Distributed Transactions from the Python SDK

    A practical guide to using Couchbase distributed ACID transactions with the Python SDK.

    This document presents a practical HOWTO on using Couchbase transactions, following on from our transactions documentation.


    • Couchbase Server 6.6.1 or above.

    • Couchbase Python SDK 4.0.0 or above.

    • NTP should be configured so nodes of the Couchbase cluster are in sync with time.

    • The application, if it is using extended attributes (XATTRs), must avoid using the XATTR field txn, which is reserved for Couchbase use.

    If using a single node cluster (for example, during development), then note that the default number of replicas for a newly created bucket is 1. If left at this default, then all Key-Value writes performed at with durability will fail with a DurabilityImpossibleException. In turn this will cause all transactions (which perform all Key-Value writes durably) to fail. This setting can be changed via GUI or command line. If the bucket already existed, then the server needs to be rebalanced for the setting to take affect.

    Getting Started

    Couchbase transactions require no additional components or services to be configured. Simply pip install the most recent version of the SDK. You may, on occasion, need to import some enumerations for particular settings, but in basic cases nothing is needed.


    Transactions can optionally be globally configured when configuring the Cluster. For example, if you want to change the level of durability which must be attained, this can be configured as part of the connect options:

    opts = ClusterOptions(authenticator=PasswordAuthenticator("Administrator", "password"),
    cluster = Cluster.connect('couchbase://localhost', opts)

    The default configuration will perform all writes with the durability setting Majority, ensuring that each write is available in-memory on the majority of replicas before the transaction continues. There are two higher durability settings available that will additionally wait for all mutations to be written to physical storage on either the active or the majority of replicas, before continuing. This further increases safety, at a cost of additional latency.

    A level of None is present but its use is discouraged and unsupported. If durability is set to None, then ACID semantics are not guaranteed.

    Creating a Transaction

    A core idea of Couchbase transactions is that an application supplies the logic for the transaction inside a lambda, including any conditional logic required, and the transaction is then automatically committed. If a transient error occurs, such as a temporary conflict with another transaction, then the transaction will rollback what has been done so far and run the lambda again. The application does not have to do these retries and error handling itself.

    Each run of the lambda is called an attempt, inside an overall transaction.

    def txn_logic_ex(ctx  # type: AttemptContext
        … Your transaction logic here …
        'txn_logic_ex' is a Python closure that takes an AttemptContext. The
        AttemptContext permits getting, inserting, removing and replacing documents,
        performing N1QL queries, etc.
        Committing is implicit at the end of the closure.
    except TransactionFailed as ex:
        print(f'Transaction did not reach commit point.  Error: {ex}')
    except TransactionCommitAmbiguous as ex:
        print(f'Transaction possibly committed.  Error: {ex}')

    The lambda gets passed a AttemptContext object, generally referred to as ctx here.

    Since the lambda may be rerun multiple times, it is important that it does not contain any side effects. In particular, you should never perform regular operations on a Collection, such as collection.insert(), inside the lambda. Such operations may be performed multiple times, and will not be performed transactionally. Instead such operations must be done through the ctx object, e.g. ctx.insert().


    A code example is worth a thousand words, so here is a quick summary of the main transaction operations. They are described in more detail below.

    inventory = cluster.bucket("travel-sample").scope("inventory")
    def txn_example(ctx):
        # insert doc
        ctx.insert(collection, 'doc-a', {})
        # get a doc
        doc_a = ctx.get(collection, 'doc-a')
        # replace a doc
        doc_b = ctx.get(collection, 'doc-b')
        content = doc_b.content_as[dict]
        content['transactions'] = 'are awesome!'
        ctx.replace(doc_b, content)
        # remove a doc
        doc_c = ctx.get(collection, 'doc-c')
        query_str = 'SELECT * FROM `travel-sample`.inventory.hotel WHERE country = "United Kingdom" LIMIT 2;'
        res = ctx.query(query_str)
        rows = [r for r in res.rows()]
        query_str = 'UPDATE `travel-sample`.inventory.route SET airlineid = "airline_137" WHERE airline = "AF"'
        res = ctx.query(query_str)
        rows = [r for r in res.rows()]
    except TransactionFailed as ex:
        print(f'Transaction did not reach commit point.  Error: {ex}')
    except TransactionCommitAmbiguous as ex:
        print(f'Transaction possibly committed.  Error: {ex}')

