Data pruning is the process of excluding irrelevant data when processing database queries in order to minimize the amount of data read, processed, and transferred. Databases employ techniques to analyze queries and safely eliminate portions of datasets, partitions, indexes, and rows that cannot influence the query result.
This reduces disk I/O, memory, CPU costs and network traffic - enabling faster processing for complex analytical queries over large datasets. Advanced cost-based optimizers automatically determine what data can be pruned.
Some pruning techniques include partition elimination, row-level security filtering, and using collision-resistant hash functions for efficient filtering with Count-Min Sketches. Intelligent data pruning is key for performant analytics.
Common data pruning techniques include:
Database statistics about data distribution, indexes, and constraints enable identifying pruning opportunities during query optimization.
Data pruning provides major performance gains for analytical workloads, especially in massively parallel processing systems. Eliminating irrelevant data portions directly reduces IO, memory, CPU costs - letting queries run faster.
Pruning enables scaling to larger data volumes by minimizing what data is processed. In fasting growing datasets, pruning often makes the difference between feasible and infeasible queries.
Data pruning provides the largest gains for:
Some challenges around extensive pruning:
Some advanced techniques include:
A Count Min Sketch is a probabilistic data structure used to estimate item frequencies and counts in data streams.
Read more ->Collision resistance is the property of cryptographic hash functions to minimize chances of different inputs mapping to the same output hash, making it difficult to intentionally cause collisions.
Read more ->Hash functions are algorithms that map data of arbitrary size to fixed-size values called hashes in a deterministic, one-way manner for purposes like data integrity and database lookup.
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