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Mastering MongoDB Indexes for 10M+ Document High-Throughput Queries

A practical deep dive into compound indexes, partial filters, and explain-plan analysis to slash query execution times from 850ms to 2ms.

Sep 8, 20268 min read2180 views145 likes
Mastering MongoDB Indexes for 10M+ Document High-Throughput Queries

Why Unindexed Queries Cripple Scaling Applications

As your database collections grow past millions of documents, COLLSCAN (collection scans) exhaust CPU and memory bandwidth.

The Anatomy of Compound Indexes (ESR Rule)

When designing compound indexes, always abide by the ESR Rule:

  1. Equality: Exact matches first
  2. Sort: Ordering criteria next
  3. Range: Range filters ($gt, $lt, $in) last
// Optimal compound index following ESR
schema.index({ status: 1, createdAt: -1, price: 1 });

Analyzing Query Plans

Always verify index utilization with .explain("executionStats"):

  • Target totalDocsExamined / nReturned = 1
  • Look for IXSCAN stages and confirm zero temporary in-memory sorts!
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