    Transaction Mechanics

    While this document is focussed on presenting how transactions are used at the API level, it is useful to have a high-level understanding of the mechanics. Reading this section is completely optional.

    Recall that the application-provided lambda (containing the transaction logic) may be run multiple times by Couchbase transactions. Each such run is called an attempt inside the overall transaction.

    Active Transaction Record Entries

    The first mechanic is that each of these attempts adds an entry to a metadata document in the Couchbase cluster. These metadata documents:

    • Are named Active Transaction Records, or ATRs.

    • Are created and maintained automatically.

    • Begin with "_txn:atr-".

    • Each contain entries for multiple attempts.

    • Are viewable, and they should not be modified externally.

    Each such ATR entry stores some metadata and, crucially, whether the attempt has committed or not. In this way, the entry acts as the single point of truth for the transaction, which is essential for providing an 'atomic commit' during reads.

    Staged Mutations

    The second mechanic is that mutating a document inside a transaction, does not directly change the body of the document. Instead, the post-transaction version of the document is staged alongside the document (technically in its extended attributes (XATTRs)). In this way, all changes are invisible to all parts of the Couchbase Data Platform until the commit point is reached.

    These staged document changes effectively act as a lock against other transactions trying to modify the document, preventing write-write conflicts.


    There are safety mechanisms to ensure that leftover staged changes from a failed transaction cannot block live transactions indefinitely. These include an asynchronous cleanup process that is started with the first transaction, and scans for expired transactions created by any application, on the relevant collections.

    Note that if an application is not running, then this cleanup is also not running.

    The cleanup process is detailed below in Asynchronous Cleanup.


    Only once the lambda has successfully run to conclusion, will the attempt be committed. This updates the ATR entry, which is used as a signal by transactional actors to use the post-transaction version of a document from its XATTRs. Hence, updating the ATR entry is an 'atomic commit' switch for the transaction.

    After this commit point is reached, the individual documents will be committed (or "unstaged"). This provides an eventually consistent commit for non-transactional actors.

    Key-Value Mutations


    Replacing a document requires a ctx.get() call first. This is necessary so the transaction can check that the document is not involved in another transaction. If it is, then the transaction will handle this at the ctx.replace() point. Generally, this involves rolling back what has been done so far, and retrying the lambda. Handling errors should be done through try/except as in the example above.

    def txn_logic(ctx):
        doc = ctx.get(collection, key)
        content = doc.content_as[dict]
        content['transactions'] = 'are awesome!'
        ctx.replace(doc, content)


    As with replaces, removing a document requires a ctx.get() call first.

    def txn_logic(ctx):
        doc = ctx.get(collection, key)


    def txn_logic(ctx):
        ctx.insert(collection, key, content)

    Key-Value Reads

    From a transaction context you may get a document:

    def txn_logic(ctx):
        doc = ctx.get(collection, key)
        doc_content = doc.content_as[dict]

    get may cause the transaction to fail with TransactionFailed (after rolling back any changes, of course). It is provided as a convenience method so the developer does not have to check the Optional if the document must exist for the transaction to succeed.

    Gets will 'read your own writes', e.g. this will succeed:

    def txn_logic(ctx):
        ctx.insert(collection, key, content)
        doc = ctx.get(collection, key)
        doc_content = doc.content_as[dict]

    N1QL Queries

    As of Couchbase Server 7.0, N1QL queries may be used inside the transaction lambda, freely mixed with Key-Value operations.


