Because Riak is an eventually consistent, clustered database, conflicts between object replicas stored on different nodes are inevitable, particularly when multiple clients update an object simultaneously.
The Problem of Conflicting Values
To illustrate this problem, imagine that you’re building a
application and storing customer information in Riak. Now imagine that
information about a particular user is being stored in the key
mariejohnston in the bucket
What happens if Marie has two browser windows open and changes her phone
number to 555-1337 in one window and saves it, and then also changes it
to 555-1212 in another window and saves it?
This means that two different values are sent into Riak. So what happens at that point? There are several possible outcomes:
- Riak is able to discern that one object is more causally recent than the other (in this case 555-1212) and chooses to store that value as the “correct” value.
The two operations hit the database at roughly the same time, i.e. two concurrent updates have been completed, and Riak is unable to determine which value “wins.” In this scenario, one of three things can happen:
a. The object is a CRDT, so Riak is able to resolve conflicting values by type-specific rules
b. Riak creates sibling values, aka siblings, for the object
c. Riak resolves the values on the basis of timestamps
In the case of outcome 1 above, Riak uses causal context metadata to make that decision. This metadata is attached to every object in Riak. Causal context comes in two forms in Riak: vector clocks and dotted version vectors. More information in both can be found in the sections below.
In the case of outcome 2, the choice between a, b and c is determined by settings. If you set the
allow_mult parameter to
true for a bucket type, all non-CRDT writes to that bucket type will create siblings in the case of concurrent writes (and occasionally under other
scenarios, e.g. healed network partitions).
allow_mult is set to
false, then Riak will not generate
siblings, instead relying on simple timestamp resolution to decide which value
“wins.” In general, we recommend always setting
true. A more complete discussion can be found in our documentation on
In versions of Riak prior to 1.4, Riak used vector clocks as the sole means of tracking the history of object updates. In Riak versions 2.0 and later, we recommend using dotted version vectors instead, for reasons that are explained in that section.
Like dotted version vectors, vector clocks are a means of tracking events in distributed systems. Unlike normal clocks, vector clocks have no sense of chronological time, i.e. they don’t care if something happened at 6 pm today or back in 1972. They care only about sequences of events. More specifically, they keep track of who—i.e. which actor in the system—has modified an object and how many times they’ve done so.
In a distributed system like Riak, multiple replicas of each object are active in the cluster all the time. Because it’s inevitable that objects will have conflicting values due to events like concurrent updates and healed network partitions, Riak needs a mechanism to keep track of which replica of an object is more current than another. In versions of Riak prior to 2.0, vector clocks were the means employed by Riak to do precisely that.
A number of important aspects of the relationship between object replicas can be determined using vector clocks:
- Whether one object is a direct descendant of the other
- Whether the objects are direct descendants of a common parent
- Whether the objects are unrelated in recent heritage
Behind the scenes, Riak uses vector clocks as an essential element of its active anti-entropy subsystem and of its automatic read repair capabilities.
Vector clocks are non-human-readable metadata attached to all Riak objects. They look something like this:
While vector clocks quite often resolve object conflicts without trouble, there are times when they can’t, i.e. when it’s unclear which value of an object is most current. When that happens, Riak, if configured to do so, will create siblings.
More Information on Vector Clocks
Additional information on vector clocks:
- Conflict Resolution in Riak KV
- Vector Clocks on Wikipedia
- Why Vector Clocks are Easy
- Why Vector Clocks are Hard
- The vector clocks used in Riak are based on the work of Leslie Lamport.
It is possible, though not recommendable, to configure Riak to ensure that only one copy of an object ever exists in a
specific location. This will ensure that at most one value is returned
when a read is performed on a bucket type/bucket/key location (and no
value if Riak returns
It’s also possible, however, to configure Riak to store multiple objects
in a single key if necessary, i.e. for an object to have different
values on different nodes. Objects stored this way have what are called
sibling values. You can instruct Riak to allow for sibling creation by
setting the the
allow_mult bucket property to
true for a specific
bucket, preferably using bucket types.
