Active Anti-Entropy

In a clustered, eventually consistent system like Riak, conflicts between object replicas stored on different nodes are an expected byproduct of node failure, concurrent client updates, physical data loss and corruption, and other events that distributed systems are built to handle. These conflicts occur when objects are either

• missing, as when one node holds a replica of the object and another node does not, or
• divergent, as when the values of an existing object differ across nodes.

Riak KV offers two means of resolving object conflicts: read repair and active anti-entropy (AAE). Both of these conflict resolution mechanisms apply both to normal key/value data in Riak as well as to [search indexes][usage search]

Read Repair vs. Active Anti-Entropy

In versions of Riak prior to 1.3, replica conflicts were healed via read repair which is a passive anti-entropy mechanism that heals object conflicts only when a read request reaches Riak from a client. Under read repair, if the vnode coordinating the read request determines that different nodes hold divergent values for the object, the repair process will be set in motion.

One advantage of using read repair alone is that it doesn’t require any kind of background process to take effect, which can cut down on CPU resource usage. The drawback of the read repair-only approach, however, is that the healing process only can only ever reach those objects that are read by clients. Any conflicts in objects that are not read by clients will go undetected.

The active anti-entropy (AAE) subsystem was added to Riak in versions 1.3 and later to enable conflict resolution to run as a continuous background process, in contrast with read repair, which does not run continuously. AAE is most useful in clusters containing so- called “cold data” that may not be read for long periods of time, even months or years, and is thus not reachable by read repair.

Although AAE is enabled by default, it can be turned off if necessary. See our documentation on managing active anti-entropy for information on how to enable and disable AAE, as well as on configuring and monitoring AAE.

Active Anti-Entropy and Hash Tree Exchange

In order to compare object values between replicas without using more resources than necessary, Riak relies on Merkle tree hash exchanges between nodes.

Using this type of exchange enables Riak to compare a balanced tree of Riak object hashes. Any difference at a higher level in the hierarchy means that at least one value has changed at a lower level. AAE recursively compares the tree, level by level, until it pinpoints exact values with a difference between nodes. The result is that AAE is able to run repair operations efficiently regardless of how many objects are stored in a cluster, since it need only repair specific objects instead of all objects.

In contrast with related systems, Riak uses persistent, on-disk hash trees instead of in-memory hash trees. The advantages of this approach are twofold:

• Riak can run AAE operations with a minimal impact on memory usage
• Riak nodes can be restarted without needing to rebuild hash trees

In addition, hash trees are updated in real time as new writes come in, which reduces the time that it takes to detect and repair missing or divergent replicas.

As an additional fallback measure, Riak periodically clears and regenerates all hash trees from on-disk key/value data, which enables Riak to detect silent data corruption to on-disk data arising from disk failure, faulty hardware, and other sources. The default time period for this regeneration is one week, but this can be adjusted in each node’s configuration file.