Dataset Scaling & Batching
Prevent large datasets from degrading performance by enforcing bounded pagination, streaming, and efficient batch processing.
Define your dataset scaling
patterns once, enforce them org-wide, always.
Standardize how large datasets are paginated, streamed, and batched to keep resource usage bounded and performance predictable as data grows.
Any code path that processes a dataset whose size can grow with customer or system data must remain bounded end-to-end. Pagination, streaming, and batching must be preserved across every layer so no downstream query, transformation, accumulator, or export step can rematerialize the full dataset in memory or a single request.
Enterprise scale coverage.
Enforced everywhere the code goes
Pull Request Checks
Runs on every push so violations of your patterns never reaches main.
Targeted Scans
Scans a repo, service or path on demand when auditing codebase.
Scheduled Scans
Runs a targeted scan automatically on a recurring schedule you define.
Large dataset pagination
and batch-sizing.
12 Guidelines · CCovering pagination, streaming, batch sizing, data bounds and more.
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