Batchers

A batcher is a component or role in manufacturing and computing that collects individual items, tasks, or messages and groups them into batches for processing, storage, or shipment. Batchers are used to improve efficiency, coordinate resource use, and control quality through standardized processing steps.

In manufacturing, batchers organize production runs into batches based on product type, process requirements, and quality checks. Batch sizes are chosen to balance throughput, equipment utilization, changeover costs, and inspection needs. Batch records track materials, processes, and outcomes to support traceability and compliance. Batch-based production can simplify scheduling and inventory control but may introduce setup times and potential excess work-in-progress.

In computing, batchers support batch processing by accumulating events, records, or messages and applying operations to the whole batch. This approach improves throughput and reduces per-item overhead but introduces latency. Common patterns include fixed-size batching, where a batch is processed after reaching a certain count, and time-based batching, where a batch is emitted after a time window. Adaptive batching adjusts batch size in response to throughput and latency targets. Many streaming and data-pipelines systems employ micro-batching, processing small batches at short intervals to approximate real-time results while retaining batch efficiency.

Key considerations for batchers include memory usage, processing latency, order preservation, and idempotence. Failure handling and retry semantics must ensure correctness when a batch partially fails. In machine learning, batchers determine minibatch size, influencing convergence speed and training stability.

See also: batching, batch processing, micro-batching.