# PostgreSQL CDC using Kafka and Debezium How to propagate Change Data Capture (CDC) data from a PostgreSQL database to Materialize using Kafka and Debezium > **Warning:** You can use [Debezium](https://debezium.io/) to propagate Change Data Capture > (CDC) data to Materialize from a PostgreSQL database, but we **strongly > recommend** using the native [PostgreSQL](/sql/create-source/postgres/) source > instead. For help getting started with your own data, you can schedule a [free guided trial](https://materialize.com/demo/?utm_campaign=General&utm_source=documentation). Change Data Capture (CDC) allows you to track and propagate changes in a PostgreSQL database to downstream consumers based on its Write-Ahead Log (`WAL`). In this guide, we’ll cover how to use Materialize to create and efficiently maintain real-time views with incrementally updated results on top of CDC data. ## Kafka + Debezium You can use [Debezium](https://debezium.io/) and the [Kafka source](/sql/create-source/kafka/#debezium-envelope) to propagate CDC data from PostgreSQL to Materialize in the unlikely event that using the[native PostgreSQL source](/sql/create-source/postgres/) is not an option. Debezium captures row-level changes resulting from `INSERT`, `UPDATE` and `DELETE` operations in the upstream database and publishes them as events to Kafka using Kafka Connect-compatible connectors. ### A. Configure database **Minimum requirements:** PostgreSQL 11+ Before deploying a Debezium connector, you need to ensure that the upstream database is configured to support [logical replication](https://www.postgresql.org/docs/current/logical-replication.html). **Self-hosted:** As a _superuser_: 1. Check the [`wal_level` configuration](https://www.postgresql.org/docs/current/wal-configuration.html) setting: ```postgres SHOW wal_level; ``` The default value is `replica`. For CDC, you'll need to set it to `logical` in the database configuration file (`postgresql.conf`). Keep in mind that changing the `wal_level` requires a restart of the PostgreSQL instance and can affect database performance. 1. Restart the database so all changes can take effect. **AWS RDS:** We recommend following the [AWS RDS](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/CHAP_PostgreSQL.html#PostgreSQL.Concepts.General.FeatureSupport.LogicalReplication) documentation for detailed information on logical replication configuration and best practices. As a _superuser_ (`rds_superuser`): 1. Create a custom RDS parameter group and associate it with your instance. You will not be able to set custom parameters on the default RDS parameter groups. 1. In the custom RDS parameter group, set the `rds.logical_replication` static parameter to `1`. 1. Add the egress IP addresses associated with your Materialize region to the security group of the RDS instance. You can find these addresses by querying the `mz_egress_ips` table in Materialize. 1. Restart the database so all changes can take effect. **AWS Aurora:** > **Note:** Aurora Serverless (v1) [does **not** support](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-serverless.html#aurora-serverless.limitations) > logical replication, so it's not possible to use this service with > Materialize. We recommend following the [AWS Aurora](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.Replication.Logical.html#AuroraPostgreSQL.Replication.Logical.Configure) documentation for detailed information on logical replication configuration and best practices. As a _superuser_: 1. Create a DB cluster parameter group for your instance using the following settings: Set **Parameter group family** to your version of Aurora PostgreSQL. Set **Type** to **DB Cluster Parameter Group**. 1. In the DB cluster parameter group, set the `rds.logical_replication` static parameter to `1`. 1. In the DB cluster parameter group, set reasonable values for `max_replication_slots`, `max_wal_senders`, `max_logical_replication_workers`, and `max_worker_processes parameters` based on your expected usage. 1. Add the egress IP addresses associated with your Materialize region to the security group of the DB instance. You can find these addresses by querying the `mz_egress_ips` table in Materialize. 1. Restart the database so all changes can take effect. **Azure DB:** We recommend following the [Azure DB for PostgreSQL](https://docs.microsoft.com/en-us/azure/postgresql/flexible-server/concepts-logical#pre-requisites-for-logical-replication-and-logical-decoding) documentation for detailed information on logical replication configuration and best practices. 1. In the Azure portal, or using the Azure CLI, [enable logical replication](https://docs.microsoft.com/en-us/azure/postgresql/concepts-logical#set-up-your-server) for the PostgreSQL instance. 