Apache Paimon for CDC: Low‑Cost, Low‑Latency Data Lake Ingestion and Performance Comparison with Hive and Hudi

Preface

Apache Paimon (incubating) is a streaming data‑lake storage technology that offers high‑throughput, low‑latency data ingestion, streaming subscription, and real‑time query capabilities. It is designed for CDC (Change Data Capture) scenarios, providing a simple, low‑cost, low‑latency one‑click solution for moving data from databases into a lake.

Why Move CDC from Hive to Paimon?

Traditional CDC to Hive involves full‑and‑incremental offline merges, which cause high latency, complex architecture, heavy storage and compute costs, and can impact the source database. Maintaining immutable views and handling large full‑table partitions are also problematic.

CDC to Paimon Architecture

Flink CDC writes directly to Paimon, automatically creating Tags that map to Hive partitions, preserving compatibility with existing Hive SQL. The workflow includes a single Flink job that handles both full and incremental data, eliminating the need for separate DataX or Sqoop processes.

Tag and Hive Compatibility

Each write generates an immutable snapshot (Tag). Tags can be auto‑created (e.g., daily) and mapped to Hive partitions via the metastore.tag-to-partition and metastore.tag-to-partition.preview settings, allowing Hive users to query data without noticing the underlying Paimon tables.

Cost Reduction

Thanks to LSM‑based file reuse, multiple Tags share the same bottom‑level files, dramatically reducing storage (e.g., 100‑day data requires only a few snapshots instead of 100 copies). Async compaction and configurable compaction triggers lower compute overhead.

Best Practices – Small Table Full‑Library Sync

parallelism.default: 16
jobmanager.memory.process.size: 4g
taskmanager.numberOfTaskSlots: 1
taskmanager.memory.process.size: 8g
execution.checkpointing.interval: 2min
execution.checkpointing.max-concurrent-checkpoints: 3
taskmanager.memory.managed.size: 1m
state.backend: rocksdb
state.backend.incremental: true
table.exec.sink.upsert-materialize: NONE

MySQL source, combined sink mode, tag auto‑creation, tag‑to‑partition mapping, and optional full‑async compaction settings are recommended.

Best Practices – Large Table

Use dynamic bucket mode ( bucket = -1) with snapshot management, tag auto‑creation, and optional async compaction based on resource availability.

Performance Comparison

Benchmarks on an Alibaba Cloud EMR 5.14.0 cluster (Paimon 0.6, Hudi 0.13.1, Flink 1.15) show:

For MOR (Merge‑On‑Read) writes, Paimon achieves ~4× higher throughput than Hudi, while Hudi suffers from poor query performance due to unmerged logs.

For COW (Copy‑On‑Write) writes, Paimon is >10× faster than Hudi, with significantly faster compaction.

These results indicate that replacing Hudi with Paimon can reduce resource usage to about one‑third while improving both write and read performance.

Conclusion

Apache Paimon provides a streamlined, low‑cost, low‑latency CDC ingestion pipeline, seamless Hive compatibility via Tags, and superior performance compared to traditional Hive and Hudi solutions, making it a compelling choice for modern data‑lake architectures.