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Centricone Technologies
Database Management foreCommerce

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.

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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.

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  • 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 page

Performance 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.

Because contention is a function of concurrency, not of volume. Queries that are fine at ten a second serialize at a thousand, usually on inventory or on a sequence. It shows up nowhere except under real concurrency, which is why load testing against the checkout path is the only reliable way to find it beforehand.
Yes — with the right locking strategy, reservation model, or queue, chosen against your catalog shape. The naive approaches either oversell or serialize the entire checkout, and both are avoidable.
Usually, once the table starts dominating your working set. Partitioning by date and moving cold periods to cheaper storage keeps operational queries fast while leaving history reachable for finance and support.
A search engine, once the catalogue is more than small. Databases do exact matching well and relevance ranking, typo tolerance, and faceting poorly, and the queries you write to compensate are the ones that collapse under load during a sale. Keeping search off the transactional database also removes it as a failure mode at peak.
With one authoritative stock figure and a deliberate decision about how stale each channel is allowed to be. Overselling almost always traces to two systems each believing they own the number. Reserving stock at the point of commitment rather than at payment is usually the change that fixes it.
Yes, and the reliable way is to stop reporting from running against the store's database. A replica or a warehouse gives merchandisers the freedom to run heavy queries without a finance report being able to affect checkout — which is the risk every time both live on the same instance.

Keep reading

Database Management

The full capability page — what we build, how we run it, the stack, and how an engagement works.

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eCommerce

Everything we do for eCommerce teams, not only database management.

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