PostgreSQL CDC Source Connector (Debezium) [Legacy] for Confluent Cloud

Tip

Confluent recommends using version 2 of this connector. For more information, see PostgreSQL CDC Source Connector V2 (Debezium) for Confluent Cloud and Moving from V1 to V2.

The fully-managed PostgreSQL Change Data Capture (CDC) Source connector (Debezium) [Legacy] for Confluent Cloud can obtain a snapshot of the existing data in a PostgreSQL database and then monitor and record all subsequent row-level changes to that data. The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output data formats. All of the events for each table are recorded in a separate Apache Kafka® topic. The events can then be easily consumed by applications and services.

Note

• This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Debezium PostgreSQL Source Connector for Confluent Platform.
• See the Debezium docs for more information.

Features

The PostgreSQL CDC Source connector (Debezium) [Legacy] provides the following features:

• Topics created automatically: The connector automatically creates Kafka topics using the naming convention: <database.server.name>.<schemaName>.<tableName>. The topics are created with the properties: topic.creation.default.partitions=1 and topic.creation.default.replication.factor=3. For more information, see Maximum message size.
• Logical decoding plugins supported: wal2json, wal2json_rds, wal2json_streaming, wal2json_rds_streaming, pgoutput, decoderbufs. The default used is pgoutput.
• Database authentication: Uses password authentication.
• SSL support: Supports one-way SSL.
• Output data formats: The connector supports Avro, JSON Schema, Protobuf, or JSON (schemaless) output Kafka record value format. It supports Avro, JSON Schema, Protobuf, JSON (schemaless), and String output record key format. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
• Tasks per connector: Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").
• Select configuration properties:
  • Tables included and Tables excluded: Allows you to set whether a table is or is not monitored for changes. By default, the connector monitors every non-system table.
  • Snapshot mode: Allows you to specify the criteria for running a snapshot.
  • Tombstones on delete: Allows you to configure whether a tombstone event should be generated after a delete event. Default is true.
  • Other configuration properties:
    • poll.interval.ms
    • max.batch.size
    • max.queue.size
• Incremental snapshot: Supports incremental snapshotting via signaling.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Limitations

Be sure to review the following information.

• For connector limitations, see PostgreSQL CDC Source (Debezium) [Legacy] Connector limitations.
• If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
• If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

Maximum message size

This connector creates topics automatically. When it creates topics, the internal connector configuration property max.message.bytes is set to the following:

• Basic cluster: 8 MB
• Standard cluster: 8 MB
• Enterprise cluster: 8 MB
• Dedicated cluster: 20 MB

For more information about Confluent Cloud clusters, see Kafka Cluster Types in Confluent Cloud.

Log retention during snapshot

When launched, the CDC connector creates a snapshot of the existing data in the database to capture the nominated tables. To do this, the connector executes a “SELECT *” statement. Completing the snapshot can take a while if one or more of the nominated tables is very large.

During the snapshot process, the database server must retain redo logs and transaction logs so that when the snapshot is complete, the CDC connector can start processing database changes that have completed since the snapshot process began. These logs are retained in a binary log (binlog) on the database server.

If one or more of the tables are very large, the snapshot process could run longer than the binlog retention time set on the database server (that is, expire_logs_days = <number-of-days>). To capture very large tables, you should temporarily retain the binlog for longer than normal by increasing the expire_logs_days number.

Quick Start

Use this quick start to get up and running with the Confluent Cloud PostgreSQL CDC Source (Debezium) [Legacy] connector. The quick start provides the basics of selecting the connector and configuring it to obtain a snapshot of the existing data in a PostgreSQL database and then monitoring and recording all subsequent row-level changes.

Prerequisites

• Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud (Google Cloud).

• The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.

• Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

• You cannot use a basic database with Azure. You must use a general purpose or memory-optimized PostgreSQL database.

• The PostgreSQL database must be configured for CDC. For details, see PostgreSQL in the Cloud.

• Clients from Azure Virtual Networks are not allowed to access the server by default. Check that your Azure Virtual Network is correctly configured and that Allow access to Azure Services is enabled.

