Debezium connector for PostgreSQL

Overview

PostgreSQL’s logical decoding feature was introduced in version 9.4. It is a mechanism that allows the extraction of the changes that were committed to the transaction log and the processing of these changes in a user-friendly manner with the help of an output plug-in. The output plug-in enables clients to consume the changes.

The PostgreSQL connector contains two main parts that work together to read and process database changes:

• A logical decoding output plug-in. You might need to install the output plug-in that you choose to use. You must configure a replication slot that uses your chosen output plug-in before running the PostgreSQL server. The plug-in can be one of the following:

  • decoderbufs is based on Protobuf and maintained by the Debezium community.

  • pgoutput is the standard logical decoding output plug-in in PostgreSQL 10+. It is maintained by the PostgreSQL community, and used by PostgreSQL itself for logical replication. This plug-in is always present so no additional libraries need to be installed. The Debezium connector interprets the raw replication event stream directly into change events.

• Java code (the actual Kafka Connect connector) that reads the changes produced by the chosen logical decoding output plug-in. It uses PostgreSQL’s streaming replication protocol, by means of the PostgreSQL JDBC driver

The connector produces a change event for every row-level insert, update, and delete operation that was captured and sends change event records for each table in a separate Kafka topic. Client applications read the Kafka topics that correspond to the database tables of interest, and can react to every row-level event they receive from those topics.

PostgreSQL normally purges write-ahead log (WAL) segments after some period of time. This means that the connector does not 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, the connector starts with a consistent view of all of the data, and does not omit any changes that were made while the snapshot was being taken.

The connector is tolerant of failures. As the connector reads changes and produces events, it records the WAL position for each event. If the connector stops for any reason (including communication failures, network problems, or crashes), upon restart the connector continues reading the WAL where it last left off. This includes snapshots. If the connector stops during a snapshot, the connector begins a new snapshot when it restarts.

How the connector works

To optimally configure and run a Debezium PostgreSQL connector, it is helpful to understand how the connector performs snapshots, streams change events, determines Kafka topic names, and uses metadata.

Security

To use the Debezium connector to stream changes from a PostgreSQL database, the connector must operate with specific privileges in the database. Although one way to grant the necessary privileges is to provide the user with superuser privileges, doing so potentially exposes your PostgreSQL data to unauthorized access. Rather than granting excessive privileges to the Debezium user, it is best to create a dedicated Debezium replication user to which you grant specific privileges.

For more information about configuring privileges for the Debezium PostgreSQL user, see Setting up permissions. For more information about PostgreSQL logical replication security, see the PostgreSQL documentation.

Snapshots

Most PostgreSQL servers are configured to not retain the complete history of the database in the WAL segments. This means that the PostgreSQL connector would be unable to see the entire history of the database by reading only the WAL. Consequently, the first time that the connector starts, it performs an initial consistent snapshot of the database.

Default workflow behavior of initial snapshots

The default behavior for performing a snapshot consists of the following steps. You can change this behavior by setting the snapshot.mode connector configuration property to a value other than initial.

1. Start a transaction with a SERIALIZABLE, READ ONLY, DEFERRABLE isolation level to ensure that subsequent reads in this transaction are against a single consistent version of the data. Any changes to the data due to subsequent INSERT, UPDATE, and DELETE operations by other clients are not visible to this transaction.

2. Read the current position in the server’s transaction log.

3. Scan the database tables and schemas, generate a READ event for each row and write that event to the appropriate table-specific Kafka topic.

4. Commit the transaction.

5. 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 5 completes, upon restart the connector begins a new snapshot. After the connector completes its initial snapshot, the PostgreSQL connector continues streaming from the position that it read in Step 2. This ensures that the connector does not miss any updates. If the connector stops again for any reason, upon restart, the connector continues streaming changes from where it previously left off.

Ad hoc snapshots

By default, a connector runs an initial snapshot operation only after it starts for the first time. Following this initial snapshot, under normal circumstances, the connector does not repeat the snapshot process. Any future change event data that the connector captures comes in through the streaming process only.

However, in some situations the data that the connector obtained during the initial snapshot might become stale, lost, or incomplete. To provide a mechanism for recapturing table data, Debezium includes an option to perform ad hoc snapshots. You might want to perform an ad hoc snapshot after any of the following changes occur in your Debezium environment:

• The connector configuration is modified to capture a different set of tables.

• Kafka topics are deleted and must be rebuilt.

• Data corruption occurs due to a configuration error or some other problem.

You can re-run a snapshot for a table for which you previously captured a snapshot by initiating a so-called ad-hoc snapshot. Ad hoc snapshots require the use of signaling tables. You initiate an ad hoc snapshot by sending a signal request to the Debezium signaling table.

When you initiate an ad hoc snapshot of an existing table, the connector appends content to the topic that already exists for the table. If a previously existing topic was removed, Debezium can create a topic automatically if automatic topic creation is enabled.

Ad hoc snapshot signals specify the tables to include in the snapshot. The snapshot can capture the entire contents of the database, or capture only a subset of the tables in the database. Also, the snapshot can capture a subset of the contents of the table(s) in the database.

You specify the tables to capture by sending an execute-snapshot message to the signaling table. Set the type of the execute-snapshot signal to incremental or blocking, and provide the names of the tables to include in the snapshot, as described in the following table:

Triggering an ad hoc incremental snapshot

You initiate an ad hoc incremental snapshot by adding an entry with the execute-snapshot signal type to the signaling table. After the connector processes the message, it begins the snapshot operation. The snapshot process reads the first and last primary key values and uses those values as the start and end point for each table. Based on the number of entries in the table, and the configured chunk size, Debezium divides the table into chunks, and proceeds to snapshot each chunk, in succession, one at a time.

For more information, see Incremental snapshots.

Triggering an ad hoc blocking snapshot

You initiate an ad hoc blocking snapshot by adding an entry with the execute-snapshot signal type to the signaling table. After the connector processes the message, it begins the snapshot operation. The connector temporarily stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot. After the snapshot completes, the connector resumes streaming.

For more information, see Blocking snapshots.

Incremental snapshots

To provide flexibility in managing snapshots, Debezium includes a supplementary snapshot mechanism, known as incremental snapshotting. Incremental snapshots rely on the Debezium mechanism for sending signals to a Debezium connector. Incremental snapshots are based on the DDD-3 design document.

In an incremental snapshot, instead of capturing the full state of a database all at once, as in an initial snapshot, Debezium captures each table in phases, in a series of configurable chunks. You can specify the tables that you want the snapshot to capture and the size of each chunk. The chunk size determines the number of rows that the snapshot collects during each fetch operation on the database. The default chunk size for incremental snapshots is 1024 rows.

As an incremental snapshot proceeds, Debezium uses watermarks to track its progress, maintaining a record of each table row that it captures. This phased approach to capturing data provides the following advantages over the standard initial snapshot process:

• You can run incremental snapshots in parallel with streamed data capture, instead of postponing streaming until the snapshot completes. The connector continues to capture near real-time events from the change log throughout the snapshot process, and neither operation blocks the other.

• If the progress of an incremental snapshot is interrupted, you can resume it without losing any data. After the process resumes, the snapshot begins at the point where it stopped, rather than recapturing the table from the beginning.

• You can run an incremental snapshot on demand at any time, and repeat the process as needed to adapt to database updates. For example, you might re-run a snapshot after you modify the connector configuration to add a table to its table.include.list property.

Incremental snapshot process

When you run an incremental snapshot, Debezium sorts each table by primary key and then splits the table into chunks based on the configured chunk size. Working chunk by chunk, it then captures each table row in a chunk. For each row that it captures, the snapshot emits a READ event. That event represents the value of the row when the snapshot for the chunk began.

As a snapshot proceeds, it’s likely that other processes continue to access the database, potentially modifying table records. To reflect such changes, INSERT, UPDATE, or DELETE operations are committed to the transaction log as per usual. Similarly, the ongoing Debezium streaming process continues to detect these change events and emits corresponding change event records to Kafka.

