⏱️Micro-batching

NEW

Aggregating small time-sliced batches of events to approximate real-time processing with simplified execution semantics.

DESCRIPTION

Micro-batching groups incoming events into small, discrete batches processed at short intervals, striking a balance between latency and throughput. Frameworks like Spark Structured Streaming implement micro-batch triggers to schedule computation. This approach leverages batch execution engines while delivering near-real-time results. It simplifies fault tolerance and state management compared to pure record-at-a-time streaming. Trade-offs include added latency relative to continuous processing and trigger tuning complexity.

ALTERNATIVE DEFINITIONS

Apache Spark

In micro-batch mode, the engine processes data streams in small batches at a user-specified trigger interval.

Clarifies scheduling and user control over batch trigger intervals.

Source

Databricks

In the micro-batch processing mode, Spark Structured Streaming processes data streams as a series of small batch jobs.

Emphasizes batch job execution for streaming workloads.

Source

RELATED TERMS

Streaming Spark Structured Streaming Trigger Interval Delays Exactly-Once Processing Backpressure Checkpointing Windowing Event Time

COMPETING TERMS

Continuous Processing Batch Processing

KNOWLEDGE AREAS

Real-time & Streaming Data Data Engineering Data Architecture Platform & Tooling

TARGET AUDIENCE

Data Engineer MLOps Engineer AI Engineer Software Developer Solution Architect

EXTERNAL SOURCES

• Wikipedia - Stream processing
• Apache Spark - Trigger configuration for micro-batches
• Apache Flink - Mini-batch concept in Flink table API

TOP QUESTIONS

When should I use Micro-batching?

Use it when you need near-real-time results but prefer batch engine reliability and simpler fault tolerance.

Category: Example

How do I choose the trigger interval for Micro-batching?

Balance latency and throughput; start with seconds, measure end-to-end SLAs, and tune based on backpressure and costs.

Category: Requirements

What are common mistakes with Micro-batching?

Setting overly aggressive triggers, ignoring checkpointing, and not accounting for late data can cause instability and inaccuracies.

Category: Mistakes

What risks come with Micro-batching?

Too-small batches increase overhead and costs; too-large batches raise latency and state spikes during catch-up.

Category: Risks

How does Micro-batching compare to continuous processing?

Micro-batching adds small latency but simplifies execution; continuous mode minimizes latency with more complex runtime semantics.

Category: Alternatives

Does Micro-batching support exactly-once semantics?

Yes, with checkpointing and idempotent sinks, most frameworks can achieve effectively exactly-once outcomes.

Category: Requirements

Is Micro-batching suitable for ML feature pipelines?

Yes, it provides predictable cadence for feature computation and materialization with manageable latency.

Category: Example

How does Micro-batching affect costs?

Frequent triggers may increase compute overhead; right-sizing batch intervals and autoscaling helps control spend.

Category: Opinion

What is a hot topic around Micro-batching?

Unifying micro-batch and streaming semantics in declarative APIs is an active area.

Category: Topic