From the standpoint of application development, the difficulty with siblings is that they by definition conflict with one another. When an application attempts to read an object that has siblings, multiple replicas will be stored in the location where the application is looking. This means that the application will need to develop a strategy for conflict resolution, i.e. the application will need to decide which value is more correct depending on the use case.
Dotted Version Vectors
In versions of Riak prior to 2.0, all causality-based conflict resolution, whether on the client side or in Riak, was achieved using [vector clocks][concept causal context]. In version 2.0, Riak added the option of using dotted version vectors (DVVs) instead.
Like vector clocks, dotted version vectors are a mechanism for tracking object update causality in terms of logical time rather than chronological time (as with timestamps), enabling Riak to make decisions about which objects are more current than others in cases of conflict.
Note: DVVs Recommended Over Vector Clocks
If you are using Riak version 2.0 or later, we strongly recommend using dotted version vectors instead of vector clocks, as DVVs are far better at limiting the number of siblings produced in a cluster, which can prevent a wide variety of potential issues.
DVVs Versus Vector Clocks
The role that DVVs play in Riak is directly analogous to that of vector clocks, as both are used to resolve object conflicts, whether during background operations like active anti-entropy or read repair, or when applications engage in client-side conflict resolution. The crucial difference between them, however, lies in the way that they handle concurrent updates.
Vector clocks can detect concurrent updates to the same object but they
can’t identify which value was associated with each update. If an object
stored in the bucket
frequent_updates with the key
updated by five different clients concurrently and tagged with the same
vector clock, then five values should be created as siblings. However,
depending on the order of delivery of those updates to the different
replicas, sibling values may be duplicated, which can in turn lead to
sibling explosion and thus undue
DVVs, on the other hand, identify each value with the update that
created it. If five clients concurrently update the object above (in the
frequent_updates, with the key
update_me), each of these
updates will be marked with a dot (a minimal vector clock) that indicates the specific event that introduced it. This
means that duplicate values can always be identified and removed,
reducing the likelihood of sibling explosion. Rather than being potentially unbounded, the
number of sibling values will be proportional to the number of
In terms of performance, the difference between vector clocks and DVVs should be minimal in most cases. Because DVVs de-duplicate updates, they should generally be smaller than objects that use vector clocks.
From an application’s perspective, vector clocks and DVVs function in exactly the same fashion. Object updates using DVVs involve the same sequence in interacting with Riak:
- fetch an object from Riak,
- fetch the object’s metadata, which will contain an opaque context
a85hYGBgzGDKBVIcWu/1S4Pjin9lMCWy5bEycN1/cYYvCwA=) for the vector clock or DVV attached to that version of the object, and finally
- pass that opaque context object back to Riak when you update the object.
You will not need to modify your application code when switching from
vector clocks to DVVs, even if you choose to switch all Riak objects in
your cluster to DVVs. You should make sure, however, that the right
bucket types and buckets are being targeted by your application after
dvv_enabled parameter has been changed.
More on using vector clocks and DVVs on the application side can be found in our documentation on conflict resolution.
Note on DVVs and bucket types
The choice between vector clocks and DVVs can be made at the bucket level, using bucket types. This enables you to employ a mixed conflict resolution strategy in your Riak cluster, using DVVs in some buckets and vector clocks in others if you wish. DVVs can be enabled by setting the
dvv_enabledbucket property to
truefor one or more bucket types.
Vector clocks remain the default if you are not using bucket types. However, any bucket type that you create and activate will have
true. And so if you wish to create a bucket type that uses traditional vector clocks, you will need to explicitly set
falsefor that bucket type.
Sibling explosion occurs when an object rapidly collects siblings that are not reconciled. This can lead to a variety of problems, including degraded performance, especially if many objects in a cluster suffer from siblings explosion. At the extreme, having an enormous object in a node can cause reads of that object to crash the entire node. Other issues include undue latency and out-of-memory errors.
To prevent sibling explosion, we recommend the following:
- Use dotted version vectors instead of vector clocks for causal context.
- Always update mutable objects within a read/modify/write cycle. More information can be found in the Object Updates doc.