How Debezium resolves collisions among records with the same primary key

In some cases, the UPDATE or DELETE events that the streaming process emits are received out of sequence. That is, the streaming process might emit an event that modifies a table row before the snapshot captures the chunk that contains the READ event for that row. When the snapshot eventually emits the corresponding READ event for the row, its value is already superseded. To ensure that incremental snapshot events that arrive out of sequence are processed in the correct logical order, Debezium employs a buffering scheme for resolving collisions. Only after collisions between the snapshot events and the streamed events are resolved does Debezium emit an event record to Kafka.

Snapshot window

To assist in resolving collisions between late-arriving READ events and streamed events that modify the same table row, Debezium employs a so-called snapshot window. The snapshot windows demarcates the interval during which an incremental snapshot captures data for a specified table chunk. Before the snapshot window for a chunk opens, Debezium follows its usual behavior and emits events from the transaction log directly downstream to the target Kafka topic. But from the moment that the snapshot for a particular chunk opens, until it closes, Debezium performs a de-duplication step to resolve collisions between events that have the same primary key..

For each data collection, the Debezium emits two types of events, and stores the records for them both in a single destination Kafka topic. The snapshot records that it captures directly from a table are emitted as READ operations. Meanwhile, as users continue to update records in the data collection, and the transaction log is updated to reflect each commit, Debezium emits UPDATE or DELETE operations for each change.

As the snapshot window opens, and Debezium begins processing a snapshot chunk, it delivers snapshot records to a memory buffer. During the snapshot windows, the primary keys of the READ events in the buffer are compared to the primary keys of the incoming streamed events. If no match is found, the streamed event record is sent directly to Kafka. If Debezium detects a match, it discards the buffered READ event, and writes the streamed record to the destination topic, because the streamed event logically supersede the static snapshot event. After the snapshot window for the chunk closes, the buffer contains only READ events for which no related transaction log events exist. Debezium emits these remaining READ events to the table’s Kafka topic.

The connector repeats the process for each snapshot chunk.

Triggering an incremental snapshot

Currently, the only way to initiate an incremental snapshot is to send an ad hoc snapshot signal to the signaling table on the source database.

You submit a signal to the signaling table as SQL INSERT queries.

After Debezium detects the change in the signaling table, it reads the signal, and runs the requested snapshot operation.

The query that you submit specifies the tables to include in the snapshot, and, optionally, specifies the kind of snapshot operation. Currently, the only valid option for snapshots operations is the default value, incremental.

To specify the tables to include in the snapshot, provide a data-collections array that lists the tables or an array of regular expressions used to match tables, for example,

{"data-collections": ["public.MyFirstTable", "public.MySecondTable"]}

The data-collections array for an incremental snapshot signal has no default value. If the data-collections array is empty, Debezium detects that no action is required and does not perform a snapshot.

Prerequisites

• Signaling is enabled.

  • A signaling data collection exists on the source database.

  • The signaling data collection is specified in the signal.data.collection property.

Using a source signaling channel to trigger an incremental snapshot

1. Send a SQL query to add the ad hoc incremental snapshot request to the signaling table:

   INSERT INTO <signalTable> (id, type, data) VALUES ('<id>', '<snapshotType>', '{"data-collections": ["<tableName>","<tableName>"],"type":"<snapshotType>","additional-conditions":[{"data-collection": "<tableName>", "filter": "<additional-condition>"}]}');

   For example,

   INSERT INTO myschema.debezium_signal (id, type, data) (1)
   values ('ad-hoc-1',   (2)
       'execute-snapshot',  (3)
       '{"data-collections": ["schema1.table1", "schema2.table2"], (4)
       "type":"incremental"}, (5)
       "additional-conditions":[{"data-collection": "schema1.table1" ,"filter":"color='blue'"}]}'); (6)

   The values of the id,type, and data parameters in the command correspond to the fields of the signaling table.

   The following table describes the parameters in the example:

   Table 3. Descriptions of fields in a SQL command for sending an incremental snapshot signal to the signaling table

   Item	Value	Description
   1	myschema.debezium_signal	Specifies the fully-qualified name of the signaling table on the source database.
   2	ad-hoc-1	The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request. Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string. Rather, during the snapshot, Debezium generates its own id string as a watermarking signal.
   3	execute-snapshot	The type parameter specifies the operation that the signal is intended to trigger.
   4	data-collections	A required component of the data field of a signal that specifies an array of table names or regular expressions to match table names to include in the snapshot. The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the signal.data.collection configuration property.
   5	incremental	An optional type component of the data field of a signal that specifies the kind of snapshot operation to run. Currently, the only valid option is the default value, incremental. If you do not specify a value, the connector runs an incremental snapshot.
   6	additional-conditions	An optional array that specifies a set of additional conditions that the connector evaluates to determine the subset of records to include in a snapshot. Each additional condition is an object with data-collection and filter properties. You can specify different filters for each data collection. * The data-collection property is the fully-qualified name of the data collection for which the filter will be applied. For more information about the additional-conditions parameter, see Ad hoc incremental snapshots with additional-conditions.

Ad hoc incremental snapshots with additional-conditions

If you want a snapshot to include only a subset of the content in a table, you can modify the signal request by appending an additional-conditions parameter to the snapshot signal.

The SQL query for a typical snapshot takes the following form:

SELECT * FROM <tableName> ....

By adding an additional-conditions parameter, you append a WHERE condition to the SQL query, as in the following example:

SELECT * FROM <data-collection> WHERE <filter> ....

The following example shows a SQL query to send an ad hoc incremental snapshot request with an additional condition to the signaling table:

INSERT INTO <signalTable> (id, type, data) VALUES ('<id>', '<snapshotType>', '{"data-collections": ["<tableName>","<tableName>"],"type":"<snapshotType>","additional-conditions":[{"data-collection": "<tableName>", "filter": "<additional-condition>"}]}');

For example, suppose you have a products table that contains the following columns:

• id (primary key)

• color

• quantity

If you want an incremental snapshot of the products table to include only the data items where color=blue, you can use the following SQL statement to trigger the snapshot:

INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-conditions":[{"data-collection": "schema1.products", "filter": "color=blue"}]}');

The additional-conditions parameter also enables you to pass conditions that are based on more than one column. For example, using the products table from the previous example, you can submit a query that triggers an incremental snapshot that includes the data of only those items for which color=blue and quantity>10:

INSERT INTO myschema.debezium_signal (id, type, data) VALUES('ad-hoc-1', 'execute-snapshot', '{"data-collections": ["schema1.products"],"type":"incremental", "additional-conditions":[{"data-collection": "schema1.products", "filter": "color=blue AND quantity>10"}]}');

The following example, shows the JSON for an incremental snapshot event that is captured by a connector.

Example: Incremental snapshot event message

{
    "before":null,
    "after": {
        "pk":"1",
        "value":"New data"
    },
    "source": {
        ...
        "snapshot":"incremental" (1)
    },
    "op":"r", (2)
    "ts_ms":"1620393591654",
    "transaction":null
}

Using the Kafka signaling channel to trigger an incremental snapshot

You can send a message to the configured Kafka topic to request the connector to run an ad hoc incremental snapshot.

The key of the Kafka message must match the value of the topic.prefix connector configuration option.

The value of the message is a JSON object with type and data fields.

The signal type is execute-snapshot, and the data field must have the following fields:

An example of the execute-snapshot Kafka message:

Key = `test_connector`
Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`

Ad hoc incremental snapshots with additional-condition

Debezium uses the additional-conditions field to select a subset of a table’s content.

Typically, when Debezium runs a snapshot, it runs a SQL query such as:

SELECT * FROM _<tableName>_ …​.

When the snapshot request includes an additional-conditions property, the data-collection and filter parameters of the property are appended to the SQL query, for example:

SELECT * FROM _<data-collection>_ WHERE _<filter>_ …​.

