Ciel, mes données ne sont plus relationnelles
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The document discusses various non-relational database types, including key-value, document, graph, and column-oriented databases, along with their strengths and weaknesses. It emphasizes the importance of understanding the complexity and scale of data to choose suitable database architectures for modern applications. Key insights include the trend towards non-relational databases and the necessity for denormalizing data to optimize application performance.
Ciel, mes données ne sont plus relationnelles
- 1. Ciel ! Mes données ne sont plus relationnelles BLEND WEB MIX 01 Octobre 2013 1
- 2. Xavier Gorse 2 @xgorse
- 3. 3 Association Française des Utilisateurs de PHP • Crée en 2001 • Forum PHP ( 21 & 22 Novembre 2013 à Paris) • AperoPHP et Rendez Vous • Antennes Locale • Président en 2009 www.afup.org Association Francophone des utilisateurs de SYmfony • Initié en 2010 par Hugo Hamon • Pas encore une vraie association • Sfpot mensuel avec conférence suivie d’un apéro • Antenne à Marseille, Lyon ?? www.afsy.fr
- 4. 4 Elao • Fondateur en 2005 • Lyon & Paris • Agence Web Technique de 15 personnes • Symfony depuis 2006 • Partenaire officiel SensioLabs www.elao.com
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- 6. Plan • Trend • Key-value databases • Document databases • Graph databases • Column-oriented databases 6
- 7. RDBMS performance 7 Data complexity Performance Relational database Requirement of application Salary&list& Most&Web&apps& Social&Network& Loca5on7based&services& Source @ianSrobinson - @jimwebber from NeoTechnology
- 8. complexity = f(size, connectedness, uniformity) 8
- 9. Data Size 9 2007 2008 2009 2010 2011 2012 2013
- 10. Data Size • 500 million page views a day • ~3TB of new data to store a day • Posts are about 50GB a day. Follower list updates are about 2.7TB a day. 10
- 11. Connectedness 11 Wikis) Blogs) Tagging) Ontologies) RDFa) web 1.0 web 2.0 “web 3.0” 1990 2000 2010 2020 Source @ianSrobinson - @jimwebber from NeoTechnology Informa(on)connec(vity) Text) Documents) Hypertext) Feeds) UGC) Folksonomies) GGG)
- 12. Uniformity • Semi-‐structured data • Different data lifecycle • Store more data about each en7ty • Individualisa7on & decentraliza7on of content genera7on 12
- 13. NoSQL Not Only SQL 13
- 14. NoSQL • Non-‐Rela7onal • Cluster Friendly • Schema less • Distributed architecture 14
- 15. ACID & CAP Theorem ACID • Atomicity • Consistency • Isola7on • Durability 15 Cap Theorem • Consistency • Availability • Par77on Tolerance
- 16. Column 1 : value Column 2 : value Column 3 : value Key Key Key/Value Column-oriented Field 1 : value Field A : value Field B : value Field 2 : value Node 1 Node 3 Node 2 Node 4 Node 5 Document oriented Graph Key Value Key Value Key Value Key Value 16
- 17. Column 1 : value Column 2 : value Column 3 : value Key Key Key/Value Column-oriented Field 1 : value Field A : value Field B : value Field 2 : value Node 1 Node 3 Node 2 Node 4 Node 5 Document oriented Graph Key Value Key Value Key Value Key Value 17
- 18. Key-value databases • Inspired by Amazon’s Dynamo (2007) • Global collection of key-value • Big scalable HashMap 18
- 19. • Strengths • Simple data model • High performance • Great at scaling out horizontally • Weaknesses • Simplistic data model • Poor for complex data 19 Key-value databases
- 20. • Written in C - BSD License - 2009 • Very fast and light-weigth • All data in memory • Persistence • Master/Slave Replication • Used for caching, session or working queue 20 Key-value databases http://redis.io/
- 21. • Riak • Memcache (RAM) • Voldemort • Amazon DynamoDB (Saas) • IronCache (Saas) 21 Key-value databases
- 22. Column 1 : value Column 2 : value Column 3 : value Key Key Key/Value Column-oriented Field 1 : value Field A : value Field B : value Field 2 : value Node 1 Node 3 Node 2 Node 4 Node 5 Document oriented Graph Key Value Key Value Key Value Key Value 22
- 23. Document databases • Inspired by IBM Lotus Notes/Domino • Idem from Key/Value with value as a document • A document is a key-value collection • Flexible schema • Non-relational, data is de-normalized 23
- 24. Document databases • Strengths • Simple, powerful data model • Good scaling, Easy/Auto sharding • Usually “ACID” compliant • Weaknesses • Unsuited for interconnected data • Query model limited to keys (and indexes) 24
- 25. Document databases • Written in C++ - License AGPL - 2009 • JSON-style documents • Full Index Support • Fast In-Place Updates • Auto-Sharding • Replication & High Availability • A lot of Connector • Big Community • Commercial Support 25 http://www.mongodb.org
- 26. Document databases • Lotus Notes / Domino • CouchDB written in Erlang, Javascript for Query • OrientDB written in Java, relationship as graph 26
- 27. Column 1 : value Column 2 : value Column 3 : value Key Key Key/Value Column-oriented Field 1 : value Field A : value Field B : value Field 2 : value Node 1 Node 3 Node 2 Node 4 Node 5 Document oriented Graph Key Value Key Value Key Value Key Value 27
- 28. Graph databases • Nodes with properties • Named relationships with properties • Focus on the data structure • Direct pointer to its adjacent element and no indexlookups are necessary 28
- 29. Graph databases • Strengths • Powerful data model • Fast for connected data • A new data architecture • Weaknesses • No Sharding : All data in one instance • Using Node/Relation property for Query kill performance • A new data architecture 29
- 30. Graph databases • Java - GPL/Commercial - 2007 • Query language : Cypher / Gremlin • REST Interface • Embed Mode • High Availability ( Master / Slave) • Commercial Support http://neo4j.org 30
- 31. GraphDB - Products • Titan • OrientDB • InfiniteGraph • AllegroGraph 31
- 32. Column 1 : value Column 2 : value Column 3 : value Key Key Key/Value Column-oriented Field 1 : value Field A : value Field B : value Field 2 : value Node 1 Node 3 Node 2 Node 4 Node 5 Document oriented Graph Key Value Key Value Key Value Key Value 32
- 33. Column-oriented database • A big table, with column families • Data stored by column instead of row • Build for distributed architecture • Map-reduce for querying/processing • Flexible schema • Easy sharding (partitioning) 33
- 34. Column-oriented database • Strengths • Data model supports semi-structured data • Naturally indexed (columns) • Horizontally scalable – RW increase linearly • Fault tolerant – no single point of failure • Weaknesses • Unsuited for interconnected data 34
- 35. Column-oriented database • Java - Apache License 2 - 2008 • Developed by Facebook • Decentralized • Supports replication and multi data center replication • Scalability • Fault-tolerant • MapReduce support http://cassandra.apache.org/ 35
- 36. Column-oriented database • HBase (Apache) • HyperTable • BigTable (Google) 36
- 37. Conclusion • Application architecture impact • Store your data in the way you want to query it • Denormalize your data and try to keep them up-to-date ! 37
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- 39. Merci