How Apache Hudi & Pulsar Enable Real‑Time CDC Data Lake Ingestion

CDC Background Introduction

Change Data Capture (CDC) captures database changes and forwards them downstream for synchronization, distribution, ETL, and analytics. Two main CDC types exist: query‑based (SQL polling) and log‑based (binlog parsing), with log‑based being non‑intrusive but more complex.

Log‑based CDC is often implemented with tools like Debezium, Canal, or Maxwell, enabling ETL pipelines that feed change events into messaging systems.

CDC Data Lake Ingestion Methods

Typical CDC‑to‑lake pipelines ingest change streams into Kafka or Pulsar, then use Flink or Spark to write data into Apache Hudi tables. Real‑time streams parse binlog via Canal, write to Kafka, and sync hourly to Hive; offline jobs perform full loads to ensure completeness. Hudi provides transactional writes, MVCC, optimistic concurrency, small‑file management, and clustering for query optimization.

Optimizations include schema validation with automatic field补全, flexible primary‑key and partition mapping, automatic table discovery, and batch‑vs‑upsert decisions based on event types to improve performance by 30‑50%.

Hudi Core Design

Hudi is a streaming data‑lake platform supporting massive updates, table services (clean, archive, compaction, clustering), and integrates with storage systems like HDFS and cloud object stores. It uses a file‑slice architecture with base Parquet/ORC files and incremental log files, enabling efficient upserts and deletes.

Key components include file groups for reduced compaction overhead, Avro‑based schema evolution, primary‑key indexing for fast lookups, pluggable index types (Bloom filter, HBase), optimistic concurrency control, and a metadata table that accelerates file‑list queries and supports global indexing.

Hudi Future Planning

Upcoming work focuses on tighter Pulsar integration, DeltaStreamer enhancements, Spark SQL integration, support for ORC format, metadata‑table‑driven query optimization, DataSourceV2 migration, catalog integration, and advanced clustering and schema‑evolution features.