Precalculation

As explained in Machine Learning System Design

Precomputation performs a calculation before its result is needed so that later processing can reuse the stored result.

Precalculation moves feature computation ahead of dataset retrieval, so a feature store can reuse stored results instead of repeating expensive work. This matters when read latency is critical: batch features can be prepared after daily data processing, while the freshest events remain real-time inputs. section 11.4.2

A practical pattern is to:

• Preaggregate raw clicks, prices, or revenue into item-day sums. section 11.4.2
• Aggregate those intermediate results into reusable 7-, 14-, 30-, or 60-day windows. section 11.4.2
• Combine precomputed features with real-time features during reads when recent events are essential. section 11.4.2

Figure 11.14 A hierarchy of aggregating used in a feature store

Use the aggregation hierarchy to see how raw events become reusable intermediate and windowed feature results. section 11.4.2

How Machine Learning System Design covers it

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.

How it connects

Connects feature stores with feature engineering, batch processing, stream processing, and feature versioning.

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Also explained in

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  LatencyThe calculation of results before they are needed so later operations can retrieve or reuse them without repeating expensive computation.

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Related concepts

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• 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.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.