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FAQ: Concurrency

Changed in version 3.0.

MongoDB allows multiple clients to read and write the same data. In order to ensure consistency, it uses locking and other concurrency control measures to prevent multiple clients from modifying the same piece of data simultaneously. Together, these mechanisms guarantee that all writes to a single document occur either in full or not at all and that clients never see an inconsistent view of the data.

What type of locking does MongoDB use?

MongoDB uses multi-granularity locking [1] that allows operations to lock at the global, database or collection level, and allows for individual storage engines to implement their own concurrency control below the collection level (e.g., at the document-level in WiredTiger).

MongoDB uses reader-writer locks that allow concurrent readers shared access to a resource, such as a database or collection, but in MMAPv1, give exclusive access to a single write operation.

In addition to a shared (S) locking mode for reads and an exclusive (X) locking mode for write operations, intent shared (IS) and intent exclusive (IX) modes indicate an intent to read or write a resource using a finer granularity lock. When locking at a certain granularity, all higher levels are locked using an intent lock.

For example, when locking a collection for writing (using mode X), both the corresponding database lock and the global lock must be locked in intent exclusive (IX) mode. A single database can simultaneously be locked in IS and IX mode, but an exclusive (X) lock cannot coexist with any other modes, and a shared (S) lock can only coexists with intent shared (IS) locks.

Locks are fair, with reads and writes being queued in order. However, to optimize throughput, when one request is granted, all other compatible requests will be granted at the same time, potentially releasing them before a conflicting request. For example, consider a case in which an X lock was just released, and in which the conflict queue contains the following items:

IS → IS → X → X → S → IS

In strict first-in, first-out (FIFO) ordering, only the first two IS modes would be granted. Instead MongoDB will actually grant all IS and S modes, and once they all drain, it will grant X, even if new IS or S requests have been queued in the meantime. As a grant will always move all other requests ahead in the queue, no starvation of any request is possible.

In db.serverStatus() and db.currentOp() output, the lock modes are represented as follows:

Lock Mode Description
R Represents Shared (S) lock.
W Represents Exclusive (X) lock.
r Represents Intent Shared (IS) lock.
w Represents Intent Exclusive (IX) lock.
[1]See the Wikipedia page on Multiple granularity locking for more information.

How granular are locks in MongoDB?

Changed in version 3.0.

For WiredTiger

Beginning with version 3.0, MongoDB ships with the WiredTiger storage engine.

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 dropping a collection, still require an exclusive database lock.

For MMAPv1

The MMAPv1 storage engine uses collection-level locking as of the 3.0 release series, an improvement on earlier versions in which the database lock was the finest-grain lock. Third-party storage engines may either use collection-level locking or implement their own finer-grained concurrency control.

For example, if you have six collections in a database using the MMAPv1 storage engine and an operation takes a collection-level write lock, the other five collections are still available for read and write operations. An exclusive database lock makes all six collections unavailable for the duration of the operation holding the lock.

How do I see the status of locks on my mongod instances?

For reporting on lock utilization information on locks, use any of the following methods:

Specifically, the locks document in the output of serverStatus, or the locks field in the current operation reporting provides insight into the type of locks and amount of lock contention in your mongod instance.

In db.serverStatus() and db.currentOp() output, the lock modes are represented as follows:

Lock Mode Description
R Represents Shared (S) lock.
W Represents Exclusive (X) lock.
r Represents Intent Shared (IS) lock.
w Represents Intent Exclusive (IX) lock.

To terminate an operation, use db.killOp().

Does a read or write operation ever yield the lock?

In some situations, read and write operations can yield their locks.

Long running read and write operations, such as queries, updates, and deletes, yield under many conditions. MongoDB operations can also yield locks between individual document modifications in write operations that affect multiple documents like update() with the multi parameter.

For storage engines supporting document level concurrency control, such as WiredTiger, yielding is not necessary when accessing storage as the intent locks, held at the global, database and collection level, do not block other readers and writers. However, operations will periodically yield, such as:

  • to avoid long-lived storage transactions because these can potentially require holding a large amount of data in memory;
  • to serve as interruption points so that you can kill long running operations;
  • to allow operations that require exclusive access to a collection such as index/collection drops and creations.

MongoDB’s MMAPv1 storage engine uses heuristics based on its access pattern to predict whether data is likely in physical memory before performing a read. If MongoDB predicts that the data is not in physical memory, an operation will yield its lock while MongoDB loads the data into memory. Once data is available in memory, the operation will reacquire the lock to complete the operation.

What locks are taken by some common client operations?

