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

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
java, python
Área
data-engineering, stream-processing

Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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.
Lenguaje dominante
Java
Estrellas
8.7k
Forks
4.8k
Merge medio
2 d 12 h
PR fusionados (30 d)
371

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