The difference between read committed, repeatable read, and serializable isolation in operational terms — and why most applications are running with weaker guarantees than engineers assume.
What CAP theorem actually says about distributed database tradeoffs, why the CP vs AP framing is more useful than the theory, and what it means for your system when the network fails.
The decision framework for choosing between a cache, a queue, and a database — including the failure modes that appear when engineers use the wrong one for the job.
How PostgreSQL estimates row counts, why those estimates are wrong for correlated columns and skewed distributions, and what engineers can do when the planner picks a bad plan.
Why a low-cardinality index is often worse than no index, how the query planner uses selectivity estimates, and when to build a partial index instead.
How to read PostgreSQL EXPLAIN output, what seq scan vs index scan actually means in practice, and the three numbers that matter most in any query plan.
Why PostgreSQL connections are expensive, what a connection pool actually does, and the difference between session mode, transaction mode, and statement mode in PgBouncer.
What replication lag actually measures in PostgreSQL, the three distinct lag components that most monitoring tools conflate, and which one matters for your RPO.
What a checkpoint actually does in PostgreSQL, why dirty page flush matters for recovery time, and what engineers should monitor to avoid checkpoint pressure.
The two mechanisms databases use to survive crashes — redo brings committed changes forward, undo rolls back uncommitted ones — and why the distinction matters operationally.
Why PostgreSQL and MySQL use B-trees while Cassandra and RocksDB use LSM trees — the read/write tradeoff that determines which storage engine fits your workload.
What write-ahead logging is, why every ACID database uses it, and what engineers need to know about LSN ordering, crash recovery, and replication lag.