perrecord

Perrecord is a term used in computing to denote processing that operates on individual records within a dataset rather than on blocks of data or on whole files. Also referred to as per-record processing or row-level processing, perrecord describes design choices where each input item receives independent treatment during computation, transformation, or auditing. The term is not a formal standard, but appears in software design discussions, database documentation, and data pipelines to distinguish row-by-row work from batch-oriented approaches.

Contexts where perrecord processing is common include relational databases, where actions such as triggers, constraints, and row-level updates execute per row. In extract-transform-load pipelines, per-record transformations apply a function to each row in a stream or batch. In streaming and event-driven systems, each incoming event can be treated as a record and processed individually, enabling fine-grained error handling and parallelism. Some services also bill per record processed as part of pricing models.

Advantages of perrecord processing include isolation and straightforward auditing, since each record has a discrete processing path. It can improve correctness when records are independent and simplifies state management in stateless designs. However, it may incur higher overhead, reducing throughput for very large datasets due to numerous function calls, and can increase latency or complicate handling when records are interdependent.

Implementation considerations involve idempotency, ordering guarantees, and fault tolerance. In practice, per-record pipelines often rely on stateless processing with idempotent operations or on mechanisms to reconcile per-record outcomes in case of partial failures.

See also: row-level processing, per-batch processing, data pipeline design.