In today’s fast paced data landscape, real time data capture and event streaming are crucial for building responsive applications and analytics platforms. Debezium, an open source distributed platform for change data capture (CDC), traditionally integrates with Apache Kafka to stream database changes. However, Apache Pulsar is emerging as a powerful alternative messaging system offering multi tenancy, geo replication, and flexible messaging models.

This post guides intermediate to advanced users through the technical nuances of integrating Debezium with Apache Pulsar, enabling efficient real time data capture across diverse messaging infrastructures. We will cover the architectural considerations, setup, and optimization strategies to maximize throughput and reliability.

Why Choose Apache Pulsar with Debezium

While Kafka remains the most popular choice for CDC pipelines, Pulsar offers unique advantages that make it attractive for modern data ecosystems:

  • Multi-Tenancy and Isolation: Pulsar supports namespaces and tenants enabling better logical separation for data streams.
  • Geo-Replication: Built-in geo replication with configurable consistency models allows global data distribution.
  • Topic Compaction and Retention: Pulsar’s advanced retention policies optimize storage for CDC use cases.
  • Flexible Messaging APIs: Pulsar supports both queue-based and stream-based messaging, enhancing integration possibilities.

Integrating Debezium with Pulsar provides a robust, scalable solution that leverages these features, especially for organizations seeking alternatives to Kafka or requiring multi region data pipelines.

Architectural Overview of Debezium with Pulsar

The core workflow involves Debezium connectors capturing changes from your source databases (e.g., MySQL, PostgreSQL, MongoDB) and publishing change events to Pulsar topics. Pulsar then acts as the messaging backbone, distributing these events to downstream consumers such as analytics engines, search indexes, or data lakes.

Key components include:

  • Debezium Connector: Responsible for CDC, monitoring database transaction logs and converting changes into event streams.
  • Pulsar Sink/Producer: Custom or community-supported connectors that push Debezium event streams into Pulsar topics.
  • Pulsar Topics and Subscriptions: Manage event storage and consumption with schemas and subscription models.
  • Consumers: Applications or microservices subscribing to Pulsar topics for real time data processing.

Setting Up Debezium with Apache Pulsar

  1. Prerequisites:
    • Running Pulsar cluster (standalone for testing or production multi-node cluster)
    • Debezium connectors configured for your database source
    • Pulsar client libraries compatible with your environment
  2. Configure Debezium to Output to Pulsar:

Debezium primarily supports Kafka out of the box, so integrating with Pulsar requires an adapter or configuring Pulsar’s Kafka compatibility layer. Two approaches:

  • Pulsar Kafka Protocol Handler: Pulsar provides a Kafka-on-Pulsar (KoP) protocol handler that lets Kafka clients (like Debezium) write to Pulsar topics transparently.
  • Custom Sink Connectors: Use or develop a Pulsar sink connector for Kafka Connect that routes Debezium CDC events into Pulsar topics directly.

Example configuration snippet for Debezium to connect via KoP:

{
  "name": "mysql-connector",
  "connector.class": "io.debezium.connector.mysql.MySqlConnector",
  "database.hostname": "dbhost",
  "database.port": "3306",
  "database.user": "debezium",
  "database.password": "dbpass",
  "database.server.id": "184054",
  "database.server.name": "mysqlserver",
  "database.history.kafka.bootstrap.servers": "pulsar-broker:9092",
  "database.history.kafka.topic": "schema-changes.mysqlserver",
  "key.converter": "org.apache.kafka.connect.json.JsonConverter",
  "value.converter": "org.apache.kafka.connect.json.JsonConverter",
  "key.converter.schemas.enable": false,
  "value.converter.schemas.enable": false
}

Here, the database.history.kafka.bootstrap.servers points to the Pulsar Kafka protocol handler endpoint.

  1. Create Pulsar Topics for CDC Events:

Use Pulsar CLI or admin API to create topics corresponding to Debezium event streams with appropriate retention and compaction policies:

pulsar-admin topics create persistent://public/default/mysqlserver.inventory.customers  
pulsar-admin topics set-retention --size -1 --time 72h persistent://public/default/mysqlserver.inventory.customers  
pulsar-admin topics set-compaction-frequency --frequency 1h persistent://public/default/mysqlserver.inventory.customers
  1. Consume Pulsar CDC Events:

Consumers can subscribe using Pulsar client APIs in Java, Python, or other supported languages. Use schema-aware consumers to deserialize Debezium events efficiently.

Optimizing Performance and Reliability

To ensure your CDC pipeline is performant and fault tolerant:

  • Tune Pulsar Broker and BookKeeper settings: Adjust bookie cache sizes and broker threading pools for high throughput.
  • Leverage Pulsar’s Message Deduplication: Prevent duplicate events during retries by enabling deduplication on relevant topics.
  • Configure Debezium Snapshot and CDC Modes: Balance initial snapshot size and incremental event streaming to minimize lag.
  • Monitor Lag and Metrics: Use Pulsar and Debezium metrics exposed via Prometheus or Grafana to proactively detect bottlenecks.
  • Implement Dead Letter Queues: Capture failed CDC events for reprocessing and troubleshooting.

Use Cases and Applications

Combining Debezium with Pulsar unlocks diverse real time data scenarios:

  • Event Driven Microservices: Trigger business workflows instantly on data changes.
  • Search Indexing Pipelines: Update Elasticsearch or Solr indexes in near real time.
  • Data Lake Ingestion: Stream changes into data lakes such as Apache Hudi or Delta Lake for analytics.
  • Multi Region Replication: Geo distribute data with Pulsar’s native replication.

Conclusion

Implementing Debezium with Apache Pulsar offers a powerful combination for real time change data capture across modern messaging systems. Leveraging Pulsar’s advanced features such as multi tenancy, geo replication, and flexible topic management enhances CDC pipelines beyond traditional Kafka setups. With proper configuration and optimization, this integration can scale to meet demanding enterprise data streaming needs. Start experimenting with Pulsar’s Kafka protocol handler today to unlock seamless CDC streaming with Debezium in your architecture.

Boost your data infrastructure’s responsiveness and scalability by adopting Debezium and Apache Pulsar together — the future-ready solution for real time event streaming.