Monitoring a Self-Managed MongoDB Deployment
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Monitoring is a critical component of all database administration. A firm grasp of MongoDB's reporting will allow you to assess the state of your database and maintain your deployment without crisis. Additionally, a sense of MongoDB's normal operational parameters will allow you to diagnose problems before they escalate to failures.
This document presents an overview of the available monitoring utilities and the reporting statistics available in MongoDB. It also introduces diagnostic strategies and suggestions for monitoring replica sets and sharded clusters.
Monitoring Strategies
MongoDB provides various methods for collecting data about the state of a running MongoDB instance:
MongoDB distributes a set of utilities that provides real-time reporting of database activities.
MongoDB provides various database commands that return statistics regarding the current database state with greater fidelity.
MongoDB Atlas is a cloud-hosted database-as-a-service for running, monitoring, and maintaining MongoDB deployments.
MongoDB Cloud Manager is a hosted service that monitors running MongoDB deployments to collect data and provide visualization and alerts based on that data.
MongoDB Ops Manager is an on-premise solution available in MongoDB Enterprise Advanced that monitors running MongoDB deployments to collect data and provide visualization and alerts based on that data.
Each strategy can help answer different questions and is useful in different contexts. These methods are complementary.
MongoDB Reporting Tools
This section provides an overview of the reporting methods distributed with MongoDB. It also offers examples of the kinds of questions that each method is best suited to help you address.
Utilities
The MongoDB distribution includes a number of utilities that quickly return statistics about instances' performance and activity. Typically, these are most useful for diagnosing issues and assessing normal operation.
mongostat
mongostat
captures and returns the counts of database
operations by type (e.g. insert, query, update, delete, etc.). These
counts report on the load distribution on the server.
Use mongostat
to understand the distribution of operation
types and to inform capacity planning. See the mongostat
reference page for details.
mongotop
mongotop
tracks and reports the current read and write
activity of a MongoDB instance, and reports these statistics on a per
collection basis.
Use mongotop
to check if your database activity and use
match your expectations. See the mongotop
reference page
for details.
Commands
MongoDB includes a number of commands that report on the state of the database.
These data may provide a finer level of granularity than the utilities
discussed above. Consider using their output in scripts and programs to
develop custom alerts, or to modify the behavior of your application in
response to the activity of your instance. The db.currentOp()
method is another useful tool for identifying the database instance's
in-progress operations.
serverStatus
The serverStatus
command, or db.serverStatus()
from the shell, returns a general overview of the status of the
database, detailing disk usage, memory use, connection, journaling,
and index access. The command returns quickly and does not impact
MongoDB performance.
serverStatus
outputs an account of the state of a MongoDB
instance. This command is rarely run directly. In most cases, the data
is more meaningful when aggregated, as one would see with monitoring
tools including MongoDB Cloud Manager and Ops Manager. Nevertheless, all
administrators should be familiar with the data provided by
serverStatus
.
dbStats
The dbStats
command, or db.stats()
from the shell,
returns a document that addresses storage use and data volumes. The
dbStats
reflect the amount of
storage used, the quantity of data contained in the database, and
object, collection, and index counters.
Use this data to monitor the state and storage capacity of a specific database. This output also allows you to compare use between databases and to determine the average document size in a database.
collStats
The collStats
or db.collection.stats()
from the
shell that provides statistics that resemble dbStats
on
the collection level, including a count of the objects in the
collection, the size of the collection, the amount of disk space used
by the collection, and information about its indexes.
replSetGetStatus
The replSetGetStatus
command (rs.status()
from
the shell) returns an overview of your replica set's status. The replSetGetStatus document details the
state and configuration of the replica set and statistics about its members.
Use this data to ensure that replication is properly configured, and to check the connections between the current host and the other members of the replica set.
Hosted (SaaS) Monitoring Tools
These are monitoring tools provided as a hosted service, usually through a paid subscription.