For example, given a products table with the columns id (primary key), color, and brand, if you want a snapshot to include only content for which color='blue', when you request the snapshot, you could add the additional-conditions property to filter the content:

Key = `test_connector`
Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-conditions": [{"data-collection": "schema1.products" ,"filter":"color='blue'"}]}}`

You can use the additional-conditions property to pass conditions based on multiple columns. For example, using the same products table as in the previous example, if you want a snapshot to include only the content from the products table for which color='blue', and brand='MyBrand', you could send the following request:

Key = `test_connector`
Value = `{"type":"execute-snapshot","data": {"data-collections": ["schema1.products"], "type": "INCREMENTAL", "additional-conditions": [{"data-collection": "schema1.products" ,"filter":"color='blue' AND brand='MyBrand'"}]}}`

Stopping an incremental snapshot

You can also stop an incremental snapshot by sending a signal to the table on the source database. You submit a stop snapshot signal to the table by sending a SQL INSERT query.

After Debezium detects the change in the signaling table, it reads the signal, and stops the incremental snapshot operation if it’s in progress.

The query that you submit specifies the snapshot operation of incremental, and, optionally, the tables of the current running snapshot to be removed.

Prerequisites

• Signaling is enabled.

  • A signaling data collection exists on the source database.

  • The signaling data collection is specified in the signal.data.collection property.

Using a source signaling channel to stop an incremental snapshot

1. Send a SQL query to stop the ad hoc incremental snapshot to the signaling table:

   INSERT INTO <signalTable> (id, type, data) values ('<id>', 'stop-snapshot', '{"data-collections": ["<tableName>","<tableName>"],"type":"incremental"}');

   For example,

   INSERT INTO myschema.debezium_signal (id, type, data) (1)
   values ('ad-hoc-1',   (2)
       'stop-snapshot',  (3)
       '{"data-collections": ["schema1.table1", "schema2.table2"], (4)
       "type":"incremental"}'); (5)

   The values of the id, type, and data parameters in the signal command correspond to the fields of the signaling table.

   The following table describes the parameters in the example:

   Table 5. Descriptions of fields in a SQL command for sending a stop incremental snapshot signal to the signaling table

   Item	Value	Description
   1	myschema.debezium_signal	Specifies the fully-qualified name of the signaling table on the source database.
   2	ad-hoc-1	The id parameter specifies an arbitrary string that is assigned as the id identifier for the signal request. Use this string to identify logging messages to entries in the signaling table. Debezium does not use this string.
   3	stop-snapshot	Specifies type parameter specifies the operation that the signal is intended to trigger.
   4	data-collections	An optional component of the data field of a signal that specifies an array of table names or regular expressions to match table names to remove from the snapshot. The array lists regular expressions which match tables by their fully-qualified names, using the same format as you use to specify the name of the connector’s signaling table in the signal.data.collection configuration property. If this component of the data field is omitted, the signal stops the entire incremental snapshot that is in progress.
   5	incremental	A required component of the data field of a signal that specifies the kind of snapshot operation that is to be stopped. Currently, the only valid option is incremental. If you do not specify a type value, the signal fails to stop the incremental snapshot.

Using the Kafka signaling channel to stop an incremental snapshot

You can send a signal message to the configured Kafka signaling topic to stop an ad hoc incremental snapshot.

The key of the Kafka message must match the value of the topic.prefix connector configuration option.

The value of the message is a JSON object with type and data fields.

The signal type is stop-snapshot, and the data field must have the following fields:

The following example shows a typical stop-snapshot Kafka message:

Key = `test_connector`
Value = `{"type":"stop-snapshot","data": {"data-collections": ["schema1.table1", "schema1.table2"], "type": "INCREMENTAL"}}`

Custom snapshotter SPI

For more advanced uses, you can provide an implementation of the io.debezium.connector.postgresql.spi.Snapshotter interface. This interface allows control of most of the aspects of how the connector performs snapshots. This includes whether or not to take a snapshot, the options for opening the snapshot transaction, and whether to take locks.

Following is the full API for the interface. All built-in snapshot modes implement this interface.

/**
 * This interface is used to determine details about the snapshot process:
 *
 * Namely:
 * - Should a snapshot occur at all
 * - Should streaming occur
 * - What queries should be used to snapshot
 *
 * While many default snapshot modes are provided with Debezium,
 * a custom implementation of this interface can be provided by the implementor, which
 * can provide more advanced functionality, such as partial snapshots.
 *
 * Implementations must return true for either {@link #shouldSnapshot()} or {@link #shouldStream()}
 * or true for both.
 */
@Incubating
public interface Snapshotter {
    void init(PostgresConnectorConfig config, OffsetState sourceInfo,
              SlotState slotState);
    /**
     * @return true if the snapshotter should take a snapshot
     */
    boolean shouldSnapshot();
    /**
     * @return true if the snapshotter should stream after taking a snapshot
     */
    boolean shouldStream();
    /**
     *
     * @return true if streaming should resume from the start of the snapshot
     * transaction, or false for when a connector resumes and takes a snapshot,
     * streaming should resume from where streaming previously left off.
     */
    default boolean shouldStreamEventsStartingFromSnapshot() {
        return true;
    }
    /**
     * Generate a valid postgres query string for the specified table, or an empty {@link Optional}
     * to skip snapshotting this table (but that table will still be streamed from)
     *
     * @param tableId the table to generate a query for
     * @param snapshotSelectColumns the columns to be used in the snapshot select based on the column
     *                              include/exclude filters
     * @return a valid query string, or none to skip snapshotting this table
     */
    Optional<String> buildSnapshotQuery(TableId tableId, List<String> snapshotSelectColumns);
    /**
     * Return a new string that set up the transaction for snapshotting
     *
     * @param newSlotInfo if a new slow was created for snapshotting, this contains information from
     *                    the `create_replication_slot` command
     */
    default String snapshotTransactionIsolationLevelStatement(SlotCreationResult newSlotInfo) {
        // we're using the same isolation level that pg_backup uses
        return "SET TRANSACTION ISOLATION LEVEL SERIALIZABLE, READ ONLY, DEFERRABLE;";
    }
    /**
     * Returns a SQL statement for locking the given tables during snapshotting, if required by the specific snapshotter
     * implementation.
     */
    default Optional<String> snapshotTableLockingStatement(Duration lockTimeout, Set<TableId> tableIds) {
        String lineSeparator = System.lineSeparator();
        StringBuilder statements = new StringBuilder();
        statements.append("SET lock_timeout = ").append(lockTimeout.toMillis()).append(";").append(lineSeparator);
        // we're locking in ACCESS SHARE MODE to avoid concurrent schema changes while we're taking the snapshot
        // this does not prevent writes to the table, but prevents changes to the table's schema....
        // DBZ-298 Quoting name in case it has been quoted originally; it doesn't do harm if it hasn't been quoted
        tableIds.forEach(tableId -> statements.append("LOCK TABLE ")
                .append(tableId.toDoubleQuotedString())
                .append(" IN ACCESS SHARE MODE;")
                .append(lineSeparator));
        return Optional.of(statements.toString());
    }
    /**
     * Lifecycle hook called once the snapshot phase is finished.
     */
    default void snapshotCompleted() {
        // no operation
    }
}

Blocking snapshots

To provide more flexibility in managing snapshots, Debezium includes a supplementary ad hoc snapshot mechanism, known as a blocking snapshot. Blocking snapshots rely on the Debezium mechanism for sending signals to a Debezium connector.

A blocking snapshot behaves just like an initial snapshot, except that you can trigger it at run time.

You might want to run a blocking snapshot rather than use the standard initial snapshot process in the following situations:

• You add a new table and you want to complete the snapshot while the connector is running.

• You add a large table, and you want the snapshot to complete in less time than is possible with an incremental snapshot.

Blocking snapshot process

When you run a blocking snapshot, Debezium stops streaming, and then initiates a snapshot of the specified table, following the same process that it uses during an initial snapshot. After the snapshot completes, the streaming is resumed.

