How do big tech companies resolve slow SQL queries on highly transactional databases?

All answers to this question.

When dealing with enterprise-scale transactional databases, the first line of defense is setting up continuous APM profiling to catch regressions early. We usually implement read-replicas to offload reporting workloads from the main transactional DB, ensuring heavy analytical queries don't lock critical tables. For write-heavy systems, we restructure indexes to eliminate full table scans and lean into composite keys. Sharding tables horizontally or shifting towards a distributed caching architecture like Redis helps bypass the database layer entirely for frequent, non-changing reads.

   Answered 2025-05-20 by Kimberly Reynolds

Have you checked if your transaction isolation levels are causing locking issues? Sometimes it isn’t the execution plan itself but data contention blocking transactions.

   Answered 2025-06-11 by Brian Fletcher

• Brian, you are spot on. Data contention is an hidden killer. We ran into an issue last quarter where long-running reads under repeatable read isolation were blocking our core application updates. Adjusting to read committed snapshot isolation completely unblocked the system. It reduced our transaction lock times by almost 80% without modifying a single line of our complex queries.

     Commented 2025-06-15 by Jeffrey Hayes

Using database execution plans is key. Analyzing the explicit costs of nested loops vs hash joins can pinpoint exactly where things break.

   Answered 2025-07-18 by Patrick Vance

• Exactly, Patrick! Relying purely on automated tools won't cut it. Manually analyzing EXPLAIN plans lets engineers spot missing composite indexes or improper join orders that automated optimizers miss completely.

     Commented 2025-07-22 by Andrew Vance