eCommerce database work for the hour your inventory gets hammered
eCommerce databases fail in a predictable place: a few hot rows during a sale. Everything else is throughput, and throughput is the easy part.
What changes here
Database Management in eCommerce is not the same engagement
Inventory contention handled properly
Hot-row locking on popular SKUs during a flash sale is the classic checkout killer. We fix it with the right locking strategy or a queue, not with a bigger instance.
Order growth planned for, not discovered
Partitioning and archival designed before the orders table is the reason every report is slow — with historical data still reachable when finance asks.
Reads moved off the write path
Catalog, search, and reporting served from replicas or a purpose-built store, so an analyst's query cannot compete with a checkout.
The failure mode worth designing against
The expensive eCommerce database failure is not slowness — it is an order taken and lost, or stock sold twice. Both are integrity problems that appear only under concurrency.
- Idempotent order writes, so a retried payment callback cannot duplicate an order
- Oversell prevention that holds under concurrent checkout, verified by load test
- Card data kept out of your database entirely, keeping PCI DSS scope small
- Recovery tested against the scenario that matters: a failure mid-sale, not overnight
The work itself
Full database management pagePerformance tuning and query optimization
Slow-query analysis, index and plan work, connection pooling, and caching — measured against your real workload rather than a synthetic benchmark.
Migrations, upgrades, and platform moves
Version upgrades, on-premise to cloud, and engine changes run with dual-write or replication, a verified row count, and a rollback path.
Schema design and data modelling
Normalization where integrity matters and denormalization where reads do, with constraints and migrations version-controlled alongside the application.
High availability and replication
Primary/replica topologies, automatic failover, and read scaling, with the failover path exercised rather than documented and forgotten.
Backup, retention, and tested restore
Point-in-time recovery, retention set against your obligations, and restore drills that produce an actual number for how long it takes.
Monitoring and managed DBA cover
Query, lock, replication-lag, and capacity alerting, with a named engineer for the escalations that arrive at 2am.
eCommerce questions we get asked
Something more specific? Send us the situation and we’ll answer it straight.