Configure snapshot

You can set the following properties in the data component of a signal:

• data-collections: to specify which tables must be snapshot

• additional-conditions: You can specify different filters for different table.

  • The data-collection property is the fully-qualified name of the table for which the filter will be applied.

  • The filter property will have the same value used in the snapshot.select.statement.overrides

For example:

  {"type": "blocking", "data-collections": ["schema1.table1", "schema1.table2"], "additional-conditions": [{"data-collection": "schema1.table1", "filter": "SELECT * FROM [schema1].[table1] WHERE column1 = 0 ORDER BY column2 DESC"}, {"data-collection": "schema1.table2", "filter": "SELECT * FROM [schema1].[table2] WHERE column2 > 0"}]}

Possible duplicates

A delay might exist between the time that you send the signal to trigger the snapshot, and the time when streaming stops and the snapshot starts. As a result of this delay, after the snapshot completes, the connector might emit some event records that duplicate records captured by the snapshot.

Streaming changes

The PostgreSQL connector typically spends 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. This protocol enables clients to receive changes from the server as they are committed in the server’s transaction log at certain positions, which are referred to as Log Sequence Numbers (LSNs).

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

The Debezium PostgreSQL connector acts as a PostgreSQL client. When the connector receives changes it transforms the events into Debezium create, update, or delete events that include the LSN of the event. The PostgreSQL connector forwards these change events in records to the Kafka Connect framework, which is running in the same process. The Kafka Connect process asynchronously writes the change event records in the same order in which they were generated to the appropriate Kafka topic.

Periodically, Kafka Connect records the most recent offset in another Kafka topic. The offset indicates source-specific position information that Debezium includes with each event. For the PostgreSQL connector, the LSN recorded in each change event is the offset.

When Kafka Connect gracefully shuts down, it stops the connectors, flushes all event records to Kafka, and records the last offset received from each connector. When Kafka Connect restarts, it reads the last recorded offset for each connector, and starts each connector at its last recorded offset. When the connector restarts, it sends a request to the PostgreSQL server to send the events starting just after that position.

PostgreSQL 10+ logical decoding support (pgoutput)

As of PostgreSQL 10+, there is a logical replication stream mode, called pgoutput that is natively supported by PostgreSQL. This means that a Debezium PostgreSQL connector can consume that replication stream without the need for additional plug-ins. This is particularly valuable for environments where installation of plug-ins is not supported or not allowed.

For more information, see Setting up PostgreSQL.

Topic names

By default, the PostgreSQL connector writes change events for all INSERT, UPDATE, and DELETE operations that occur in a table to a single Apache Kafka topic that is specific to that table. The connector uses the following convention to name change event topics:

topicPrefix.schemaName.tableName

The following list provides definitions for the components of the default name:

topicPrefix

The topic prefix as specified by the topic.prefix configuration property.

schemaName

The name of the database schema in which the change event occurred.

tableName

The name of the database table in which the change event occurred.

For example, suppose that fulfillment is the logical server name in the configuration for a connector that is capturing changes in a PostgreSQL installation that has a postgres database and an inventory schema that contains four tables: products, products_on_hand, customers, and orders. The connector would stream records to these four Kafka topics:

• fulfillment.inventory.products

• fulfillment.inventory.products_on_hand

• fulfillment.inventory.customers

• fulfillment.inventory.orders

Now suppose that the tables are not part of a specific schema but were created in the default public PostgreSQL schema. The names of the Kafka topics would be:

• fulfillment.public.products

• fulfillment.public.products_on_hand

• fulfillment.public.customers

• fulfillment.public.orders

The connector applies similar naming conventions to label its transaction metadata topics.

If the default topic name do not meet your requirements, you can configure custom topic names. To configure custom topic names, you specify regular expressions in the logical topic routing SMT. For more information about using the logical topic routing SMT to customize topic naming, see Topic routing.

Transaction metadata

Debezium can generate events that represent transaction boundaries and that enrich data change event messages.

For every transaction BEGIN and END, Debezium generates an event that contains the following fields:

status

BEGIN or END.

id

String representation of the unique transaction identifier composed of Postgres transaction ID itself and LSN of given operation separated by colon, i.e. the format is txID:LSN.

ts_ms

The time of a transaction boundary event (BEGIN or END event) at the data source. If the data source does not provide Debezium with the event time, then the field instead represents the time at which Debezium processes the event.

event_count (for END events)

Total number of events emmitted by the transaction.

data_collections (for END events)

An array of pairs of data_collection and event_count elements that indicates the number of events that the connector emits for changes that originate from a data collection.

Example

{
  "status": "BEGIN",
  "id": "571:53195829",
  "ts_ms": 1486500577125,
  "event_count": null,
  "data_collections": null
}
{
  "status": "END",
  "id": "571:53195832",
  "ts_ms": 1486500577691,
  "event_count": 2,
  "data_collections": [
    {
      "data_collection": "s1.a",
      "event_count": 1
    },
    {
      "data_collection": "s2.a",
      "event_count": 1
    }
  ]
}

Unless overridden via the topic.transaction option, transaction events are written to the topic named .transaction.

Change data event enrichment

When transaction metadata is enabled the data message Envelope is enriched with a new transaction field. This field provides information about every event in the form of a composite of fields:

id

String representation of unique transaction identifier.

total_order

The absolute position of the event among all events generated by the transaction.

data_collection_order

The per-data collection position of the event among all events that were emitted by the transaction.

Following is an example of a message:

{
  "before": null,
  "after": {
    "pk": "2",
    "aa": "1"
  },
  "source": {
   ...
  },
  "op": "c",
  "ts_ms": "1580390884335",
  "transaction": {
    "id": "571:53195832",
    "total_order": "1",
    "data_collection_order": "1"
  }
}

Data change events

The Debezium PostgreSQL connector generates a data change event for each row-level INSERT, UPDATE, and DELETE operation. Each event contains a key and a value. The structure of the key and the value depends on the table that was changed.

Debezium and Kafka Connect are designed around continuous streams of event messages. However, the structure of these events may change over time, which can be difficult for consumers to handle. To address this, each event contains the schema for its content or, if you are using a schema registry, a schema ID that a consumer can use to obtain the schema from the registry. This makes each event self-contained.

The following skeleton JSON shows the basic four parts of a change event. However, how you configure the Kafka Connect converter that you choose to use in your application determines the representation of these four parts in change events. A schema field is in a change event only when you configure the converter to produce it. Likewise, the event key and event payload are in a change event only if you configure a converter to produce it. If you use the JSON converter and you configure it to produce all four basic change event parts, change events have this structure:

{
 "schema": { (1)
   ...
  },
 "payload": { (2)
   ...
 },
 "schema": { (3)
   ...
 },
 "payload": { (4)
   ...
 },
}

By default behavior is that the connector streams change event records to topics with names that are the same as the event’s originating table.

Change event keys

For a given table, the change event’s key has a structure that contains a field for each column in the primary key of the table at the time the event was created. Alternatively, if the table has REPLICA IDENTITY set to FULL or USING INDEX there is a field for each unique key constraint.

Consider a customers table defined in the public database schema and the example of a change event key for that table.

Example table

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)
);

Example change event key

If the topic.prefix connector configuration property has the value PostgreSQL_server, every change event for the customers table while it has this definition has the same key structure, which in JSON looks like this:

{
  "schema": { (1)
    "type": "struct",
    "name": "PostgreSQL_server.public.customers.Key", (2)
    "optional": false, (3)
    "fields": [ (4)
          {
              "name": "id",
              "index": "0",
              "schema": {
                  "type": "INT32",
                  "optional": "false"
              }
          }
      ]
  },
  "payload": { (5)
      "id": "1"
  },
}

Change event values

The value in a change event is a bit more complicated than the key. Like the key, the value has a schema section and a payload section. The schema section contains the schema that describes the Envelope structure of the payload section, including its nested fields. Change events for operations that create, update or delete data all have a value payload with an envelope structure.

