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FAQ: MongoDB Diagnostics

This document provides answers to common diagnostic questions and issues.

If you don’t find the answer you’re looking for, check the complete list of FAQs or post your question to the MongoDB User Mailing List.

Where can I find information about a mongod process that stopped running unexpectedly?

If mongod shuts down unexpectedly on a UNIX or UNIX-based platform, and if mongod fails to log a shutdown or error message, then check your system logs for messages pertaining to MongoDB. For example, for logs located in /var/log/messages, use the following commands:

sudo grep mongod /var/log/messages
sudo grep score /var/log/messages

Does TCP keepalive time affect MongoDB Deployments?

If you experience socket errors between clients and servers or between members of a sharded cluster or replica set that do not have other reasonable causes, check the TCP keepalive value (for example, the tcp_keepalive_time value on Linux systems). A common keepalive period is 7200 seconds (2 hours); however, different distributions and macOS may have different settings.

For MongoDB, you will have better results with shorter keepalive periods, on the order of 120 seconds (two minutes).

If your MongoDB deployment experiences keepalive-related issues, you must alter the keep alive value on all machines hosting MongoDB processes. This includes all machines hosting mongos or mongod processes and all machines hosting client processes that connect to MongoDB.

Note

For non-Linux systems, values greater than or equal to 600 seconds (10 minutes) will be ignored by mongod and mongos. For Linux, values greater than 300 seconds (5 minutes) will be overridden on the mongod and mongos sockets with a maximum of 300 seconds.

On Linux systems:

  • To view the keep alive setting, you can use one of the following commands:

    sysctl net.ipv4.tcp_keepalive_time
    

    Or:

    cat /proc/sys/net/ipv4/tcp_keepalive_time
    

    The value is measured in seconds.

    Note

    Although the path includes ipv4, the tcp_keepalive_time value applies to both IPv4 and IPv6.

  • To change the tcp_keepalive_time value, you can use one of the following command:

    sudo sysctl -w net.ipv4.tcp_keepalive_time=<value>
    

    Or:

    echo <value> | sudo tee /proc/sys/net/ipv4/tcp_keepalive_time
    

    These operations do not persist across system reboots. To persist the setting, add the following line to /etc/sysctl.conf:

    net.ipv4.tcp_keepalive_time = <value>
    

    On Linux, mongod and mongos processes limit the keepalive to a maximum of 300 seconds (5 minutes) on their own sockets by overriding keepalive values greater than 5 minutes.

For macOS systems:

  • To view the keep alive setting, issue the following command:

    sysctl net.inet.tcp.keepinit
    
  • To change the net.inet.tcp.keepinit value, you can use the following command:

    sysctl -w net.inet.tcp.keepinit=<value>
    

    The above method for setting the TCP keepalive is not persistent; you will need to reset the value each time you reboot or restart a system. See your operating system’s documentation for instructions on setting the TCP keepalive value persistently.

For Windows systems:

  • To view the keep alive setting, issue the following command:

    reg query HKLM\SYSTEM\CurrentControlSet\Services\Tcpip\Parameters /v KeepAliveTime
    

    The registry value is not present by default. The system default, used if the value is absent, is 7200000 milliseconds or 0x6ddd00 in hexadecimal.

  • To change the KeepAliveTime value, use the following command in an Administrator Command Prompt, where <value> is expressed in hexadecimal (e.g. 120000 is 0x1d4c0):

    reg add HKLM\SYSTEM\CurrentControlSet\Services\Tcpip\Parameters\ /t REG_DWORD /v KeepAliveTime /d <value>
    

    Windows users should consider the Windows Server Technet Article on KeepAliveTime for more information on setting keep alive for MongoDB deployments on Windows systems.

You will need to restart mongod and mongos processes for new system-wide keepalive settings to take effect.

Why does MongoDB log so many “Connection Accepted” events?

If you see a very large number connection and re-connection messages in your MongoDB log, then clients are frequently connecting and disconnecting to the MongoDB server. This is normal behavior for applications that do not use request pooling, such as CGI. Consider using FastCGI, an Apache Module, or some other kind of persistent application server to decrease the connection overhead.

If these connections do not impact your performance you can use the run-time quiet option or the command-line option --quiet to suppress these messages from the log.

What tools are available for monitoring MongoDB?

Starting in version 4.0, MongoDB offers free Cloud monitoring for standalones and replica sets. Free monitoring provides information about your deployment, including:

  • Operation Execution Times
  • Memory Usage
  • CPU Usage
  • Operation Counts

For more information, see Free Monitoring.

The MongoDB Cloud Manager and Ops Manager, an on-premise solution available in MongoDB Enterprise Advanced include monitoring functionality, which collects data from running MongoDB deployments and provides visualization and alerts based on that data.

