Navigation

MongoDB Performance

On this page

As you develop and operate applications with MongoDB, you may need to analyze the performance of the application and its database. When you encounter degraded performance, it is often a function of database access strategies, hardware availability, and the number of open database connections.

Some users may experience performance limitations as a result of inadequate or inappropriate indexing strategies, or as a consequence of poor schema design patterns. Locking Performance discusses how these can impact MongoDB’s internal locking.

Performance issues may indicate that the database is operating at capacity and that it is time to add additional capacity to the database. In particular, the application’s working set should fit in the available physical memory.

In some cases performance issues may be temporary and related to abnormal traffic load. As discussed in Number of Connections, scaling can help relax excessive traffic.

Database Profiling can help you to understand what operations are causing degradation.

Locking Performance

MongoDB uses a locking system to ensure data set consistency. If certain operations are long-running or a queue forms, performance will degrade as requests and operations wait for the lock.

Lock-related slowdowns can be intermittent. To see if the lock has been affecting your performance, refer to the locks section and the globalLock section of the serverStatus output.

Dividing locks.timeAcquiringMicros by locks.acquireWaitCount can give an approximate average wait time for a particular lock mode.

locks.deadlockCount provide the number of times the lock acquisitions encountered deadlocks.

If globalLock.currentQueue.total is consistently high, then there is a chance that a large number of requests are waiting for a lock. This indicates a possible concurrency issue that may be affecting performance.

If globalLock.totalTime is high relative to uptime, the database has existed in a lock state for a significant amount of time.

Long queries can result from ineffective use of indexes; non-optimal schema design; poor query structure; system architecture issues; or insufficient RAM resulting in disk reads.

Number of Connections

In some cases, the number of connections between the applications and the database can overwhelm the ability of the server to handle requests. The following fields in the serverStatus document can provide insight:

If there are numerous concurrent application requests, the database may have trouble keeping up with demand. If this is the case, then you will need to increase the capacity of your deployment.

For read-heavy applications, increase the size of your replica set and distribute read operations to secondary members.

For write-heavy applications, deploy sharding and add one or more shards to a sharded cluster to distribute load among mongod instances.

Spikes in the number of connections can also be the result of application or driver errors. All of the officially supported MongoDB drivers implement connection pooling, which allows clients to use and reuse connections more efficiently. An extremely high number of connections, particularly without corresponding workload, is often indicative of a driver or other configuration error.

Unless constrained by system-wide limits, the maximum number of incoming connections supported by MongoDB is configured with the maxIncomingConnections setting. On Unix-based systems, system-wide limits can be modified using the ulimit command, or by editing your system’s /etc/sysctl file. See UNIX ulimit Settings for more information.

Database Profiling

The Database Profiler collects detailed information about operations run against a mongod instance. The profiler’s output can help to identify inefficient queries and operations.

You can enable and configure profiling for individual databases or for all databases on a mongod instance. Profiler settings affect only a single mongod instance and will not propagate across a replica set or sharded cluster.

See Database Profiler for information on enabling and configuring the profiler.

The following profiling levels are available:

Level Description
0 The profiler is off and does not collect any data. This is the default profiler level.
1 The profiler collects data for operations that take longer than the value of slowms.
2 The profiler collects data for all operations.

Important

Profiling can impact performance and shares settings with the system log. Carefully consider any performance and security implications before configuring and enabling the profiler on a production deployment.

See Profiler Overhead for more information on potential performance degradation.

Note

When logLevel is set to 0, MongoDB records slow operations to the diagnostic log at a rate determined by slowOpSampleRate. Starting in MongoDB 4.2, the secondaries of replica sets log all oplog entry messages that take longer than the slow operation threshold to apply regardless of the sample rate.

At higher logLevel settings, all operations appear in the diagnostic log regardless of their latency with the following exception: the logging of slow oplog entry messages by the secondaries. The secondaries log only the slow oplog entries; increasing the logLevel does not log all oplog entries.

Starting in MongoDB 4.2, the profiler entries and the diagnostic log messages (i.e. mongod/mongos log messages) for read/write operations include:

  • queryHash to help identify slow queries with the same query shape.
  • planCacheKey to provide more insight into the query plan cache for slow queries.

Full Time Diagnostic Data Capture

To facilitate analysis of the MongoDB server behavior by MongoDB Inc. engineers, mongod and mongos processes include a Full Time Diagnostic Data Collection (FTDC) mechanism. FTDC data files are compressed, are not human-readable, and inherit the same file access permissions as the MongoDB data files. Only users with access to FTDC data files can transmit the FTDC data. MongoDB Inc. engineers cannot access FTDC data independent of system owners or operators. MongoDB processes run with FTDC on by default. For more information on MongoDB Support options, visit Getting Started With MongoDB Support.

FTDC Privacy

FTDC data files are compressed and not human-readable. MongoDB Inc. engineers cannot access FTDC data without explicit permission and assistance from system owners or operators.

FTDC data never contains any of the following information:

  • Samples of queries, query predicates, or query results
  • Data sampled from any end-user collection or index
  • System or MongoDB user credentials or security certificates

FTDC data contains certain host machine information such as hostnames, operating system information, and the options or settings used to start the mongod or mongos. This information may be considered protected or confidential by some organizations or regulatory bodies, but is not typically considered to be Personally Identifiable Information (PII). For clusters where these fields were configured with protected, confidential, or PII data, please notify MongoDB Inc. engineers before sending the FTDC data so appropriate measures can be taken.

FTDC periodically collects statistics produced by the following commands:

Depending on the host operating system, the diagnostic data may include one or more of the following utilization statistics:

  • CPU utilization
  • Memory utilization
  • Disk utilization related to performance. FTDC does not include data related to storage capacity.
  • Network performance statistics. FTDC only captures metadata and does not capture or inspect any network packets.

Note

Starting in MongoDB 4.4, if the mongod process runs in a container, FTDC will report utilization statistics from the perspective of the container instead of the host operating system. For example, if a the mongod runs in a container that is configured with RAM restrictions, FTDC will calculate memory utilization against the container’s RAM limit, as opposed to the host operating system’s total available RAM.

FTDC collects statistics produced by the following commands on file rotation or startup:

mongod processes store FTDC data files in a diagnostic.data directory under the instances storage.dbPath. All diagnostic data files are stored under this directory. For example, given a dbPath of /data/db, the diagnostic data directory would be /data/db/diagnostic.data.

mongos processes store FTDC data files in a diagnostic directory relative to the systemLog.path log path setting. MongoDB truncates the logpath’s file extension and concatenates diagnostic.data to the remaining name. For example, given a path setting of /var/log/mongodb/mongos.log, the diagnostic data directory would be /var/log/mongodb/mongos.diagnostic.data.

FTDC runs with the following defaults:

  • Data capture every 1 second
  • 200MB maximum diagnostic.data folder size.

These defaults are designed to provide useful data to MongoDB Inc. engineers with minimal impact on performance or storage size. These values only require modifications if requested by MongoDB Inc. engineers for specific diagnostic purposes.

You can view the FTDC source code on the MongoDB Github Repository. The ftdc_system_stats_*.ccp files specifically define any system-specific diagnostic data captured.

To disable FTDC, start up the mongod or mongos with the diagnosticDataCollectionEnabled: false option specified to the setParameter setting in your configuration file:

copy 
setParameter:
  diagnosticDataCollectionEnabled: false

Disabling FTDC may increase the time or resources required when analyzing or debugging issues with support from MongoDB Inc. engineers.

© MongoDB, Inc 2008-present. MongoDB, Mongo, and the leaf logo are registered trademarks of MongoDB, Inc.