• Public access may be required for your database. See Manage Networking for Confluent Cloud Connectors for details. The following example shows the AWS Management Console when setting up a PostgreSQL database.

  

  Public access enabled

• A parameter group with the property rds.logical_replication=1 is required. An example is shown below. Once created, you must reboot the database.

  

  Parameter group

  

  RDS logical replication

• For networking considerations, see Networking, DNS, and service endpoints. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors. The following example shows the AWS Management Console when setting up security group rules for the VPC.

  

  Open inbound traffic

  Note

  See your specific cloud platform documentation for how to configure security rules for your VPC.

• Kafka cluster credentials. The following lists the different ways you can provide credentials.
  • Enter an existing service account resource ID.
  • Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
  • Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

See the Quick Start for Confluent Cloud for installation instructions.

Step 2: Add a connector

In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector

Click the PostgreSQL CDC Source (Debezium) [Legacy] connector card.

Step 4: Enter the connector details

Note

• Make sure you have all your prerequisites completed.
• An asterisk ( * ) designates a required entry.

At the Add Postgres CDC Source (Debezium) [Legacy] Connector screen, complete the following:

Kafka credentialsAuthenticationConfigurationSizingReview and Launch

1. Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
   • Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
   • Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
   • Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
2. Click Continue.

Step 5: Check the Kafka topic

After the connector is running, verify that messages are populating your Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

{
  "connector.class": "PostgresCdcSource",
  "name": "PostgresCdcSourceConnector_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "****************************************************************",
  "database.hostname": "debezium-1.<host-id>.us-east-2.rds.amazonaws.com",
  "database.port": "5432",
  "database.user": "postgres",
  "database.password": "**************",
  "database.dbname": "postgres",
  "database.server.name": "cdc",
  "table.include.list":"public.passengers",
  "plugin.name": "pgoutput",
  "output.data.format": "JSON",
  "tasks.max": "1"
}

Note the following property definitions:

• "connector.class": Identifies the connector plugin name.
• "name": Sets a name for your new connector.

• "kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:

  confluent iam service-account list
  

  For example:

  confluent iam service-account list
  
     Id     | Resource ID |       Name        |    Description
  +---------+-------------+-------------------+-------------------
     123456 | sa-l1r23m   | sa-1              | Service account 1
     789101 | sa-l4d56p   | sa-2              | Service account 2
  

• "table.includelist": (Optional) Enter a comma-separated list of fully-qualified table identifiers for the connector to monitor. By default, the connector monitors all non-system tables. A fully-qualified table name is in the form schemaName.tableName.

• "database.sslmode": If not entered, disable is the default option used. If you enter "database.sslmode" : "require", the connector uses a secure (encrypted) connection. The connector fails if a secure connection cannot be established. This mode does not do Certification Authority (CA) validation.

• "output.data.format": Sets the output record format (data coming from the connector). Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based record format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

• "after.state.only": (Optional) Defaults to true, which results in the Kafka record having only the record state from change events applied. Enter false to maintain the prior record states after applying the change events. For additional details, see After-state only output limitation.

• "json.output.decimal.format": (Optional) Defaults to BASE64. Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

  • BASE64 to serialize DECIMAL logical types as base64 encoded binary data.
  • NUMERIC to serialize Connect DECIMAL logical type values in JSON or JSON_SR as a number representing the decimal value.

• "column.exclude.list": (Optional) A comma-separated list of regular expressions that match the fully-qualified names of columns to exclude from change event record values. Fully-qualified names for columns are in the form databaseName.tableName.columnName.

• "plugin.name": (Optional) Sets the plugin to use. Options are wal2json, wal2json_rds, wal2json_streaming, wal2json_rds_streaming, pgoutput, and decoderbufs. The default is pgoutput.

• "slot.name": (Optional) The name of the PostgreSQL logical decoding slot created for streaming changes from a plugin and for a database. The slot name can contain only lower-case letters, numbers, and the underscore character. The default value is debezium.

• "publication.name": (Optional) The name of the PostgreSQL publication created for streaming changes when using pgoutput. The default is dbz_publication.