Name | Notes |
---|---|
MongoDB Cloud Manager is a cloud-based suite of services for managing MongoDB
deployments. MongoDB Cloud Manager provides monitoring, backup, and automation
functionality. For an on-premise solution, see also
Ops Manager, available in MongoDB Enterprise Advanced. | |
VividCortex provides deep insights into MongoDB production
database workload and query performance -- in
one-second resolution. Track latency, throughput, errors, and
more to ensure scalability and exceptional performance of your
application on MongoDB. | |
Dashboard for MongoDB, MongoDB
specific alerts, replication failover timeline and iPhone, iPad
and Android mobile apps. | |
IBM has an Application Performance Management SaaS offering that
includes monitor for MongoDB and other applications and middleware. | |
New Relic offers full support for application performance
management. In addition, New Relic Plugins and Insights enable you to view
monitoring metrics from Cloud Manager in New Relic. | |
Infrastructure monitoring to visualize
the performance of your MongoDB deployments. | |
Monitoring, Anomaly Detection and Alerting SPM monitors all key MongoDB metrics together with infrastructure incl. Docker and other application metrics, e.g. Node.js, Java, NGINX, Apache, HAProxy or Elasticsearch. SPM provides correlation of metrics and logs. | |
Pandora FMS provides the PandoraFMS-mongodb-monitoring
plugin to monitor MongoDB. |
Process Logging
During normal operation, mongod
and mongos
instances report a live account of all server activity and operations
to either
standard output or a log file. The following runtime settings
control these options.
quiet
. Limits the amount of information written to the log or output.verbosity
. Increases the amount of information written to the log or output. You can also modify the logging verbosity during runtime with thelogLevel
parameter or thedb.setLogLevel()
method in the shell.path
. Enables logging to a file, rather than the standard output. You must specify the full path to the log file when adjusting this setting.logAppend
. Adds information to a log file instead of overwriting the file.
Note
The following database commands also affect logging:
getLog
. Displays recent messages from themongod
process log.logRotate
. Rotates the log files formongod
processes only. See Rotate Log Files.
Log Redaction
Available in MongoDB Enterprise only
A mongod
or mongos
running with
redactClientLogData
redacts any message accompanying a given log
event before logging, leaving only metadata, source files, or line numbers
related to the event. redactClientLogData
prevents
potentially sensitive information from entering the system log at the cost of
diagnostic detail.
For example, the following operation inserts a document into a
mongod
running without log redaction. The mongod
has the log verbosity level set to
1
:
db.clients.insertOne( { "name" : "Joe", "PII" : "Sensitive Information" } )
This operation produces the following log event:
{ "t": { "$date": "2024-07-19T15:36:55.024-07:00" }, "s": "I", "c": "COMMAND", ... "attr": { "type": "command", ... "appName": "mongosh 2.2.10", "command": { "insert": "clients", "documents": [ { "name": "Joe", "PII": "Sensitive Information", "_id": { "$oid": "669aea8792c7fd822d3e1d8c" } } ], "ordered": true, ... } ... } }
When mongod
runs with redactClientLogData
and
performs the same insert operation, it produces the following log event:
{ "t": { "$date": "2024-07-19T15:36:55.024-07:00" }, "s": "I", "c": "COMMAND", ... "attr": { "type": "command", ... "appName": "mongosh 2.2.10", "command": { "insert": "###", "documents": [ { "name": "###", "PII": "###", "_id": "###" } ], "ordered": "###", ... } ... } }
Use redactClientLogData
in conjunction with
Encryption at Rest and TLS/SSL (Transport Encryption) to assist
compliance with regulatory requirements.
Diagnosing Performance Issues
As you develop and operate applications with MongoDB, you may want to analyze the performance of the database as the application. MongoDB Performance discusses some of the operational factors that can influence performance.
Replication and Monitoring
Beyond the basic monitoring requirements for any MongoDB instance, for replica sets, administrators must monitor replication lag. "Replication lag" refers to the amount of time that it takes to copy (i.e. replicate) a write operation on the primary to a secondary. Some small delay period may be acceptable, but significant problems emerge as replication lag grows, including:
Growing cache pressure on the primary.
Operations that occurred during the period of lag are not replicated to one or more secondaries. If you're using replication to ensure data persistence, exceptionally long delays may impact the integrity of your data set.
If the replication lag exceeds the length of the operation log (oplog) then MongoDB will have to perform an initial sync on the secondary, copying all data from the primary and rebuilding all indexes. [1] This is uncommon under normal circumstances, but if you configure the oplog to be smaller than the default, the issue can arise.
Note
The size of the oplog is only configurable during the first run using the
--oplogSize
argument to themongod
command, or preferably, theoplogSizeMB
setting in the MongoDB configuration file. If you do not specify this on the command line before running with the--replSet
option,mongod
will create a default sized oplog.By default, the oplog is 5 percent of total available disk space on 64-bit systems. For more information about changing the oplog size, see the Change the Oplog Size of Self-Managed Replica Set Members.
Flow Control
Starting in MongoDB 4.2, administrators can limit the rate at which
the primary applies its writes with the goal of keeping the majority
committed
lag under
a configurable maximum value flowControlTargetLagSeconds
.
By default, flow control is enabled
.
