Creating a real-time data replication pipeline from PostgreSQL to ClickHouse can be complex. This detailed runbook with a practical example outlines the steps required to set up this process:

Prerequisites

• PostgreSQL Server with data to replicate.
• ClickHouse Server for data ingestion.
• A Kafka Cluster for message queuing (optional, but recommended for scalable, real-time replication).
• Change Data Capture (CDC) tool like Debezium.

Example Setup

• PostgreSQL Database: sales_db
• Table to Replicate: orders
• ClickHouse Database: analytics_db
• Kafka Topic: pg_orders

Step 1: Prepare the PostgreSQL Server

• Install PostgreSQL and set up the sales_db database.
• Ensure that the PostgreSQL server is configured to allow logical replication (wal_level = logical in postgresql.conf).
• Create a replication user with the necessary permissions.

Step 2: Set Up ClickHouse

• Install ClickHouse.

• Create a database named analytics_db.

• Define a schema in ClickHouse that corresponds to the PostgreSQL orders table. For example:

  CREATE TABLE analytics_db.orders (
      order_id Int32,
      order_date Date,
      amount Float32,
      customer_id Int32
  ) ENGINE = MergeTree()
  ORDER BY order_id;
  
  

Step 3: Install and Configure a CDC Tool (e.g., Debezium)

• Install Debezium and connect it to your PostgreSQL instance.

• Configure Debezium to track changes on the orders table. Example configuration snippet:

  {
      "name": "sales_db_connector",
      "config": {
          "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
          "database.hostname": "localhost",
          "database.port": "5432",
          "database.user": "replication_user",
          "database.password": "password",
          "database.dbname": "sales_db",
          "table.include.list": "public.orders",
          "plugin.name": "pgoutput"
      }
  }
  
  

Step 4: Set Up Kafka for Message Queuing (Optional)

• Install and start Kafka.
• Create a new Kafka topic pg_orders for the orders table events.

Step 5: Stream Data from PostgreSQL to Kafka

• Debezium will capture changes in the orders table and push them to the pg_orders Kafka topic.

Step 6: Stream Data from Kafka to ClickHouse

• In ClickHouse, create a Kafka engine table to consume data from the Kafka topic:

  CREATE TABLE analytics_db.kafka_orders (
      order_id Int32,
      order_date Date,
      amount Float32,
      customer_id Int32
  ) ENGINE = Kafka()
  SETTINGS kafka_broker_list = 'kafka:9092',
           kafka_topic_list = 'pg_orders',
           kafka_group_name = 'clickhouse_group',
           kafka_format = 'JSONEachRow';
  
  

• Create a materialized view to read from kafka_orders and write to the orders table in ClickHouse:

  CREATE MATERIALIZED VIEW analytics_db.orders_consumer TO analytics_db.orders AS
  SELECT * FROM analytics_db.kafka_orders;
  
  

Step 7: Monitoring and Validation

• Monitor the Debezium and Kafka logs for errors or lags.
• Regularly verify data consistency between PostgreSQL and ClickHouse.

Step 8: Maintenance and Troubleshooting

• Regularly back up both PostgreSQL and ClickHouse databases.
• Update your replication setup as needed for any schema changes in PostgreSQL.

Conclusion

This runbook provides a framework for setting up real-time replication from PostgreSQL to ClickHouse using Debezium and Kafka. Always test in a staging environment before deploying to production. Regular monitoring and maintenance are essential to ensure the pipeline's reliability and efficiency.