Performance Considerations

Atlas Search runs a new process, called mongot, alongside the mongod process on each host in your Atlas cluster. mongot maintains all Atlas Search indexes on collections in your Atlas databases. The amount of CPU, memory, and disk resources mongot consumes depends on several factors, including your index configuration and the complexity of your queries.

Index Size and Configuration

When you create an Atlas Search index, the default configuration sets field mapping to dynamic, which means that all the data in your collection is actively added to your Atlas Search index. Other options such as enabling highlights can also result in your index taking up more disk space. You can reduce the size and performance footprint of your Atlas Search index by:

  • Specifying a custom index definition to narrow the amount and type of data that is indexed.
  • Setting the store option to false when specifying a string type in an index definition.

Creating and Updating an Atlas Search Index

Creating an Atlas Search index is resource-intensive. The performance of your Atlas cluster may be impacted while the index builds.

If you change the configuration of an Atlas Search index it must rebuild, which also consumes resources and may affect database performance.


Do not run queries against an Atlas Search index while it is building. Ensure that all nodes in your cluster have the Active status before running Atlas Search queries.

Query Operators and Query Complexity

The complexity level of Atlas Search queries and the type of operators used can affect database performance. Highly complex queries with multiple clauses are resource-intensive, as are queries which use the regex (regular expression) operator.

Atlas Search queries are ranked by score. Queries that return a large number of results are more computationally intensive because they must keep track of all the scores for the result set.

Performance Monitoring

You can monitor your Atlas cluster and view charts with performance statistics on the Atlas Metrics tab. These metrics can help you see how Atlas Search queries and index building affect your cluster’s performance.


If your cluster’s resources are stretched or near the limits of acceptable performance, consider upgrading to a larger cluster tier before implementing Atlas Search functionality.

$match Aggregation Stage Usage

Using a $match aggregation pipeline stage after a $search stage can drastically slow down query results. If possible, design your $search query so that all necessary filtering occurs in the $search stage to remove the need for a $match stage. The $compound Atlas Search operator is helpful for queries that require multiple filtering operations.