    There are two ways to initiate a transaction with Couchbase 7.x: via the SDK, and via the query service directly using BEGIN TRANSACTION. The latter is intended for those using query via the REST API, or using the query workbench in the UI, and it is strongly recommended that application writers instead use the SDK. This provides these benefits:

    • It automatically handles errors and retrying.

    • It allows Key-Value operations and N1QL queries to be freely mixed.

    • It takes care of issuing BEGIN TRANSACTION, END TRANSACTION, COMMIT and ROLLBACK automatically. These become an implementation detail and you should not use these statements inside the lambda.

    Supported N1QL

    The majority of N1QL DML statements are permitted within a transaction. Specifically: INSERT, UPSERT, DELETE, UPDATE, MERGE and SELECT are supported.

    DDL statements, such as CREATE INDEX, are not.

    Using N1QL

    If you already use N1QL from the Python SDK, then its use in transactions is very similar. It returns the same QueryResult you are used to, and takes most of the same options.

    You must take care to write ctx.query() inside the lambda however, rather than cluster.query() or scope.query().

    An example of selecting some rows from the travel-sample bucket:

    def txn_select(ctx):
        query_str = 'SELECT * FROM `travel-sample`.inventory.hotel WHERE country = "United Kingdom" LIMIT 2;'
        res = ctx.query(query_str)
        rows = [r for r in res.rows()]

    And an example combining SELECTs and UPDATEs. It’s possible to call regular Python functions from the lambda, as shown here, permitting complex logic to be performed. Just remember that since the lambda may be called multiple times, so may the method.

    def txn_complex(ctx):
        # find all hotels of the chain
        res = ctx.query(
            'SELECT reviews FROM `travel-sample`.inventory.hotel WHERE url = "http://marriot%" AND country = "United States"')
        # This function (not provided here) will use a trained machine learning model to provide a
        # suitable price based on recent customer reviews.
        updated_price = price_from_recent_reviews(res)
        # Set the price of all hotels in the chain
        query_str = f'UPDATE `travel-sample`.inventory.hotel SET price = {updated_price} WHERE url LIKE "http://marriot%" AND country = "United States"'

    Read Your Own Writes

    As with Key-Value operations, N1QL queries support Read Your Own Writes.

    This example shows inserting a document and then selecting it again.

    def txn_logic(ctx):
            "INSERT INTO `travel-sample` VALUES ('doc', {'hello':'world'})")  (1)
        query_str = "SELECT hello FROM `travel-sample` WHERE META().id = 'doc'"  (2)
        res = ctx.query(query_str)
    1 The inserted document is only staged at this point, as the transaction has not yet committed. Other transactions, and other non-transactional actors, will not be able to see this staged insert yet.
    2 But the SELECT can, as we are reading a mutation staged inside the same transaction.

    Mixing Key-Value and N1QL

    Key-Value operations and queries can be freely intermixed, and will interact with each other as you would expect.

    In this example we insert a document with Key-Value, and read it with a SELECT.

    def txn_logic(ctx):
        collection = cluster.defaultCollection()
        ctx.insert(collection, 'doc-greeting',
                   {'greeting': 'hello world'})  (1)
        query_str = "SELECT greeting FROM `travel-sample` WHERE META().id = 'doc-greeting'"  (2)
        res = ctx.query(query_str)
    1 As with the 'Read Your Own Writes' example, here the insert is only staged, and so it is not visible to other transactions or non-transactional actors.
    2 But the SELECT can view it, as the insert was in the same transaction.

    Query Options

    Query options can be provided via TransactionQueryOptions, which provides a subset of the options in the Python SDK’s QueryOptions.

    def txn_logic(ctx):
        res = ctx.query(
            "INSERT INTO `travel-sample` VALUES ('doc-abc', {'hello':'world'})",

    Some of the supported options are:

    • scan_consistency

    • client_context_id

    • scan_wait

    • scan_cap

    • pipeline_batch

    • pipeline_cap

    • profile

    • read_only

    • adhoc

    • raw

    See the QueryOptions documentation for details on these.

