Performance Considerations¶

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.

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.

$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.

$sort Aggregation Stage Usage¶

Using a $sort aggregation pipeline stage after a $search stage can drastically slow down query results. If possible, design your $search query so that all necessary sorting occurs in the $search stage to remove the need for a $sort stage. In general, the Atlas Search $compound operator is helpful for queries that require multiple sorting operations. To sort documents based on a numeric, date, or geo field, consider using the Atlas Search $near operator.