Slow Queries Articles

The planner is only as good as its statistics. When autoanalyze lags behind a bulk load, the bad plans arrive long before the next analyze run does.

MySQL leans on the slow query log and performance_schema digests; PostgreSQL leans on pg_stat_statements and auto_explain. Here is how the workflows map, and where each side needs supplementing.

MySQL gives every connection a thread; PostgreSQL gives every connection a process. That one difference changes capacity planning, pooling strategy, and what breaks under transaction pooling.

Deep OFFSET pagination punishes InnoDB and PostgreSQL alike. Here is how keyset pagination works in each engine, including composite cursors, row-value comparisons, and covering-index tricks.

JIT compilation is supposed to make big PostgreSQL queries faster, but bad cost estimates fire it up on cheap ones. Here is how to spot the regression and switch it off safely.

The wrong-index ORDER BY LIMIT regression bites MySQL and PostgreSQL alike: the planner walks an ordering index expecting early matches that never come. Here is why LIMIT tempts both optimizers, and how to diagnose and fix it in each.

MySQL EXPLAIN and PostgreSQL EXPLAIN describe query plans in different vocabularies with different cost models. This guide translates between them and shows what each output quietly hides.

The planner assumes your columns are independent. When they're actually correlated, its row estimates collapse, it picks a nested loop over a hash join, and a fast query becomes a slow one. CREATE STATISTICS fixes the math.

Prepared statements reduce parse and planning overhead, but generic plans can become a production regression when tenant skew, partial indexes, and parameter-sensitive predicates arrive.

pg_stat_statements is where PostgreSQL performance work becomes concrete. The trick is ranking by impact, variance, I/O, and calls instead of staring at one dramatic slow query.