1. Add the egress IP addresses associated with your Materialize region to the list of allowed IP addresses under the "Connections security" menu. You can find these addresses by querying the `mz_egress_ips` table in Materialize. 1. Restart the database so all changes can take effect. **Cloud SQL:** We recommend following the [Cloud SQL for PostgreSQL](https://cloud.google.com/sql/docs/postgres/replication/configure-logical-replication#configuring-your-postgresql-instance) documentation for detailed information on logical replication configuration and best practices. As a _superuser_ (`cloudsqlsuperuser`): 1. In the Google Cloud Console, enable logical replication by setting the `cloudsql.logical_decoding` configuration parameter to `on`. 1. Add the egress IP addresses associated with your Materialize region to the list of allowed IP addresses. You can find these addresses by querying the `mz_egress_ips` table in Materialize. 1. Restart the database so all changes can take effect. Once logical replication is enabled: 1. Grant enough privileges to ensure Debezium can operate in the database. The specific privileges will depend on how much control you want to give to the replication user, so we recommend following the [Debezium documentation](https://debezium.io/documentation/reference/connectors/postgresql.html#postgresql-replication-user-privileges). 1. Specify the `REPLICA IDENTITY` value: - If a table that you want to replicate has a **primary key** defined, you can use your default replica identity value. However, without `REPLICA IDENTITY FULL`, you cannot ingest unchanged [TOASTed](https://www.postgresql.org/docs/current/storage-toast.html) values. - If a table you want to replicate has **no primary key** defined, you must set the replica identity value to `FULL`: ```postgres ALTER TABLE repl_table REPLICA IDENTITY FULL; ``` This setting determines the amount of information that is written to the WAL in `UPDATE` and `DELETE` operations. Setting it to `FULL` will include the previous values of all the table’s columns in the change events. As a heads up, you should expect a performance hit in the database from increased CPU usage. For more information, see the [PostgreSQL documentation](https://www.postgresql.org/docs/current/logical-replication-publication.html). ### B. Deploy Debezium **Minimum requirements:** Debezium 1.5+ Debezium is deployed as a set of Kafka Connect-compatible connectors, so you first need to define a SQL connector configuration and then start the connector by adding it to Kafka Connect. > **Warning:** If you deploy the PostgreSQL Debezium connector in [Confluent Cloud](https://docs.confluent.io/cloud/current/connectors/cc-mysql-source-cdc-debezium.html), > you **must** override the default value of `After-state only` to `false`. **Debezium 1.5+:** 1. Create a connector configuration file and save it as `register-postgres.json`: ```json { "name": "your-connector", "config": { "connector.class": "io.debezium.connector.postgresql.PostgresConnector", "tasks.max": "1", "plugin.name":"pgoutput", "database.hostname": "postgres", "database.port": "5432", "database.user": "postgres", "database.password": "postgres", "database.dbname" : "postgres", "database.server.name": "pg_repl", "table.include.list": "public.table1", "publication.autocreate.mode":"filtered", "key.converter": "io.confluent.connect.avro.AvroConverter", "value.converter": "io.confluent.connect.avro.AvroConverter", "value.converter.schemas.enable": false } } ``` You can read more about each configuration property in the [Debezium documentation](https://debezium.io/documentation/reference/1.6/connectors/postgresql.html#postgresql-connector-properties). By default, the connector writes events for each table to a Kafka topic named `serverName.schemaName.tableName`. 1. Start the PostgreSQL Debezium connector using the configuration file: ```bash export CURRENT_HOST='' curl -i -X POST -H "Accept:application/json" -H "Content-Type:application/json" \ http://$CURRENT_HOST:8083/connectors/ -d @register-postgres.json ``` 1. Check that the connector is running: ```bash curl http://$CURRENT_HOST:8083/connectors/your-connector/status ``` The first time it connects to a PostgreSQL server, Debezium takes a [consistent snapshot](https://debezium.io/documentation/reference/1.6/connectors/postgresql.html#postgresql-snapshots) of the tables selected for replication, so you should see that the pre-existing records in the replicated table are initially pushed into your Kafka topic: ```bash /usr/bin/kafka-avro-console-consumer \ --bootstrap-server