How Does MongoDB Consume and Use Memory?

By Youdong Zhang

We have seen many online cases and community issue inquiries regarding MongoDB, which mainly focus on its memory consumption and usage. These questions include:

  • Why does my MongoDB use XX GB of memory?
  • Several Mongod instances/processes are deployed on one device. How should the WiredTiger cache be disposed?
  • Should the MongoDB use the SWAP space to reduce the memory pressure?

In this article, we hope to address some of the most common queries regarding MongoDB usage, focusing on its memory usage.

Where Is MongoDB Used?

After the Mongod process boots up, in addition to being the same as an ordinary process, it loads each binary and reliant library the memory, which serve as the DBMS. It must also be responsible for a large number of tasks such as client connection management, request processing, database metadata and storage engine. All of these tasks involve allocation and freeing up memory, and under default conditions the MongoDB uses Google tcmalloc as a memory allocator. The largest shares of the memory are occupied by “storage engine” and “customer terminal connection and request processing”.

Storage Engine Cache

MongoDB 3.2 and after use as a default the WiredTiger storage engine. The upper limit of the memory used by the WiredTiger engine can be set by , and the general recommended setting is about 60% of the system's usable memory (default setting).

To cite an example, if is set at 10 GB, you can think of it as if the total amount of memory allocated through tcmalloc by the WiredTiger engine does not exceed 10 GB. In order to control the use of memory, WiredTiger begins elimination when the memory use approaches a certain threshold, to prevent user requests from being blocked when the memory use is full.

At present there are 4 configurable parameters that support the eviction policy of the WiredTiger storage engine (in general it does not require revision), which means:

Under this rule, in an instance where MongoDB is operating normally, the cache used will generally be and under, and there is no major problem if this is occasionally exceeded; if used > = 95% or dirty >= 20% occurs and is sustained for a time, this means that the memory elimination pressure is very large, and the users' request threads will be blocked from participating in page elimination; when a request is delayed the pressure increases, and at this time "increasing memory" or "switching to a faster disk to raise IO capabilities" can be considered.

TCP Connection and Request Processing

The MongoDB Driver will establish a TCP connection with the Mongod process, and will send database requests and receive responses on the connection. In addition to the TCP protocol stack maintaining the socket database for connection, each connection will have a read buffer and a write buffer, and the users receive and send network packets. The size of the buffer is set by sysctl system parameters like those below, and these are respectively the minimum value, default value and maximum value of the buffers. Google can be used for the detailed interpretation.

On redhat7 (such detailed information has moreover not been derived on redhat6) it is possible to check the information of the buffer of each connection by . An illustration is provided below. The read and write buffers account for 2357478 bytes and 2626560 bytes respectively, that is about 2 MB; 500 similar connections take up about 1 GB of memory; the question of how much is taken up by the buffer is determined by the size of the data packets that are sent/received in response on the connection, the network quality, etc.; this buffer also will not increase too much; and if a packet is relatively large, it will be relatively easy for this buffer to increase a lot.

In addition to the memory overhead on the protocol stack, Mongod will start a separate thread for each connection, and this will be exclusively responsible for processing the request on this connection. Mongod has set a protocol stack of a maximum of 1 MB for the thread for processing the connection request, and ordinarily the actual use is around several tens of KB, and the actual overhead of these stack threads are seen through the proc document system. In addition to the thread that processes the request, Mongod also has a series of back end threads, for example, such threads as master-slave synchronization, regular refreshed journal, TTL, and evict. The default is the thread stack of each thread’s largest (typically 10 MB) of each thread. Due to the fact that the total number of threads in this batch is relatively fixed, the memory occupied is also relatively controllable.

When a thread is processing a request, it must allocate a temporary buffer to store the data packet that is received, and in order to request to establish the context (OperationContext) it stores the results of interim processing (e.g. ordering, aggregation, etc.) and the final response results, etc.

When a large number of requests are concurrent, one may observe a rise in the memory used by Mongod, and then a gradual release after the number of requests declines. This is due to the tcmalloc memory management policy. tcmalloc takes performance into consideration. Each thread will have its own local free page cache and central free page cache; during memory application, the memory can be used according to a local thread free page cache ==> central free page cache search. If usable memory can’t be found, then it will only apply from the stack; when the memory is released, it will then return to the cache, and the tcmalloc back end will be slowly returned to the OS. Under default conditions, tcmalloc will at most [something missing] memory of cache min (1GB,1/8 * system_memory), and it is possible to set this value by . However, in general, revision is not recommended, and adjustment and optimization are done at the access level to the maximum extent possible)

As far as the management policy of the tcmalloc cache is concerned, the MongoDB level exposes several parameters for adjustment. In general adjustment is not required. If it is possible to clearly understand the tcmalloc principles and parameter meaning, then it is possible to do targeted adjustment and optimization; the memory status of MongoDB tcmalloc can be viewed through . You can see the tcmalloc file for specific meaning. For emphasis you can focus on the . This value tells you how much memory is self-cached and has not been returned to the OS.

How Can You Control Memory Usage?

Configuring the WiredTiger cacheSizeGB Rationally

  • If only Mongod is deployed on a device, and Mongod can use all of the available memory, the default settings can be used.
  • If several Mongods are mixed and deployed on a device, or Mongod is deployed together with some other processes, it is necessary to set the based on the memory quota allocated to Mongod, and this may be set according to about 60% of the quota.

Controlling the Number of Concurrent Connections

A detailed analysis has already been done regarding the TCP connection for the memory overhead upper surface of Mongod. Many fellow students have a certain misunderstanding about concurrence, and believe that “The higher the number of concurrent connections is, the higher the QPS of the database will be.” Actually, in the network models of the majority of databases, an excessively high number of connections will cause the back end memory pressure to become large and the context switching overhead to become large, resulting in a decline in performance.

When the MongoDB driver is connected to Mongod, it will maintain a connection pool (ordinarily a default of 100), and when a large number of clients simultaneously access the same Mongod, it is necessary to consider reducing the size of each client connection pool. In the case of Mongod, it is possible to limit the maximum number of concurrent connections by configuring the settings, and thereby preventing an overload of the database pressure.

Should I Set SWAP or Not?

The recommendation in the official file is as follows, with the gist being that you should set SWAP and avoid OOM of Mongod because too much memory is being used.

There are advantages and disadvantages to both turning on and not turning on SWAP. If SWAP is turned on, then when the memory pressure is large, it will use the SWAP disk space to alleviate the memory pressure. At this time the entire database service will slow down, but the specific extent to which it will slow down is uncontrollable. If SWAP is not turned on, when the entire memory exceeds the device memory online, this will trigger the OOM killer to kill the process, which is actually telling you that it might be necessary to expand the memory resources or to optimize access to the database.

The question of whether or not to turn on SWAP is actually a choice between a “good death” and “barely living”. Speaking in terms of some important business scenarios, you should first plan enough memory for the database, and when the memory is insufficient, “adjusting capacity expansion in a timely manner” is better than “uncontrollably slow”.

Other Features

  • Reduce situations involving memory sorting as much as possible. Memory sorting generally requires more temporary memory.
  • The master-slave node setting difference must not be too large. The slave node will maintain a buffer (the default is a maximum of 256 MB) that is used to store pull to oplog, and the back end takes the oplog from the buffer and endlessly replays it. When slave synchronization is slow, this buffer will continuously use the maximum memory.
  • It controls the number of sets and indexes, and reduces the database management metadata memory overhead; if there are too many collections and indexes, the metadata memory overhead is the effect on the one hand, and if there are more, then this will affect the efficiency of boot up and loading, as well as performance during operation.


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