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.
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.
the number of times the lock acquisitions encountered deadlocks.
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
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:
connectionsis a container for the following two fields:
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.
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
for more information.
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
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:
The profiler is off and does not collect any data. This is the default profiler level.
The profiler collects data for operations that take longer than the value of
The profiler collects data for all operations.
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.
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.
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
Starting in MongoDB 4.2, the profiler entries and the diagnostic log messages (i.e. mongod/mongos log messages) for read/write operations include:
Full Time Diagnostic Data Capture¶
To facilitate analysis of the MongoDB server behavior by MongoDB Inc.
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 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
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.
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
/data/db, the diagnostic data directory would be
mongos processes store FTDC data files in a
diagnostic directory relative to the
path setting. MongoDB truncates the logpath's file extension and
diagnostic.data to the remaining name. For example,
path setting of
/var/log/mongodb/mongos.log, the diagnostic data directory would be
FTDC runs with the following defaults:
- Data capture every 1 second
- 200MB maximum
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.
ftdc_system_stats_*.ccp files specifically define any
system-specific diagnostic data captured.
setParameter: diagnosticDataCollectionEnabled: false
Disabling FTDC may increase the time or resources required when analyzing or debugging issues with support from MongoDB Inc. engineers.