For more information, see also the MongoDB Cloud Manager documentation and Ops Manager documentation.

A full list of third-party tools is available as part of the Monitoring for MongoDB documentation.

Memory Diagnostics for the WiredTiger Storage Engine

Must my working set size fit RAM?

No.

If the cache does not have enough space to load additional data, WiredTiger evicts pages from the cache to free up space.

Note

The storage.wiredTiger.engineConfig.cacheSizeGB limits the size of the WiredTiger internal cache. The operating system will use the available free memory for filesystem cache, which allows the compressed MongoDB data files to stay in memory. In addition, the operating system will use any free RAM to buffer file system blocks and file system cache.

To accommodate the additional consumers of RAM, you may have to decrease WiredTiger internal cache size.

The default WiredTiger internal cache size value assumes that there is a single mongod instance per machine. If a single machine contains multiple MongoDB instances, then you should decrease the setting to accommodate the other mongod instances.

If you run mongod in a container (e.g. lxc, cgroups, Docker, etc.) that does not have access to all of the RAM available in a system, you must set storage.wiredTiger.engineConfig.cacheSizeGB to a value less than the amount of RAM available in the container. The exact amount depends on the other processes running in the container.

To see statistics on the cache and eviction, use the serverStatus command. The wiredTiger.cache field holds the information on the cache and eviction.

...
"wiredTiger" : {
   ...
   "cache" : {
      "tracked dirty bytes in the cache" : <num>,
      "bytes currently in the cache" : <num>,
      "maximum bytes configured" : <num>,
      "bytes read into cache" :<num>,
      "bytes written from cache" : <num>,
      "pages evicted by application threads" : <num>,
      "checkpoint blocked page eviction" : <num>,
      "unmodified pages evicted" : <num>,
      "page split during eviction deepened the tree" : <num>,
      "modified pages evicted" : <num>,
      "pages selected for eviction unable to be evicted" : <num>,
      "pages evicted because they exceeded the in-memory maximum" : <num>,,
      "pages evicted because they had chains of deleted items" : <num>,
      "failed eviction of pages that exceeded the in-memory maximum" : <num>,
      "hazard pointer blocked page eviction" : <num>,
      "internal pages evicted" : <num>,
      "maximum page size at eviction" : <num>,
      "eviction server candidate queue empty when topping up" : <num>,
      "eviction server candidate queue not empty when topping up" : <num>,
      "eviction server evicting pages" : <num>,
      "eviction server populating queue, but not evicting pages" : <num>,
      "eviction server unable to reach eviction goal" : <num>,
      "pages split during eviction" : <num>,
      "pages walked for eviction" : <num>,
      "eviction worker thread evicting pages" : <num>,
      "in-memory page splits" : <num>,
      "percentage overhead" : <num>,
      "tracked dirty pages in the cache" : <num>,
      "pages currently held in the cache" : <num>,
      "pages read into cache" : <num>,
      "pages written from cache" : <num>,
   },
   ...

For an explanation of some key cache and eviction statistics, such as wiredTiger.cache.bytes currently in the cache and wiredTiger.cache.tracked dirty bytes in the cache, see wiredTiger.cache.

To adjust the size of the WiredTiger internal cache, see storage.wiredTiger.engineConfig.cacheSizeGB and --wiredTigerCacheSizeGB. Avoid increasing the WiredTiger internal cache size above its default value.

How do I calculate how much RAM I need for my application?

With WiredTiger, MongoDB utilizes both the WiredTiger internal cache and the filesystem cache.

Starting in 3.4, the WiredTiger internal cache, by default, will use the larger of either:

  • 50% of (RAM - 1 GB), or
  • 256 MB.

For example, on a system with a total of 4GB of RAM the WiredTiger cache will use 1.5GB of RAM (0.5 * (4 GB - 1 GB) = 1.5 GB). Conversely, a system with a total of 1.25 GB of RAM will allocate 256 MB to the WiredTiger cache because that is more than half of the total RAM minus one gigabyte (0.5 * (1.25 GB - 1 GB) = 128 MB < 256 MB).

By default, WiredTiger uses Snappy block compression for all collections and prefix compression for all indexes. Compression defaults are configurable at a global level and can also be set on a per-collection and per-index basis during collection and index creation.

Different representations are used for data in the WiredTiger internal cache versus the on-disk format:

  • Data in the filesystem cache is the same as the on-disk format, including benefits of any compression for data files. The filesystem cache is used by the operating system to reduce disk I/O.
  • Indexes loaded in the WiredTiger internal cache have a different data representation to the on-disk format, but can still take advantage of index prefix compression to reduce RAM usage. Index prefix compression deduplicates common prefixes from indexed fields.
  • Collection data in the WiredTiger internal cache is uncompressed and uses a different representation from the on-disk format. Block compression can provide significant on-disk storage savings, but data must be uncompressed to be manipulated by the server.

