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.. _troubleshooting:
FAQ: MongoDB Diagnostics
.. default-domain:: mongodb
This document provides answers to common diagnostic questions and
If you don't find the answer you're looking for, check
the :doc:`complete list of FAQs </faq>` or post your question to the
`MongoDB User Mailing List <!forum/mongodb-user>`_.
Where can I find information about a ``mongod`` process that stopped running unexpectedly?
If :program:`mongod` shuts down unexpectedly on a UNIX or UNIX-based
platform, and if :program:`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:
.. code-block:: sh
sudo grep mongod /var/log/messages
sudo grep score /var/log/messages
.. _faq-keepalive:
Does TCP ``keepalive`` time affect sharded clusters and replica sets?
If you experience socket errors between members of a sharded cluster
or replica set, that do not have other reasonable causes, check the
TCP keep alive value, which Linux systems store as the
``tcp_keepalive_time`` value. A common keep alive period is ``7200``
seconds (2 hours); however, different distributions and OS X may have
different settings. For MongoDB, you will have better experiences with
shorter keepalive periods, on the order of ``300`` seconds (five minutes).
On Linux systems you can use the following operation to check the
value of ``tcp_keepalive_time``:
.. code-block:: sh
cat /proc/sys/net/ipv4/tcp_keepalive_time
You can change the ``tcp_keepalive_time`` value with the following
.. code-block:: sh
echo 300 > /proc/sys/net/ipv4/tcp_keepalive_time
The new ``tcp_keepalive_time`` value takes effect without requiring
you to restart the :program:`mongod` or :program:`mongos`
servers. When you reboot or restart your system you will need to set
the new ``tcp_keepalive_time`` value, or see your operating system's
documentation for setting the TCP keepalive value persistently.
For OS X systems, issue the following command to view the keep alive
.. code-block:: sh
sysctl net.inet.tcp.keepinit
To set a shorter keep alive period use the following invocation:
.. code-block:: sh
sysctl -w net.inet.tcp.keepinit=300
If your replica set or sharded cluster experiences keepalive-related
issues, you must alter the ``tcp_keepalive_time`` value on all machines
hosting MongoDB processes. This includes all machines hosting
:program:`mongos` or :program:`mongod` servers.
Windows users should consider the `Windows Server Technet Article on
KeepAliveTime configuration
for more information on setting keep alive for MongoDB deployments on
Windows systems.
What tools are available for monitoring MongoDB?
The `MongoDB Management Services <>` includes
monitoring. MMS Monitoring is a free, hosted services for monitoring
MongoDB deployments. A full list of third-party tools is available as
part of the :doc:`/administration/monitoring/` documentation. Also
consider the `MMS Documentation <>`_.
.. _faq-memory:
Memory Diagnostics
Do I need to configure swap space?
Always configure systems to have swap space. Without swap, your system
may not be reliant in some situations with extreme memory constraints,
memory leaks, or multiple programs using the same memory. Think of
the swap space as something like a steam release valve that allows the
system to release extra pressure without affecting the overall
functioning of the system.
Nevertheless, systems running MongoDB *do not* need swap for routine
operation. Database files are :ref:`memory-mapped
<faq-storage-memory-mapped-files>` and should constitute most of your
MongoDB memory use. Therefore, it is unlikely that :program:`mongod`
will ever use any swap space in normal operation. The operating system
will release memory from the memory mapped files without needing
swap and MongoDB can write data to the data files without needing the swap
.. _faq-fundamentals-working-set:
What is "working set" and how can I estimate its size?
The *working set* for a MongoDB database is the portion of your data
that clients access most often. You can estimate size of the working
set, using the :data:`~serverStatus.workingSet` document in the output
of :dbcommand:`serverStatus`. To return :dbcommand:`serverStatus` with
the :data:`~serverStatus.workingSet` document, issue a command in the
following form:
.. code-block:: javascript
db.runCommand( { serverStatus: 1, workingSet: 1 } )
Must my working set size fit RAM?
Your working set should stay in memory to achieve good performance.
Otherwise many random disk IO's will occur, and unless you are using
SSD, this can be quite slow.
One area to watch specifically in managing the size of your working set
is index access patterns. If you are inserting into indexes at random
locations (as would happen with id's that are randomly
generated by hashes), you will continually be updating the whole index.
If instead you are able to create your id's in approximately ascending
order (for example, day concatenated with a random id), all the updates
will occur at the right side of the b-tree and the working set size for
index pages will be much smaller.
It is fine if databases and thus virtual size are much larger than RAM.
