Atomicity and Transactions¶
In MongoDB, a write operation is atomic on the level of a single document, even if the operation modifies multiple embedded documents within a single document.
When a single write operation modifies multiple documents, the modification of each document is atomic, but the operation as a whole is not atomic and other operations may interleave. However, you can isolate a single write operation that affects multiple documents using the $isolated operator.
Using the $isolated operator, a write operation that affects multiple documents can prevent other processes from interleaving once the write operation modifies the first document. This ensures that no client sees the changes until the write operation completes or errors out.
An isolated write operation does not provide “all-or-nothing” atomicity. That is, an error during the write operation does not roll back all its changes that preceded the error.
$isolated operator causes write operations to acquire an exclusive lock on the collection, even for document-level locking storage engines such as WiredTiger. That is, $isolated operator will make WiredTiger single-threaded for the duration of the operation.
The $isolated operator does not work on sharded clusters.
Since a single document can contain multiple embedded documents, single-document atomicity is sufficient for many practical use cases. For cases where a sequence of write operations must operate as if in a single transaction, you can implement a two-phase commit in your application.
However, two-phase commits can only offer transaction-like semantics. Using two-phase commit ensures data consistency, but it is possible for applications to return intermediate data during the two-phase commit or rollback.
For more information on two-phase commit and rollback, see Perform Two Phase Commits.
Concurrency control allows multiple applications to run concurrently without causing data inconsistency or conflicts.
One approach is to create a unique index on a field that can only have unique values. This prevents insertions or updates from creating duplicate data. Create a unique index on multiple fields to force uniqueness on that combination of field values. For examples of use cases, see update() and Unique Index and findAndModify() and Unique Index.
Another approach is to specify the expected current value of a field in the query predicate for the write operations. The two-phase commit pattern provides a variation where the query predicate includes the application identifier as well as the expected state of the data in the write operation.