Database Optimization at Scale: From 2s to 20ms Query Times
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BackendApril 25, 202610 min read

Database Optimization at Scale: From 2s to 20ms Query Times

MV

Manthan Vaghasiya

Founder & Lead Architect

#PostgreSQL#Caching#Performance
Learn the indexing strategies, query planner tricks, and caching architectures we used to achieve sub-20ms response times on a 50M-row PostgreSQL database serving 100K RPM.

The Problem

Our client's dashboard was loading in 2+ seconds. The PostgreSQL database had grown to 50 million rows, and queries that were once fast had become painfully slow.

Root Cause Analysis

Using EXPLAIN ANALYZE, we identified three critical bottlenecks:

  • Missing composite indexes on frequently joined columns
  • Sequential scans on large tables due to outdated statistics
  • N+1 query patterns in the ORM layer

The Solution

  • Created targeted composite indexes reducing scan time by 95%
  • Implemented Redis caching layer for frequently accessed aggregations
  • Rewrote critical queries using CTEs and materialized views
  • Set up pg_stat_statements monitoring for ongoing optimization

Results

  • Query times dropped from 2s to 20ms (100x improvement)
  • Database CPU utilization reduced by 60%
  • Application could handle 4x more concurrent users
MV

Manthan Vaghasiya

Founder & Lead Architect