Change Data Capture (CDC)

Description

Change Data Capture (CDC) enables you to ingest data changes from tables in a relational database and publish them to a sink. In a CDC pipeline, data changes in the source relational table are captured at the row-level operations (INSERT, DELETE, UPDATE) based on the table's PRIMARY KEY. Then they're pushed to the downstream sink in real-time.

Currently, you can create a CDC pipeline with a source backed by a table in a POSTGRESQL Store.

How a CDC Pipeline Works

Source Record Structure

DeltaStream uses Debezium to capture changes in the source relation table. This means when defining a CDC source backed by a given table in a POSTGRESQL store, you can use this JSON skeleton:

{
 "op": ...,
 "ts_ms": ...,
 "before": {...},
 "after": {...},
 "system": {...}
}

op: Describes the row-level operation that caused a change in the source. It can be c for CREATE, u for UPDATE, d for DELETE or r for READ.
ts_ms: Shows the time at which the change event is processed by the CDC pipeline, in the source.
before and after: These specify the state of the row at which the change occurred, before and/or after the change, depending on the semantics of the operation.
system: Shows the metadata in the source about the operation. Some of its included fields are:
- db (String): The relational database name containing the source table.
- schema (String): The relational schema name containing the source table.
- table (String): The relational source table name.
- lsn (BigInt): The log sequence number of the change.

Source Store Requirements

Debezium uses Postgresql's logical decoding slot for streaming changes from the given relational source table. When you create the CDC pipeline, you can specify an existing slot's name to use for the job. If not, DeltaStream creates a new replication slot. When creating the source POSTGRESQL Store, the provided username must have enough privileges to use an existing replication slot or create a new one. For more information on replication slots in PostgreSQL, see the Postgres documentation. Moreover, the source POSTGRESQL store must use logical decoding with the write-ahead log (WAL). This means you should set wal_level to logical in the source relational database.

Considerations

Storage TOAST

PostgreSQL uses a fixed page size (commonly 8 kb), so rows with large field values are compressed and/or broken up into multiple physical rows. This technique is referred to as TOAST. See PostgreSQL documentation on the subject to learn more.

DeltaStream follows the Debezium default behavior when generating CDC events containing TOASTed fields. If the TOASTed field is unchanged, then the value in the CDC event is replaced with __debezium_unavailable_value.

How to Create a CDC Pipeline

You create a CDC pipeline in two steps:

Define the CDC source
Define the sink and the query to write CDC changes into it

Step 1. Define DDL for CDC source

Using CREATE STREAM, define a backed by the source relational table. The parameters below are used as CDC parameters in the WITH clause in CREATE STREAM:

Parameter Name

Description

postgresql.db.name

Name of the database in the POSTGRESQL store containing the source table.

Required: Yes Type: String Valid values: See LIST ENTITIES

postgresql.schema.name

Name of the schema in the POSTGRESQL store containing the source table.

Required: Yes Type: String Valid values: See LIST ENTITIES

postgresql.table.name

Name of the source table in the POSTGRESQL store.

Required: Yes Type: String Valid values: See LIST ENTITIES

value.format

Format of the CDC record coming from the source.

Required: Yes Type: String Valid values: JSON

store

Name of the POSTGRESQL store that hosts the relational table backing the CDC source.

Required: Yes Type: String Valid values: See LIST STORES

Step 2. Define the CSAS for CDC Sink Stream and Query

Using CREATE STREAM AS SELECT, define a backed by the sink Streaming Entity and the query to insert CDC records into it.

Sink parameters

The CDC sink can write into an existing entity in the sink store or create a new entity. The below parameters are used in the WITH clause for the sink in CSAS to create the desired behavior:

Parameter Name

Description

topic

Name of the Streaming Entity into which the data for the CDC sink is written. If the entity doesn't exist, an entity with this name is created in the corresponding store. Required: No Default value: Lowercase sink name Type: String

topic.partitions

The number of partitions to use when creating the sink entity, if applicable. If the entity already exists, this value must equal the number of partitions in the existing entity.

