Batch processing

Reduces per-item overhead by amortizing setup, network, or transaction costs across many items in a single call.

Database access: inserting thousands of rows from a CSV import; API integration: syncing records to a remote service within rate limits; File processing: applying transformations to a large collection in memory-efficient chunks

Database systems (SQL bulk INSERT, MongoDB insertMany), REST APIs (batch endpoints), ORM libraries (Django bulk_create, SQLAlchemy bulk_insert_mappings), ETL pipelines, message queues

collect items into batch → execute single bulk operation → handle partial failures

• batch_size
• transaction
• chunk
• insertMany
• bulk_create
• batch endpoint
• rate limit
• partial failure

• Streaming/iterative processing — lower memory footprint but higher latency
• individual operations — simpler error handling but much slower at scale
• async parallel calls — faster than sequential but still incurs per-call overhead

Sending the entire dataset as one bulk call without chunking — causes memory exhaustion or request timeouts on huge inputs. Ignoring partial-failure semantics — bulk operations may succeed for some items and fail for others, requiring per-item error handling. Forgetting to disable per-row triggers or constraints — bulk inserts can be slowed to a crawl by row-level hooks. Treating bulk operations as fully atomic — many APIs only guarantee per-batch atomicity, not all-or-nothing across the whole dataset. Not tuning batch_size — too small wastes the overhead benefit; too large exceeds server limits.

Streaming — processes items continuously rather than in discrete batches; Transaction batching — groups operations for atomicity rather than performance; Pipeline parallelism — overlaps execution stages rather than grouping data

Coming from Python: may assume list comprehensions or map() are 'bulk' — true bulk operations push work to the data layer (DB, API) rather than iterating in application code. Coming from JavaScript: may use Promise.all on individual fetches — this is parallel, not bulk; a true bulk endpoint accepts an array in one request.

• batch processing
• transaction batching
• pipeline parallelism
• bulk API design

When NOT to use: when individual items require distinct validation or authorization that the bulk endpoint cannot enforce, or when failure isolation per item is critical. Performance: bulk operations can be 10–100x faster than individual calls but consume more memory per request and may lock resources longer. Boundary conditions: most bulk APIs enforce a maximum batch size (e.g., 1000 items) and may rate-limit batch throughput separately from individual calls.

Enables systems to handle millions of records within acceptable latency budgets and prevents request storms that would otherwise trigger rate limits or overload downstream services.

Bulk operations: like moving furniture with one truck instead of making a hundred trips with a shopping cart — same work, one trip, far less overhead.

Streaming bulk processing with backpressure (e.g., Kafka batches, PostgreSQL COPY with progress tracking, async pipeline batches)