Reducing database calls is one of the most effective ways to improve the performance, scalability, and reliability of a web application. Every unnecessary round-trip to the database adds latency, consumes resources, and increases load — especially under high traffic.

In this post, we’ll explore practical strategies for reducing database calls, improving query efficiency, and designing applications that read and write data more intelligently.

Using JOINs

Multiple small queries can often be replaced with a single query using JOINs. Instead of fetching related data in separate requests, join tables at the database level and return only the fields you need.

Example problem:
Fetching a list of orders, then fetching the customer for each order → causes an N+1 query problem.

Solution:
Use a JOIN to fetch orders and customers in one query.

Guidelines

  • Only select required columns

  • Prefer indexed join keys

  • Avoid unnecessary nested joins

  • Consider database views for repeated join patterns

Cache Data That Doesn’t Change Often

If data is read frequently but changes rarely, cache it instead of hitting the database every time.

Common caching targets include:

  • configuration values

  • reference data (countries, currencies, roles)

  • user preferences

  • rendered HTML fragments

  • API responses

Tools & approaches

  • In-memory cache (in-process, LRU)

  • Distributed cache (Redis, Memcached)

  • HTTP cache headers

  • CDN for edge caching

Always define:

  • cache invalidation strategy

  • expiration policy (TTL)

  • cache key format

De-Normalise Data for Faster Reads

Normalisation is great for consistency — but not always for performance. For read-heavy applications, selective denormalisation can reduce joins and repeated lookups.

Examples:

  • store a user’s display name directly on related records

  • copy aggregated counts (likes, comments)

  • persist computed summaries

Use carefully

  • keep a single source of truth

  • update denormalised copies via background jobs or events

  • document relationships clearly

Pre-Compute Expensive Data with Background Workers

Some values are expensive to calculate in real-time — so compute them asynchronously.

Good candidates:

  • analytics & reports

  • recommendation scores

  • financial rollups

  • machine-learning features

Patterns

  • scheduled batch jobs

  • event-driven processing

  • materialised views

  • write-time computation

This shifts load away from request time, reducing latency and database pressure.

Batch Changes

Instead of performing many small writes, group them into batches.

Examples:

  • bulk insert/update instead of per-row queries

  • queue writes and flush periodically

  • debounce state updates from the UI

Benefits:

  • fewer network round-trips

  • better transaction efficiency

  • improved throughput

Be mindful of:

  • transaction size limits

  • failure + retry behaviour

Read Replicas

Read replicas allow you to scale read traffic without overloading the primary database.

Typical uses:

  • analytics queries

  • dashboards

  • heavy read endpoints

  • background tasks

Caveats

  • replicas are eventually consistent

  • design for stale-read tolerance

  • route writes to the primary only

Additional Strategies You Should Consider

Avoid the N+1 Query Problem

This often appears when ORMs lazily load relations.

Fixes

  • eager loading

  • query prefetching

  • dataloader / batching utilities

Add Proper Indexing

Indexes reduce the need for repeated scans and improve lookup performance.

Checklist:

  • index foreign keys

  • index frequently filtered columns

  • avoid over-indexing writes

Regularly review slow query logs.

Use Pagination and Result Limits

Never return unbounded datasets.

Good practices:

  • enforce default limits

  • cursor-based pagination for large tables

  • avoid deep offsets when possible

Optimise ORM Usage

ORMS are convenient — but can generate inefficient queries.

Watch for:

  • implicit queries inside loops

  • unnecessary field selection

  • unused relationships

Profile queries in development and staging.

Use Stored Procedures or Prepared Statements (Where Appropriate)

They can:

  • reduce repeated query parsing

  • optimise execution plans

  • encapsulate complex logic

Use judiciously to avoid coupling too tightly to a specific database.

Profile, Monitor, and Measure

You can’t optimise what you can’t see.

Use:

  • query profiling tools

  • APM monitoring

  • slow query logs

  • performance budgets

Review metrics regularly — especially after feature releases.

Summary

Reducing database calls is about more than just writing fewer queries — it’s about designing smarter data flows. Use JOINs when appropriate, cache read-heavy data, batch writes, and pre-compute expensive values. Scale reads with replicas, avoid N+1 queries, index effectively, and monitor performance continuously.

When done well, these techniques improve:

  • response times

  • system scalability

  • database longevity

  • user experience