Case Study: Reducing Query Costs 3x with Partial Indexes and Profiling on Mongoose.Cloud
A practical case study: profiling the top 200 queries, adding partial indexes, and introducing runtime guards cut query costs by 3x. Step-by-step actions, pitfalls, and how to measure impact.
Case Study: Reducing Query Costs 3x with Partial Indexes and Profiling on Mongoose.Cloud
Hook: Cost reductions are repeatable if you combine profiling, smart indexing and runtime guards. Here’s a field-tested playbook and the numbers that prove it.
Problem statement
A SaaS customer was facing runaway query costs from a mix of analytic background jobs and high-cardinality user queries. The goal: reduce daily query-related spend by 2–4x without losing feature fidelity.
Approach
- Profile the top 200 queries by cost using query sampling and trace attribution.
- Identify candidates for partial indexes and denormalized materialized views.
- Introduce runtime query guards and scheduled throttles for expensive background jobs.
For teams planning governance around expensive queries, pair this work with the practical guidance in Hands-on: Building a Cost-Aware Query Governance Plan.
Implementation details
- Used Mongoose’s index management API to create partial indexes that targeted active tenants only.
- Refactored heavy analytics into nightly aggregations and materialized them into region-local stores.
- Added CI checks to block merges that introduced full-collection scans without index ownership documentation.
Results
Within six weeks:
- Query-related spend decreased by 3x.
- P95 latency for customer-facing queries improved by 20–40%.
- Background job windows were cut in half, improving predictability.
Pitfalls and mitigations
Partial indexes introduce complexity in query planning. Ensure your QA environment mirrors production index layouts and use schema diffs in PRs. For teams looking to prototype safety checks, our earlier article on observability includes trace patterns to track index usage (Observability Patterns).
How to replicate in your org
- Start by ranking queries by cost (not just frequency).
- Use partial indexes for high-cardinality dimensions where applicable.
- Gate index changes behind documented ownership and CI preflight checks.
Further learning
Related Topics
Ava Martinez
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