Database Performance Tuning and Scaling Questions

Approach summary
Design capacity planning as a repeatable lifecycle: measure → model → provision (headroom) → validate → operate. Prioritize minimal disruption for stateful shards with limited maintenance windows.

Workload modeling

• Collect metrics per-shard: QPS, read/write ratio, op latency histogram, item size, connection count, memory, IO, compaction/GC events.
• Build statistical models (p50/p95/p99) and peak profiles (daily, weekly, release-driven). Tag by tenant/team to predict growth.
• Example: model shard CPU = baseline + alpha * write_qps + beta * compaction_rate.

Headroom planning

• Set target utilization envelopes (e.g., CPU 50–60%, disk IOPS 60%, memory 70%) to allow burst/repairs.
• Reserve soft headroom per shard and cluster for maintenance and failover (e.g., +25% spare capacity).
• Translate to capacity units: shards-per-node, nodes-per-cluster, spare nodes.

Failover strategies

• Prefer stateful graceful failover: synchronous replication + leader election; use async replicas for read-only degrade.
• Automated failover with cooldowns and circuit-breakers; avoid domino re-sharding during failover.
• Maintain standby nodes with warmed caches for rapid promotion.

Safe shard-splitting & rebalancing

• Implement online split: split metadata, stream a copy to new shard, switch a small percentage of traffic, monitor, ramp.
• Use incremental rebalancing throttled by IOPS/replication lag; enforce safety checks (replication lag < threshold, CPU < threshold).
• Keep split operations in maintenance windows if heavy; otherwise use background throttling.

Runbooks for emergency scaling

• Quick steps: identify hot shard → enable traffic routing to replica(s) → spin prebuilt node image and attach as replica → promote if needed → trigger controlled split if sustained.
• Playbook commands, expected metrics, rollback steps, runbook owner and Slack/Oncall actions.

Validation: load tests & SLOs

• Synthetic soak tests per workload profile: ramp to p95 and p99 targets, include background tasks (compaction, backups).
• Chaos tests: kill nodes, introduce replication lag, network latency.
• Define SLOs (latency p95/p99, error rate, recovery time). Use load tests to validate headroom and failover within SLOs.

Metrics & automation

• Automate alerting for headroom breaches, automated scale-up when safe, and dashboards by tenant. Regularly review model vs. observed and iterate.