Required: Yes, unless topic already exists Type: Integer Valid values: [1, ...]

topic.replicas

The number of replicas to use when creating the sink entity, if applicable. If the entity already exists, this value must equal the number of replicas in the existing entity.

Required: Yes, unless topic already exists Type: Integer Valid values: [1, ...]

Source parameters

The source stream in the CDC pipeline captures the changes from a relational table in the source POSTGRESQL Store. Here are the parameters you can specify in the WITH clause for the source in CSAS:

Parameter Name

Description

postgresql.slot.name

Name of the existing PostgreSQL replication slot name to use for the CDC pipeline. Note that the username you provide for the source PostgreSQL store should have the required privileges to use this slot. Required: No Default value: ds_cdc Type: String

postgresql.decoding.plugin.name

Name of the Postgres logical decoding plug-in on the source store. Supported values are decoderbufs, pgoutput, wal2json, wal2json_rds, wal2json_streaming, and wal2json_rds_streaming. Required: No Default value: pgoutput Type: String

Example

Assume a POSTGRESQL Store is defined with the name pgstore and it has a table named pageviews under the schema named public in the report database. Here is how the data displays in pageviews. Each row has 3 columns — viewtime, userid, and pageid — and shows when a given page was visited by a specific user:

demodb.public/pgstore# PRINT ENTITY "public".pageviews;
+-----------------+-------------+-------------+
| viewtime        | userid      | pageid      |
+=================+=============+=============+
| 1694124853651   | User_4      | Page_29     |
+-----------------+-------------+-------------+
| 1694124856731   | User_1      | Page_59     |
+-----------------+-------------+-------------+
| 1694124857732   | User_1      | Page_63     |
+-----------------+-------------+-------------+

Use the DDL statement below to create a named pageviews_cdc,backed by the pageviews table to capture its CDC records:

CREATE STREAM pageviews_cdc(
  op VARCHAR,
  ts_ms BIGINT,
  `before` STRUCT<viewtime BIGINT, userid VARCHAR, pageid VARCHAR>, 
  `after`  STRUCT<viewtime BIGINT, userid VARCHAR, pageid VARCHAR>, 
  `source` STRUCT<db VARCHAR, `schema` VARCHAR, `table` VARCHAR, `lsn` BIGINT>)
WITH (
  'store'='pgstore', 
  'value.format'='json',
  'postgresql.db.name'='report',
  'postgresql.schema.name'='public',
  'postgresql.table.name'='pageviews');

The pageviews_cdc stream is used as the source in the CDC pipeline. Whenever an INSERT, DELETE, or UPDATE happens on pageviews in the relational database, a corresponding record is generated in pageviews_cdc to capture the change. Now use the below CSAS statement to define a stream as the sink and run a query to write those changes into it:

CREATE STREAM cdc_sink WITH (
  'topic.partitions' = 1,
  'topic.replicas' = 1) AS 
SELECT * FROM pageviews_cdc;

Note that in the above CSAS you must provide the topic properties, as a new topic is created in the sink store. To use a pre-existing topic such as cdc_logs in the sink store, you can replace topic properties with the topic name in the WITH clause:

CREATE STREAM cdc_sink WITH ('topic' = 'cdc_logs') AS 
SELECT * FROM pageviews_cdc;

As an example, assume a new record is added to pageviews showing that User_5 visited Page_94. Following this INSERT operation, you see a record similar to the below in cdc_sink published via the CDC pipeline defined above:

{
 "op":"c",
 "ts_ms":1693430399726,
 "before":null,
 "after":{"viewtime":1693430399292,"userid":"User_5","pageid":"Page_94"},
 "source":{"db":"report","schema":"public","table":"pageviews","lsn":38990016}
}

Now imagine you're only interested in the DELETE events in the source and wish to write the userid and lsn for each DELETE. You can use the query below to create such a CDC pipeline:

CREATE STREAM cdc_sink WITH ('topic' = 'cdc_logs') AS 
SELECT ts_ms AS event_ts,
       `before`->userid AS uid,
       `source`->`lsn` AS seq_num
FROM pageviews_cdc
WHERE op = 'd';

PreviousAPPLICATION NextCREATE CHANGELOG AS SELECT

Last updated 8 days ago