Query Performance

Analyze and optimize MongoDB query performance with MongoDash's performance analysis tools.

Query Execution Stats

Business

View detailed execution statistics for any query:

• Execution time - Total time in milliseconds
• Documents examined - Number of documents scanned
• Documents returned - Number of results
• Index used - Which index (if any) was used
• Stage details - Performance per pipeline stage

Explain Plan

View MongoDB's explain output:

1. Run a query
2. Click Explain button
3. Review execution plan:
   • Query planner analysis
   • Winning plan selection
   • Rejected plan alternatives
   • Index usage details

Understanding Explain Output

COLLSCAN - Full collection scan (slow on large collections) IXSCAN - Index scan (fast) FETCH - Document retrieval after index lookup SORT - In-memory sort (expensive without index)

Performance Metrics

Efficiency Ratio

Documents examined vs documents returned:

• Good - Ratio close to 1 (examining only needed documents)
• Poor - High ratio (scanning many unnecessary documents)

Execution Time

Benchmark query speed:

• < 10ms - Excellent
• 10-100ms - Good
• 100-1000ms - Acceptable for complex queries
• > 1000ms - Needs optimization

Index Recommendations

MongoDash suggests indexes based on query patterns:

Single Field Index

For simple equality queries:

{ "status": 1 }

Compound Index

For queries with multiple conditions:

{ "status": 1, "createdAt": -1 }

Text Index

For full-text search:

{ "description": "text", "title": "text" }

Optimization Strategies

1. Add Indexes

Create indexes on frequently queried fields:

• Equality filters
• Sort fields
• Range queries

2. Use Projections

Limit returned fields to reduce data transfer:

{
  "find": { "status": "active" },
  "projection": { "name": 1, "email": 1 }
}

3. Limit Results

Add limits to queries that don't need all documents:

{
  "find": { "status": "active" },
  "limit": 100
}

4. Filter Early in Pipelines

Put $match stages early in aggregation pipelines to reduce documents processed by later stages.

Query Monitoring

Enterprise

Monitor query performance across your workspace:

• Slow query log (queries > 100ms)
• Query frequency analysis
• Index usage statistics
• Performance trends over time

Performance Testing

Test query performance before deploying:

1. Run query on production-sized dataset
2. Review execution stats
3. Test with and without indexes
4. Verify performance meets requirements

What's Next?

• Query Builder
• Aggregation Pipelines