Best practices for performance

1. Missing indexes

Problem:

// No index on 'email' field
db.users.find({ email: "user@example.com" })

Solution:

// Create index
db.users.createIndex({ email: 1 })

2. Inefficient regex queries

Problem:

// Case-insensitive regex without index
db.users.find({ name: /john/i })

Solution:

// Use text index or exact match
db.users.createIndex({ name: "text" })
db.users.find({ $text: { $search: "john" } })

3. Large skip() operations

Problem:

// Skipping thousands of documents
db.collection.find().skip(10000).limit(10)

Solution:

// Use range queries with indexed field
db.collection.find({ _id: { $gt: lastSeenId } }).limit(10)

4. Selecting unnecessary fields

Problem:

// Fetching entire documents
db.users.find({ status: "active" })

Solution:

// Use projection
db.users.find({ status: "active" }, { name: 1, email: 1 })

5. Inefficient aggregation pipelines

Problem:

// $match after $lookup
db.orders.aggregate([
  { $lookup: { ... } },
  { $match: { status: "completed" } }
])

Solution:

// $match first to reduce documents
db.orders.aggregate([
  { $match: { status: "completed" } },
  { $lookup: { ... } }
])

1. Unbounded arrays

Problem:

// Array grows indefinitely
{
  userId: 123,
  activities: [/* thousands of items */]
}

Solution:

// Use separate collection or bucketing
{
  userId: 123,
  month: "2024-01",
  activities: [/* limited items */]
}

2. Excessive embedding

Problem:

// Deeply nested documents
{
  user: {
    profile: {
      settings: {
        preferences: {
          // many levels deep
        }
      }
    }
  }
}

Solution:

// Flatten or use references
{
  userId: 123,
  profileId: 456
}

3. Large documents

Problem:

// Documents approaching 16MB limit
{
  data: "very large string...",
  attachments: [/* large binary data */]
}

Solution:

// Store large data separately (GridFS or object storage)
{
  dataRef: "s3://bucket/key",
  attachments: [{ ref: "gridfs://id" }]
}

1. Not using connection pooling

Problem:

// Creating new connection per request
app.get('/api/users', async (req, res) => {
  const client = await MongoClient.connect(uri);
  // ...
  await client.close();
});

Solution:

// Reuse connection pool
const client = new MongoClient(uri, { maxPoolSize: 50 });
await client.connect();

app.get('/api/users', async (req, res) => {
  const db = client.db();
  // ...
});

2. Not closing cursors

Problem:

// Cursor left open
const cursor = db.collection.find();
// Never closed

Solution:

// Always close cursors
const cursor = db.collection.find();
try {
  await cursor.forEach(doc => { /* process */ });
} finally {
  await cursor.close();
}

3. Too many connections

Problem:

// One connection per user session
const connections = new Map();
users.forEach(user => {
  connections.set(user.id, new MongoClient(uri));
});

Solution:

// Share connection pool across application
const client = new MongoClient(uri);
// All users share the same pool

1. Too many indexes

Problem:

// Index on every field
db.collection.createIndex({ field1: 1 })
db.collection.createIndex({ field2: 1 })
db.collection.createIndex({ field3: 1 })
// ... 20+ indexes

Impact: Slows down writes and increases storage.

Solution: Keep only necessary indexes and use compound indexes.

2. Wrong index order in compound indexes

Problem:

// Query: { status: "active", createdAt: { $gt: date } }
// Index: { createdAt: 1, status: 1 }  // Wrong order

Solution:

// Correct order: equality first, range second
db.collection.createIndex({ status: 1, createdAt: 1 })

3. Not using covered queries

Problem:

// Index exists but query not covered
db.users.createIndex({ email: 1 })
db.users.find({ email: "user@example.com" }, { name: 1, email: 1 })
// Still fetches documents

Solution:

// Include all projected fields in index
db.users.createIndex({ email: 1, name: 1 })
db.users.find({ email: "user@example.com" }, { name: 1, email: 1, _id: 0 })

CPU alert

Condition: CPU > 80% for 10 consecutive minutes
Action: Send notification to ops team
Escalation: Page on-call if > 90% for 15 minutes

Memory alert

Condition: Memory > 85% for 15 consecutive minutes
Action: Send notification to ops team
Escalation: Auto-scale if > 95% for 10 minutes

Replication lag alert

Condition: Lag > 10 seconds
Action: Send notification immediately
Escalation: Page on-call if > 60 seconds

Disk space alert

Condition: Disk > 80%
Action: Send notification to ops team
Escalation: Create incident if > 90%