    Query Concurrency

    Only one query statement will be performed by the query service at a time. Non-blocking mechanisms can be used to perform multiple concurrent query statements, but this may result internally in some added network traffic due to retries, and is unlikely to provide any increased performance.

    Query Performance Advice

    This section is optional reading, and only for those looking to maximize transactions performance.

    After the first query statement in a transaction, subsequent Key-Value operations in the lambda are converted into N1QL and executed by the query service rather than the Key-Value data service. The operation will behave identically, and this implementation detail can largely be ignored, except for these two caveats:

    • These converted Key-Value operations are likely to be slightly slower, as the query service is optimized for statements involving multiple documents. Those looking for the maximum possible performance are recommended to put Key-Value operations before the first query in the lambda, if possible.

    • Those using non-blocking mechanisms to achieve concurrency should be aware that the converted Key-Value operations are subject to the same parallelism restrictions mentioned above, e.g. they will not be executed in parallel by the query service.

    Single Query Transactions

    This section is mainly of use for those wanting to do large, bulk-loading transactions.

    The query service maintains where required some in-memory state for each document in a transaction, that is freed on commit or rollback. For most use-cases this presents no issue, but there are some workloads, such as bulk loading many documents, where this could exceed the server resources allocated to the service. Solutions to this include breaking the workload up into smaller batches, and allocating additional memory to the query service. Alternatively, single query transaction, described here, may be used.

    Single query transactions have these characteristics:

    • They have greatly reduced memory usage inside the query service.

    • As the name suggests, they consist of exactly one query, and no Key-Value operations.

    You will see reference elsewhere in Couchbase documentation to the tximplicit query parameter. Single query transactions internally are setting this parameter. In addition, they provide automatic error and retry handling.

    Single query transactions may be initiated like so:

    bulk_load_statement = ""  # a bulk-loading N1QL statement not provided here
    def txn_logic(ctx):
    except TransactionFailed as ex:
        print(f'Transaction did not reach commit point.  Error: {ex}')
    except TransactionCommitAmbiguous as ex:
        print(f'Transaction possibly committed.  Error: {ex}')

    Query with KV Roles

    To execute a key-value operation within a transaction, users must have the relevant Administrative or Data RBAC roles, and permissions on the relevant buckets, scopes, and collections.

    Similarly, to run a query statement within a transaction, users must have the relevant Administrative or Query & Index RBAC roles, and permissions on the relevant buckets, scopes and collections.

    Refer to Roles for details.

    Query Mode
    When a transaction executes a query statement, the transaction enters query mode, which means that the query is executed with the user’s query permissions. Any key-value operations which are executed by the transaction after the query statement are also executed with the user’s query permissions. These may or may not be different to the user’s data permissions; if they are different, you may get unexpected results.


    Committing is automatic at the end of the code block with the transaction context. If no exception is thrown, it will be committed. If you want to rollback the transaction, simply throw an exception. Transactions may rollback from the transaction logic itself, various failure conditions, or from your application logic by throwing an exception.

    As soon as the transaction is committed, all its changes will be atomically visible to reads from other transactions. The changes will also be committed (or "unstaged") so they are visible to non-transactional actors, in an eventually consistent fashion.

    Commit is final: after the transaction is committed, it cannot be rolled back, and no further operations are allowed on it.

    An asynchronous cleanup process ensures that once the transaction reaches the commit point, it will be fully committed - even if the application crashes.

    A Full Transaction Example

    Let’s pull together everything so far into a more real-world example of a transaction.

    This example simulates a simple Massively Multiplayer Online game, and includes documents representing:

    • Players, with experience points and levels;

    • Monsters, with hitpoints, and the number of experience points a player earns from their death.

