Infrastructure & Operations

Database Design & Optimization

Data models, indexes and queries designed around how your application actually reads and writes. We build databases that stay fast, consistent and recoverable as your data grows.

Discuss your project How we work
psql
EXPLAIN ANALYZE SELECT id, total, created_at
FROM orders
WHERE customer_id = 4821
ORDER BY created_at DESC LIMIT 20;

Limit  (actual time=0.041..0.058 rows=20)
  -> Index Scan using orders_customer_created_idx
Planning Time: 0.121 ms
Execution Time: 0.079 ms

01 · Overview

The foundation everything else stands on

Most slow applications are slow in the database. Missing indexes, unbounded queries and data models that fight the access pattern add up quietly until they become incidents. We design schemas from the questions your application needs to answer, and enforce integrity with constraints rather than hope.

We specialise in PostgreSQL and also work with MySQL, MongoDB and Redis. For existing systems we start with measurement: query statistics, execution plans and lock analysis, so optimisation targets the real bottleneck instead of a guess.

Focus areas

  • Normalised schema design
  • Query performance tuning
  • Indexing and partitioning
  • Backup and recovery

Technologies

  • PostgreSQL
  • MySQL and MariaDB
  • MongoDB
  • Redis
  • pg_stat_statements
  • PgBouncer

02 · Capabilities

What we deliver

Design, tuning and care for the data your business depends on.

Data modelling

Entities, relationships and constraints designed from real access patterns and business rules.

Database architecture

The right mix of relational, document and cache stores, with replication and connection pooling where needed.

Query optimisation

Execution plans analysed, queries rewritten and N+1 patterns in ORMs removed, with before-and-after measurements.

Indexing strategy

Composite, partial and expression indexes justified by workload, and unused indexes removed to keep writes fast.

Migrations

Schema changes and data backfills that run safely on live systems, with tested rollback steps.

Data integrity and recovery

Constraints, point-in-time recovery, tested restores and row-level security where the data calls for it.

03 · Approach

Measure, model, then optimise

A data-first approach that grounds every change in evidence about your workload.

  1. 01

    Analysis and modelling

    We document data requirements and how the application uses them.

    • Data requirements
    • Entity relationship diagrams
    • Normalisation
    • Access patterns
  2. 02

    Schema and indexing

    Tables, constraints and indexes designed and reviewed together.

    • Table design
    • Constraints and foreign keys
    • Composite and partial indexes
    • Migration plan
  3. 03

    Performance and scaling

    Query tuning and capacity measures, introduced where data shows they help.

    • Query optimisation
    • Connection pooling
    • Read replicas
    • Partitioning where justified
  4. 04

    Security and resilience

    Protection and recovery built in and rehearsed.

    • Encryption at rest
    • Access control
    • Automated backups
    • Restore drills

04 · Technical considerations

The details that decide success

Database work rewards patience and punishes shortcuts. These are the details we watch.

Normalise first, denormalise deliberately
We start from a clean model and denormalise only where measurements show a clear benefit, documenting the trade-off.
The cost of every index
Each index speeds some reads and slows every write. We measure before adding one and remove those that are never used.
Locking during migrations
Large table changes are split into safe steps, such as building indexes concurrently, so production traffic is not blocked.
Connection management
Pooling with PgBouncer or an equivalent protects the database from connection storms during traffic spikes.
Backups you have tested
A backup only matters if it restores. We schedule restore drills and verify recovery objectives.
Planning for growth
Partitioning, archiving and retention policies keep large tables manageable as data accumulates.

05 · Outcomes

What you can expect

  • Faster queries with the evidence to prove it
  • Integrity enforced by the database itself
  • Schema changes without downtime
  • Backups that are tested, not assumed

06 · FAQ

Database Design & Optimization: common questions

Ask us something else
Should we use SQL or NoSQL?

It depends on your data and access patterns. Relational databases such as PostgreSQL suit structured data, complex queries and transactions. Document and key-value stores such as MongoDB and Redis suit flexible schemas, caching and very high write rates. Many systems use both.

How do you optimise slow queries?

We measure first with query statistics and EXPLAIN ANALYZE, then fix the cause: missing or unsuitable indexes, inefficient joins, N+1 query patterns or queries that fetch more than they need. Every change is confirmed with before-and-after measurements.

Do you handle database migrations?

Yes, from schema changes on live systems to moving between database engines or hosting providers. We plan cutovers carefully, validate data integrity and keep a rollback path until the migration is confirmed.

What about backups and recovery?

We configure automated backups with point-in-time recovery where supported, store copies off-site, and run documented restore drills so recovery is proven, not assumed.

Can you help our database scale?

Yes. Depending on the workload we use connection pooling, caching, read replicas, partitioning and vertical scaling, and add monitoring so decisions are based on real data.

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Make your data work faster

Share what is slow or what you are planning to build. We will start with measurements and a clear plan.

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