Building Real-Time Data Pipelines from PostgreSQL Using Flink CDC

Overview
In today's data-driven world, businesses require timely insights to make informed decisions. Traditional batch processing methods often fall short in providing real-time analytics. Enter Change Data Capture (CDC) with Apache Flink—a powerful combination that enables the continuous streaming of database changes. This article delves into building real-time data pipelines from PostgreSQL using Flink CDC, highlighting its advantages, challenges, and best practices.
We’ll capture inserts, updates, and deletes from PostgreSQL and stream them to a sink system like Kafka or it could be integrated with other services like OpenSearch or Delta Lakes

What Is Flink CDC?
Apache Flink is a distributed stream processing framework renowned for its scalability and fault tolerance. Flink CDC (Change Data Capture) extends Flink's capabilities by allowing it to capture and stream real-time changes from databases like PostgreSQL. This means any insert, update, or delete operation in the database can be immediately reflected in downstream systems, ensuring up-to-date data across applications.

Why Use Flink CDC with PostgreSQL?
PostgreSQL, a popular relational database, doesn't natively support real-time data streaming. Flink CDC bridges this gap by:
Real-time Data Streaming: Capturing database changes as they happen.

Fault Tolerant: Ensuring data consistency even in the event of failures.

Scalable: Handling large volumes of data efficiently.

Schema Evolution Handling: Adapting to changes in database schema without disrupting the pipeline.

Common Use Cases
Data Warehousing: Continuously syncing transactional data from PostgreSQL to data lakes or warehouses like Apache Iceberg or Hudi.

Microservices Communication: Propagating database changes to other microservices in real-time.

Analytics: Feeding real-time data into analytics platforms for up-to-date reporting.

Here is the high level Architecture flow :

Building a Real-Time Data Pipeline: A Step-by-Step Guide
Prerequisites
Docker / Docker Compose (recommended)

PostgreSQL 10+

Apache Flink 1.16+ or Flink SQL CLI

Optional: Kafka (for sink)

Java 8/11+ and Maven (if using Java API)

Step 1: Enable Logical Replication in PostgreSQL
Flink CDC requires logical replication to read changes from the WAL (Write-Ahead Log).
This is a crucial step, if you are using AWS - update the Parameter groups with the following settings.
1.1 Update postgresql.conf
ini
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wal_level = logical
max_replication_slots = 10
max_wal_senders = 10

1.2 Update pg_hba.conf
ini
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host replication cdc_user 0.0.0.0/0 md5

1.3 Create CDC User
sql
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CREATE ROLE cdc_user WITH REPLICATION LOGIN PASSWORD 'cdc_pass';
GRANT SELECT ON ALL TABLES IN SCHEMA public TO cdc_user;

Note: Restart PostgreSQL after configuration changes.

Step 2: Start Apache Flink and Optional Kafka via Docker
Create a docker-compose.yml with the following services:
PostgreSQL (with CDC enabled)

Apache Flink

Optional: Kafka + Zookeeper (if you want to use Kafka as a sink)

Here is the sample code:

version: '3.8'
services:
db:
image: postgres
restart: always
environment:
POSTGRES_USER: postgres
POSTGRES_PASSWORD: postgres
POSTGRES_DB: jsonforms
ports:
- '5432:5432'
volumes:
- pgdata:/var/lib/postgresql/data

backend:
build: ./backend
ports:
- '3000:3000'
depends_on:
- db
environment:
DB_HOST: db

frontend:
build: ./frontend
ports:
- '4200:80'
depends_on:
- backend

volumes:
pgdata:

Step 3: Create a PostgreSQL Table with Sample Data
sql
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CREATE TABLE customers (
id SERIAL PRIMARY KEY,
name VARCHAR(100),
email VARCHAR(100),
updated_at TIMESTAMP DEFAULT now()
);

INSERT INTO customers (name, email) VALUES
('Alice', '[e-mail]'),
('Bob', '[e-mail]');

