Atlas Search Query Performance

Consider 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) or the wildcard operator.

Use $search Instead of $text or $regex

For applications that rely heavily on MongoDB $text and $regex queries, use the following recommendations to determine whether to refactor your applications or migrate your applications to Atlas Search $search. The $search aggregation pipeline stage provides features that are either not available through the MongoDB operators or are available through the MongoDB operators but not as performant as Atlas Search $search.

The following table shows how MongoDB $regex, $text, and Atlas Search $search address your application's requirements.

Use $limit Before $facet

Using a $limit aggregation pipeline stage after a $facet aggregation pipeline stage might negatively impact query performance. To avoid performance bottlenecks, use $limit before $facet.

{
  {
    "$search": {...}
  },
  { "$limit": 20 },
  {
    "$facet": {
      "results": [],
      "totalCount": $$SEARCH_META
    }
  }
}

For a demonstration, see the following examples:

• Metadata and Search Results Example

• Facet Example

Avoid $match After $search

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 Atlas Search compound operator helps with queries that require multiple filtering operations. If you must use the $match stage in your aggregation pipeline, consider using the storedSource option to store only the fields that your $match condition needs. You can then use the returnStoredSource option to retrieve stored fields.

Avoid $group After $search

Using a $group aggregation pipeline stage after a $search stage can slow down query results. If you use $group to get basic counts for field aggregations, we recommend that you use facet inside the $searchMeta stage instead.

Avoid $count After $search

Using a $count aggregation pipeline stage after a $search stage can slow down query results. If you use $count to get a count of the number of documents, we recommend that you use count inside the $search or $searchMeta stage instead.

Avoid $sort After $search

Using a $sort aggregation pipeline stage after a $search stage can drastically slow down query results.

• For sorting numeric, date, or string fields, use the sort option with the $search stage. To learn more, see Sort Atlas Search Results.

• For sorting geo fields, use the near operator.

• To sort other fields, use $sort and returnStoredSource fields.

Limit Use of $skip and $limit After $search

Using $skip and $limit to retrieve results non-sequentially might be slow if the results for your query are large. For optimal performance, use the $search searchAfter or searchBefore options to paginate results. To learn more, see Paginate the Results.

To return non-sequential results, such as jumping from page 2 to page 5, you can use the following pipeline stages:

• $search searchAfter the last result on Page 2

• $skip documents on Pages 3 and 4

• $limit results for Page 5

Here, your query is optimized to skip only 2 pages of results, instead of skipping 4 pages if you didn't use searchAfter. For a demonstration of this, see How to Paginate the Query Results.

Monitor Performance

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. To learn more, see Review Atlas Search Metrics.

Atlas might trigger some Atlas alerts when:

• Atlas Search queries your clusters, which can impact Atlas performance metrics, such as the query targeting metrics.

  The change streams cursors that the Atlas Search process (mongot) uses to keep Atlas Search indexes updated can contribute to the query targeting ratio and trigger query targeting alerts if the ratio is high.

• Atlas Search replicates data from MongoDB, which contributes to the metrics measured in Atlas, such as the number of getmore operations.

Continue Learning

Follow along with this video to learn how to understand, iterate, and improve your Atlas Search results using explain and $search score details.

Duration: 5 Minutes