Consider the same sample table that was used to show an example of a change event key:

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)
);

The value portion of a change event for a change to this table varies according to the REPLICA IDENTITY setting and the operation that the event is for.

Replica identity

REPLICA IDENTITY is a PostgreSQL-specific table-level setting that determines the amount of information that is available to the logical decoding plug-in for UPDATE and DELETE events. More specifically, the setting of REPLICA IDENTITY controls what (if any) information is available for the previous values of the table columns involved, whenever an UPDATE or DELETE event occurs.

There are 4 possible values for REPLICA IDENTITY:

• DEFAULT - The default behavior is that UPDATE and DELETE events contain the previous values for the primary key columns of a table if that table has a primary key. For an UPDATE event, only the primary key columns with changed values are present.

  If a table does not have a primary key, the connector does not emit UPDATE or DELETE events for that table. For a table without a primary key, the connector emits only create events. Typically, a table without a primary key is used for appending messages to the end of the table, which means that UPDATE and DELETE events are not useful.

• NOTHING - Emitted events for UPDATE and DELETE operations do not contain any information about the previous value of any table column.

• FULL - Emitted events for UPDATE and DELETE operations contain the previous values of all columns in the table.

• INDEX index-name - Emitted events for UPDATE and DELETE operations contain the previous values of the columns contained in the specified index. UPDATE events also contain the indexed columns with the updated values.

create events

The following example shows the value portion of a change event that the connector generates for an operation that creates data in the customers table:

{
    "schema": { (1)
        "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", (2)
                "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", (3)
                "field": "source"
            },
            {
                "type": "string",
                "optional": false,
                "field": "op"
            },
            {
                "type": "int64",
                "optional": true,
                "field": "ts_ms"
            }
        ],
        "optional": false,
        "name": "PostgreSQL_server.inventory.customers.Envelope" (4)
    },
    "payload": { (5)
        "before": null, (6)
        "after": { (7)
            "id": 1,
            "first_name": "Anne",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": { (8)
            "version": "2.4.1.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": true,
            "db": "postgres",
            "sequence": "[\"24023119\",\"24023128\"]",
            "schema": "public",
            "table": "customers",
            "txId": 555,
            "lsn": 24023128,
            "xmin": null
        },
        "op": "c", (9)
        "ts_ms": 1559033904863 (10)
    }
}

update events

The value of a change event for an update in the sample customers table has the same schema as a create event for that table. Likewise, the event value’s payload has the same structure. However, the event value payload contains different values in an update event. Here is an example of a change event value in an event that the connector generates for an update in the customers table:

{
    "schema": { ... },
    "payload": {
        "before": { (1)
            "id": 1
        },
        "after": { (2)
            "id": 1,
            "first_name": "Anne Marie",
            "last_name": "Kretchmar",
            "email": "annek@noanswer.org"
        },
        "source": { (3)
            "version": "2.4.1.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": false,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 24023128,
            "xmin": null
        },
        "op": "u", (4)
        "ts_ms": 1465584025523  (5)
    }
}

Primary key updates

An UPDATE operation that changes a row’s primary key field(s) is known as a primary key change. For a primary key change, in place of sending an UPDATE event record, the connector sends a DELETE event record for the old key and a CREATE event record for the new (updated) key. These events have the usual structure and content, and in addition, each one has a message header related to the primary key change:

• The DELETE event record has __debezium.newkey as a message header. The value of this header is the new primary key for the updated row.

• The CREATE event record has __debezium.oldkey as a message header. The value of this header is the previous (old) primary key that the updated row had.

delete events

The value in a delete change event has the same schema portion as create and update events for the same table. The payload portion in a delete event for the sample customers table looks like this:

{
    "schema": { ... },
    "payload": {
        "before": { (1)
            "id": 1
        },
        "after": null, (2)
        "source": { (3)
            "version": "2.4.1.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": false,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 46523128,
            "xmin": null
        },
        "op": "d", (4)
        "ts_ms": 1465581902461 (5)
    }
}

A delete change event record provides a consumer with the information it needs to process the removal of this row.

PostgreSQL connector events are designed to work with Kafka log compaction. Log compaction enables removal of some older messages as long as at least the most recent message for every key is kept. This lets Kafka reclaim storage space while ensuring that the topic contains a complete data set and can be used for reloading key-based state.

Tombstone events

When a row is deleted, the delete event value still works with log compaction, because Kafka can remove all earlier messages that have that same key. However, for Kafka to remove all messages that have that same key, the message value must be null. To make this possible, the PostgreSQL connector follows a delete event with a special tombstone event that has the same key but a null value.

truncate events

A truncate change event signals that a table has been truncated. The message key is null in this case, the message value looks like this:

{
    "schema": { ... },
    "payload": {
        "source": { (1)
            "version": "2.4.1.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": false,
            "db": "postgres",
            "schema": "public",
            "table": "customers",
            "txId": 556,
            "lsn": 46523128,
            "xmin": null
        },
        "op": "t", (2)
        "ts_ms": 1559033904961 (3)
    }
}

In case a single TRUNCATE statement applies to multiple tables, one truncate change event record for each truncated table will be emitted.

Note that since truncate events represent a change made to an entire table and don’t have a message key, unless you’re working with topics with a single partition, there are no ordering guarantees for the change events pertaining to a table (create, update, etc.) and truncate events for that table. For instance a consumer may receive an update event only after a truncate event for that table, when those events are read from different partitions.

message events

A message event signals that a generic logical decoding message has been inserted directly into the WAL typically with the pg_logical_emit_message function. The message key is a Struct with a single field named prefix in this case, carrying the prefix specified when inserting the message. The message value looks like this for transactional messages:

{
    "schema": { ... },
    "payload": {
        "source": { (1)
            "version": "2.4.1.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": false,
            "db": "postgres",
            "schema": "",
            "table": "",
            "txId": 556,
            "lsn": 46523128,
            "xmin": null
        },
        "op": "m", (2)
        "ts_ms": 1559033904961, (3)
        "message": { (4)
            "prefix": "foo",
            "content": "Ymfy"
        }
    }
}

Unlike other event types, non-transactional messages will not have any associated BEGIN or END transaction events. The message value looks like this for non-transactional messages:

{
    "schema": { ... },
    "payload": {
        "source": { (1)
            "version": "2.4.1.Final",
            "connector": "postgresql",
            "name": "PostgreSQL_server",
            "ts_ms": 1559033904863,
            "snapshot": false,
            "db": "postgres",
            "schema": "",
            "table": "",
            "lsn": 46523128,
            "xmin": null
        },
        "op": "m", (2)
        "ts_ms": 1559033904961 (3)
        "message": { (4)
            "prefix": "foo",
            "content": "Ymfy"
    }
}

Data type mappings

The PostgreSQL connector represents changes to rows with events that are structured like the table in which the row exists. The event contains a field for each column value. How that value is represented in the event depends on the PostgreSQL data type of the column. The following sections describe how the connector maps PostgreSQL data types to a literal type and a semantic type in event fields.

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

• 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.

If the default data type conversions do not meet your needs, you can create a custom converter for the connector.

Basic types

The following table describes how the connector maps basic types.

Temporal types

Other than PostgreSQL’s TIMESTAMPTZ and TIMETZ data types, which contain time zone information, how temporal types are mapped depends on the value of the time.precision.mode connector configuration property. The following sections describe these mappings:

• time.precision.mode=adaptive

• time.precision.mode=adaptive_time_microseconds

• time.precision.mode=connect

time.precision.mode=adaptive

When the time.precision.mode property is set to adaptive, the default, the connector determines the literal type and semantic type based on the column’s data type definition. This ensures that events exactly represent the values in the database.

time.precision.mode=adaptive_time_microseconds

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

time.precision.mode=connect

When the time.precision.mode configuration property is set to connect, the connector uses Kafka Connect logical types. This may be useful when consumers can handle only the built-in Kafka Connect logical types and are unable to handle variable-precision time values. However, since PostgreSQL supports microsecond precision, the events generated by a connector with the connect time precision mode results in a loss of precision when the database column has a fractional second precision value that is greater than 3.

