WiredTiger Storage Engine

Operation and Limitations

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 mongod instance. WiredTiger doesn't allocate cache on a per-database or per-collection level.

Transaction (Read and Write) Concurrency

Starting in version 7.0, MongoDB uses a default algorithm to dynamically adjust the maximum number of concurrent storage engine transactions (read and write tickets). The dynamic concurrent storage engine transaction algorithm optimizes database throughput during cluster overload. The maximum number of concurrent storage engine transactions (read and write tickets) never exceeds 128 read tickets and 128 write tickets and may differ across nodes in a cluster. The maximum number of read tickets and write tickets within a single node are always equal.

To specify a maximum number of read and write transactions (read and write tickets) that the dynamic maximum can not exceed, use storageEngineConcurrentReadTransactions and storageEngineConcurrentWriteTransactions.

If you want to disable the dynamic concurrent storage engine transactions algorithm, file a support request to work with a MongoDB Technical Services Engineer.

To view the number of concurrent read transactions (read tickets) and write transactions (write tickets) allowed in the WiredTiger storage engine, use the serverStatus command and see the wiredTiger.concurrentTransactions parameter.

Document Level Concurrency

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 renameCollection, still require an exclusive database lock in certain circumstances.

Snapshots and Checkpoints

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.

MongoDB configures WiredTiger to create checkpoints, specifically, writing the snapshot data to disk at intervals of 60 seconds.

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.

Journal

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 storage.wiredTiger.engineConfig.journalCompressor setting. For details on changing the journal compressor, see Change WiredTiger Journal Compressor.

Compression

With WiredTiger, MongoDB supports compression for all collections and indexes. Compression minimizes storage use at the expense of additional CPU.

By default, WiredTiger uses block compression with the snappy compression library for all collections and prefix compression for all indexes.

For collections, the following block compression libraries are also available:

• zlib

• zstd

To specify an alternate compression algorithm or no compression, use the storage.wiredTiger.collectionConfig.blockCompressor setting.

For indexes, to disable prefix compression, use the storage.wiredTiger.indexConfig.prefixCompression setting.

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.

Memory Use

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

The default WiredTiger internal cache size is 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 uses 1.5GB of RAM (0.5 * (4 GB - 1 GB) = 1.5 GB). Conversely, on a system with a total of 1.25 GB of RAM WiredTiger allocates 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.

With 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.