MongoDB Performance & Scale Readiness Checklist
A checklist by engineers, for engineers, designed to help you scale smart
As your user base scales, so do the demands on your database. This checklist helps engineering teams evaluate their MongoDB architecture across key areas— so you can spot performance issues before they become production problems.
Use this checklist to:
- Identify scale blockers early
- Avoid costly architectural rewrites
- Prepare your environment for what’s next
Section 1: Schema Design & Data Modeling
Every scalable MongoDB deployment starts with smart modeling.
- Are you following the “Data that is accessed together should be stored together” principle?
- Tip: If multiple queries are needed to build a single view, consider the Extended Reference Pattern to reduce lookups.
- Do your documents stay under 16MB, even as data grows?
- Tip: Estimate future document size based on projected growth over the next two years. For time-series data, use MongoDB’s native time series collections.
- Are you avoiding unbounded arrays?
- Tip: Unbounded arrays can hurt performance. Use the Subset Pattern for items like comments or activity logs. Try to keep arrays under 100 documents when possible.
- Have you denormalized data for read-heavy workloads?
- Tip: Duplicating data to avoid expensive $lookup operations is acceptable with the right consistency strategies. Consider using transactions, the Computed Pattern for pre-calculated values, or the Extended Reference Pattern for partial duplication.
- Are field names optimized for scale?
- Tip: Short field names (e.g., “fn” instead of “firstName”) can reduce storage by up to 30% in large collections. Use a mapping layer in your app to keep code readable while storing efficiently.
Section 2: Query Performance & Optimization
Fast queries = happy users.
- Do all production queries use indexes?
- Tip: Run explain(“executionStats”) on your queries. If you see “COLLSCAN”, an index is missing.
- Are your indexes following the ESR rule (Equality, Sort, Range)?
- Tip: The order of fields in compound indexes matters—equality fields come first, then sort, then range. Take a look to ESR.
- Have you enabled query profiling to catch slow queries?
- Tip: Set profiling level to catch queries over 100ms with db.setProfilingLevel(1, { slowms: 100 })
- Are you removing unused indexes?
- Tip: Use $indexStats to find and remove zero-use indexes, which add unnecessary write overhead.
- Do you have a pagination strategy for large result sets?
- Tip: Skip the skip(). Use range queries on indexed fields for efficient pagination and lower system load.
Section 3: Infrastructure & Cluster Health
Infrastructure reliability = system stability.
- Is your replica set configured with at least three members?
- Tip: Use an odd number for proper elections and distribute across availability time zones.
- Are you monitoring key metrics and setting alerts?
- Tip: Watch CPU, memory, disk I/O, and replication lag. Trigger alerts before hitting 80% thresholds.
- Are you using WiredTiger cache appropriately?
- Tip: Default is 50% of RAM minus 1GB. Tune based on workload using cache sizing guidelines.
- Have you tested backup & recovery procedures?
- Tip: Schedule quarterly recovery drills—untested backups are unreliable backups.
- Is your oplog sized to handle 24–48 hours of operations?
- Tip: Monitor your oplog window to avoid replication lag during spikes.
Section 4: Scalability Planning
Future-proofing requires planning ahead.
- Do you have a sharding strategy for large data sets?
- Tip: Choosing the right shard key upfront is critical- changing it later is difficult. Review shard key selection.
- Have you load tested with 10x your current traffic?
- Tip: Use tools like mongoperf to simulate real workload patterns.
- Is your connection pooling configured correctly?
- Tip: Avoid creating new connections per operation. Tune your pool size based on concurrency needs.
- Do you have a plan for data archival?
- Tip: Archive old data with TTL indexes or cold storage to keep your system lean and performant.
How to Interpret Your Results
- ✔️ All boxes checked → Excellent scale readiness
- ✔️ 80–99% → Strong foundation, with room to optimize
- ✔️ 60–79% → Moderate risk—address key areas before scaling
- ❌ < 60% → High risk—act now to avoid system strain
Build with Confindence
This checklist is just the beginning. Optimizing MongoDB can get complex, but you don’t have to do it alone. Our MongoDB Health Check & Scale Review delivers:
- A deep dive into your current environment
- A tailored optimization roadmap
- Implementation of proven best practices
- Expert guidance from our MongoDB Special Ops team to future-proof your stack
Let’s make sure your foundation scales with you.