Docs Menu
Docs Home
/
MongoDB Ops Manager
/ /

Monitor and Improve Slow Queries

On this page

  • Common Reasons for Slow Queries
  • Index Considerations
  • Access Performance Advisor
  • Index Suggestions
  • Create Suggested Indexes

The Performance Advisor monitors any operation with a query predicate that MongoDB considers slow and suggests new indexes to improve query performance. For the selected host and time period, the Performance Advisor evaluates up to the 20,000 most recent slow queries found in the logs.

Recommended indexes are accompanied by sample queries, grouped by query shape, that were run against a collection that would benefit from the suggested index. The Performance Advisor does not negatively affect the performance of your Ops Manager clusters.

Note

If a query is slow, common reasons include:

  • The query is unsupported by your current indexes.

  • Some documents in your collection have large array fields that are costly to search and index.

  • One query retrieves information from multiple collections with $lookup.

Indexes improve read performance, but a large number of indexes can negatively impact write performance since indexes must be updated during writes. If your collection already has several indexes, consider this tradeoff of read and write performance when deciding whether to create new indexes. Examine whether a query for such a collection can be modified to take advantage of existing indexes, as well as whether a query occurs often enough to justify the cost of a new index.

The Performance Advisor can help identify and remove unnecessary indexes. To learn more, see Review Drop Index Recommendations.

To access the Performance Advisor:

1
2

If the replica set resides in a sharded cluster, first click the sharded cluster containing the replica set.

3
4
5

The Performance Advisor displays up to 20 query shapes across all collections in the cluster and suggested indexes for those shapes. The Performance Advisor ranks the indexes according to their Impact, which indicates High or Medium based on the total wasted bytes read. To learn more about index ranking, see Review Index Ranking.

Note

If the slow query log contains consecutive $match stages in the aggregation pipeline, the two stages can coalesce into the first $match stage and result in a single $match stage. As a result, the query shape in the Performance Advisor might differ from the actual query you ran.

The Performance Advisor ranks the indexes that it suggests according to their Impact, which indicates High or Medium based on the total wasted bytes read. To learn more about how the Performance Advisor ranks indexes, see Review Index Ranking.

To learn how to create indexes that the Performance Advisor suggests, see Create Suggested Indexes.

Each index that the Performance Advisor suggests contains the following metrics. These metrics apply specifically to queries which would be improved by the index:

Metric
Description
Execution Count
Number of queries executed per hour which would be improved.
Average Execution Time
Current average execution time in milliseconds for affected queries.
Average Query Targeting
Average number of documents read per document returned by affected queries. A higher query targeting score indicates a greater degree of inefficiency. For more information on query targeting, see Query Targeting.
In Memory Sort
Current number of affected queries per hour that needed to be sorted in memory.
Average Docs Scanned
Average number of documents scanned.
Average Docs Returned
Average number of documents returned.
Avgerage Object Size
Average object size.

For each suggested index, the Performance Advisor shows the most commonly executed query shapes that the index would improve. For each query shape, the Performance Advisor displays the following metrics:

Metric
Description
Execution Count
Number of queries executed per hour which match the query shape.
Average Execution Time
Average execution time in milliseconds for queries which match the query shape.
Average Query Targeting
Average number of documents read for every document returned by matching queries. A higher query targeting score indicates a greater degree of inefficiency. For more information on query targeting, see Query Targeting.
Average Docs Scanned
Average number of documents scanned.
Average Docs Returned
Average number of documents returned.

The Performance Advisor also shows each executed sample query that matches the query shape, with specific metrics for that query.

Each index suggestion includes an Average Query Targeting score indicating how many documents were read for every document returned for the index's corresponding query shapes. A score of 1 represents very efficient query shapes because every document read matched the query and was returned with the query results. All suggested indexes represent an opportunity to improve query performance.

By default, the Performance Advisor suggests indexes for all clusters in the deployment. To only show suggested indexes from a specific collection, use the Collection dropdown at the top of the Performance Advisor.

You can also adjust the time range the Performance Advisor takes into account when suggesting indexes by using the Time Range dropdown at the top of the Performance Advisor.

