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Starting in MongoDB 3.2, the WiredTiger storage engine is the default
storage engine. For existing deployments, if you do not specify the
--storageEngine or the
storage.engine setting, the
mongod instance can automatically determine
the storage engine used to create the data files in the
storage.dbPath. See Default Storage Engine Change.
Deployments hosted in the following environments can use the WiredTiger storage engine:
MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud
All MongoDB Atlas deployments use the WiredTiger storage engine.
MongoDB Enterprise: The subscription-based, self-managed version of MongoDB
MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB
To learn more about WiredTiger memory use for deployments hosted in MongoDB Atlas, see Memory.
The following operational notes and limitations apply to the WiredTiger engine:
You can't pin documents to the WiredTiger cache.
WiredTiger doesn't reserve a portion of the cache for reads and another for writes.
A heavy write workload can affect performance, but WiredTiger prioritizes index caching in such cases.
WiredTiger allocates its cache to the entire
mongodinstance. WiredTiger doesn't allocate cache on a per-database or per-collection level.
WiredTiger uses document-level concurrency control for write operations. As a result, multiple clients can modify different documents of a collection at the same time.
For most read and write operations, WiredTiger uses optimistic concurrency control. WiredTiger uses only intent locks at the global, database and collection levels. When the storage engine detects conflicts between two operations, one will incur a write conflict causing MongoDB to transparently retry that operation.
Some global operations, typically short lived operations involving
multiple databases, still require a global "instance-wide" lock.
Some other operations, such as
collMod, still require
an exclusive database lock.
WiredTiger uses MultiVersion Concurrency Control (MVCC). At the start of an operation, WiredTiger provides a point-in-time snapshot of the data to the operation. A snapshot presents a consistent view of the in-memory data.
When writing to disk, WiredTiger writes all the data in a snapshot to disk in a consistent way across all data files. The now-durable data act as a checkpoint in the data files. The checkpoint ensures that the data files are consistent up to and including the last checkpoint; i.e. checkpoints can act as recovery points.
Starting in version 3.6, MongoDB configures WiredTiger to create checkpoints (i.e. write the snapshot data to disk) at intervals of 60 seconds. In earlier versions, MongoDB sets checkpoints to occur in WiredTiger on user data at an interval of 60 seconds or when 2 GB of journal data has been written, whichever occurs first.
During the write of a new checkpoint, the previous checkpoint is still valid. As such, even if MongoDB terminates or encounters an error while writing a new checkpoint, upon restart, MongoDB can recover from the last valid checkpoint.
The new checkpoint becomes accessible and permanent when WiredTiger's metadata table is atomically updated to reference the new checkpoint. Once the new checkpoint is accessible, WiredTiger frees pages from the old checkpoints.
Starting in MongoDB 5.0, you can use the
minSnapshotHistoryWindowInSeconds parameter to specify how
long WiredTiger keeps the snapshot history.
Increasing the value of
increases disk usage because the server must maintain the history of
older modified values within the specified time window. The amount of
disk space used depends on your workload, with higher volume workloads
requiring more disk space.
MongoDB maintains the snapshot history in the
located in your specified
WiredTiger uses a write-ahead log (i.e. journal) in combination with checkpoints to ensure data durability.
The WiredTiger journal persists all data modifications between checkpoints. If MongoDB exits between checkpoints, it uses the journal to replay all data modified since the last checkpoint. For information on the frequency with which MongoDB writes the journal data to disk, see Journaling Process.
WiredTiger journal is compressed using the snappy compression
library. To specify a different compression algorithm or no
compression, use the
For details on changing the journal compressor, see
Change WiredTiger Journal Compressor.
If a log record less than or equal to 128 bytes (the mininum log record size for WiredTiger), WiredTiger does not compress that record.
With WiredTiger, MongoDB supports compression for all collections and indexes. Compression minimizes storage use at the expense of additional CPU.
For collections, the following block compression libraries are also available:
To specify an alternate compression algorithm or no compression, use
Compression settings are also configurable on a per-collection and per-index basis during collection and index creation. See Specify Storage Engine Options and db.collection.createIndex() storageEngine option.
For most workloads, the default compression settings balance storage efficiency and processing requirements.
The WiredTiger journal is also compressed by default. For information on journal compression, see Journal.
With WiredTiger, MongoDB utilizes both the WiredTiger internal cache and the filesystem cache.
Starting in MongoDB 3.4, the default WiredTiger internal cache size is the larger of either:
50% of (RAM - 1 GB), or
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).
In some instances, such as when running in a container, the database can have memory constraints that are lower than the total system memory. In such instances, this memory limit, rather than the total system memory, is used as the maximum RAM available.
To see the memory limit, see
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
--wiredTigerCacheSizeGB. Avoid increasing the WiredTiger
internal cache size above its default value.