Bathcamp 2010-riak

Timothy Perrett
Bath Camp 2010

What is Riak?
Documented orientated database
Written in Erlang
Based on Dynamo[1] and CAP Theorem[2]
Highly fault tolerant
HTTP and ProtoBuff interface
Write MapReduce in Erlang or JavaScript
1. http://goo.gl/r8Np
2. http://www.julianbrowne.com/article/viewer/brewers-cap-theorem

Same, Same but different
Riak solves similar problems to MongoDB
Semi-structured data modeled as "documents”
Storage of non-document data in the database
High write-availability
Riak is intrinsically multi-node scalable
Mongo in comparison is single system (+ sharding)
Riak achieves availability via quorum writes
Mongo uses performant in-place writes
Riak uses “masterless” replication

N/R/W – Dynamo
N = Number of replicas to store
R = Number of replicas needed to read
W = Number of replicas needed to read
These principals first appeared in an Amazon research paper known as Dynamo

160bit integer key space. Each node that joins is assigned part of that space for consistent hashing
Hashing means any node can service any request making the cluster masterless and eventually consistant
Number of replicas

Number of replies before Riak gives the client a successful reply.
Tries to access all nodes, but as soon as the N/R is satisfied a response is given
Reads

Same as reads; W implies the number of successful nodes that must reply before the write is considered consistent by the client
Writes

Extreme example
Given N=10, R=W=2 we could have 8 nodes down and the cluster would still be fully available to all clients

What does this all mean?
N/R/W specified at request time, so eachclient can specify its own tolerance foroutages dynamically
Despite any outages within the cluster, the whole cluster can still appear available based on N/R/W
Given N=3 and R=W=2, we can have 3-2=1 node down/unreachable/laggy in the cluster
Stupidly high availability complete with eventual consistency controlled by dynamic clients

Brewer’s CAP Theorem
Consistency
Availability
Partition Tolerance
You cant have all things, all the time…
…but you can have some of each, all the time!
Riak is about choosing your own levels of each according to your use case

Consistency
Start with document version zero
Things get redistributed and n0 and n2 are sitting in NYC and n1 and n3 are in London
What if stuff changes??

Consistency
Uh oh: inconsistency
Both parts of the cluster are still fully available
NYC serves v1 whilst London serves v0
The network resumes and Riak determines the latest version by using vector clocks

Consistency
What if both sides of the Atlantic changed?
Riak is unable to determine which is the right document, both are returned to the client with an indication of the inconsistency

Distributed, fault-tolerant full-text searching
Lucene syntax for queries
No need for index sharding
Linier scaling
Double the number of nodes to get double the search capacity (awesome!)
Search via:
Fields, wildcards, fuzzy text or token proximity
Riak Search

Questions?
basho.com/riak.html
github.com/basho/riak
twitter.com/timperrett
github.com/timperrett
blog.getintheloop.eu

Bathcamp 2010-riak

by Timothy Perrett

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