Via the filesystem cache, MongoDB automatically uses all free memory that is not used by the WiredTiger cache or by other processes.

To adjust the size of the WiredTiger internal cache, see storage.wiredTiger.engineConfig.cacheSizeGB and --wiredTigerCacheSizeGB. Avoid increasing the WiredTiger internal cache size above its default value.

Note

The storage.wiredTiger.engineConfig.cacheSizeGB limits the size of the WiredTiger internal cache. The operating system will use the available free memory for filesystem cache, which allows the compressed MongoDB data files to stay in memory. In addition, the operating system will use any free RAM to buffer file system blocks and file system cache.

To accommodate the additional consumers of RAM, you may have to decrease WiredTiger internal cache size.

The default WiredTiger internal cache size value assumes that there is a single mongod instance per machine. If a single machine contains multiple MongoDB instances, then you should decrease the setting to accommodate the other mongod instances.

If you run mongod in a container (e.g. lxc, cgroups, Docker, etc.) that does not have access to all of the RAM available in a system, you must set storage.wiredTiger.engineConfig.cacheSizeGB to a value less than the amount of RAM available in the container. The exact amount depends on the other processes running in the container.

To view statistics on the cache and eviction rate, see the wiredTiger.cache field returned from the serverStatus command.

Sharded Cluster Diagnostics

The two most important factors in maintaining a successful sharded cluster are:

You can prevent most issues encountered with sharding by ensuring that you choose the best possible shard key for your deployment and ensure that you are always adding additional capacity to your cluster well before the current resources become saturated. Continue reading for specific issues you may encounter in a production environment.

In a new sharded cluster, why does all data remains on one shard?

Your cluster must have sufficient data for sharding to make sense. Sharding works by migrating chunks between the shards until each shard has roughly the same number of chunks.

The default chunk size is 64 megabytes. MongoDB will not begin migrations until the imbalance of chunks in the cluster exceeds the migration threshold. This behavior helps prevent unnecessary chunk migrations, which can degrade the performance of your cluster as a whole.

If you have just deployed a sharded cluster, make sure that you have enough data to make sharding effective. If you do not have sufficient data to create more than eight 64 megabyte chunks, then all data will remain on one shard. Either lower the chunk size setting, or add more data to the cluster.

As a related problem, the system will split chunks only on inserts or updates, which means that if you configure sharding and do not continue to issue insert and update operations, the database will not create any chunks. You can either wait until your application inserts data or split chunks manually.

Finally, if your shard key has a low cardinality, MongoDB may not be able to create sufficient splits among the data.

Why would one shard receive a disproportion amount of traffic in a sharded cluster?

In some situations, a single shard or a subset of the cluster will receive a disproportionate portion of the traffic and workload. In almost all cases this is the result of a shard key that does not effectively allow write scaling.

It’s also possible that you have “hot chunks.” In this case, you may be able to solve the problem by splitting and then migrating parts of these chunks.

In the worst case, you may have to consider re-sharding your data and choosing a different shard key to correct this pattern.

What can prevent a sharded cluster from balancing?

If you have just deployed your sharded cluster, you may want to consider the troubleshooting suggestions for a new cluster where data remains on a single shard.

If the cluster was initially balanced, but later developed an uneven distribution of data, consider the following possible causes:

  • You have deleted or removed a significant amount of data from the cluster. If you have added additional data, it may have a different distribution with regards to its shard key.
  • Your shard key has low cardinality and MongoDB cannot split the chunks any further.
  • Your data set is growing faster than the balancer can distribute data around the cluster. This is uncommon and typically is the result of:
    • a balancing window that is too short, given the rate of data growth.
    • an uneven distribution of write operations that requires more data migration. You may have to choose a different shard key to resolve this issue.
    • poor network connectivity between shards, which may lead to chunk migrations that take too long to complete. Investigate your network configuration and interconnections between shards.

Why do chunk migrations affect sharded cluster performance?

If migrations impact your cluster or application’s performance, consider the following options, depending on the nature of the impact:

  1. If migrations only interrupt your clusters sporadically, you can limit the balancing window to prevent balancing activity during peak hours. Ensure that there is enough time remaining to keep the data from becoming out of balance again.
  2. If the balancer is always migrating chunks to the detriment of overall cluster performance:

It’s also possible that your shard key causes your application to direct all writes to a single shard. This kind of activity pattern can require the balancer to migrate most data soon after writing it. Consider redeploying your cluster with a shard key that provides better write scaling.