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.
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
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
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
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.
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 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
This often appears when ORMs lazily load relations.
Fixes
eager loading
query prefetching
dataloader / batching utilities
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.
Never return unbounded datasets.
Good practices:
enforce default limits
cursor-based pagination for large tables
avoid deep offsets when possible
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.
They can:
reduce repeated query parsing
optimise execution plans
encapsulate complex logic
Use judiciously to avoid coupling too tightly to a specific database.
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.
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