Note
For flow control to engage, the replica set/sharded cluster must
have: featureCompatibilityVersion (fCV) of
4.2
and read concern majority enabled
. That is, enabled flow
control has no effect if fCV is not 4.2
or if read concern
majority is disabled.
See also: Check the Replication Lag.
Replica Set Status
Replication issues are most often the result of network connectivity
issues between members, or the result of a primary that does not
have the resources to support application and replication traffic. To
check the status of a replica, use the replSetGetStatus
or
the following helper in the shell:
rs.status()
The replSetGetStatus
reference provides a more in-depth
overview view of this output. In general, watch the value of
optimeDate
, and pay particular attention
to the time difference between the primary and the
secondary members.
[1] | The oplog can grow past its configured size
limit to avoid deleting the majority commit point . |
Slow Application of Oplog Entries
Secondary members of a replica set now log oplog entries that take longer than the slow operation threshold to apply. These slow oplog messages:
Are logged for the secondaries in the
diagnostic log
.Are logged under the
REPL
component with the textapplied op: <oplog entry> took <num>ms
.Do not depend on the log levels (either at the system or component level)
Do not depend on the profiling level.
Are affected by
slowOpSampleRate
.
The profiler does not capture slow oplog entries.
Sharding and Monitoring
In most cases, the components of sharded clusters benefit from the same monitoring and analysis as all other MongoDB instances. In addition, clusters require further monitoring to ensure that data is effectively distributed among nodes and that sharding operations are functioning appropriately.
Config Servers
The config database maintains a map identifying which
documents are on which shards. The cluster updates this map as
chunks move between shards. When a configuration
server becomes inaccessible, certain sharding operations become
unavailable, such as moving chunks and starting mongos
instances. However, clusters remain accessible from already-running
mongos
instances.
Because inaccessible configuration servers can seriously impact
the availability of a sharded cluster, you should monitor your
configuration servers to ensure that the cluster remains well
balanced and that mongos
instances can restart.
MongoDB Cloud Manager and Ops Manager monitor config servers and can create notifications if a config server becomes inaccessible. See the MongoDB Cloud Manager documentation and Ops Manager documentation for more information.
Balancing and Chunk Distribution
The most effective sharded cluster deployments evenly balance chunks among the shards. To facilitate this, MongoDB has a background balancer process that distributes data to ensure that chunks are always optimally distributed among the shards.
Issue the db.printShardingStatus()
or sh.status()
command to the mongos
from within mongosh
.
This returns an overview of the entire cluster including the
database name, and a list of the chunks.
Stale Locks
To check the lock status of the database, connect to a
mongos
instance using mongosh
. Issue the
following command sequence to switch to the config
database and
display all outstanding locks on the shard database:
use config db.locks.find()
The balancing process takes a special "balancer" lock that prevents
other balancing activity from transpiring. In the config
database,
use the following command to view the "balancer" lock.
db.locks.find( { _id : "balancer" } )
The primary of the CSRS config server holds the "balancer" lock, using a process ID named "ConfigServer". This lock is never released. To determine if the balancer is running, see Check if Balancer is Running.
Storage Node Watchdog
Note
The Storage Node Watchdog is available in both the Community and MongoDB Enterprise editions.
The Storage Node Watchdog monitors the following MongoDB directories to detect filesystem unresponsiveness:
The
--dbpath
directoryThe
journal
directory inside the--dbpath
directory ifjournaling
is enabledThe directory of
--logpath
fileThe directory of
--auditPath
file
By default, the Storage Node Watchdog is disabled. You can only enable
the Storage Node Watchdog on a mongod
at startup time by
setting the watchdogPeriodSeconds
parameter to an integer
greater than or equal to 60. However, once enabled, you can pause the
Storage Node Watchdog and restart during runtime. See
watchdogPeriodSeconds
parameter for details.
If any of the filesystems containing the monitored directories become
unresponsive, the Storage Node Watchdog terminates the
mongod
and exits with a status code of 61. If the
mongod
is the primary of a replica set, the
termination initiates a failover, allowing another member to
become primary.
Once a mongod
has terminated, it may not be possible to cleanly
restart it on the same machine.
Note
Symlinks
If any of its monitored directories is a symlink to other volumes, the Storage Node Watchdog does not monitor the symlink target.
For example, if the mongod
uses
storage.directoryPerDB: true
(or
--directoryperdb
) and symlinks a
database directory to another volume, the Storage Node Watchdog does
not follow the symlink to monitor the target.
The maximum time the Storage Node Watchdog can
take to detect an unresponsive filesystem and terminate is nearly twice the
value of watchdogPeriodSeconds
.