Full benchmark, execution stats, and reasoning here 👇 https://t.co/EFJImfD9Pn #MongoDB #DatabasePerformance #BackendEngineering

Takeaways: • ESR is a guideline, not a rule • Cardinality > theoretical order • Multi-key fields amplify scans • Always benchmark with real data

Putting a bounded, selective field early in the index reduces seeks drastically. ESR ignores: • cardinality differences • multi-key fan-out • $in behaving like range at scale

Quick math: • age=40 → ~25K docs • 1 departmentId → ~300 docs • 250 departmentIds → ~75K docs Index order decides when MongoDB can prune this search space.

Why ESR failed here 👇 • departmentId is multi-key (array) • salary has very high cardinality • $in with 250 values behaves like a range query This explodes index scans.

Surprise: The ESR-correct index performed the worst in terms of keys examined and execution time. The fastest index was: {age, departmentId, salary}

I tested 4 compound indexes: • {age, salary, departmentId} (ESR-correct) • {departmentId, age, salary} • {departmentId, salary, age} • {age, departmentId, salary} • {age, salary, departmentId} Same query. Same data. Different results.

Test setup: • 1M documents • age (41 values) • salary (~1M unique values) • departmentIds (array of 3, from 10K total) Query: age = 40 AND departmentId IN (250 ids) ORDER BY salary

MongoDB recommends ordering compound indexes as: 1️⃣ Equality 2️⃣ Sort 3️⃣ Range This usually works — but I wanted to test it with real data, not assumptions.

I benchmarked MongoDB’s ESR (Equality–Sort–Range) index rule on 1M documents — and found cases where following the guideline gives the worst performance. Cardinality, multi-key indexes, and $in queries change everything. 🧵👇

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