Overview of no sql

Overview of no sql

• 1.
  Overview of NoSQL ...motivation, technologies,should you care?
• 2.
  Overview ● Evolution of/motivationfor NoSQL databases ● Characterization of NoSQL databases ● Classification of NoSQL databases ● Popularity/usage of NoSQL systems
• 3.
  A brief historyof NoSQL ● Originally coined in 1998 by Strozzi for specific non-rel database ○ easy to use, free, text based data storage, easy manipulation of contents of db ● Reintroduced by Evans (Rackspace) in 2009 for conf on open source distributed databases ○ in response to increase in interest in non RDBMS solutions ■ bringing together Cassandra, Mongo, Couch, etc ● Has grown as a movement over last 3 years
• 4.
  Current status ● Significantbuzz within community in 2010 ○ initial development of technology ○ pioneer deployments ○ lots of meetups/conferences/birds of feathers ● Many key technologies evolved later 2010, 2011 ○ more large deployments for some technologies ○ small companies with no legacy basing operations on NoSQL
• 5.
  Current Status ● 2012 ○ buzz/hype is fading ○ technology continues to mature ○ increased number of deployments ○ skills sought in job market
• 6.
  NoSQL - anegative definition ● NoSQL simply defined by being non- relational ○ diverse set of technologies fall into NoSQL camp ● Motivations mixed ○ open source ○ scale - TB, PB - particulary for read/write latency ○ increased flexibility over RDBMS systems ○ ability to work with raw data ○ ACID not always most appropriate design choice ■ analytics data is excellent example ● Results in many different NoSQL technologies
• 7.
  Typical characteristics ● Don'tuse SQL! ● Open Source ● Intended to deliver performance ○ in some dimension ● Typically JOIN not supported ○ performance hit ● Consistency often relaxed ○ eventual consistency ● More flexibility in schema ○ if schema used at all!
• 8.
  Diversity of NoSQL databases ●122 seperate technologies listed on http: //nosql-database.org/ ○ mix of commercial, open source and some inbetween ● Vary in many dimensions: ○ architecture ○ interfaces ■ api/languages ○ internal data storage ○ distribution mechanisms ■ redundancy, reliability ○ usage - deployments & support community ○ maturity
• 9.
  Classification of NoSQL systems ● Column based solutions ● Document store solutions ● Key/Value solutions ● Graph based solutions ● Less significantly: ○ XML databases ○ Object databases ○ Mulitvalue databases
• 10.
  Column based solutions ●Structured data ○ similar to classical tables ● Generally much more flexible ○ no rigorous schema necessary ○ can typically add columns in ad hoc fashion ■ often without explicitly declaring column ● However, can result in very different usage ○ eg can have millions of columns associated with given row ● Examples: Hadoop/HBase, Cassandra, Hypertable, SimpleDB
• 11.
  Document based solutions ●Less structured data ○ DB composed of 'documents' containing arbitrary data ■ usually containing longer form content eg CMS ● Documents contain some structure to support query/search/filter, etc ● Somewhat less emphasis on a key ○ can be autogenerated ● Quite unlike classical databases ● Examples: MongoDB, CouchDB
• 12.
  Key/value stores ● DBsinspired by memcache ○ simple, fast key/value stores ● Attempt to retain most of DB in memory ○ fast response times ● Different designs for scalability ○ single node/multi node ● Much emphasis on the keys in this type of DB ● Write usually overwrites entire previous entry ● Examples: Redis, Couchbase/Membase, DynamoDB, Riak
• 13.
  Graph based solutions ●Obviously different from previous categories ○ Focus specifically on graphs ● Queries supported are graph-specific ○ eg get nodes related to specified node ● Typically support for solving standard graph problems ○ eg shortest path, general graph traversal ● Can deliver very significant performance over non-graph specific solutions ○ for graph problems! ● Examples: Neo4j
• 14.
  It's a noisyspace... ● Very many candidate technologies ● Relatively small amount of real world solutions ● Differences between classifications above is one of emphasis... ○ column based and document based arrive at semi- structured sweet spot from opposite ends of spectrum ● ...although this results in different preferred use cases... ○ document based solution better for document problems, eg CMS
• 15.
  Common techniques used ●Hashing techniques used to map data to nodes in cluster ● Internode communication via Gossip ● Common replication techniques ● Thrift is used in a few cases ● MapReduce often used to search over distributed system
• 16.
  Comparison (oldish)...
• 17.
  Comparison (oldish)
• 18.
  Comparison (oldish)
• 19.
  Horses for courses... ●SQL is perfectly good solution for many problems ○ tried and tested ● Some problems require alternative solution ○ typically driven by scale and/or flexibility ● NoSQL offers (many) alternatives ○ although relatively easy to identify realistic options ● Column based approaches good for mostly structured data with enhanced flexibility ● Document based approaches good for document oriented problems
• 20.
  ...so let's diveinto one NoSQL database... ● Cassandra...