MongoDB for Web Apps: Indexes, Replication, Optimization

Problem: catalog with arbitrary attributes "breaks" the relational model

We set up MongoDB for an electronics online store: each category has a unique set of characteristics (diagonal, core count, memory size). In PostgreSQL we would need to build EAV or JSON fields — complex queries and performance degradation with 500 products. MongoDB solved this without pain: the schema is not fixed, and BSON documents fit the product structure perfectly. Our client — a store with 5000 products in 20 categories — got query response times under 10 ms without special optimization. We will tell you how to set up such a database turnkey.

Problems we solve

• N+1 queries when working with documents — a typical mistake when instead of nested arrays, related documents are fetched with separate queries. Solution: use $lookup in aggregation or store nested subarrays.
• Slow writes without replication — when a single node fails, data is lost for minutes. A Replica Set of three servers gives RPO=0 and automatic failover.
• Indexes do not cover queries — without partial and compound indexes, aggregations by status and date take seconds instead of milliseconds. With properly configured indexes, query performance improves 10–100 times.

How we set up MongoDB: Replica Set case

For the same online store, we deployed a cluster on three servers (MongoDB 7.0, Ubuntu 22.04). We configured a Replica Set with two regular nodes and one hidden node for backups. Application connection string: mongodb://myapp:password@mongo1:27017,mongo2:27017/myapp?replicaSet=rs0&readPreference=secondaryPreferred. The result — fault tolerance and read balancing to secondary nodes. A load of 10,000 requests per second is sustained without delays.

Installation and configuration

# Install MongoDB 7.0
curl -fsSL https://www.mongodb.org/static/pgp/server-7.0.asc | gpg --dearmor -o /usr/share/keyrings/mongodb-server-7.0.gpg
echo "deb [arch=amd64,arm64 signed-by=/usr/share/keyrings/mongodb-server-7.0.gpg] https://repo.mongodb.org/apt/ubuntu jammy/mongodb-org/7.0 multiverse" > /etc/apt/sources.list.d/mongodb-org-7.0.list
apt update && apt install -y mongodb-org
systemctl enable mongod && systemctl start mongod

# /etc/mongod.conf (main settings)
net:
  port: 27017
  bindIp: 127.0.0.1  # for production replace with internal IP
security:
  authorization: enabled
storage:
  dbPath: /var/lib/mongodb
  wiredTiger:
    engineConfig:
      cacheSizeGB: 2  # 50% RAM
replication:
  replSetName: "rs0"
operationProfiling:
  slowOpThresholdMs: 100
  mode: slowOp

Indexes for typical queries

// Create indexes immediately when designing the schema
// Unique index on email
db.users.createIndex({ email: 1 }, { unique: true, background: true })

// Compound for sorting user orders
db.orders.createIndex({ userId: 1, createdAt: -1 })

// Partial — only active sessions
db.sessions.createIndex(
  { userId: 1, expiresAt: 1 },
  { partialFilterExpression: { revokedAt: { $exists: false } } }
)

// TTL index — auto-delete logs after 30 days
db.logs.createIndex({ createdAt: 1 }, { expireAfterSeconds: 2592000 })

// Text search with Russian language
db.articles.createIndex({ title: "text", body: "text" }, { default_language: "russian" })

// Wildcard for catalog with arbitrary attributes
db.products.createIndex({ "attributes.$**": 1 })

Aggregation: revenue by category

db.orders.aggregate([
  {
    $match: {
      createdAt: { $gte: ISODate("2024-01-01"), $lt: ISODate("2024-04-01") },
      status: "paid"
    }
  },
  { $unwind: "$items" },
  {
    $lookup: {
      from: "products",
      localField: "items.productId",
      foreignField: "_id",
      as: "product"
    }
  },
  { $unwind: "$product" },
  {
    $group: {
      _id: "$product.category",
      revenue: { $sum: { $multiply: ["$items.price", "$items.quantity"] } },
      orders: { $addToSet: "$_id" }
    }
  },
  {
    $project: {
      category: "$_id",
      revenue: { $round: ["$revenue", 2] },
      orderCount: { $size: "$orders" }
    }
  },
  { $sort: { revenue: -1 } }
])

How to choose MongoDB topology for your project?

Why Replica Set is mandatory for production?

Replica Set is the minimum configuration for production. A single node does not provide fault tolerance: if the server fails, data is unavailable until restored. A Replica Set of three nodes guarantees automatic failover to a secondary node within seconds. RPO (recovery point) approaches zero. For most applications this is the optimal balance of reliability and cost. Contact us — we will audit your current schema and suggest the optimal topology.

Process

What is included

• Server and network configuration (authorization, TLS)
• Replica Set or sharded cluster setup
• Index creation for load (partial, TTL, text)
• Aggregation query optimization
• Integration with Mongoose/Node.js (schemas, hooks, virtuals)
• Monitoring setup (MongoDB Atlas, Prometheus + Grafana)
• Backup (mongodump + automation)
• Documentation and instructions for the team

Timeline and cost

Basic setup of a Replica Set with indexes and monitoring — from 2 to 5 days. The cost is calculated individually depending on the schema complexity and number of nodes. Contact us for a project estimate — we will prepare a commercial proposal within one business day. Order MongoDB setup from certified specialists with 10 years of experience.

Why order setup from us?

We are certified MongoDB specialists (over 10 years of experience in NoSQL administration). We have set up more than 50 clusters for projects with loads up to 100,000 requests per second. We provide a warranty on all work — from 3 to 12 months depending on SLA. Get a consultation right now — we will evaluate your current architecture for free.

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.