• "publication.autocreate.mode": (Optional) Determines how creation of publication should work when using pgoutput. Possible options are: all_tables, disabled and filtered.

  • all_tables (default): If a publication exists, the connector uses it. If a publication does not exist, the connector creates a publication for all tables in the database for which the connector is capturing changes. This requires that the database user that has permission to perform replications also has permission to create a publication.
  • disabled: The connector does not attempt to create a publication. A database administrator or the user configured to perform replications must have created the publication before running the connector. If the connector cannot find the publication, the connector throws an exception and stops.
  • filtered: If a publication exists, the connector uses it. If no publication exists, the connector creates a new publication for tables that match the current filter configuration as specified by the database.exclude.list, schema.include.list, schema.exclude.list, and table.include.list connector configuration properties.

• "snapshot.mode": (Optional) Specifies the criteria for performing a database snapshot when the connector starts.

  • The default setting is initial. When selected, the connector takes a snapshot of the structure and data from captured tables. This is useful if you want the topics populated with a complete representation of captured table data when the connector starts.
  • never specifies that the connector should never perform snapshots, and that when starting for the first time, the connector starts reading from where it last left off.
  • exported specifies that the database snapshot is based on the point in time when a replication slot was created. Note that this is a good way to perform a lock-free snapshot (see Snapshot isolation).

• "signal.data.collection": (Optional) Fully-qualified name of the data collection that is used to send signals to the connector. The collection name is of the form schemaName.tableName. These signals can be used to perform incremental snapshotting.

• "tasks.max": Enter the number of tasks in use by the connector. Organizations can run multiple connectors with a limit of one task per connector (that is, "tasks.max": "1").

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. For additional information about the Debezium SMTs ExtractNewRecordState and EventRouter (Debezium), see Debezium transformations.

See Configuration Properties for all property values and definitions.

Step 4: Load the properties file and create the connector

Enter the following command to load the configuration and start the connector:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file postgresql-cdc-source.json

Example output:

Created connector PostgresCdcSourceConnector_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |            Name              | Status  |  Type
+-----------+------------------------------+---------+-------+
lcc-ix4dl   | PostgresCdcSourceConnector_0 | RUNNING | source

Step 6: Check the Kafka topic.

After the connector is running, verify that messages are populating your Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Managed and Custom Connectors section.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

How should we connect to your data?

name

Sets a name for your connector.

• Type: string
• Valid Values: A string at most 64 characters long
• Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

• Type: string
• Default: KAFKA_API_KEY
• Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
• Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

• Type: password
• Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

• Type: string
• Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

• Type: password
• Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

• Type: string
• Default: default
• Importance: medium

How should we connect to your database?

database.hostname

The address of the PostgreSQL server.

• Type: string
• Importance: high

database.port

Port number of the PostgreSQL server.

• Type: int
• Valid Values: [0,…,65535]
• Importance: high

database.user

The name of the PostgreSQL user that has the required authorization.

• Type: string
• Importance: high

database.password

The password for the PostgreSQL user that has the required authorization.

• Type: password
• Importance: high

database.dbname

The name of the PostgreSQL database to connect to.

• Type: string
• Importance: high

database.server.name

The logical name of the PostgreSQL server/cluster. This logical name forms a namespace and is used in all the names of the Kafka topics and the Kafka Connect schema names. The logical name is also used for the namespaces of the corresponding Avro schema, if Avro data format is used. Kafka topics should/will be created with the prefix database.server.name. Only alphanumeric characters, underscores, hyphens and dots are allowed.

• Type: string
• Importance: high

database.sslmode

What SSL mode should we use to connect to your database. require establishes an encrypted connection or fails if one cannot be made for any reason. If your database server enforces SSL, please use require. disable uses an unencrypted connection.

• Type: string
• Default: disable
• Importance: low

Database details

signal.data.collection

Fully-qualified name of the data collection that needs to be used to send signals to the connector. Use the following format to specify the fully-qualified collection name: schemaName.tableName

• Type: string
• Importance: medium

publication.autocreate.mode

Determines how creation of a publication should work when using pgoutput. Possible options are: all_tables, disabled, and filtered. Check the documentation for more details on each option.