TIMESTAMP type

The TIMESTAMP type represents a timestamp without time zone information. Such columns are converted into an equivalent Kafka Connect value based on UTC. For example, the TIMESTAMP value “2018-06-20 15:13:16.945104” is represented by an io.debezium.time.MicroTimestamp with the value “1529507596945104” when time.precision.mode is not set to connect.

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

PostgreSQL supports using +/-infinite values in TIMESTAMP columns. These special values are converted to timestamps with value 9223372036825200000 in case of positive infinity or -9223372036832400000 in case of negative infinity. This behavior mimics the standard behavior of the PostgreSQL JDBC driver. For reference, see the org.postgresql.PGStatement interface.

Decimal types

The setting of the PostgreSQL connector configuration property decimal.handling.mode determines how the connector maps decimal types.

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

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

When the decimal.handling.mode property is set to double, the connector represents all DECIMAL, NUMERIC and MONEY values as Java double values and encodes them as shown in the following table.

The last possible setting for the decimal.handling.mode configuration property is string. In this case, the connector represents DECIMAL, NUMERIC and MONEY values as their formatted string representation, and encodes them as shown in the following table.

PostgreSQL supports NaN (not a number) as a special value to be stored in DECIMAL/NUMERIC values when the setting of decimal.handling.mode is string or double. In this case, the connector encodes NaN as either Double.NaN or the string constant NAN.

HSTORE type

The setting of the PostgreSQL connector configuration property hstore.handling.mode determines how the connector maps HSTORE values.

When the dhstore.handling.mode property is set to json (the default), the connector represents HSTORE values as string representations of JSON values and encodes them as shown in the following table. When the hstore.handling.mode property is set to map, the connector uses the MAP schema type for HSTORE values.

Domain types

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

Network address types

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

PostGIS types

The PostgreSQL connector supports all PostGIS data types.

Toasted values

PostgreSQL has a hard limit on the page size. This means that values that are larger than around 8 KBs need to be stored by using TOAST storage. This impacts replication messages that are coming from the database. Values that were stored by using the TOAST mechanism and that 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 the database, as this would potentially lead to race conditions. Consequently, Debezium follows these rules to handle toasted values:

• Tables with REPLICA IDENTITY FULL - TOAST column values are part of the before and after fields in change events just like any other column.

• Tables with REPLICA IDENTITY DEFAULT - When receiving an UPDATE event from the database, any unchanged TOAST column value that is not part of the replica identity is not contained in the event. Similarly, when receiving a DELETE event, no TOAST columns, if any, are in the before field. As Debezium cannot safely provide the column value in this case, the connector returns a placeholder value as defined by the connector configuration property, unavailable.value.placeholder.

Default values

If a default value is specified for a column in the database schema, the PostgreSQL connector will attempt to propagate this value to the Kafka schema whenever possible. Most common data types are supported, including:

• BOOLEAN

• Numeric types (INT, FLOAT, NUMERIC, etc.)

• Text types (CHAR, VARCHAR, TEXT, etc.)

• Temporal types (DATE, TIME, INTERVAL, TIMESTAMP, TIMESTAMPTZ)

• JSON, JSONB, XML

• UUID

Note that for temporal types, parsing of the default value is provided by PostgreSQL libraries; therefore, any string representation which is normally supported by PostgreSQL should also be supported by the connector.

In the case that the default value is generated by a function rather than being directly specified in-line, the connector will instead export the equivalent of 0 for the given data type. These values include:

• FALSE for BOOLEAN

• 0 with appropriate precision, for numeric types

• Empty string for text/XML types

• {} for JSON types

• 1970-01-01 for DATE, TIMESTAMP, TIMESTAMPTZ types

• 00:00 for TIME

• EPOCH for INTERVAL

• 00000000-0000-0000-0000-000000000000 for UUID

This support currently extends only to explicit usage of functions. For example, CURRENT_TIMESTAMP(6) is supported with parentheses, but CURRENT_TIMESTAMP is not.

Setting up Postgres

Before using the PostgreSQL connector to monitor the changes committed on a PostgreSQL server, decide which logical decoding plug-in you intend to use. If you plan not to use the native pgoutput logical replication stream support, then you must install the logical decoding plug-in into the PostgreSQL server. Afterward, enable a replication slot, and configure a user with sufficient privileges to perform the replication.

If your database is hosted by a service such as Heroku Postgres you might be unable to install the plug-in. If so, and if you are using PostgreSQL 10+, you can use the pgoutput decoder support to capture changes in your database. If that is not an option, you are unable to use Debezium with your database.

PostgreSQL in the Cloud

PostgreSQL on Amazon RDS

It is possible to capture changes in a PostgreSQL database that is running in Amazon RDS. To do this:

• Set the instance parameter rds.logical_replication to 1.

• Verify that the wal_level parameter is set to logical by running the query SHOW wal_level as the database RDS master user. This might not be the case in multi-zone replication setups. You cannot set this option manually. It is automatically changed when the rds.logical_replication parameter is set to 1. If the wal_level is not set to logical after you make the preceding change, it is probably because the instance has to be restarted after the parameter group change. Restarts occur during your maintenance window, or you can initiate a restart manually.

• Set the Debezium plugin.name parameter to pgoutput.

• Initiate logical replication from an AWS account that has the rds_replication role. The role grants permissions to manage logical slots and to stream data using logical slots. By default, only the master user account on AWS has the rds_replication role on Amazon RDS. To enable a user account other than the master account to initiate logical replication, you must grant the account the rds_replication role. For example, grant rds_replication to _<my_user>_. You must have superuser access to grant the rds_replication role to a user. To enable accounts other than the master account to create an initial snapshot, you must grant SELECT permission to the accounts on the tables to be captured. For more information about security for PostgreSQL logical replication, see the PostgreSQL documentation.

PostgreSQL on Azure

It is possible to use Debezium with Azure Database for PostgreSQL, which has support for the pgoutput logical decoding plug-in, which is supported by Debezium.

Set the Azure replication support to logical. You can use the Azure CLI or the Azure Portal to configure this. For example, to use the Azure CLI, here are the az postgres server commands that you need to execute:

az postgres server configuration set --resource-group mygroup --server-name myserver --name azure.replication_support --value logical
az postgres server restart --resource-group mygroup --name myserver

PostgreSQL on CrunchyBridge

It is possible to use Debezium with CrunchyBridge; logical replication is already turned on. The pgoutput plugin is available. You will have to create a replication user and provide correct privileges.

Installing the logical decoding output plug-in

As of PostgreSQL 9.4, the only way to read changes to the write-ahead-log is to install a logical decoding output plug-in. Plug-ins are written in C, compiled, and installed on the machine that runs the PostgreSQL server. Plug-ins use a number of PostgreSQL specific APIs, as described by the PostgreSQL documentation.

The PostgreSQL connector works with one of Debezium’s supported logical decoding plug-ins to receive change events from the database in either the Protobuf format or the pgoutput format. The pgoutput plugin comes out-of-the-box with the PostgreSQL database. For more details on using Protobuf via the decoderbufs plug-in, see the plug-in documentation which discusses its requirements, limitations, and how to compile it.

For simplicity, Debezium also provides a container image based on the upstream PostgreSQL server image, on top of which it compiles and installs the plug-ins. You can use this image as an example of the detailed steps required for the installation.

Plug-in differences

Plug-in behavior is not completely the same for all cases. These differences have been identified:

• While all plug-ins will refresh schema metadata from the database upon detection of a schema change during streaming, the pgoutput plug-in is somewhat more ‘eager’ about triggering such refreshes. For example, a change to the default value for a column will trigger a refresh with pgoutput, while other plug-ins will not be aware of this change until another change triggers a refresh (eg. addition of a new column.) This is due to the behaviour of pgoutput, rather than Debezium itself.