Step 4: Add Flink CDC Connector to Your Flink Project
Option 1: Flink SQL
Download the Flink PostgreSQL CDC JAR from Ververica Maven repo and place it in Flink’s /lib directory. (Its already added as a mount to Docker Compose)
Option 2: Java (Maven Dependency)
xml
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com.ververica
flink-connector-postgres-cdc
3.0.1

🔧 Step 5: Define the PostgreSQL Source in Flink SQL
sql
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CREATE TABLE postgres_source (
id INT,
name STRING,
email STRING,
updated_at TIMESTAMP(3),
PRIMARY KEY (id) NOT ENFORCED
) WITH (
'connector' = 'postgres-cdc',
'hostname' = 'postgres',
'port' = '5432',
'username' = 'cdc_user',
'password' = 'cdc_pass',
'database-name' = 'mydb',
'schema-name' = 'public',
'table-name' = 'customers'
);

Step 6: Define the Sink
Option 1: Print to Console
sql
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CREATE TABLE print_sink (
id INT,
name STRING,
email STRING,
updated_at TIMESTAMP(3)
) WITH (
'connector' = 'print'
);

Option 2: Kafka Sink (Optional)
sql
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CREATE TABLE kafka_sink (
id INT,
name STRING,
email STRING,
updated_at TIMESTAMP(3)
) WITH (
'connector' = 'kafka',
'topic' = 'customer_changes',
'properties.bootstrap.servers' = 'kafka:9092',
'format' = 'json'
);

Step 7: Start the Flink Job
Run the SQL insert:
sql
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INSERT INTO print_sink
SELECT * FROM postgres_source;

Or to Kafka:
sql
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INSERT INTO kafka_sink
SELECT * FROM postgres_source;

🔁 Step 8: Test Real-Time Streaming
Insert a new row into PostgreSQL:
sql
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INSERT INTO customers (name, email) VALUES ('Charlie', '[e-mail]');

You should see the new change printed in Flink logs or appear in Kafka!

Optional: Flink CDC with Java DataStream API
If you want to build your own Java app:
java
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PostgreSQLSource source = PostgreSQLSource.builder()
.hostname("localhost")
.port(5432)
.username("cdc_user")
.password("cdc_pass")
.database("mydb")
.schemaList("public")
.tableList("public.customers")
.decodingPluginName("pgoutput")
.deserializer(new JsonDebeziumDeserializationSchema())
.build();

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

env.fromSource(source, WatermarkStrategy.noWatermarks(), "PostgresSource")
.print();

env.execute();

Challenges and Pain Points
While Flink CDC offers robust features, there are challenges to consider:
Schema Changes: Handling changes in the database schema (like adding or removing columns) can be complex. Flink CDC provides mechanisms to detect and adapt to these changes, but careful planning is required.

Latency: Ensuring low-latency data processing can be challenging, especially when dealing with large volumes of data.

Resource Management: Efficiently managing resources to handle high-throughput data streams without overloading the system.

Alternatives to Flink CDC
While Flink CDC is a powerful tool, it's essential to consider alternatives based on specific requirements:
Debezium: An open-source CDC platform that integrates with Kafka, suitable for microservices architectures.

Apache Kafka Connect: Provides connectors for various databases, including PostgreSQL, to stream data into Kafka topics.

AWS DMS (Database Migration Service): A managed service that supports real-time data replication across databases.

Each of these tools has its strengths and is better suited for different scenarios.
Best Practices
To ensure a successful implementation of Flink CDC with PostgreSQL:
Monitor Lag: Regularly monitor the lag between the source database and the Flink job to detect potential issues.

Handle Failures Gracefully: Implement retry mechanisms and ensure idempotent processing to handle transient failures.

Optimize Performance: Tune Flink's checkpointing and parallelism settings to balance performance and fault tolerance.

Real-World Use Cases
Sync PostgreSQL changes to a Kafka event bus

Feed real-time dashboards from operational data

Create CDC-based data lake ingestion to S3, Iceberg, or Delta Lake

Trigger downstream event-driven microservices

Summary
Building real-time data pipelines from PostgreSQL using Flink CDC enables businesses to process and analyze data as it arrives, leading to timely insights and informed decision-making. While there are challenges to consider, the benefits of real-time data streaming make it a compelling choice for modern data architectures.