[Feature Request]: [Iceberg/Managed] Continuous unbounded streaming support in beam.managed.Write(ICEBERG) (Append & CDC)

オープン
  • 何か問題がありますか？
#41,226 コメント 0 件 リアクション 0 件 担当者 0 名 GitHub で見る

メンテナーはふだん 1 日以内に返信

まだ誰も着手していません。

評価

難易度
5/5
見積もり時間
1週間以上
初心者へのやさしさ
25/100
issue の種類
機能追加
明瞭さ
おおむね明確
活発さ
活発
技術スタック
java, python
領域
data-engineering, stream-processing

調査の方向性

Start by reading the implementation of beam.managed.Write for Iceberg and the existing managed write tests; the issue does not name specific files. Trace how bounded writes build and commit Iceberg snapshots, then check streaming runner checkpoint and watermark integration. Done means unbounded writes support append and CDC modes with periodic or checkpoint-aligned commits and watermark metadata.

索引モデルが issue の本文から書いたものです。

説明

Description

Currently, beam.managed.Write(beam.managed.ICEBERG) is designed primarily for bounded (batch) PCollections. When writing unbounded streaming data (e.g., Kafka event logs, Pub/Sub streams, or continuous CDC streams from Debezium/Spanner), Beam lacks native streaming sink support to coordinate periodic snapshot commits against Iceberg catalogs (Google Cloud BigLake REST Catalog, Apache Polaris, AWS Glue).

Because Iceberg query engines (BigQuery, Trino, Spark) only read committed table snapshots, continuous streaming pipelines need native checkpoint-aligned snapshot commits so data becomes queryable with low latency.

Proposed Architecture

Extend beam.managed.Write(beam.managed.ICEBERG) to natively accept unbounded PCollections and manage streaming snapshot commits:

beam.managed.Write(
    beam.managed.ICEBERG,
    config={
        "table": "my_catalog.db.orders",
        "warehouse": "gs://my-bucket/lakehouse/wh",
        "commit_interval": "60s",      # maximum time between snapshot commits
        "mode": "append",              # "append" for event logs, or "merge-on-read" for CDC
        "catalog_properties": {
            "type": "rest",
            "uri": "https://biglake.googleapis.com/iceberg/v1/restcatalog",
        }
    }
)
Key Capabilities
  1. Unbounded Streaming Ingestion: Native execution on streaming runners (Dataflow Streaming Engine with Windmill, FlinkRunner).
  2. Checkpoint-Aligned Snapshot Commits: Flushes data files and writes atomic Iceberg snapshots coordinated with runner checkpoints or periodic commit intervals.
  3. Dual Ingestion Semantics:
    • Append-only Streams: High-throughput Parquet DataFile appends (Kafka, Pub/Sub).
    • CDC Streams (Merge-on-Read): Automatically generates Iceberg v2 Equality Delete files alongside DataFiles for row-level UPSERT and DELETE operations.
  4. Watermark Propagation: Records the pipeline's event-time watermark directly into the Iceberg snapshot summary metadata (snapshot.summary['beam.watermark']), giving downstream readers a verified consistency bound.
主要言語
Java
スター
8.7k
フォーク
4.8k
平均マージ
2日 12時間
マージ済み PR（30日）
371

環境構築

はじめの一歩

  1. issue を最後まで読み、次にプロジェクトのコントリビューションガイドを読みます。
  2. 着手することを issue にコメントします — 二人が同じ作業をするのを防げます。
  3. リポジトリをフォークし、ブランチを切って変更します。
  4. issue 番号を参照したプルリクエストを送ります。

apache/beam のほかの issue

apache/beam の issue をすべて見る

似ている issue

Java の issue をもっと見る

新しい issue をメールで受け取る

初心者向けの GitHub issue を短くまとめたダイジェスト。