All up-to-date differences are tracked in a test suite Java class.

Configuring the PostgreSQL server

If you are using a logical decoding plug-in other than pgoutput, after installing it, configure the PostgreSQL server as follows:

1. To load the plug-in at startup, add the following to the postgresql.conf file::

   # MODULES
   shared_preload_libraries = 'decoderbufs' (1)

   1	Instructs the server to load the decoderbufs logical decoding plug-ins at startup (the name of the plug-in is set in the Protobuf make file).

2. To configure the replication slot regardless of the decoder being used, specify the following in the postgresql.conf file:

   # REPLICATION
   wal_level = logical             (1)

   1	Instructs the server to use logical decoding with the write-ahead log.

Depending on your requirements, you may have to set other PostgreSQL streaming replication parameters when using Debezium. Examples include max_wal_senders and max_replication_slots for increasing the number of connectors that can access the sending server concurrently, and wal_keep_size for limiting the maximum WAL size which a replication slot will retain. For more information about configuring streaming replication, see the PostgreSQL documentation.

Debezium uses PostgreSQL’s logical decoding, which uses replication slots. Replication slots are guaranteed to retain all WAL segments required for Debezium even during Debezium outages. For this reason, it is important 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, see the PostgreSQL streaming replication documentation.

If you are working with a synchronous_commit setting other than on, the recommendation is to set wal_writer_delay to a value such as 10 milliseconds to achieve a low latency of change events. Otherwise, its default value is applied, which adds a latency of about 200 milliseconds.

Setting up permissions

Setting up a PostgreSQL server to run a Debezium connector requires a database user that can perform replications. Replication can be performed only by a database user that has appropriate permissions and only for a configured number of hosts.

Although, by default, superusers have the necessary REPLICATION and LOGIN roles, as mentioned in Security, it is best not to provide the Debezium replication user with elevated privileges. Instead, create a Debezium user that has the minimum required privileges.

Prerequisites

• PostgreSQL administrative permissions.

Procedure

1. To provide a user with replication permissions, define a PostgreSQL role that has at least the REPLICATION and LOGIN permissions, and then grant that role to the user. For example:

   CREATE ROLE <name> REPLICATION LOGIN;

Setting privileges to enable Debezium to create PostgreSQL publications when you use pgoutput

If you use pgoutput as the logical decoding plugin, Debezium must operate in the database as a user with specific privileges.

Debezium streams change events for PostgreSQL source tables from publications that are created for the tables. Publications contain a filtered set of change events that are generated from one or more tables. The data in each publication is filtered based on the publication specification. The specification can be created by the PostgreSQL database administrator or by the Debezium connector. To permit the Debezium PostgreSQL connector to create publications and specify the data to replicate to them, the connector must operate with specific privileges in the database.

There are several options for determining how publications are created. In general, it is best to manually create publications for the tables that you want to capture, before you set up the connector. However, you can configure your environment in a way that permits Debezium to create publications automatically, and to specify the data that is added to them.

Debezium uses include list and exclude list properties to specify how data is inserted in the publication. For more information about the options for enabling Debezium to create publications, see publication.autocreate.mode.

For Debezium to create a PostgreSQL publication, it must run as a user that has the following privileges:

• Replication privileges in the database to add the table to a publication.

• CREATE privileges on the database to add publications.

• SELECT privileges on the tables to copy the initial table data. Table owners automatically have SELECT permission for the table.

To add tables to a publication, the user must be an owner of the table. But because the source table already exists, you need a mechanism to share ownership with the original owner. To enable shared ownership, you create a PostgreSQL replication group, and then add the existing table owner and the replication user to the group.

Procedure

1. Create a replication group.

   CREATE ROLE <replication_group>;

2. Add the original owner of the table to the group.

   GRANT REPLICATION_GROUP TO <original_owner>;

3. Add the Debezium replication user to the group.

   GRANT REPLICATION_GROUP TO <replication_user>;

4. Transfer ownership of the table to <replication_group>.

   ALTER TABLE <table_name> OWNER TO REPLICATION_GROUP;

For Debezium to specify the capture configuration, the value of publication.autocreate.mode must be set to filtered.

Configuring PostgreSQL to allow replication with the Debezium connector host

To enable Debezium to replicate PostgreSQL data, you must configure the database to permit replication with the host that runs the PostgreSQL connector. To specify the clients that are permitted to replicate with the database, add entries to the PostgreSQL host-based authentication file, pg_hba.conf. For more information about the pg_hba.conf file, see the PostgreSQL documentation.

Procedure

• Add entries to the pg_hba.conf file to specify the Debezium connector hosts that can replicate with the database host. For example,

  pg_hba.conf file example:

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

  1	Instructs the server to allow replication for <youruser> locally, that is, on the server machine.
  2	Instructs the server to allow <youruser> on localhost to receive replication changes using IPV4.
  3	Instructs the server to allow <youruser> on localhost to receive replication changes using IPV6.

Supported PostgreSQL topologies

The PostgreSQL connector can be used with a standalone PostgreSQL server or with a cluster of PostgreSQL servers.

As mentioned in the beginning, PostgreSQL (for all versions ⇐ 12) supports logical replication slots on only primary servers. This means that a replica in a PostgreSQL cluster cannot be configured for logical replication, and consequently that the Debezium PostgreSQL connector can connect and communicate with only the primary server. Should this server fail, the connector stops. When the cluster is repaired, if the original primary server is once again promoted to primary, you can restart the connector. However, if a different PostgreSQL server with the plug-in and proper configuration is promoted to primary, you must change the connector configuration to point to the new primary server and then you can restart the connector.

WAL disk space consumption

In certain cases, it is possible for PostgreSQL disk space consumed by WAL files to spike or increase out of usual proportions. There are several possible reasons for this situation:

• The LSN up to which the connector has received data is available in the confirmed_flush_lsn column of the server’s pg_replication_slots view. Data that is older than this LSN is no longer available, and the database is responsible for reclaiming the disk space.

  Also in the pg_replication_slots view, the restart_lsn column contains the LSN of the oldest WAL that the connector might require. If the value for confirmed_flush_lsn is regularly increasing and the value of restart_lsn lags then the database needs to reclaim the space.

  The database typically reclaims disk space in batch blocks. This is expected behavior and no action by a user is necessary.

• There are many updates in a database that is being tracked but only a tiny number of updates are related to the table(s) and schema(s) for which the connector is capturing changes. This situation can be easily solved with periodic heartbeat events. Set the heartbeat.interval.ms connector configuration property.

  For the connector to detect and process events from a heartbeat table, you must add the table to the PostgreSQL publication specified by the publication.name property. If this publication predates your Debezium deployment, the connector uses the publications as defined. If the publication is not already configured to automatically replicate changes FOR ALL TABLES in the database, you must explicitly add the heartbeat table to the publication, for example, ALTER PUBLICATION <publicationName> ADD TABLE <heartbeatTableName>;

• The PostgreSQL instance contains multiple databases and one of them is a high-traffic database. Debezium captures changes in another database that is low-traffic in comparison to the other database. Debezium then cannot confirm the LSN as replication slots work per-database and Debezium is not invoked. As WAL is shared by all databases, the amount used tends to grow until an event is emitted by the database for which Debezium is capturing changes. To overcome this, it is necessary to:

  • Enable periodic heartbeat record generation with the heartbeat.interval.ms connector configuration property.

  • Regularly emit change events from the database for which Debezium is capturing changes.

  A separate process would then periodically update the table by either inserting a new row or repeatedly updating the same row. PostgreSQL then invokes Debezium, which confirms the latest LSN and allows the database to reclaim the WAL space. This task can be automated by means of the heartbeat.action.query connector configuration property.

