Apache Beam Unified Batch And Stream

Apache Beam Unified Batch And Stream

Overview

Use this skill when Apache Beam is the abstraction layer for both batch and streaming data processing. It helps agents preserve portability without hiding time semantics or runner-specific constraints.

When to Use

• building Apache Beam pipelines for batch, streaming, or unified workloads
• targeting multiple runners such as Dataflow, Flink, Spark, or Direct Runner
• sharing transform logic across batch and streaming modes
• managing windowing, watermarks, triggers, and late data handling
• designing portable pipelines that must run across environments

Do not use this when the workload is locked to a single runner and Beam portability is not a goal.

Workflow

1. Define the pipeline contract and time semantics. Include:

   • input PCollections and their bounded or unbounded nature
   • event-time versus processing-time expectations
   • output schema, grain, and freshness requirements
   • delivery guarantees expected by downstream consumers

2. Separate portable pipeline logic from runner-specific deployment.

   • keep transforms, DoFns, and combiners runner-agnostic
   • isolate runner configuration (parallelism, autoscaling, resource hints) into pipeline options
   • document which runners are supported and tested
   • avoid runner-specific APIs unless portability is explicitly sacrificed

3. Design windowing and trigger strategy explicitly. Account for:

   • fixed, sliding, session, or global windows
   • trigger behavior: when to emit, accumulate, or discard
   • allowed lateness and late data routing
   • watermark advancement assumptions per source

4. Handle state and side inputs carefully.

   • stateful DoFns bind to a specific key space — document key cardinality
   • side inputs can become bottlenecks at scale — prefer bounded and small
   • timers must account for watermark-driven versus processing-time semantics
   • state cleanup must be explicit for unbounded pipelines

5. Make testing and local validation part of the workflow.

   • use DirectRunner for correctness tests
   • validate windowing behavior with synthetic watermark progression
   • test exactly-once semantics through pipeline drains and restarts
   • confirm output idempotency for at-least-once runners

6. Plan deployment, scaling, and operational observability.

   • define autoscaling boundaries and cost limits per runner
   • expose transform-level metrics for latency, throughput, and backlog
   • plan drain and update strategies for streaming pipelines
   • document rollback: can the pipeline be redeployed at an earlier version safely?

Common Rationalizations

Red Flags

• pipeline runs on DirectRunner only and has never been tested on the production runner
• windowing strategy is undefined or uses global windows for unbounded sources
• no documentation of which runners are supported or tested
• stateful DoFns have no key cardinality analysis or state cleanup plan
• pipeline updates require full reprocessing because no drain or savepoint strategy exists
• side inputs are unbounded or grow without limit
• no metrics or observability beyond runner-provided defaults

Verification

• Pipeline contract defines inputs, outputs, time semantics, and delivery guarantees
• Windowing, triggers, and allowed lateness are explicitly designed and tested
• Runner-specific configuration is isolated from portable transform logic
• State and side inputs are bounded and have cleanup or eviction plans
• Pipeline has been tested on the target production runner, not just DirectRunner
• Drain, update, and rollback strategies are documented
• Operational metrics cover latency, throughput, backlog, and error rates per transform