I used to think more indexes always meant faster queries. Production taught me about write amplification and table bloat instead. We had three indexes that nothing queried, slowing every insert. EXPLAIN ANALYZE finally showed which plans actually used which indexes. After dropping the dead ones, writes got healthier without hurting reads. Now I review unused indexes in the same ritual as reviewing slow queries.
I used to think more indexes always meant faster queries. Production taught me about write amplification and table bloat instead. We had three indexes that nothing queried, slowing every insert. EXPLAIN ANALYZE finally showed which plans actually used which indexes. After dropping the dead ones, writes got healthier without hurting reads. Now I review unused indexes in the same ritual as reviewing slow queries.
We needed locks so queue workers did not process the same job twice. Redis SET NX was faster under load and easy to expire automatically. Postgres advisory locks were simpler operationally for our small team. Failover behavior mattered more than raw latency in our case. We chose Postgres first, then moved hot paths to Redis later. Pick the lock store you can operate confidently at 3am.
We needed locks so queue workers did not process the same job twice. Redis SET NX was faster under load and easy to expire automatically. Postgres advisory locks were simpler operationally for our small team. Failover behavior mattered more than raw latency in our case. We chose Postgres first, then moved hot paths to Redis later. Pick the lock store you can operate confidently at 3am.
I used to think more indexes always meant faster queries. Production taught me about write amplification and table bloat instead. We had three indexes that nothing queried, slowing every insert. EXPLAIN ANALYZE finally showed which plans actually used which indexes. After dropping the dead ones, writes got healthier without hurting reads. Now I review unused indexes in the same ritual as reviewing slow queries.
I used to think more indexes always meant faster queries. Production taught me about write amplification and table bloat instead. We had three indexes that nothing queried, slowing every insert. EXPLAIN ANALYZE finally showed which plans actually used which indexes. After dropping the dead ones, writes got healthier without hurting reads. Now I review unused indexes in the same ritual as reviewing slow queries.
I used to think more indexes always meant faster queries. Production taught me about write amplification and table bloat instead. We had three indexes that nothing queried, slowing every insert. EXPLAIN ANALYZE finally showed which plans actually used which indexes. After dropping the dead ones, writes got healthier without hurting reads. Now I review unused indexes in the same ritual as reviewing slow queries.
We needed locks so queue workers did not process the same job twice. Redis SET NX was faster under load and easy to expire automatically. Postgres advisory locks were simpler operationally for our small team. Failover behavior mattered more than raw latency in our case. We chose Postgres first, then moved hot paths to Redis later. Pick the lock store you can operate confidently at 3am.
We needed locks so queue workers did not process the same job twice. Redis SET NX was faster under load and easy to expire automatically. Postgres advisory locks were simpler operationally for our small team. Failover behavior mattered more than raw latency in our case. We chose Postgres first, then moved hot paths to Redis later. Pick the lock store you can operate confidently at 3am.
We needed locks so queue workers did not process the same job twice. Redis SET NX was faster under load and easy to expire automatically. Postgres advisory locks were simpler operationally for our small team. Failover behavior mattered more than raw latency in our case. We chose Postgres first, then moved hot paths to Redis later. Pick the lock store you can operate confidently at 3am.