Understanding High CPU Usage and Slow Queries in MongoDB

High CPU usage and slow queries in MongoDB occur due to inefficient indexing, excessive aggregation operations, long-running queries, and improper resource allocation.

Root Causes

1. Missing or Inefficient Indexes

Queries without indexes require full collection scans:

# Example: Check if a query is using an index
EXPLAIN("executionStats") db.users.find({ email: "This email address is being protected from spambots. You need JavaScript enabled to view it." })

2. Expensive Aggregation Pipelines

Complex aggregations increase CPU load:

# Example: Aggregation with multiple stages
EXPLAIN("executionStats") db.orders.aggregate([
  { "$match": { "status": "completed" } },
  { "$group": { "_id": "$customerId", "total": { "$sum": "$amount" } } }
])

3. Large Data Transfers and Unbounded Queries

Fetching too much data strains CPU and memory:

# Example: Query fetching all records without pagination
EXPLAIN("executionStats") db.logs.find({}).sort({ timestamp: -1 })

4. Frequent Updates to Large Documents

Modifying large documents causes excessive I/O:

# Example: Updating a large document
UPDATE users SET profileData = { huge JSON object } WHERE userId = "123"

5. Inefficient Sharding and Replication

Poorly distributed shards overload specific nodes:

# Example: Check shard key distribution
sh.status()

Step-by-Step Diagnosis

To diagnose high CPU usage and slow queries in MongoDB, follow these steps:

1. Identify Slow Queries: Analyze slow query logs:

# Example: View slow queries
use admin;
db.system.profile.find({ millis: { $gt: 1000 } }).sort({ ts: -1 })

2. Check Query Execution Plans: Determine if indexes are used:

# Example: Explain query execution
EXPLAIN("executionStats") db.orders.find({ customerId: "123" })

3. Monitor CPU Usage: Track CPU consumption by MongoDB:

# Example: Check MongoDB CPU usage
mongostat --host mongodb://myserver

4. Analyze Index Usage: Identify inefficient indexes:

# Example: List all indexes
use mydatabase;
db.users.getIndexes()

5. Optimize Sharding: Ensure balanced workload distribution:

# Example: Check shard distribution
sh.status()

Solutions and Best Practices

1. Create and Optimize Indexes

Ensure queries use indexes to avoid full scans:

# Example: Create a compound index
CREATE INDEX idx_user_email ON users(email)

2. Optimize Aggregation Pipelines

Reduce the number of aggregation stages:

# Example: Streamline aggregation
EXPLAIN("executionStats") db.orders.aggregate([
  { "$match": { "status": "completed" } },
  { "$group": { "_id": "$customerId", "total": { "$sum": "$amount" } } }
])

3. Implement Query Pagination

Use limit() and skip() to reduce load:

# Example: Paginate large queries
EXPLAIN("executionStats") db.logs.find({}).sort({ timestamp: -1 }).limit(100)

4. Optimize Document Updates

Minimize updates to large documents:

# Example: Use partial updates
UPDATE users SET profileData.address = "New Address" WHERE userId = "123"

5. Balance Sharding and Replication

Ensure shards are evenly distributed:

# Example: Migrate imbalanced chunks
sh.moveChunk("mydb.users", { "_id": ObjectId("123") }, "shard002")

Conclusion

High CPU usage and slow query performance in MongoDB can severely impact database efficiency. By optimizing indexes, streamlining aggregations, implementing pagination, managing document updates, and properly balancing sharding, developers can significantly improve MongoDB performance.

FAQs

• Why is MongoDB using high CPU? High CPU usage occurs due to inefficient queries, missing indexes, or heavy aggregation operations.
• How do I reduce MongoDB query time? Use indexed queries, optimize aggregation pipelines, and limit query result sets.
• Why are my MongoDB queries slow? Queries can be slow due to full collection scans, large result sets, or frequent updates to large documents.
• How can I monitor MongoDB performance? Use mongostat, explain(), and db.system.profile to analyze query execution and CPU usage.
• What is the best way to optimize MongoDB indexes? Create compound indexes based on query patterns and remove unused indexes to reduce storage overhead.