Why Riak KV?
What is Riak?
Riak is a distributed database designed to deliver maximum data availability by distributing data across multiple servers. As long as your Riak client can reach one Riak server, it should be able to write data.
While Riak is typically known as an eventually consistent system, beginning with version 2.0 it can be used either as an eventually or strongly consistent system, and these two approaches can be mixed and matched in a single cluster.
When Riak is used as an eventually consistent system, the data that you want to read should remain available in most failure scenarios, although it may not be the most up-to-date version of that data.
When Riak is used as a strongly consistent system, on the other hand, reads will return the most up-to-date version of data, with the drawback that some nodes will be temporarily unavailable to receive writes in certain rare situations.
Riak’s goals for Riak
|Availability||Riak writes to and reads from multiple servers to offer data availability even when hardware or the network itself are experiencing failure conditions|
|Operational simplicity||Easily add new machines to your Riak cluster without incurring a larger operational burden|
|Scalability||Riak automatically distributes data around the cluster and yields a near-linear performance increase as you add capacity|
|Masterless||Your requests are not held hostage to a specific server in the cluster that may or may not be available|
When Riak makes sense
If your data does not fit on a single server and demands a distributed database architecture, you should take a close look at Riak as a potential solution to your data availability issues. Getting distributed databases right is very difficult, and Riak was built to address the problem of data availability with as few trade-offs and downsides as possible.
Riak’s focus on availability makes it a good fit whenever downtime is unacceptable. No one can promise 100% uptime, but Riak is designed to survive network partitions and hardware failures that would significantly disrupt most databases. An exception to Riak’s high availability approach is the optional strong consistency feature, which can be applied on a selective basis.
A less-heralded feature of Riak is its predictable latency. Because its fundamental operations—read, write, and delete—do not involve complex data joins or locks, it services those requests promptly. Thanks to this capability, Riak is often selected as a data storage backend for data management software from a variety of paradigms, such as Datomic.
From the standpoint of the actual content of your data, Riak might also be a good choice if your data can be modeled as one of Riak’s currently available Data Types: flags, registers, counters, sets, or maps. These Data Types enable you to take advantage of Riak’s high availability approach while simplifying application development.
When Riak is Less of a Good Fit
We recommend running no fewer than 5 data servers in a cluster. This means that Riak can be overkill for small databases. If you’re not already sure that you will need a distributed database, there’s a good chance that you won’t need Riak.
If explosive growth is a possibility, however, you are always highly advised to prepare for that in advance. Scaling at Internet speeds is sometimes compared to overhauling an airplane mid-flight. If you feel that such a transition might be necessary in the future, then you might want to consider Riak.
Riak’s simple data model, consisting of keys and values as its atomic elements, means that your data must be denormalized if your system is to be reasonably performant. For most applications this is not a serious hurdle. But if your data simply cannot be effectively managed as keys and values, Riak will most likely not be the best fit for you.
Correspondingly, if your application demands a high query load by any
means other than key/value lookup—e.g. SQL-style
SELECT * FROM table
operations—Riak will not be as efficient as other databases. If you
wish to compare Riak with other data technologies, Riak offers a tool
called Riak Bench to help measure its performance, so that you can
decide whether the availability and operational benefits of Riak
outweigh its disadvantages.
How Does a Riak Cluster Work?
A Riak cluster is a group of nodes that are in constant communication to ensure data availability and partition tolerance.
What is a Riak Node?
A Riak node is not quite the same as a server, but in a production environment the two should be equivalent. A developer may run multiple nodes on a single laptop, but this would never be advisable in a real production cluster.
Each node in a Riak cluster is equivalent, containing a complete, independent copy of the whole Riak package. There is no “master” node; no node has more responsibilities than others; and no node has special tasks not performed by other nodes. This uniformity provides the basis for Riak’s fault tolerance and scalability.
Each node is responsible for multiple data partitions, as discussed below:
Riak Automatically Re-Distributes Data When Capacity is Added
When you add (or remove) machines, data is rebalanced automatically with no downtime. New machines claim data until ownership is equally spread around the cluster, with the resulting cluster status updates shared to every node via a gossip protocol and used to route requests. This is what makes it possible for any node in the cluster to receive requests. The end result is that developers don’t need to deal with the underlying complexity of where data lives.
Data is distributed across nodes using consistent hashing. Consistent hashing ensures that data is evenly distributed around the cluster and makes possible the automatic redistribution of data as the cluster scales.
Riak’s replication scheme ensures that you can still read, write, and
update data if nodes go down. Riak allows you to set a replication
variable, N (also known as the
n_val), that specifies the number of
nodes on which a value will be replicated.
n_val value of 3 (the default) means that each object is replicated
3 times. When an object’s key is mapped onto a given node, Riak will
continue on and automatically replicate the data onto two more nodes.
This parameter enables you to replicate values to 7 nodes in a 10-node
cluster, 10 nodes in a 15-node cluster, and so on.
When Things Go Wrong
Riak retains fault tolerance, data integrity, and availability even in failure conditions such as hardware failure and network partitions. Riak has a number of means of addressing these scenarios and other bumps in the road, like version conflicts in data.
Hinted handoff enables Riak to handle node failure. If a node goes down, a neighboring node will take over its storage operations. When the failed node returns, the updates received by the neighboring node are handed back to it. This ensures that availability for writes and updates is maintained automatically, minimizing the operational burden of failure conditions.
In any system that replicates data, conflicts can arise, for example when two clients update the same object at the exact same time or when not all updates have yet reached hardware that is experiencing lag.
In Riak, replicas are eventually consistent, meaning that while data is always available, not all replicas may have the most recent update at the exact same time, causing brief periods—generally on the order of milliseconds—of inconsistency while all state changes are synchronized.
Riak addresses data conflicts as follows: When you make a read request, Riak looks up all replicas for that object. By default, Riak will return the most recently updated version, determined by looking at the object’s vector clock. Vector clocks are metadata attached to each replica when it is created. They are extended each time a replica is updated to keep track of versions. You can also allow clients to resolve conflicts themselves if that is a better fit for your use case.
Riak Data Types
If you are not interested in dealing with version conflicts on the application side, Riak Data Types offer a powerful yet easy-to-use means of storing certain types of data while allowing Riak to handle merge conflicts. These conflicts are resolved automatically by Riak using Data Type-specific algorithms inspired by research into convergent replicated data types.
When an outdated replica is returned as part of a read request, Riak
will automatically update the out-of-sync replica to make it consistent.
Read repair, a self-healing property of
the database, will even update a replica that returns a
the event that a node loses the data due to physical failure.
Reading and Writing Data in Failure Conditions
In Riak, you can set an R value for reads and a W value for writes. These values give you control over how many replicas must respond to a request for it to succeed.
Let’s say that you have an N value of 3 (aka
n_val=3) for a particular
key/value pair, but one of the physical nodes responsible for a replica
is down. With an
r=2 setting, only 2 replicas must return results for
read to be deemed successful. This allows Riak to provide read
availability even when nodes are down or laggy. The same applies for the
W in writes. If this value is not specified, Riak defaults to
according to which the majority of nodes must respond.
There is more on replication properties elsewhere in the documentation.