MongoDB Tuning: WiredTiger, Indexes, Query Optimization

Imagine: a server with 32 GB RAM, MongoDB using 16 GB for the WiredTiger cache, but queries still take seconds. The cause — inefficient indexes and incorrect configuration. We've seen this many times: COLLSCAN instead of IXSCAN, cache eviction from disk pages, and aggregations consuming all memory. Optimizing MongoDB is a complex task involving WiredTiger engine tuning, index design, and slow query profiling. Without a systematic approach, even powerful servers run inefficiently. Our engineers hold MongoDB certifications and have 10+ years of experience in this field. In this article, we share practical techniques that helped our clients reduce response time by 50%. We'll cover common mistakes and how to fix them. Special attention goes to the ESR rule for indexes and WiredTiger cache tuning — these two areas yield the greatest impact.

Problems We Solve

Inefficient WiredTiger Cache Configuration

By default, MongoDB allocates 50% of RAM for the cache. On a server with 32 GB, that's 16 GB. But without explicitly setting cacheSizeGB, the cache can be evicted by other processes. We always set it manually, leaving headroom for the OS and disk cache.

# /etc/mongod.conf
storage:
  wiredTiger:
    engineConfig:
      cacheSizeGB: 12  # For a 32 GB server
      journalCompressor: snappy
    collectionConfig:
      blockCompressor: snappy
    indexConfig:
      prefixCompression: true

Monitoring: db.serverStatus().wiredTiger.cache. If pages evicted by application threads > 0, the cache is under pressure — increase cacheSizeGB.

Indexes: Missing or Wrong Order

Without an index, any query becomes a COLLSCAN. The key rule is ESR (Equality, Sort, Range). Here's how to build a compound index:

// Query: find active orders for a user, sorted by date
db.orders.find({ user_id: ObjectId("..."), status: "active" }).sort({ created_at: -1 })
// Correct index: equality → sort
db.orders.createIndex({ user_id: 1, status: 1, created_at: -1 })

Slow Aggregations with $lookup

The most common mistake is placing $match after $lookup. Optimal order:

db.orders.aggregate([
  { $match: { status: "completed", created_at: { $gte: ISODate("current year-01-01") } } },
  { $lookup: { from: "users", localField: "user_id", foreignField: "_id", as: "user",
      pipeline: [{ $match: { country: "RU" } }, { $project: { name: 1, email: 1 } }]
  }},
  { $project: { _id: 1, total: 1, "user.name": 1 } }
])

For parallel pipelines, use $facet.

WiredTiger Cache Tuning: Key Parameters

The key parameter is cacheSizeGB. Set it to 60–70% of available RAM, but not more than 20 GB on modern versions (according to MongoDB official documentation). Leave the rest to the OS and disk cache. For a 64 GB server, optimal cacheSizeGB is 40, but considering other processes, usually 35–40.

Indexes: Design and Maintenance

Use the ESR rule for compound indexes. Regularly check for unused indexes: db.aggregate([{ $indexStats: {} }]). Indexes with accesses.ops == 0 are dead weight — they should be dropped. This can save up to 20% of RAM.

When to Shard MongoDB?

Sharding is justified if data exceeds 200 GB or write load is above 10,000 RPS on a single server. Choose a shard key with high cardinality, e.g., a hash of user_id.

How We Do It: A Case Study

We optimized MongoDB for an e-commerce client — with a catalog of 5 million products. Query times for categories and prices were 2-3 seconds. Our solution:

• Compound indexes on (category, price, created_at).
• cacheSizeGB = 20 on a 64 GB server.
• Analytical queries routed to secondary (readPreference: secondaryPreferred).
• Replaced $lookup with post-filtering using pipeline.

Result: response time dropped to 50 ms, CPU load reduced by 30%. Our team guarantees that this indexing approach cuts response time by half compared to a typical configuration. For urgent cases, express diagnosis is available in 1 day — contact us.

Regular Check for Unused Indexes

Indexes with accesses.ops == 0 consume memory and slow down writes. Run $indexStats monthly and drop unused ones. This saves up to 20% of RAM on indexes. Correct cacheSizeGB gives a 40% speed improvement over default settings.

Process and Cost

1. Audit: profiler, explain, index analysis.
2. Design: calculate cacheSizeGB, indexes per ESR.
3. Implementation: configure, create/drop indexes, optimize queries.
4. Testing: load testing, metric comparison.
5. Deployment: apply changes, monitor.

Optimization timelines range from 3 to 10 business days. Cost is calculated individually after a free audit. Savings on server infrastructure can reach 30% due to reduced load. Our optimization pays for itself in 2-3 months.

What's Included in MongoDB Optimization

• Full audit of current configuration and performance.
• Index schema design aligned with business logic.
• WiredTiger tuning (cacheSizeGB, compression, journal).
• Slow query and aggregation optimization.
• Documentation of changes and operational recommendations.
• Team training and access handover.
• 30 days of post-deployment support.

Tuning Checklist

Comparison of Common Indexing Approaches

Example: Enabling Profiler

db.setProfilingLevel(1, { slowms: 50 });
db.getProfilingStatus();
db.system.profile.aggregate([
  { $group: { _id: "$ns", avgMillis: { $avg: "$millis" }, count: { $sum: 1 } } },
  { $sort: { avgMillis: -1 } },
  { $limit: 10 }
])

Order a MongoDB performance audit — get an engineer consultation and an optimization plan.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.