The Performance Advisor can't suggest indexes for MongoDB databases configured to use the ctime timestamp format. As a workaround, set the timestamp format for such databases to either iso8601-utc or iso8601-local.

The Performance Advisor analyzes up to 200,000 of your cluster's most recent log lines.

You can create indexes suggested by the Performance Advisor directly within the Performance Advisor itself. When you create indexes, keep the ratio of reads to writes on the target collection in mind. Indexes come with a performance cost, but are more than worth the cost for frequent queries on large data sets. To learn more about indexing strategies, see Indexing Strategies.

  • You can only create one index at a time through the Performance Advisor. If you want to create more simultaneously, you can do so using Data Explorer or the shell

  • Ops Manager always creates indexes at the top level of the deployment. If you create an index while viewing the Performance Advisor for a single shard in a sharded cluster, Ops Manager creates that index for the entire sharded cluster.

To create a suggested index:

1

The Performance Advisor opens the Create Index dialog and prepopulates the Fields based on the index you selected.

2
{ <option1>: <value1>, ... }

Example

The following options document specifies the unique option and the name for the index:

{ unique: true, name: "myUniqueIndex" }
3

Use collation to specify language-specific rules for string comparison, such as rules for lettercase and accent marks. The collation document contains a locale field which indicates the ICU Locale code, and may contain other fields to define collation behavior.

Example

The following collation option document specifies a locale value of fr for a French language collation:

{ "locale": "fr" }

To review the list of locales that MongoDB collation supports, see the list of languages and locales. To learn more about collation options, including which are enabled by default for each locale, see Collation in the MongoDB manual.

4

Warning

Due to critical issue SERVER-68925, Ops Manager deployments using certain versions of the MongoDB Agent should not perform automated rolling index builds on clusters running the following MongoDB versions:

  • MongoDB 4.2.19-4.2.22

  • MongoDB 4.4.13-4.4.16

  • MongoDB 5.0.6-5.0.11

  • MongoDB 6.0.0-6.0.1

You can continue to perform manual rolling index builds safely on your clusters. To perform automated rolling index builds safely, upgrade the MongoDB Agent to 12.0.11.7606 or later or upgrade your clusters to:

  • MongoDB 4.2.23 or later

  • MongoDB 4.4.17 or later

  • MongoDB 5.0.12 or later

  • MongoDB 6.0.2 or later

Important

Rolling index builds succeed only when they meet certain conditions. To ensure your index build succeeds, avoid the following design patterns that commonly trigger a restart loop:

Note

Data Explorer doesn't support building indexes in a rolling fashion for standalone deployments.

Building indexes in a rolling fashion reduces the performance impact of building indexes on replica sets and sharded clusters. To maintain cluster availability, Ops Manager removes one node from the cluster at a time starting with a secondary.

After you build an index in a rolling fashion, if your MongoDB database runs with an FCV less than 4.2, resync the head database to ensure that the head database takes the new index into account.

Ops Manager automatically cancels rolling index builds that don't succeed on all nodes. When a rolling index build completes on some nodes, but fails on others, Ops Manager cancels the build and removes the index from any nodes that it was successfully built on.

In the event of a rolling index build cancellation, Ops Manager generates an activity feed event and sends a notification email to the project owner with the following information:

  • Name of the cluster on which the rolling index build failed

  • Namespace on which the rolling index build failed

  • Project that contains the cluster and namespace

  • Organization that contains the project

  • Link to the activity feed event

To learn more about rebuilding indexes, see Build Indexes on Replica Sets.

Note

The following index options are incompatible with building indexes in a rolling fashion:

Ops Manager ignores these options if you specify them in the Options pane.

5
6

Important

When an index build completes, Ops Manager generates an activity feed event and sends a notification email to the project owner with the following information:

  • Completion date of the index build

  • Name of the cluster on which the index build completed

  • Namespace on which the index build completed

  • Project containing the cluster and namespace

  • Organization containing the project

  • Link to the activity feed event

Back

Analyze Slow Queries