Apache Paimon 1.3: 150x Faster Queries, Pure Python SDK, AI-Ready Lakehouse

Overview

Apache Paimon 1.3, released after three months and 500+ commits, delivers major breakthroughs in lakehouse fusion performance, multi-ecosystem compatibility, and AI scenario adaptation. The release targets three core pain points: slow queries on append-only tables, expensive wide-table updates, and fragmented ecosystem tooling.

Lakehouse Fusion Performance Revolution

1. Incremental Clustering: 150x Query Speedup, 20x Lower Write Cost

Traditional append tables (logs, behavior data) suffer from unorganized files, forcing full scans during queries. Paimon 1.3 introduces incremental clustering , which organizes data into a tiered layout:

Level-0 : Newly written data stays unsorted to preserve write throughput.

Level-1+ : Background asynchronous compaction sorts and archives data by clustering keys without blocking foreground writes.

Because only incremental changes are rewritten, write amplification drops dramatically. Benchmarks show append-table query efficiency improves up to 150x and write cost falls 20x . For log analysis and user-behavior tracking, queries that previously took 10 minutes now return in 3 seconds.

2. Data Evolution: Wide-Table Updates in Minutes, 99.4% Storage Reduction

Wide tables (hundreds of columns) typically require full-row rewrites for a single column change. Paimon 1.3 adds data evolution with two hidden columns: _ROW_ID (unique row identifier) and _SEQUENCE_NUMBER (version). Updates now write only changed columns, leaving original data untouched.

In a MERGE INTO test, runtime dropped from 27 minutes to 17 minutes, and storage shrank from 170 GB to 1 GB — a 99.4% reduction . This directly benefits financial risk control and precision marketing workloads.

Ecosystem Compatibility: Seamless Multi-Engine Access

1. PyPaimon Reconstruction: Pure Python SDK, 95x Faster Reads

The legacy Python SDK depended on a JVM, causing slow startup and poor performance for AI developers. Paimon 1.3 rewrites PyPaimon as a pure Python library:

No JVM dependency — lightweight startup.

Built on Apache Arrow for native read/write performance.

In a 100k-row, 25-column benchmark, read throughput is 95x faster than the old Python SDK and even 6x faster than the Java SDK . Pandas and NumPy can now consume Paimon data directly without format conversion.

2. Virtual File System (PVFS): Unified Permissions, Cross-Engine Access

When using a REST catalog, file paths are opaque UUIDs, making direct engine access (e.g., Spark reading CSV) difficult. PVFS exposes a virtual path scheme: pvfs://catalog/database/table/file.csv. It reuses Paimon's permission model, so a single ACL configuration governs access across Spark, Flink, Hive, and other engines.

Enterprises eliminate cross-system adaptation overhead: data lake files become directly readable by all engines with permissions configured once.

AI Scenario Adaptation: Multi-Modal Data Lake

1. Fast Column Updates: Doubling AI Labeling Efficiency

AI training pipelines frequently add or update label columns on multi-modal datasets (images, audio, video). Paimon 1.3 supports fast column addition (seconds to materialize) and partial column updates (only changed label data rewritten), doubling labeling throughput compared to full-table rewrites.

2. Vector Storage on the Roadmap: Native Multi-Modal Retrieval

The community is developing Blob storage (KB to GB unstructured objects) and vector storage . Future versions will store raw media files and their embedding vectors together in Paimon, enabling global-index-powered multi-modal retrieval in seconds.

Conclusion

Paimon 1.3 moves the project from "usable" to "production-grade, cost-efficient, and future-ready." Performance breakthroughs solve efficiency bottlenecks, ecosystem compatibility lowers migration friction, and AI-native features position the lakehouse for emerging generative AI workloads.