Phase 15Advanced16 of 18 in Database Design

Production Engineering: Observability, Performance, Failure, Capacity and Cost

The database metrics, logs and traces that matter; read and write performance engineering; a failure catalogue with detection and recovery for every component; capacity planning from QPS to IOPS; and database cost trade-offs.

Running a database is a discipline: measure it, know how it fails, plan its growth, and pay only for what the workload needs. This phase gives you checklists you can apply to any production database.

0/5 · 0%

5 topics ~42 min 7 code blocks & diagrams

Start with the first topic

15.1

Database Observability: Metrics, Logs and Traces

Watch four layers: workload (QPS, latency percentiles, errors per query shape), resources (CPU, memory, disk space, IOPS, throughput), database internals (connections and pool saturation, cache hit ratio, locks and deadlocks, replication lag, vacuum and xid age, checkpoints), and correlation (slow-query logs, plans, and traces linking requests to queries).

8 min 2 code practice

15.2

Performance Engineering: Writes, Reads and the Performance Lab

Improve performance in order of leverage: fix queries and indexes, fix schema and access patterns, tune pooling and memory, add caching and replicas, then partition or shard. For writes, batch and use COPY, size transactions, limit indexes, and move work async. For reads, use covering indexes, caches, read models and keyset pagination. Measure each change in a repeatable benchmark.

9 min 2 code practice

15.3

Database Failure Engineering and the Failure Lab

For every architecture, ask what happens when each part fails: primary, replica, replication, disk, connections, slow queries, lock contention, a shard, the cache, the network, data corruption, and a bad migration. For each, know the detection signal, the impact, the immediate mitigation, the recovery, and the prevention, and practise them deliberately in a failure lab.

9 min 1 code practice

15.4

Capacity Planning

Estimate reads, writes and transactions per second at peak; storage per day and per year including indexes, WAL and backups; memory for the working set; connections; IOPS and network. Then choose instance sizes, replicas and partitioning with headroom (target ~50–60% utilisation at peak) and a growth horizon of 12–18 months.

8 min 1 code practice

15.5

Database Cost Engineering

Database cost is compute, storage, IOPS, backups, replicas, cross-region and data-transfer charges, caches and monitoring. Reduce it by fixing queries before buying hardware, right-sizing, tiering cold data, using reserved or committed pricing, and choosing managed versus self-hosted deliberately.