    In this example, the player is dealing damage to the monster. The player’s client has sent this instruction to a central server, where we’re going to record that action. We’re going to do this in a transaction, as we don’t want a situation where the monster is killed, but we fail to update the player’s document with the earned experience.

    (Though this is just a demo - in reality, the game would likely live with the small risk and limited impact of this, rather than pay the performance cost for using a transaction.)

    def player_hits_monster(damage, player_id, monster_id, cluster, collection):
            def txn_logic(ctx):
                monster_doc = (ctx.get(collection, monster_id)
                player_doc = (ctx.get(collection, player_id)).content_as[dict]
                monster_hit_points = monster_doc["hitpoints"]
                monster_new_hitpoints = monster_hit_points - damage
                if monster_new_hitpoints <= 0:
                    # Monster is killed. The remove is just for demoing, and a more realistic
                    # example would set a "dead" flag or similar.
                    # The player earns experience for killing the monster
                    experience_for_killing_monster = monster_doc["experience_when_killed"]
                    player_experience = player_doc["experience"]
                    player_new_experience = player_experience + experience_for_killing_monster
                    player_new_level = calculate_level_for_experience(
                    player_content = player_doc.copy()
                    player_content["experience"] = player_new_experience
                    player_content["level"] = player_new_level
                    ctx.replace(player_doc, player_content)
        except TransactionFailed as ex:
            print(f'Transaction did not reach commit point.  Error: {ex}')
            # The operation failed. Both the monster and the player will be untouched.
            # Situations that can cause this would include either the monster
            # or player not existing (as get is used), or a persistent
            # failure to be able to commit the transaction, for example on
            # prolonged node failure.
        except TransactionCommitAmbiguous as ex:
            print(f'Transaction possibly committed.  Error: {ex}')
            # Indicates the state of a transaction ended as ambiguous and may or
            # may not have committed successfully.
            # Situations that may cause this would include a network or node failure
            # after the transactions operations completed and committed, but before the
            # commit result was returned to the client.

    Concurrency with Non-Transactional Writes

    This release of transactions for Couchbase requires a degree of co-operation from the application. Specifically, the application should ensure that non-transactional writes are never done concurrently with transactional writes, on the same document.

    This requirement is to ensure that the strong Key-Value performance of Couchbase was not compromised. A key philosophy of our transactions is that you 'pay only for what you use'.

    If two such writes do conflict then the behaviour is undefined: either write may 'win', overwriting the other. This still applies if the non-transactional write is using CAS.

    Note this only applies to writes. Any non-transactional reads concurrent with transactions are fine, and are at a Read Committed level.


    If an exception is thrown, either by the application from the lambda, or by the transaction internally, then that attempt is rolled back. The transaction logic may or may not be retried, depending on the exception.

    If the transaction is not retried then it will throw an exception, and its message property can be used to inspect the details of the failure.

    The application can use this to signal why it triggered a rollback, as so:

        def txn_logic(ctx):
            customer = ctx.get(collection, "customer-name")
            if customer.content_as[dict]["balance"] < cost_of_item:
                raise InsufficientBalanceException()
            # else continue transaction
    except TransactionCommitAmbiguous:
        # This exception can only be thrown at the commit point, after the
        # BalanceInsufficient logic has been passed, so there is no need to
        # check the cause property here.
    except InsufficientBalanceException as e:
        raise InsufficientBalanceException("user had Insufficient balance", e)

    After a transaction is rolled back, it cannot be committed, no further operations are allowed on it, and the system will not try to automatically commit it at the end of the code block.

    Error Handling

    As discussed previously, Couchbase transactions will attempt to resolve many errors for you, through a combination of retrying individual operations and the application’s lambda. This includes some transient server errors, and conflicts with other transactions.

    But there are situations that cannot be resolved, and total failure is indicated to the application via errors. These situations include:

    • Any error thrown by your transaction lambda, either deliberately or through an application logic bug.