kafka:9092 \ --from-beginning \ --topic pg_repl.public.table1 ``` **Debezium 2.0+:** 1. Beginning with Debezium 2.0.0, Confluent Schema Registry support is not included in the Debezium containers. To enable the Confluent Schema Registry for a Debezium container, install the following Confluent Avro converter JAR files into the Connect plugin directory: * `kafka-connect-avro-converter` * `kafka-connect-avro-data` * `kafka-avro-serializer` * `kafka-schema-serializer` * `kafka-schema-registry-client` * `common-config` * `common-utils` You can read more about this in the [Debezium documentation](https://debezium.io/documentation/reference/stable/configuration/avro.html#deploying-confluent-schema-registry-with-debezium-containers). 1. Create a connector configuration file and save it as `register-postgres.json`: ```json { "name": "your-connector", "config": { "connector.class": "io.debezium.connector.postgresql.PostgresConnector", "tasks.max": "1", "plugin.name":"pgoutput", "database.hostname": "postgres", "database.port": "5432", "database.user": "postgres", "database.password": "postgres", "database.dbname" : "postgres", "topic.prefix": "pg_repl", "schema.include.list": "public", "table.include.list": "public.table1", "publication.autocreate.mode":"filtered", "key.converter": "io.confluent.connect.avro.AvroConverter", "value.converter": "io.confluent.connect.avro.AvroConverter", "key.converter.schema.registry.url": "http://:8081", "value.converter.schema.registry.url": "http://:8081", "value.converter.schemas.enable": false } } ``` You can read more about each configuration property in the [Debezium documentation](https://debezium.io/documentation/reference/2.4/connectors/postgresql.html#postgresql-connector-properties). By default, the connector writes events for each table to a Kafka topic named `serverName.schemaName.tableName`. 1. Start the Debezium Postgres connector using the configuration file: ```bash export CURRENT_HOST='' curl -i -X POST -H "Accept:application/json" -H "Content-Type:application/json" \ http://$CURRENT_HOST:8083/connectors/ -d @register-postgres.json ``` 1. Check that the connector is running: ```bash curl http://$CURRENT_HOST:8083/connectors/your-connector/status ``` The first time it connects to a Postgres server, Debezium takes a [consistent snapshot](https://debezium.io/documentation/reference/1.6/connectors/postgresql.html#postgresql-snapshots) of the tables selected for replication, so you should see that the pre-existing records in the replicated table are initially pushed into your Kafka topic: ```bash /usr/bin/kafka-avro-console-consumer \ --bootstrap-server kafka:9092 \ --from-beginning \ --topic pg_repl.public.table1 ``` ### C. Create a source
NOTE: Currently, Materialize only supports Avro-encoded Debezium records. If you're interested in JSON support, please reach out in the community Slack or submit a feature request.
Debezium emits change events using an envelope that contains detailed information about upstream database operations, like the `before` and `after` values for each record. To create a source that interprets the [Debezium envelope](/sql/create-source/kafka/#debezium-envelope) in Materialize: ```mzsql CREATE SOURCE kafka_repl FROM KAFKA CONNECTION kafka_connection (TOPIC 'pg_repl.public.table1') FORMAT AVRO USING CONFLUENT SCHEMA REGISTRY CONNECTION csr_connection ENVELOPE DEBEZIUM; ``` By default, the source will be created in the active cluster; to use a different cluster, use the `IN CLUSTER` clause. This allows you to replicate tables with `REPLICA IDENTITY DEFAULT`, `INDEX`, or `FULL`. ### D. Create a view on the source A [view](/concepts/views/) saves a query under a name to provide a shorthand for referencing the query. During view creation, the underlying query is not executed. ```mzsql CREATE VIEW cnt_table1 AS SELECT field1, COUNT(*) AS cnt FROM kafka_repl GROUP BY field1; ``` ### E. Create an index on the view In Materialize, [indexes](/concepts/indexes) on views compute and, as new data arrives, incrementally update view results in memory within a [cluster](/concepts/clusters/) instead of recomputing the results from scratch. Create an index on `cnt_table1` view. Then, as new change events stream in through Kafka (as the result of `INSERT`, `UPDATE` and `DELETE` operations in the upstream database), the index incrementally updates the view results in memory, such that the in-memory up-to-date results are immediately available and computationally free to query. ```mzsql CREATE INDEX idx_cnt_table1_field1 ON cnt_table1(field1); ``` For best practices on when to index a view, see [Indexes](/concepts/indexes/) and [Views](/concepts/views/).