• Type: string
• Default: all_tables
• Valid Values: all_tables, disabled, filtered
• Importance: medium

publication.name

The name of the PostgreSQL publication created for streaming changes when using pgoutput

• Type: string
• Default: dbz_publication
• Valid Values: Must match the regex ^[^\s\"\'\`]+$
• Importance: medium

table.include.list

An optional comma-separated list of strings that match fully-qualified table identifiers for tables to be monitored. Any table not included in this config property is excluded from monitoring. Each identifier is in the form schemaName.tableName. By default the connector monitors every non-system table in each monitored schema. May not be used with “Table excluded”.

• Type: list
• Importance: medium

table.exclude.list

An optional comma-separated list of strings that match fully-qualified table identifiers for tables to be excluded from monitoring. Any table not included in this config property is monitored. Each identifier is in the form schemaName.tableName. May not be used with “Table included”.

• Type: list
• Importance: medium

snapshot.mode

Specifies the criteria for running a snapshot upon startup of the connector. The default setting is initial, and specifies the connector can run a snapshot only when no offsets have been recorded for the logical server name. 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 where it last left off (last LSN position) or start from the beginning, based on the point of the view of the logical replication slot. 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.

• Type: string
• Default: initial
• Valid Values: exported, initial, never
• Importance: low

datatype.propagate.source.type

A comma-separated list of regular expressions matching the database-specific data type names that adds the data type’s original type and original length as parameters to the corresponding field schemas in the emitted change records.

• Type: list
• Importance: low

tombstones.on.delete

Controls whether a tombstone event should be generated after a delete event. When set to true, the delete operations are represented by a delete event and a subsequent tombstone event. When set to 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.

• Type: boolean
• Default: true
• Importance: high

column.exclude.list

Regular expressions matching columns to exclude from change events

• Type: list
• Importance: medium

plugin.name

The name of the Postgres logical decoding plugin installed on the server. Note: pgoutput is only available in PostgreSQL 10+.

• Type: string
• Default: pgoutput
• Importance: high

slot.name

The name of the PostgreSQL logical decoding slot that was created for streaming changes from a particular plug-in and for a particular database/schema. The server uses this slot to stream events to the connector.

• Type: string
• Default: debezium
• Valid Values: Must match the regex ^[a-z0-9_]+$
• Importance: low

Connection details

poll.interval.ms

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.

• Type: int
• Default: 1000 (1 second)
• Valid Values: [1,…]
• Importance: low

max.batch.size

Positive integer value that specifies the maximum size of each batch of events that should be processed during each iteration of this connector.

• Type: int
• Default: 1000
• Valid Values: [1,…,5000]
• Importance: low

event.processing.failure.handling.mode

Specifies how the connector should react to exceptions during processing of binlog events.

• Type: string
• Default: fail
• Valid Values: fail, skip, warn
• Importance: low

heartbeat.interval.ms

Controls how frequently the connector sends heartbeat messages to a Kafka topic. The behavior of default value 0 is that the connector does not send heartbeat messages.

• Type: int
• Default: 0
• Valid Values: [0,…]
• Importance: low

heartbeat.action.query

Specifies a query that the connector executes on the source database when the connector sends a heartbeat message.

• Type: string
• Importance: low

Connector details

provide.transaction.metadata

Stores transaction metadata information in a dedicated topic and enables the transaction metadata extraction together with event counting.

• Type: boolean
• Default: false
• Importance: low

decimal.handling.mode

Specifies how DECIMAL and NUMERIC columns should be represented in change events, including: ‘precise’ (the default) uses java.math.BigDecimal to represent values, which are encoded in the change events using a binary representation and Kafka Connect’s ‘org.apache.kafka.connect.data.Decimal’ type; ‘string’ uses string to represent values; ‘double’ represents values using Java’s ‘double’, which may not offer the precision but will be far easier to use in consumers.

• Type: string
• Default: precise
• Valid Values: double, precise, string
• Importance: medium

binary.handling.mode

Specifies how binary (blob, binary, etc.) columns should be represented in change events, including: ‘bytes’ (the default) represents binary data as byte array; ‘base64’ represents binary data as base64-encoded string; ‘hex’ represents binary data as hex-encoded (base16) string.

