nw-query-optimization

Cardinality estimation uses histograms and density vectors to help cost-based optimizers choose efficient execution plans. Maintaining accurate statistics prevents stale data from degrading query performance.

NoSQL query optimization indexing strategies include B-Tree, Hash, compound, and covering indexes. Selecting the right index type based on query patterns reduces latency and improves data retrieval performance.

Join algorithm selection depends on data size and available indexes. Hash Joins suit large unindexed datasets, Nested Loops work best for small indexed datasets, and Merge Joins require sorted inputs.

Cost-based optimization chooses execution plans using database statistics. Stale data degrades performance by causing the optimizer to select inefficient join strategies and indexing paths based on outdated cardinality estimates.

Covering indexes apply to both SQL and NoSQL environments to reduce query latency. By including all required columns in the index, the database engine avoids accessing the underlying table data entirely.