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.
You may experience performance limitations from inadequate or inappropriate indexing strategies or 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 reduce excessive traffic.
Database profiling can help you 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 degrades as requests and operations wait for the lock.
Lock-related slowdowns can be intermittent. To see if the lock has been
affecting your performance, see the locks
section and the globalLock section of the
serverStatus output.
Note
Some serverStatus response fields are not returned on
MongoDB Atlas Free clusters or Flex clusters. For more information,
see Limited Commands in the MongoDB Atlas
documentation.
Dividing locks.<type>.timeAcquiringMicros by
locks.<type>.acquireWaitCount
can give an approximate average wait time for a particular lock mode.
locks.<type>.deadlockCount provides
the number of times the lock acquisitions encountered deadlocks.
If globalLock.currentQueue.total is consistently high,
many requests may be 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 been
in a locked state for a significant period.
Long queries can result from:
Ineffective use of indexes
Non-optimal schema design
Poor query structure
System architecture issues
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 server's ability to handle requests. The
following fields in the serverStatus document provide insight:
connectionsis a container for the following two fields:connections.currentthe total number of current clients connected to the database instance.connections.availablethe total number of unused connections available for new clients.
Many concurrent application requests may overwhelm the server's ability to keep up with demand. If this is the case, increase the capacity of your deployment.
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 result from application or driver errors. All 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, often indicates a driver or other configuration error.
Self-Managed Connection Limits
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 Self-Managed Deployments
for more information.
MongoDB Atlas Connection Limits
MongoDB Atlas sets the limit for concurrent incoming connections based on the cluster tier and class. To learn more, see Connection Limits and Cluster Tier in the Atlas documentation.