Choose Table Partitioning: Range, List, Hash or Unpartitioned

An index can reduce scanned data for suitable queries, but adds write, maintenance and storage costs. Evaluate column order, selectivity, filters and sorting together. Slow queries are not always caused by missing indexes: estimates, locks, storage latency or excessive data transfer may dominate. Inspect representative execution plans and actual row counts. Measure read improvement alongside write overhead, and avoid uncontrolled production maintenance.

Raw capacity is not the capacity available to applications. RAID or erasure coding, filesystems, reserved space, metadata, snapshots and growth headroom are separate deductions. TB and TiB representations also change the displayed number. Write calculations with units, establish protected usable capacity, then subtract operating reserves. Track growth rate as well as current utilization. The projected exhaustion date should leave enough time to procure and deploy additional capacity.

Latency is the time between starting an operation and receiving its result. An average can hide a small number of very slow operations; medians and p95/p99 percentiles answer different questions. Network RTT, storage waits, processor queues and application processing contribute to end-to-end time. State whether measurements come from the client or server. Check whether increases coincide with traffic growth, maintenance or capacity limits. Record normal and peak-hour baselines before selecting an alert threshold.

Retention defines which recovery points are kept and for how long. Daily, weekly and monthly points do not represent identical change patterns; full-copy creation and chain dependencies affect physical capacity. Retention decisions combine business requirements, applicable obligations and technical capacity. Longer retention does not automatically provide better recovery: the right point must be discoverable and readable. When changing a policy, test whether existing points are deleted immediately or handled differently by the product.

A transaction groups operations that an application treats as jointly successful or failed. Database atomicity does not automatically include email delivery or changes in other services. Define the commit point and retry behaviour. Evaluate idempotency to prevent processing the same request twice. Test connection loss before and after commit separately: a client-side error does not always mean the transaction failed.

Installability does not prove production support. Review the compatibility chain across OS, application, drivers, extensions and management tools. Update plans should record version, support end, restart needs and rollback methods. An unrepresentative test environment can produce misleading results. Validate service health and existing workflows after a change, not just version numbers. Remember that pinning a version can also prevent future security fixes.