    • Attempting to insert a document that already exists.

    • Calling ctx.get() on a document key that does not exist (if the resultant exception is not caught).

    Once one of these errors occurs, the current attempt is irrevocably failed (though the transaction may retry the lambda to make a new attempt). It is not possible for the application to catch the failure and continue (with the exception of ctx.get() raising an error). Once a failure has occurred, all other operations tried in this attempt (including commit) will instantly fail.

    Transactions, as they are multi-stage and multi-document, also have a concept of partial success or failure. This is signalled to the application through the TransactionResult.unstaging_complete property, described later.

    These are some of the exceptions that Couchbase transactions can raise to the application: TransactionFailed, TransactionExpired and TransactionCommitAmbiguous.

    TransactionFailed and TransactionExpired

    The transaction definitely did not reach the commit point. TransactionFailed indicates a fast-failure whereas TransactionExpired indicates that retries were made until the timeout was reached, but this distinction is not normally important to the application and generally TransactionExpired does not need to be handled individually.

    Either way, an attempt will have been made to rollback all changes. This attempt may or may not have been successful, but the results of this will have no impact on the protocol or other actors. No changes from the transaction will be visible, both to transactional and non-transactional actors.

    Handling: Generally, debugging exactly why a given transaction failed requires review of the logs, so it is suggested that the application log these on failure (see Logging). The application may want to try the transaction again later. Alternatively, if transaction completion time is not a priority, then transaction timeouts (which default to 15 seconds) can be extended across the board through TransactionsConfig.

    This will allow the protocol more time to get past any transient failures (for example, those caused by a cluster rebalance). The tradeoff to consider with longer timeouts, is that documents that have been staged by a transaction are effectively locked from modification from other transactions, until the timeout has been reached.

    Note that the timeout is not guaranteed to be followed precisely. For example, if the application were to do a long blocking operation inside the lambda (which should be avoided), then timeout can only trigger after this finishes. Similarly, if the transaction attempts a key-value operation close to the timeout, and that key-value operation times out, then the transaction timeout may be exceeded.


    As discussed previously, each transaction has a 'single point of truth' that is updated atomically to reflect whether it is committed.

    However, it is not always possible for the protocol to become 100% certain that the operation was successful, before the transaction expires. This potential ambiguity is unavoidable in any distributed system; a classic example is a network failure happening just after an operation was sent from a client to a server. The client will not get a response back and cannot know if the server received and executed the operation.

    The ambiguity is particularly important at the point of the atomic commit, as the transaction may or may not have reached the commit point. Couchbase transactions will raise TransactionCommitAmbiguous to indicate this state. It should be rare to receive this error.

    If the transaction had in fact successfully reached the commit point, then the transaction will be fully completed ("unstaged") by the asynchronous cleanup process at some point in the future. With default settings this will usually be within a minute, but whatever underlying fault has caused the TransactionCommitAmbiguous may lead to it taking longer.

    If the transaction had not in fact reached the commit point, then the asynchronous cleanup process will instead attempt to roll it back at some point in the future.

    Handling: This error can be challenging for an application to handle. As with TransactionFailed it is recommended that it at least writes any logs from the transaction, for future debugging. It may wish to retry the transaction at a later point, or extend transactional timeouts (as detailed above) to give the protocol additional time to resolve the ambiguity.


    This boolean flag indicates whether all documents were able to be unstaged (committed).

    For most use-cases it is not an issue if it is false. All transactional actors will still read all the changes from this transaction, as though it had committed fully. The cleanup process is asynchronously working to complete the commit, so that it will be fully visible to non-transactional actors.

    The flag is provided for those rare use-cases where the application requires the commit to be fully visible to non-transactional actors, before it may continue. In this situation the application can raise an error here, or poll all documents involved until they reflect the mutations.