Setting up multiple connectors for same database server

Debezium uses replication slots to stream changes from a database. These replication slots maintain the current position in form of a LSN (Log Sequence Number) which is pointer to a location in the WAL being consumed by the Debezium connector. This helps PostgreSQL keep the WAL available until it is processed by Debezium. A single replication slot can exist only for a single consumer or process - as different consumer might have different state and may need data from different position.

Since a replication slot can only be used by a single connector, it is essential to create a unique replication slot for each Debezium connector. Although when a connector is not active, Postgres may allow other connector to consume the replication slot - which could be dangerous as it may lead to data loss as a slot will emit each change just once [See More].

In addition to replication slot, Debezium uses publication to stream events when using the pgoutput plugin. Similar to replication slot, publication is at database level and is defined for a set of tables. Thus, you’ll need a unique publication for each connector, unless the connectors work on same set of tables. For more information about the options for enabling Debezium to create publications, see publication.autocreate.mode

See slot.name and publication.name on how to set a unique replication slot name and publication name for each connector.

Upgrading PostgreSQL

When you upgrade the PostgreSQL database that Debezium uses, you must take specific steps to protect against data loss and to ensure that Debezium continues to operate. In general, Debezium is resilient to interruptions caused by network failures and other outages. For example, when a database server that a connector monitors stops or crashes, after the connector re-establishes communication with the PostgreSQL server, it continues to read from the last position recorded by the log sequence number (LSN) offset. The connector retrieves information about the last recorded offset from the Kafka Connect offsets topic, and queries the configured PostgreSQL replication slot for a log sequence number (LSN) with the same value.

For the connector to start and to capture change events from a PostgreSQL database, a replication slot must be present. However, as part of the PostgreSQL upgrade process, replication slots are removed, and the original slots are not restored after the upgrade completes. As a result, when the connector restarts and requests the last known offset from the replication slot, PostgreSQL cannot return the information.

You can create a new replication slot, but you must do more than create a new slot to guard against data loss. A new replication slot can provide the LSNs only for changes the occur after you create the slot; it cannot provide the offsets for events that occurred before the upgrade. When the connector restarts, it first requests the last known offset from the Kafka offsets topic. It then sends a request to the replication slot to return information for the offset retrieved from the offsets topic. But the new replication slot cannot provide the information that the connector needs to resume streaming from the expected position. The connector then skips any existing change events in the log, and only resumes streaming from the most recent position in the log. This can lead to silent data loss: the connector emits no records for the skipped events, and it does not provide any information to indicate that events were skipped.

For guidance about how to perform a PostgreSQL database upgrade so that Debezium can continue to capture events while minimizing the risk of data loss, see the following procedure.

Procedure

1. Temporarily stop applications that write to the database, or put them into a read-only mode.

2. Back up the database.

3. Temporarily disable write access to the database.

4. Verify that any changes that occurred in the database before you blocked write operations are saved to the write-ahead log (WAL), and that the WAL LSN is reflected on the replication slot.

5. Provide the connector with enough time to capture all event records that are written to the replication slot.
   This step ensures that all change events that occurred before the downtime are accounted for, and that they are saved to Kafka.

6. Verify that the connector has finished consuming entries from the replication slot by checking the value of the flushed LSN.

7. Shut down the connector gracefully by stopping Kafka Connect.
   Kafka Connect stops the connectors, flushes all event records to Kafka, and records the last offset received from each connector.

   As an alternative to stopping the entire Kafka Connect cluster, you can stop the connector by deleting it. Do not remove the offset topic, because it might be shared by other Kafka connectors. Later, after you restore write access to the database and you are ready to restart the connector, you must recreate the connector.

8. As a PostgreSQL administrator, drop the replication slot on the primary database server. Do not use the slot.drop.on.stop property to drop the replication slot. This property is for testing only.

9. Stop the database.

10. Perform the upgrade using an approved PostgreSQL upgrade procedure, such as pg_upgrade, or pg_dump and pg_restore.

11. (Optional) Use a standard Kafka tool to remove the connector offsets from the offset storage topic.
    For an example of how to remove connector offsets, see how to remove connector offsets in the Debezium community FAQ.

12. Restart the database.

13. As a PostgreSQL administrator, create a Debezium logical replication slot on the database. You must create the slot before enabling writes to the database. Otherwise, Debezium cannot capture the changes, resulting in data loss.

14. Verify that the publication that defines the tables for Debezium to capture is still present after the upgrade. If the publication is not available, connect to the database as a PostgreSQL administrator to create a new publication.

15. If it was necessary to create a new publication in the previous step, update the Debezium connector configuration to add the name of the new publication to the publication.name property.

16. In the connector configuration, rename the connector.

17. In the connector configuration, set slot.name to the name of the Debezium replication slot.

18. Verify that the new replication slot is available.

19. Restore write access to the database and restart any applications that write to the database.

20. In the connector configuration, set the snapshot.mode property to never, and then restart the connector.

    If you were unable to verify that Debezium finished reading all database changes in Step 6, you can configure the connector to perform a new snapshot by setting snapshot.mode=initial. If necessary, you can confirm whether the connector read all changes from the replication slot by checking the contents of a database backup that was taken immediately before the upgrade.

Additional resources

• Configuring replication slots for Debezium

Deployment

To deploy a Debezium PostgreSQL connector, you install the Debezium PostgreSQL connector archive, configure the connector, and start the connector by adding its configuration to Kafka Connect.

Prerequisites

• Zookeeper, Kafka, and Kafka Connect are installed.

• PostgreSQL is installed and is set up to run the Debezium connector.

Procedure

1. Download the Debezium PostgreSQL connector plug-in archive.

2. Extract the files into your Kafka Connect environment.

3. Add the directory with the JAR files to Kafka Connect’s plugin.path.

4. Restart your Kafka Connect process to pick up the new JAR files.

If you are working with immutable containers, see Debezium’s Container images for Zookeeper, Kafka, PostgreSQL and Kafka Connect with the PostgreSQL connector already installed and ready to run. You can also run Debezium on Kubernetes and OpenShift.

Connector configuration example

Following is an example of the configuration for a PostgreSQL connector that connects to a PostgreSQL server on port 5432 at 192.168.99.100, whose logical name is fulfillment. Typically, you configure the Debezium PostgreSQL connector in a JSON file by setting the configuration properties available for the connector.

You can choose to produce events for a subset of the schemas and tables in a database. Optionally, you can ignore, mask, or truncate columns that contain sensitive data, are larger than a specified size, or that you do not need.

{
  "name": "fulfillment-connector",  (1)
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector", (2)
    "database.hostname": "192.168.99.100", (3)
    "database.port": "5432", (4)
    "database.user": "postgres", (5)
    "database.password": "postgres", (6)
    "database.dbname" : "postgres", (7)
    "topic.prefix": "fulfillment", (8)
    "table.include.list": "public.inventory" (9)
  }
}

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

You can send this configuration with a POST command to a running Kafka Connect service. The service records the configuration and starts one connector task that performs the following actions:

• Connects to the PostgreSQL database.

• Reads the transaction log.

• Streams change event records to Kafka topics.

Adding connector configuration

To run a Debezium PostgreSQL connector, create a connector configuration and add the configuration to your Kafka Connect cluster.

Prerequisites

• PostgreSQL is configured to support logical replication.

• The logical decoding plug-in is installed.

• The PostgreSQL connector is installed.

Procedure

1. Create a configuration for the PostgreSQL connector.

2. Use the Kafka Connect REST API to add that connector configuration to your Kafka Connect cluster.

Results

After the connector starts, it performs a consistent snapshot of the PostgreSQL server databases that the connector is configured for. The connector then starts generating data change events for row-level operations and streaming change event records to Kafka topics.

Connector properties

The Debezium PostgreSQL connector has many configuration properties that you can use to achieve the right connector behavior for your application. Many properties have default values. Information about the properties is organized as follows:

• Required configuration properties

• Advanced configuration properties

• Pass-through configuration properties

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