Principle:Apache Paimon Multi Batch Data Writing
| Knowledge Sources | |
|---|---|
| Domains | Data_Lake, Columnar_Storage |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Mechanism for writing multiple batches of data to a Paimon table before performing a single atomic commit.
Description
Multi-batch writing allows accumulating data from multiple sources or time windows into a single atomic commit. Multiple calls to write_pandas() or write_arrow() buffer data in the file store writer, and a single prepare_commit() + commit() sequence makes all data visible atomically. This is useful for Lance-format tables where multiple batches produce multiple Lance files that are all committed together.
The write pipeline follows a three-phase pattern:
- Write phase: Multiple calls to write_pandas() or write_arrow() buffer data into the file store. Each call may produce one or more data files depending on the batch size and partitioning.
- Prepare phase: A single prepare_commit() call finalizes all buffered data files and produces a list of CommitMessage objects describing the changes.
- Commit phase: A single commit() call atomically publishes all changes as a new snapshot, making all data from all batches visible at once.
Usage
Use when ingesting data in multiple batches (e.g., from multiple DataFrame operations) that should be atomically visible as a single snapshot. Common scenarios include:
- ETL pipelines that process data in chunks from a source system
- Streaming micro-batches that accumulate over a time window before committing
- Multi-source ingestion where data from different sources should appear together
- Large dataset loading where memory constraints require processing data in smaller batches
Theoretical Basis
Batch accumulation follows the write-ahead pattern where multiple writes are buffered before a single commit. This amortizes the commit overhead and ensures atomic visibility of all data.
The atomicity guarantee means that readers either see all data from all batches or none of it. There is no intermediate state where only some batches are visible. This is achieved through Paimon's snapshot-based MVCC (Multi-Version Concurrency Control) mechanism, where a new snapshot is only created when the commit succeeds.
The write-ahead pattern also provides:
- Reduced commit overhead: A single metadata update instead of one per batch
- Consistent snapshots: All data appears in one snapshot rather than spread across multiple
- Failure atomicity: If any batch fails, no partial data is committed