Key Value Operations


    The complete code sample used on this page can be downloaded from the GitHub repo for the Python docs, from which you can see in context how to authenticate and connect to a Couchbase Cluster, then perform these Bucket operations.


    A document refers to an entry in the database (other databases may refer to the same concept as a row). A document has an ID (primary key in other databases), which is unique to the document and by which it can be located. The document also has a value which contains the actual application data. See the concept guide to Documents for a deeper dive into documents in the Couchbase Data Platform. Or read on, for a hands-on introduction to working with documents from the Python SDK.

    CRUD Operations

    The core interface to Couchbase Server is simple KV operations on full documents. Make sure you’re familiar with the basics of authorization and connecting to a Cluster from the Start Using the SDK section. We’re going to expand on the short Upsert example we used there, adding options as we move through the various CRUD operations. Here is the Insert operation at its simplest:

    document = {"foo":"bar", "bar":"foo"}
    result = collection.insert("document-key", document)

    Options may be added to operations:

    Insert (with options)
    from couchbase.durability import ClientDurability, ServerDurability, Durability, ReplicateTo, PersistTo
    from datetime import timedelta
    document = {"foo":"bar", "bar":"foo"}
    result = collection.insert("document-key", document, expiry=timedelta(seconds=5))

    Setting a Compare and Swap (CAS) value is a form of optimistic locking — dealt with in depth in the CAS page. Here we just note that the CAS is a value representing the current state of an item; each time the item is modified, its CAS changes. The CAS value is returned as part of a document’s metadata whenever a document is accessed. Without explicitly setting it, a newly-created document would have a CAS value of 0.

    document = {"foo":"bar","bar":"foo"}
    result = collection.replace("document-key", document, cas = 12345, expiry = timedelta(minutes=1))

    Expiration sets an explicit time to live (TTL) for a document, for which you can also see a more detailed example of TTL discovery later in the docs. We’ll discuss modifying Expiration in more details below. For a discussion of item (Document) vs Bucket expiration, see the Expiration Overview page.

    document = {"foo": "bar", "bar": "foo"}
    result = collection.upsert("document-key", document, cas=12345, expiry=timedelta(minutes=1),
                               durability=ClientDurability(ReplicateTo.ONE, PersistTo.ONE))

    Here, we have added Durability options, namely PersistTo and ReplicateTo.


    In Couchbase Server releases before 6.5, Durability was set with these two options — see the 6.0 Durability documentation — covering how many replicas the operation must be propagated to and how many persisted copies of the modified record must exist.

    If 6.5 or above is being used, you can take advantage of the Durable Write feature, in which Couchbase Server will only return success to the SDK after the requested replication level has been achieved. The three replication levels are:

    • Majority - The server will ensure that the change is available in memory on the majority of configured replicas.

    • MajorityAndPersistToActive - Majority level, plus persisted to disk on the active node.

    • PersistToMajority - Majority level, plus persisted to disk on the majority of configured replicas.

    The options are in increasing levels of safety. Note that nothing comes for free - for a given node, waiting for writes to storage is considerably slower than waiting for it to be available in-memory. These trade offs, as well as which settings may be tuned, are discussed in the durability page.

    The following example demonstrates using the newer durability features available in Couchbase server 6.5 onwards.

    from couchbase.durability import Durability
    document = dict(foo="bar", bar="foo")
    result = collection.upsert("document-key", document,
                               cas=12345, expiry=timedelta(minutes=1),
    Sub-Document Operations

    All of these operations involve fetching the complete document from the Cluster. Where the number of operations or other circumstances make bandwidth a significant issue, the SDK can work on just a specific path of the document with Sub-Docunent Operations.

    Retrieving full documents

    Using the Get() method with the document key can be done in a similar fashion to the other operations:

    result = collection.get("document-key")
    content = result.content_as[str]


    When removing a document, you will have the same concern for durability as with any additive modification to the Bucket:

    Remove (with options)

    result = collection.remove("document-key",
                               RemoveOptions(cas=12345, durability=ClientDurability(PersistTo.ONE, ReplicateTo.ONE)))

    Expiration / TTL

    By default, Couchbase documents do not expire, but transient or temporary data may be needed for user sessions, caches, or other temporary documents. Using Touch(), you can set expiration values on documents to handle transient data:

    result = collection.touch("document-key", timedelta(seconds=10))

    Atomic document modifications

    The value of a document can be increased or decreased atomically using .increment() and .decrement().

    collection.increment("document-key", DeltaValue(1), initial=SignedInt64(1000))
    Increment (with options)
    collection.increment("document-key", DeltaValue(1), initial=SignedInt64(1000), expiry=timedelta(days=1))
    collection.decrement("document-key", DeltaValue(1), initial=SignedInt64(1000))
    Increment (with options)
    collection.decrement("document-key", DeltaValue(1), initial=SignedInt64(1000), expiry=timedelta(days=1))
    Increment & Decrement are considered part of the ‘binary’ API, and as such may still be subject to change.

    Additional Resources

    A complete Caching example for the Python 3.0 SDK, using Flask, is worked through here.

    Working on just a specific path within a JSON document will reduce network bandwidth requirements - see the Sub-Document pages.

    Our Query Engine enables retrieval of information using the SQL-like syntax of N1QL.