    If you regularly see this flag false, consider increasing the transaction timeout to reduce the possibility that the transaction times out during the commit.

    Full Error Handling Example

    Pulling all of the above together, this is the suggested best practice for error handling:

        def txn_logic(ctx):
            # ... transactional code here ...
        result = cluster.transactions.run(txn_logic)
        # the transaction definitely reached the commit point. Unstaging
        # the individual documents may or may not have completed
        # TODO find python equivalent
        # if(!result.unstaging_complete):
        # In rare cases, the application may require the commit to have
        # completed.  (Recall that the asynchronous cleanup process is
        # still working to complete the commit.)
        # The next step is application-dependent.
    except TransactionCommitAmbiguous as ex:
        # The transaction may or may not have reached commit point
            f'Transaction returned TransactionCommitAmbiguous and may have succeeded.  Error: {ex}')
    except TransactionFailed as ex:
        # The transaction definitely did not reach commit point
        print(f'Transaction failed with TransactionFailed.  Error: {ex}')

    Asynchronous Cleanup

    Transactions will try to clean up after themselves in the advent of failures. However, there are situations that inevitably created failed, or 'lost' transactions, such as an application crash.

    This requires an asynchronous cleanup task, described in this section.

    The first transaction triggered by an application will spawn a background cleanup task, whose job it is to periodically scan for expired transactions and clean them up. It does this by scanning a subset of the Active Transaction Record (ATR) transaction metadata documents, for each collection used by any transactions.

    The default settings are tuned to find expired transactions reasonably quickly, while creating negligible impact from the background reads required by the scanning process. To be exact, with default settings it will generally find expired transactions within 60 seconds, and use less than 20 reads per second, per collection of metadata documents being checked. This is unlikely to impact performance on any cluster, but the settings may be tuned as desired.

    All applications connected to the same cluster and running transactions will share in the cleanup, via a low-touch communication protocol on the "_txn:client-record" metadata document that will be created in each collection in the cluster involved with transaction metadata. This document is visible and should not be modified externally as it is maintained automatically. All ATRs will be distributed between all cleanup clients, so increasing the number of applications will not increase the reads required for scanning.

    An application may cleanup transactions created by another application.

    It is important to understand that if an application is not running, then cleanup is not running. This is particularly relevant to developers running unit tests or similar.

    Configuring Cleanup

    The cleanup settings can be configured as so:

    Setting Default Description


    60 seconds

    This determines how long a cleanup 'run' is; that is, how frequently this client will check its subset of ATR documents. It is perfectly valid for the application to change this setting, which is at a conservative default. Decreasing this will cause expiration transactions to be found more swiftly (generally, within this cleanup window), with the tradeoff of increasing the number of reads per second used for the scanning process.



    This is the thread that takes part in the distributed cleanup process described above, that cleans up expired transactions created by any client. It is strongly recommended that it is left enabled.



    This thread is for cleaning up transactions created just by this client. The client will preferentially aim to send any transactions it creates to this thread, leaving transactions for the distributed cleanup process only when it is forced to (for example, on an application crash). It is strongly recommended that it is left enabled.

    Monitoring Cleanup

    To monitor cleanup, increase the verbosity on the logging.


    To aid troubleshooting, raise the log level on the SDK.

    Please see the Python SDK logging documentation for details.

    Concurrent Operations

    The API allows operations to be performed concurrently inside a transaction, which can assist performance. There are two rules the application needs to follow:

    • The first mutation must be performed alone, in serial. This is because the first mutation also triggers the creation of metadata for the transaction.

    • All concurrent operations must be allowed to complete fully, so the transaction can track which operations need to be rolled back in the event of failure. This means the application must 'swallow' the error, but record that an error occurred, and then at the end of the concurrent operations, if an error occurred, throw an error to cause the transaction to retry.

    Further Reading

    There’s plenty of explanation about how transactions work in Couchbase in our Transactions documentation.