Precalculation
Precalculation is computing and optionally storing results before they are needed so later operations can reuse them without repeating the computation.
Latency
The calculation of results before they are needed so later operations can retrieve or reuse them without repeating expensive computation.
- How it is used
- Reduces latency by moving expensive computation out of latency-sensitive code paths.
- How deep it goes
- Develops build-time, startup, runtime-interval, lookup-table, stale-data, and incremental-update strategies.
- Examples
- Uses storage statistics involving filesystem or database queries, subscription quotas, discounts, forecasting, and periodic refreshes.
- What you can do after
- Apply it by choosing a precomputation time and serving stored results through a lookup instead of repeating expensive logic.
Machine Learning System Design
Precomputation performs a calculation before its result is needed so that later processing can reuse the stored result.
- How it is used
- Reduces feature-store read latency by calculating reusable features before dataset retrieval.
- How deep it goes
- Defines precomputation, then explains staged aggregation, reuse, overlapping windows, and batch/real-time serving tradeoffs.
- Examples
- Uses clicks, prices, revenue, item-day sums, and 7-, 14-, 30-, and 60-day aggregation windows.
- What you can do after
- Apply it via a feature store to reuse staged aggregates and combine them with real-time features.
Read how Machine Learning System Design explains precalculation →
Related concepts
- meanaddresses · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
- materialized viewimplemented by · A materialized view is a persistently stored result of a defining query that can be read without recomputing it and is refreshed or maintained from underlying data.
- Mapproduces · A map is an associative data structure that stores key-value pairs and supports retrieving values by key.
- incremental processingincludes · Incremental processing updates results or state by processing only inputs or tasks affected by changes, rather than recomputing everything.
- don't repeat yourself principleuses · The Don’t Repeat Yourself principle is a software design guideline that gives each piece of knowledge or behavior a single authoritative representation to avoid unnecessary duplication.
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