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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. See Memory and the MMAPv1 Storage Engine for more information on the working set.
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.
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.
Long queries can result from ineffective use of indexes; non-optimal schema design; poor query structure; system architecture issues; or insufficient RAM resulting in page faults and disk reads.
While this is intentional and aids performance, the memory mapped files make it difficult to determine if the amount of RAM is sufficient for the data set.
The mem.resident field provides the amount of resident memory in use. If this exceeds the amount of system memory and there is a significant amount of data on disk that isn’t in RAM, you may have exceeded the capacity of your system.
You can inspect mem.mapped to check the amount of mapped memory that mongod is using. If this value is greater than the amount of system memory, some operations will require a page faults to read data from disk.
With the MMAPv1 storage engine, page faults can occur as MongoDB reads from or writes data to parts of its data files that are not currently located in physical memory. In contrast, operating system page faults happen when physical memory is exhausted and pages of physical memory are swapped to disk.
Rapid increases in the MongoDB page fault counter may indicate that the server has too little physical memory. Page faults also can occur while accessing large data sets or scanning an entire collection.
A single page fault completes quickly and is not problematic. However, in aggregate, large volumes of page faults typically indicate that MongoDB is reading too much data from disk.
MongoDB can often “yield” read locks after a page fault, allowing other database processes to read while mongod loads the next page into memory. Yielding the read lock following a page fault improves concurrency, and also improves overall throughput in high volume systems.
Increasing the amount of RAM accessible to MongoDB may help reduce the frequency of page faults. If this is not possible, you may want to consider deploying a sharded cluster or adding shards to your deployment to distribute load among mongod instances.
See What are page faults? for more information.
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:
connections is a container for the following two fields:
connections.current the total number of current clients connected to the database instance.
- connections.available the total number of unused connections available for new clients.
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. Extremely high numbers of connections, particularly without corresponding workload is often indicative of a driver or other configuration error.
Unless constrained by system-wide limits, MongoDB has no limit on incoming connections. On Unix-based systems, you can modify system limits using the
ulimit command, or by editing your system’s
/etc/sysctl file. See UNIX ulimit Settings 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 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:
|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.
When logLevel is set to
0 , MongoDB records slow operations to the diagnostic log at a rate determined by slowOpSampleRate. For MongoDB 3.6 deployments, starting in version 3.6.11, 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.
To facilitate analysis of the MongoDB server behavior by MongoDB Inc. engineers,
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 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 statistics:
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.
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
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
FTDC runs with the following defaults:
Data capture every 1 second
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.
setParameter: diagnosticDataCollectionEnabled: false
Disabling FTDC may increase the time or resources required when analyzing or debugging issues with support from MongoDB Engineers.