The following table lists some operations and the types of locks they use for document level locking storage engines:

Operation Database Collection
Issue a query r (Intent Shared) r (Intent Shared)
Insert data w (Intent Exclusive) w (Intent Exclusive)
Remove data w (Intent Exclusive) w (Intent Exclusive)
Update data w (Intent Exclusive) w (Intent Exclusive)
Perform Aggregation r (Intent Shared) r (Intent Shared)
Create an index (Foreground) W (Exclusive)  
Create an index (Background) w (Intent Exclusive) w (Intent Exclusive)
List collections R (Shared)  
Map-reduce W (Exclusive) and R (Shared) w (Intent Exclusive) and r (Intent Shared)

Which administrative commands lock the database?

Certain administrative commands can exclusively lock the database for extended periods of time. In some deployments, for large databases, you may consider taking the mongod instance offline so that clients are not affected. For example, if a mongod is part of a replica set, take the mongod offline and let other members of the set service load while maintenance is in progress.

The following administrative operations require an exclusive lock at the database level for extended periods:

Commands Methods
copydb. This operation may lock all databases. See Does a MongoDB operation ever lock more than one database?. db.copyDatabase(). This operation may lock all databases. See Does a MongoDB operation ever lock more than one database?.
create when creating a very large (i.e. many gigabytes) capped collection db.createCollection() when creating a very large (i.e. many gigabytes) capped collection
createIndexes for indexes without background set to true db.collection.createIndex() and db.collection.createIndexes() issued without background set to true
reIndex db.collection.reIndex()
repairDatabase db.repairDatabase()

The following administrative operations lock the database but only hold the lock for a very short time:

Commands Methods
authenticate db.auth()
createUser db.createUser()
dropIndexes db.collection.dropIndex()
getLastError db.getLastError()
isMaster db.isMaster()
replSetGetStatus rs.status()
renameCollection db.collection.renameCollection()
serverStatus db.serverStatus()

Does a MongoDB operation ever lock more than one database?

The following MongoDB operations lock multiple databases:

  • db.copyDatabase() must lock the entire mongod instance at once.
  • db.repairDatabase() obtains a global write lock and will block other operations until it finishes.
  • User authentication requires a read lock on the admin database for deployments using 2.6 user credentials. For deployments using the 2.4 schema for user credentials, authentication locks the admin database as well as the database the user is accessing.
  • All writes to a replica set’s primary lock both the database receiving the writes and then the local database for a short time. The lock for the local database allows the mongod to write to the primary’s oplog and accounts for a small portion of the total time of the operation.
  • Replica set member state transitions take global exlusive lock.

How does sharding affect concurrency?

Sharding improves concurrency by distributing collections over multiple mongod instances, allowing shard servers (i.e. mongos processes) to perform any number of operations concurrently to the various downstream mongod instances.

In a sharded cluster, locks apply to each individual shard, not to the whole cluster; i.e. each mongod instance is independent of the others in the sharded cluster and uses its own locks. The operations on one mongod instance do not block the operations on any others.

How does concurrency affect a replica set primary?

With replica sets, when MongoDB writes to a collection on the primary, MongoDB also writes to the primary’s oplog, which is a special collection in the local database. Therefore, MongoDB must lock both the collection’s database and the local database. The mongod must lock both databases at the same time to keep the database consistent and ensure that write operations, even with replication, are “all-or-nothing” operations.

When writing to a replica set, the lock’s scope applies to the primary.

How does concurrency affect secondaries?

In replication, MongoDB does not apply writes serially to secondaries. Secondaries collect oplog entries in batches and then apply those batches in parallel. Secondaries do not allow reads while applying the write operations, and apply write operations in the order that they appear in the oplog.

Does MongoDB support transactions?

Because a single document can contain related data that would otherwise be modeled across separate parent-child tables in a relational schema, MongoDB’s atomic single-document operations already provide transaction semantics that meet the data integrity needs of the majority of applications. One or more fields may be written in a single operation, including updates to multiple sub-documents and elements of an array. The guarantees provided by MongoDB ensure complete isolation as a document is updated; any errors cause the operation to roll back so that clients receive a consistent view of the document.

Starting in version 4.0, for situations that require atomicity for updates to multiple documents or consistency between reads to multiple documents, MongoDB provides multi-document transactions for replica sets, and transactions for sharded clusters are scheduled for MongoDB 4.2.


In most cases, multi-document transaction incurs a greater performance cost over single document writes, and the availability of multi-document transaction should not be a replacement for effective schema design. For many scenarios, the denormalized data model (embedded documents and arrays) will continue to be optimal for your data and use cases. That is, for many scenarios, modeling your data appropriately will minimize the need for multi-document transactions.


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What isolation guarantees does MongoDB provide?

Depending on the read concern, clients can see the results of writes before the writes are durable. To control whether the data read may be rolled back or not, clients can use the readConcern option.

For information, see: