SLAs feel reassuring when signed—but their substance lies in what happens behind the scenes. Often, the most damaging breaches don’t stem from cloud outages or server failures, but from invisible issues hidden in how PostgreSQL was initially set up and configured. Increasingly sluggish queries, split-brain scenarios, silent backup failures, any of these can suddenly explode into customer-facing crises. 1. Slow Queries: The Sneaky SLA Saboteur The Hidden Cost of Delayed Queries A seemingly minor tuning oversight, like a missing index or outdated statistics, can turn a 200 ms query into a 10-second slog. It might not seem urgent initially, but as concurrency increases, cascading delays build up. A Slow Query Accelerated 1000× In one case study, an engineer faced a painfully slow query that scanned 50 million rows through a sequential scan—even though it was a simple query filtering on two columns (col_1, col_2) and selecting by id. After creating an index using those columns plus an INCLUDE (id) clause, the query performance improved dramatically: what had taken seconds dropped to just milliseconds, representing up to a 1,000× improvement in the worst-case runtime. [Ref: Learnings from a slow query analysis in PostgreSQL] This shows how even a simple query, if not indexed properly, can pose an SLA risk as data volume increases.