DataTerminology.comNEW
The practice of grouping multiple records or requests to process together for higher throughput and efficiency.
DESCRIPTION
Batching amortizes fixed costs by processing groups of items instead of one-by-one, improving throughput and resource utilization. It appears in data ingestion, API calls, messaging systems, and storage writes. Trade-offs include increased latency and potential memory pressure. Micro-batching extends batching to near-real-time systems by processing small windows rapidly. Correctness concerns include idempotency, ordering, and partial-failure handling. Choosing batch sizes requires balancing SLA latency, throughput targets, and downstream capacity.
ALTERNATIVE DEFINITIONS
Wikipedia
Batch processing is the running of a series of programs (jobs) on a computer without manual intervention.
Defines the broader paradigm; batching is the grouping mechanism enabling batch processing.
AWS
To send, receive, or delete multiple messages with a single action, use the AWS SDKs to take advantage of Amazon SQS batch API actions.
Highlights practical API-level batching for messaging efficiency.
Google Cloud
Publisher clients can batch messages to improve throughput by reducing the number of network requests.
Shows network overhead reduction as a key throughput benefit.
RELATED TERMS
Batch Processing Micro-batching Throughput Delays Backpressure Queue Bulk Load Vectorization Extract-Transform-Load
COMPETING TERMS
RELATIONS
- Broader: Batch Processing
KNOWLEDGE AREAS
Data Engineering Real-time & Streaming Data Software Engineering Platform & Tooling
TARGET AUDIENCE
Data Engineer Software Developer DevOps Engineer MLOps Engineer Application Administrator
EXTERNAL SOURCES
- Wikipedia - Batch processing
- Apache Spark - Micro-batch execution model
- Apache Kafka - Producer batching configuration
- Microsoft - Pattern for isolating workloads
TOP QUESTIONS
Why is batching important in data engineering?
Batching amortizes fixed costs, reduces network round-trips, and increases throughput. It’s essential for cost efficiency and meeting high-volume SLAs.
Category: Relevancy
What is the main trade-off of batching?
You typically trade higher throughput for increased latency. Selecting batch size balances latency budgets with system efficiency.
Category: Risks
How does micro-batching differ from traditional batching?
Micro-batching processes small groups in short, frequent intervals, enabling near-real-time processing while retaining batching’s efficiency benefits.
Category: Example
What are common mistakes when implementing batching?
Overly large batches cause timeouts or memory pressure; ignoring idempotency and partial failures can corrupt data or cause duplicates.
Category: Mistakes
How do I choose a batch size for an API?
Measure end-to-end latency, throughput, and error rates while respecting API limits. Incrementally tune to the point of diminishing returns.
Category: Requirements
How does batching interact with backpressure?
Bigger batches can exacerbate queues if consumers are slow. Use adaptive batching and flow control to maintain stability.
Category: Management
Is batching compatible with exactly-once processing?
Yes, with idempotent writes, deduplication, and transactional semantics. Systems like Kafka offer idempotent producers to help.
Category: Misunderstanding
What is a hot topic around batching today?
Adaptive batching and dynamic windowing in streaming systems optimize throughput under fluctuating load while meeting latency SLAs.
Category: Topic
How does batching improve database performance?
Batch inserts/updates reduce round-trips and enable set-oriented operations, leveraging indexes and transaction grouping for efficiency.
Category: Example