Optimize your MongoDB queries to ensure the best possible performance for your application. This can occur both during development and later, as your data usage and demand changes. As collections grow, a periodic review of query performance can help determine when clusters need to scale up or scale out.
Behavior
When you send a query to MongoDB, the server interprets the query, builds a query plan to determine how best to retrieve the data, executes the plan, then returns the results to the client.
To improve query performance, consider how MongoDB builds and executes the plan for your specific queries and identify problem areas, such as fields that would benefit from an index, inefficient filters, and issues at scale.
Indexes
Indexes store values from individual fields or sets of fields from a collection in a separate data structure. In read operations, they allow MongoDB to search in the index to identify relevant documents instead of the entire collection. In write operations, MongoDB must both write the change to the collection and update the index.
In many cases the use of indexes can speed up your query. This improvement depends on which fields are indexed and what your queries use for filters. For example, if a query applies no projections or filters, a collection scan can be more efficient than an index scan.
For more information, see Indexes.
Tasks
Troubleshooting
Solutions
Details
Atlas Query Profiler
The Atlas Query Profiler helps diagnose and monitor slow-running queries using log data from your cluster. It aggregates, filters, and visualizes performance statistics in a scatterplot chart through the Query Insights interface.
You may find this visualization more convenient in identifying slow queries and performance outliers than the Database Profiler.
With the Query Profiler, you get a macro view of your application load. This can help you identify which queries are slow and provide some insights into how you can improve their performance, such as:
How long the query takes to run
The number of documents examined by the query
Whether the query uses an index
For more information, see Monitor Query Performance with the Query Profiler.
Database Profiler
The database profiler collects detailed information about the
queries that run on MongoDB, which it stores in the
capped system.profile collection.
Important
The database profiler can affect database performance and disk usage. Consider the performance and storage implications before enabling it on a production deployment.
For more information, see Profiler Overhead.
With the database profiler, you get a micro view of slow queries in the current application load. This can provide some insights into how you can improve query performance, such as:
How long the query takes to run
The number of documents examined by the query
Whether the query uses an index
For more information, see Database Profiler Output.
Note
The database profiler is not available on mongos.
If you need to find slow queries on a sharded cluster, see
Monitor Current Operations.
Monitor Current Operations
The $currentOp aggregation stage returns information
on operations currently running on a MongoDB server or cluster.
$currentOp can help you identify performance issues, such as:
How long the query takes to run
Whether the query is waiting on a lock
Whether the query uses an index
For more information, see Monitor Slow Queries.
Explain
The explain() method retrieves query plan
information and execution statistics on queries and aggregation
pipelines. It is available for use with these query
methods:
The explain() method can help you to
identify potential issues with specific queries, such as:
Inefficient filters
Ineffective index usage
Potential performance issues at scale
For more information, see Explain Slow Queries.
Collection Stats
The $collStats aggregation stage provides
statistical information on specific collections and their usage.
You may find this useful in identifying index and performance
issues.
This can provide useful information, such as:
Storage usage for the collection and its indexes
The average size of documents
Index Stats
The $indexStats aggregation stage provides
statistical information on index usage by a collection. This can
help you identify specific index usage issues, such as:
Whether queries use the index and how often
Whether an index is redundant