database, though how quickly will depend upon the capabilities and performance of Kafka and the volume of changes being made to the data in PostgreSQL.

此内容没有您所选择的语言版本。

Chapter 2. Debezium Connector for PostgreSQL

2.1. Overview

2.1.1. Logical decoding output plugin

2.2. Setting up PostgreSQL

2.2.1. Configuring the replication slot

2.2.2. Setting up Permissions

2.2.3. WAL Disk Space Consumption

2.2.4. How the PostgreSQL connector works

2.2.4.1. Snapshots

2.2.4.2. Streaming Changes

2.2.4.3. PostgreSQL 10+ Logical Decoding Support (pgoutput)

2.2.4.4. Topics Names

2.2.4.5. Meta Information

2.2.4.6. Events

2.2.4.6.1. Change Event’s Key

2.2.4.6.2. Change Event’s Value

2.2.4.6.3. Replica Identity

2.2.4.6.4. Create Events

2.2.4.6.5. Update Events

2.2.4.6.6. Delete Events

2.2.4.7. Data Types

2.2.4.7.1. Temporal Values

2.2.4.7.2. TIMESTAMP values

2.2.4.7.3. Decimal Values

2.2.4.7.4. HStore Values

2.2.4.8. PostgreSQL Domain Types

2.2.4.8.1. Network Address Types

2.2.4.8.2. PostGIS Types

2.2.4.8.3. Toasted values

2.3. Deploying the PostgreSQL Connector

2.3.1. Example Configuration

2.3.2. Monitoring

2.3.2.1. Snapshot Metrics

2.3.2.1.1. MBean: debezium.postgres:type=connector-metrics,context=snapshot,server=<database.server.name>

2.3.2.2. Streaming Metrics

2.3.2.2.1. MBean: debezium.postres:type=connector-metrics,context=streaming,server=<database.server.name>

2.3.3. Connector Properties

2.4. PostgreSQL common issues

2.4.1. Configuration and Startup Errors

2.4.2. PostgreSQL Becomes Unavailable

2.4.3. Cluster Failures

2.4.4. Kafka Connect Process Stops Gracefully

2.4.5. Kafka Connect Process Crashes

2.4.6. Kafka Becomes Unavailable

2.4.7. Connector Is Stopped for a Duration

学习

尝试、购买和销售

社区

关于红帽文档

让开源更具包容性

關於紅帽

Red Hat legal and privacy links

Red Hat legal and privacy links

2.2.4.7.2. `TIMESTAMP` values