.. todo Commenting out for now:
.. _faq-fundamentals-working-set-size:
How can I measure working set size?
Measuring working set size can be difficult; even if it is much
smaller than total RAM. If the database is much larger than RAM in
total, all memory will be indicated as in use for the cache. Thus you
need a different way to estimate the working set size.
One technique is to use the `eatmem.cpp
utility, which reserves a certain amount of system memory for itself.
You can run the utility with a certain amount specified and see if
the server continues to perform well. If not, the working set is
larger than the total RAM minus the consumed RAM. The test will eject
some data from the file system cache, which might take time to page
back in after the utility is terminated.
Running eatmem.cpp continuously with a small percentage of total RAM,
such as 20%, is a good technique to get an early warning if memory is
too low. If disk I/O activity increases significantly, terminate
eatmem.cpp to mitigate the problem for the moment until further steps
can be taken.
In :term:`replica sets <replica set>`, if one server is underpowered
the eatmem.cpp utility could help as an early warning mechanism for
server capacity. Of course, the server must be receiving
representative traffic to get an indication.
How do I calculate how much RAM I need for my application?
.. todo Improve this FAQ
The amount of RAM you need depends on several factors, including but not
limited to:
- The relationship between :doc:`database storage </faq/storage>` and working set.
- The operating system's cache strategy for LRU (Least Recently Used)
- The impact of :doc:`journaling </core/journaling>`
- The number or rate of page faults and other MMS gauges to detect when
you need more RAM
MongoDB defers to the operating system when loading data into memory
from disk. It simply :ref:`memory maps <faq-storage-memory-mapped-files>` all
its data files and relies on the operating system to cache data. The OS
typically evicts the least-recently-used data from RAM when it runs low
on memory. For example if clients access indexes more frequently than
documents, then indexes will more likely stay in RAM, but it depends on
your particular usage.
To calculate how much RAM you need, you must calculate your working set
size, or the portion of your data that clients use most often. This
depends on your access patterns, what indexes you have, and the size of
your documents.
If page faults are infrequent, your
working set fits in RAM. If fault rates rise higher than that, you risk
performance degradation. This is less critical with SSD drives than
with spinning disks.
How do I read memory statistics in the UNIX ``top`` command
Because :program:`mongod` uses :ref:`memory-mapped files
<faq-storage-memory-mapped-files>`, the memory statistics in ``top``
require interpretation in a special way. On a large database, ``VSIZE``
(virtual bytes) tends to be the size of the entire database. If the
:program:`mongod` doesn't have other processes running, ``RSIZE``
(resident bytes) is the total memory of the machine, as this counts
file system cache contents.
For Linux systems, use the ``vmstat`` command to help determine how
the system uses memory. On OS X systems use ``vm_stat``.
Sharded Cluster Diagnostics
The two most important factors in maintaining a successful sharded cluster are:
- :ref:`choosing an appropriate shard key <sharding-internals-shard-keys>` and
- :ref:`sufficient capacity to support current and future operations
You can prevent most issues encountered with sharding by ensuring that
you choose the best possible :term:`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.
.. _sharding-troubleshooting-not-splitting:
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
:ref:`migration threshold <sharding-migration-thresholds>`. While the
default chunk size is configurable with the :setting:`chunkSize`
setting, these behaviors help 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 :ref:`chunk size
<sharding-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* :doc:`split chunks manually </tutorial/split-chunks-in-sharded-cluster>`.
Finally, if your shard key has a low :ref:`cardinality
<sharding-shard-key-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 :ref:`write scaling <sharding-shard-key-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 :ref:`choosing a different shard key <sharding-internals-choose-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 :ref:`troubleshooting suggestions for a new cluster where
data remains on a single shard <sharding-troubleshooting-not-splitting>`.
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 :term:`shard key` has low :ref:`cardinality <sharding-shard-key-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 :ref:`balancing window <sharding-schedule-balancing-window>` that
is too short, given the rate of data growth.
- an uneven distribution of :ref:`write operations
<sharding-shard-key-write-scaling>` 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:
#. If migrations only interrupt your clusters sporadically, you can
limit the :ref:`balancing window
<sharding-schedule-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.
#. If the balancer is always migrating chunks to the detriment of
overall cluster performance:
- You may want to attempt :doc:`decreasing the chunk size </tutorial/modify-chunk-size-in-sharded-cluster>`
to limit the size of the migration.
- Your cluster may be over capacity, and you may want to attempt to
:ref:`add one or two shards <sharding-procedure-add-shard>` to
the cluster to distribute load.
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 :ref:`write scaling <sharding-shard-key-write-scaling>`.
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