• Type: string
• Default: bytes
• Valid Values: base64, bytes, hex
• Importance: low

time.precision.mode

Time, date, and timestamps can be represented with different kinds of precisions, including: ‘adaptive’ (the default) bases the precision of time, date, and timestamp values on the database column’s precision; ‘adaptive_time_microseconds’ like ‘adaptive’ mode, but TIME fields always use microseconds precision; ‘connect’ always represents time, date, 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.

• Type: string
• Default: adaptive
• Valid Values: adaptive, adaptive_time_microseconds, connect
• Importance: medium

cleanup.policy

Set the topic cleanup policy

• Type: string
• Default: delete
• Valid Values: compact, delete
• Importance: medium

hstore.handling.mode

Specifies how HSTORE columns should be represented in change events.

• Type: string
• Default: json
• Valid Values: json, map
• Importance: medium

interval.handling.mode

Specifies how INTERVAL columns should be represented in change events.

• Type: string
• Default: numeric
• Valid Values: numeric, string
• Importance: medium

schema.refresh.mode

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.

• Type: string
• Default: columns_diff
• Valid Values: columns_diff, columns_diff_exclude_unchanged_toast
• Importance: medium

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

• Type: string
• Default: JSON
• Importance: high

output.key.format

Sets the output Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

• Type: string
• Default: JSON
• Valid Values: AVRO, JSON, JSON_SR, PROTOBUF, STRING
• Importance: high

after.state.only

Controls whether the generated Kafka record should contain only the state after applying change events.

• Type: boolean
• Default: true
• Importance: low

json.output.decimal.format

Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

BASE64 to serialize DECIMAL logical types as base64 encoded binary data and

NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.

• Type: string
• Default: BASE64
• Importance: low

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

• Type: int
• Valid Values: [1,…,1]
• Importance: high

Next Steps

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.

After-state only output limitation

When a connector is configured with the property After-state only=false, you expect to see the previous values of all columns under before in the record. However, for certain conditions before will contain null or partially displayed columns. If Protobuf is used, the record may not contain the before field at all. The following example shows this issue and provides a corrective action to take.

For example, the connector is configured with JSON and After-state only is set to false. When a record is updated in the PostgreSQL database, you may see a record similar to the following sample, where "before" is null.

{
  "before": null,
  "after": {
    "id": 5,
    "name": "Allen William Henry",
    "sex": "male",
    "age": 25,
    "sibsp": 0,
    "parch": 0,
    "created_at": "2018-01-02T15:22:14.831461Z"
   },
   "source": {
     "version": "1.3.1.Final",
     "connector": "postgresql",
     "name": "test",
     "ts_ms": 1621389097781,
     "snapshot": "false",
     "db": "postgres",
     "schema": "public",
     "table": "passengers",
     "txId": 572,
     "lsn": 872429856,
     "xmin": null
   },
   "op": "u",
   "ts_ms": 1621389098688,
   "transaction": null
}

For an updated record to contain the previous (before) values of all columns in the row, you need to modify the passengers table by running ALTER TABLE passengers REPLICA IDENTITY FULL. After you make this change in the PostgreSQL database, and records are updated, you should see records similar to the following sample.

{
  "before": {
    "id": 8,
    "name": "Gosta Leonard",
    "sex": "male",
    "age": 2,
    "sibsp": 3,
    "parch": 1,
    "created_at": "2018-01-03T20:53:55.955056Z"
  },
  "after": {
    "id": 8,
    "name": "Gosta Leonard",
    "sex": "male",
    "age": 25,
    "sibsp": 3,
    "parch": 1,
    "created_at": "2018-01-03T20:53:55.955056Z"
  },
  "source": {
    "version": "1.3.1.Final",
    "connector": "postgresql",
    "name": "test",
    "ts_ms": 1621390542864,
    "snapshot": "false",
    "db": "postgres",
    "schema": "public",
    "table": "passengers",
    "txId": 581,
    "lsn": 1207967968,
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