Understanding Batch Processing

Batch processing is a technique for processing large datasets by breaking them into smaller, manageable chunks (batches). This prevents memory exhaustion, improves error recovery, and enables progress tracking.

Why Use Batch Processing?

Memory Management

Loading millions of records into memory at once causes out-of-memory errors. Batching limits memory usage to a predictable amount.

• Prevents application crashes from memory exhaustion
• Allows processing datasets larger than available RAM
• Enables predictable memory consumption patterns

Better Error Handling

Processing in batches isolates errors to specific chunks:

• Easier to identify which records caused failures
• Can retry failed batches without reprocessing everything
• Reduces impact of transient errors

Progress Tracking

Batches provide natural checkpoints for monitoring:

• Display progress percentage to users
• Log progress for debugging and auditing
• Enable pause/resume functionality

Transaction Management

Smaller transactions improve database performance:

• Shorter lock durations reduce contention
• Smaller transaction logs
• Faster rollback on errors

Choosing the Right Batch Size

Too Small

Too Large

Just Right

Factors Affecting Batch Size

Available Memory

Primary constraint. Always leave headroom for:

• Operating system overhead
• Other processes sharing resources
• Temporary objects during processing
• Framework/library overhead

Record Size

Larger records mean fewer fit in memory:

• Simple records (100 bytes): Batches of 10,000+
• Medium records (1 KB): Batches of 1,000-5,000
• Large records (10+ KB): Batches of 100-1,000

Processing Complexity

• Simple transformations: Larger batches OK
• Complex computations: Smaller batches to prevent timeouts
• External API calls: Consider rate limits
• Database writes: Balance commit frequency with transaction size

Network and I/O

• Local processing: Can use larger batches
• Network transfers: Smaller batches reduce latency impact
• Cloud APIs: Check batch size limits in documentation

Implementation Best Practices

Dynamic Batch Sizing

Adjust batch size based on runtime conditions:

if memory_pressure_high:
    batch_size = batch_size // 2
elif memory_available and processing_fast:
    batch_size = min(batch_size * 1.5, max_batch_size)

Checkpoint and Resume

Track progress to enable resumption after failures:

• Save last processed record ID/offset
• Use idempotent operations (safe to retry)
• Implement proper rollback on batch failure

Parallel Processing

Process multiple batches concurrently:

• Use thread pools or process pools
• Ensure batch independence (no shared state)
• Monitor total memory across all workers
• Consider database connection pooling

Monitor and Tune

• Track memory usage per batch
• Measure processing time per batch
• Log batch sizes and performance metrics
• Adjust based on production data

Common Batch Sizes by Use Case

Use Case	Typical Batch Size	Rationale
Database inserts	1,000 - 10,000	Balance commit overhead with transaction size
API calls	100 - 1,000	Respect rate limits, handle failures
File processing	10,000 - 100,000	Minimize I/O overhead
Machine learning inference	32 - 256	GPU memory constraints, model size
Stream processing	100 - 1,000	Low latency requirements