MongoDb - Monitoring and Performance Tuning in MongoDB

In real-world systems, performance problems are common. If MongoDB is not monitored properly, you may face:

  • Slow queries

  • High CPU usage

  • Memory exhaustion

  • Disk bottlenecks

  • Application timeouts

Monitoring helps detect issues early. Performance tuning helps fix them.

1) Why Monitoring Is Important

Databases are stateful systems. Performance depends on:

  • Query patterns

  • Index design

  • RAM size

  • Disk speed

  • Network latency

Without monitoring:

  • You won’t know the root cause of slow performance.

2) Key Performance Metrics

You must track these:

CPU Usage

High CPU may indicate:

  • Heavy aggregation queries

  • Poor indexing

  • Large sorting operations

Memory Usage

MongoDB uses RAM heavily for caching.

If RAM is insufficient:

  • Disk I/O increases

  • Queries slow down

Disk I/O

Slow disks cause:

  • Write delays

  • Replication lag

  • Slow reads

Connections

Too many connections:

  • Can exhaust resources

  • May indicate connection leaks in application

Replication Lag

In replica sets:

  • Secondary nodes may fall behind

  • This affects read scaling and failover

3) Built-in Monitoring Tools

MongoDB provides command-line tools:

mongostat

Shows real-time statistics:

  • Insert rate

  • Query rate

  • Update rate

  • Memory usage

Useful for quick monitoring.

mongotop

Shows:

  • Which collections are using most read/write time

Helps identify hot collections.

4) Database Profiler

MongoDB has a built-in profiler.

It records:

  • Slow queries

  • Execution time

  • Query details

You can configure it to:

  • Log queries slower than a threshold (e.g., 100ms)

Very useful for debugging performance issues.

5) Explain Plan (Very Important)

When you run a query with explain(), MongoDB shows:

  • Whether an index was used

  • How many documents were scanned

  • Execution time details

Key terms:

COLLSCAN

Collection scan.
Means:

  • No index used

  • Entire collection scanned

This is slow for large collections.

IXSCAN

Index scan.
Means:

  • Index used

  • Much faster query

Understanding explain() is critical for interviews.

6) Index Optimization

Most performance problems come from bad indexing.

Important index types:

  • Single-field index

  • Compound index

  • Multikey index (for arrays)

  • Text index

  • TTL index

Common mistakes:

  • Creating too many indexes (slows writes)

  • Not matching index order with query pattern

  • Ignoring sort fields in compound index

Rule:
Design indexes based on actual query patterns, not assumptions.

7) Query Optimization

Avoid:

  • Unbounded $regex

  • Large skip() values

  • Sorting without index

  • Fetching unnecessary fields

Use:

  • Projection (select only needed fields)

  • Proper indexing

  • Pagination with range queries instead of skip()

8) Connection Pooling

Applications should use connection pooling.

Opening new database connections repeatedly:

  • Increases latency

  • Exhausts resources

All official drivers support connection pools.

9) Scaling Strategies

If tuning is not enough:

  • Vertical scaling (increase RAM/CPU)

  • Horizontal scaling (sharding)

Performance tuning comes before scaling.

10) Production Monitoring Tools

For enterprise setups:

  • Metrics dashboards

  • Alerting systems

  • Cloud monitoring tools

If using MongoDB Atlas, monitoring is built-in with graphs and alerts.

Interview-Level Understanding

You should clearly explain:

  • What COLLSCAN means

  • Why indexing is important

  • How explain() works

  • Why too many indexes are bad

  • What replication lag is

Monitoring and tuning knowledge separates beginners from production-level developers.