Recomputing

Recomputing is the act of performing calculations again to produce current results, usually after inputs, parameters, or state have changed. It often serves as a straightforward approach when incremental updates are too complex or unreliable. Recomputing contrasts with incremental computation and memoization, which attempt to reuse prior work.

In software and algorithms, recomputation is common when dependencies are numerous or opaque; re-running from scratch guarantees consistency, at the cost of higher CPU time and latency. It is used in debugging, recomputing derived values after batch updates, and in simple pipelines where maintaining incremental correctness is impractical.

In databases and data processing, recomputation underpins on-demand query execution and maintenance of materialized views or caches. When data changes, systems may invalidate cached results and recompute answers for affected queries, trading additional compute for simpler correctness and reduced complexity of maintaining incremental updates.

In distributed and fault-tolerant systems, recomputation enables state recovery by re-executing deterministic computations from logs or checkpoints. Checkpointing and lineage tracking help bound recomputation cost, while parallelism can mitigate latency by distributing re-execution.

In practice, the choice between recomputing and preserving or updating existing results depends on factors like data size, update frequency, computational cost, and the risk of stale results. Recomputing remains a fundamental technique across domains, offering simplicity and reliability where incremental maintenance is challenging.