Technology
Keep your databases fast, recoverable and upgradeable
PostgreSQL, MongoDB and Redis each degrade in their own way as load grows. We measure where yours are under strain, fix the causes in order of risk and leave runbooks your team can use.
- Duration
- 4 to 12 weeks
- Model
- Measure, fix and hand over
- Output
- Tuned data stores and tested runbooks
Signs you need this
Each of these can be fixed inside the engine you already run.
- A handful of queries in
pg_stat_statementstake most of the database time, and nobody has read their plans in months. - Autoscaling adds application pods, and PostgreSQL runs out of connections long before it runs out of CPU.
- Tables and indexes keep growing while row counts stay flat, and autovacuum never catches up on the largest tables.
- A routine schema migration waited for a lock and blocked every query that queued behind it.
- Failover has never been tested, or it was tested once and the application needed a restart to reconnect.
- One MongoDB shard takes most of the writes because the shard key grows with time, and queries without that key go to every shard.
- Redis started as a cache, now also holds sessions and background jobs, and a restart or an eviction quietly loses work.
Why the usual fixes do not last
Data stores rarely fail suddenly. They degrade: a query plan changes when a table grows, dead rows pile up faster than autovacuum removes them, a shard key that was fine at launch concentrates writes a year later, and a cache without an eviction policy fills up.
The usual responses buy time without removing the cause. A larger instance hides a missing index until the data doubles again. A cache in front of a slow query keeps the slow query and adds an invalidation problem. Replacing the engine moves the same access pattern to a system the team knows less well. We start from measurement instead: query plans, wait events, replication lag, evictions and key distribution.
This service works inside the engines you already run. Deciding which data lives where, or moving it between systems, belongs to Data Architecture and Migration; the two often run one after the other.
What is included, and what is not
Included
- PostgreSQL query plans, indexes and partitioning of the largest tables
- Autovacuum tuning, bloat control and transaction ID wraparound checks
- Connection pooling, for example with PgBouncer
- Replication, failover and restore tests with measured recovery times
- Schema changes and major version upgrades with minimal downtime and a rollback path
- MongoDB document model, indexes, shard key and the limits of multi-document transactions
- Redis cache patterns, invalidation, eviction policy, persistence and hot keys
Not included
- Deciding which data lives in which system across the estate
- Replacing a database engine without a measured reason
- BI, reporting and data warehouse design
- Day-to-day database administration and on-call after handover
How the work runs
- Weeks 1 to 2Measure the engines. With read-only access to query statistics, plans, replication status and memory metrics, we find the queries, tables and keys that carry most of the load.
- Week 3Order the fixes. Each finding gets a risk, an effort and a proposed change, from a single setting to a planned change such as partitioning or a new shard key.
- Week 4 onwardFix in reversible steps. Changes go through your normal pipeline one at a time, each with a measurement before and after and a way back. Write access granted for a stage is removed when the stage ends.
- ThroughoutRehearse failure. Failover, restore from backup and the loss of a cache node are tested with your team, and recovery times are measured rather than assumed.
- Final weekHand over. Dashboards, runbooks and decision records are walked through with the engineers who will own them.
What you get
All outputs belong to you, including the code and the source files of every diagram.
When this is not the right service
- Systems that are slow somewhere else. If the database is idle while requests queue, start with Performance and Scalability.
- Projects whose main question is the data model or a move to another platform; that is Data Architecture and Migration.
- Teams looking for a permanent database administrator or round-the-clock operations. We fix, document and hand over.
Frequently asked questions
Which versions and hosting options do you work with?
PostgreSQL, MongoDB and Redis, including Redis-compatible engines such as Valkey, self-hosted or on managed services such as Amazon RDS, Cloud SQL or MongoDB Atlas. Managed services change what you can tune, not how the engines behave. A major version near its end of support makes the upgrade plan part of the work.
Will you change production?
Only through your pipeline and your review. Access is read-only by default, and any write access is removed when the stage ends. Every change has a measured before and after and a way back.
How do you upgrade a major version without a long outage?
It depends on data size and acceptable downtime. For PostgreSQL, pg_upgrade is quick but needs a short outage; logical replication to a new cluster cuts the switch to a brief pause but needs more preparation. Either way it is rehearsed on a copy, query plans are compared and the rollback path is agreed first.
How much of our team’s time does it need?
Mostly the engineers who own the data layer and the busiest services. They join interviews in the first weeks and pair with us on each change, so the knowledge stays with them.
What happens after the engagement?
Your team owns the changes, dashboards and runbooks. Many continue alone; others keep an embedded architect 2 to 3 days a week for the next stage.
Start with a short technical conversation
Thirty minutes. You describe the system and where the data layer hurts, and we tell you where we would start.