Durability & Failure
Data durability refers to the fault tolerance and persistence of data in the face of software or hardware failure. Even the most reliable software and hardware might fail at some point, and along with the failures, introduce a chance of data loss. Couchbase’s durability features include Synchronous Replication, and the possibility to use distributed, multi-document ACID transactions. It is the responsibility of the development team and the software architect to evaluate the best choice for each use case.
Couchbase’s distributed and scalable nature exposes any set-up to the risk of potential network and hardware problems. The key to durability is planning for resilience, by evaluating the options on offer for persistence and replication, and carefully considering the performance trade-offs involved.
Writes in Couchbase (from the SDK or elsewhere) are written to a single node. From there, Couchbase Server will take care of sending the mutation to any configured replicas, and to disk. By default all writes are asynchronous, but levels of durability can be set, to ensure replication and/or persistence to disks, before the write is committed.
Durability has been enhanced in Couchbase Data Platform 6.5, with Durable Writes, under which mutations will not be visible to other clients until they have met their durability requirements. These requirements are now enforced on the server side, rather than requiring client logic, which means that this committed durability can now be guaranteed.
durabilityLevel parameter, which all mutating operations accept, allows the application to wait until this replication (or persistence) is successful before proceeding.
durabilityLevel() is used with no argument, the application will report success back as soon as the primary node has acknowledged the mutation in its memory.
With Couchbase Server 6.5 and above, the three replication level options 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 order of increasing levels of safety.
For a given node, waiting for writes to storage is considerably slower than waiting for it to be available in-memory.
PersistToMajority will take longer than the other two, and
timeout value needs to be selected with care — particularly for large numbers of documents — after testing on a representative network, with a realistic workload.
Variation in network speeds and conditions, inter alia, make it difficult to give blanket recommendations.
Options for making changes to
kvDurableTimeout for Durable Writes can be found on the Client Settings page.
Increasing the number of reader and writer threads for Couchbase Server storage may be advantageous: see the discussion on the Server storage page.
Durable Writes must not be made with three replicas.
Attempting this will result in an error message:
While Durable Writes are being attempted, another client cannot write to the document concerned — see the diagram and explanation in the Server Durability docs.
Durable Writes do a great deal to mitigate against network problems, but working in a distributed environment always brings its own class of extra problems to any client-server communication.
Consider carefully the case where the client has sent the operation to the server, the server has sent the mutation on to the replica(s), and the replicas have acknowledged completion of their operation(s). Now, at the point where the server is sending the successful status to the client, the network drops. The client is in an ambiguous state, not knowing whether or not the operation was successful.
In cases where the operation is idempotent (such as an upsert operation), then simply retrying is a perfectly acceptable strategy, where the cost of doing so is justified by the importance of the operation. Where the operation is an array append, or somesuch, then distributed, multi-document ACID transactions should be considered.
In a version of Couchbase Server lower than 6.5 is being used then the application can fall-back to 'client verified' durability. Here the SDK will do a simple poll of the replicas and only return once the requested durability level is achieved.
Couchbase Server versions prior to 6.5 implement durability using the
Observe operation for clients to check if a mutation has been replicated and/or persisted to a specified number of cluster nodes.
This allows applications to determine when a write is durable (that is, will still be present after some amount of failure).
The_level of durability_ is configurable with two values:
The replication factor of the operation: how many replicas must this operation be propagated to, using
The_persistence_ of the modification: how many persisted copies of the modified record must exist, using the
When an operation is propagated to a replica, it is first propagated to the replica’s memory and then placed in a queue, to be persisted to the replica’s disk.
You can control whether you desire the operation to only be propagated to a replica’s memory (
ReplicateTo), or whether to wait until it is fully persisted to the replica’s disk (
PersistTo receives a value total to the number of nodes that an operation should be persisted.
The maximum value is the number of replica nodes plus one.
A value of
1 indicates active-only persistence (and no replica persistence).
ReplicateTo receives a value total to the number of replica nodes the operation should be replicated.
Its maximum value is the number of replica nodes.
As with Ambiguity in recent server versions' Synchronous Replication, the operation may succeed before a network (or other) failure occurs — it’s up to the client application to handle such possibilities.
Take a look at the Durability in Action code sample to see how to handle ambiguous situations in your code, with appropriate use of
Note that Couchbase’s design of asynchronous persistence and replication is there for a reason: The time it takes to store an item in memory is several orders of magnitude quicker than persisting it to disk or sending it over the network. Operation times and latencies will increase when waiting on the speed of the network and the speed of the disk.
Durability operations may also cause increased network traffic since it is implemented at the client level by sending special probes to each server once the actual modification has completed.