Microsoft Openness Mongo DB

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Transcript of Microsoft Openness Mongo DB

  • 1. Microsoft andOpennessNoSQL on AzureHeriyadi JanwarPlatform Lead

2. + LinuxMicrosoft is playing quitenicely with Linux and otheropen source tools. -Robert McMillan, Wired Enterprise 3. + Apache Hadoop 4. + Java 5. + PHP 6. + Firefox 7. + Drupal 8. + Node.js 9. + SAMBAA few years back, a patchsubmission from coders atMicrosoft would have beenamazing to the point ofunthinkable, but the battlesare mostly over and timeshave changed. 10. Attract IndividualConsumers:- Provide interestingservice- Provide mobilityOnline- Provide socialMonetize the Social: Business - Improve individualMonetize Individual:experience- Upsell service - VIPApplication - Re-sell Aggregate Data(e.g., Advertisers) - Speed - Extra Capabilities 11. Social NetworkING: the Business Problem 100s of million of users Terabytes to petabytes of data Required (eventual) data consistency across users 12. Solution Shard/Partition user data across hundreds tothousands of SQL Databases Propagate data changes from one DB to other DBsusing reliable, async Message Service Provide a caching layer for performance And also used for 13. Many LARGE SCALE customers using similar patterns Patterns Sharding and reliable messaging Sharding and fan/out query layer Caching layer Customer Examples Social Networking: Facebook, MySpace, etc Online electronic stores (cannot give names ) Travel reservation systems (e.g. Choice International) MSN Casual Gaming etc. 14. Require high availability Be able to scale out: Be able to quickly grow and change:Move better support for these patterns into the Data Platform! 15. NoSQL = operational and developer agility at low CapEx and OpEx! Low Cost Processing Paradigms Data Model Paradigms Range from devices, over OLTP Web 2.0 applications to BigData Analytics 16. Data ModelExample Stores (apologies to the ones I did not list)Simple Key-Value PairsMemcache, Redis, Dynamo, Voldermort, LevelDB, Azure CachingWide Sparse Column Sets HyperTable, Big Table, Cassandra, HBASE, Hyperbase, AmazonDynamoDB, Windows Azure Tables, SQL Server/Azure SparsecolumnsBLOBs Amazon S3, Oracle Berkeley NoSQL, Windows Azure Blob Store,SQL Server RBS/FileTableJSON DocumentsMongoDB, CouchBase, Riak, RavenDBGraph Neo4J, GraphDB, HypergraphDB, Stig, IntellidimensionObjects and XML Documents Versant, Oracle Berkeley NoSQL, MarkLogic, existDB, EMCHiveDB, SQL Server/Azure, Oracle, IBM DB2Extended Relational Oracle, EMC SQLFire, IBM DB2, MySQL, Postgres, SQLServer/Azure 17. You want: You can only get 2 of 3 (CAP Theorem) In Brave New World: 18. Performance and Elastic Scale on Demand Automate management lifecycle (or fail) Simple deployment lifecycle No DB or OS Admin telling me what to do 19. Code First and revise quickly Application-model first (before database) Flexible open data models You dont know exactly what you are looking for Lower Pain of adoption and maintenance No DB or OS Admin telling me what to do 20. Low CapEx, Low OpEx Built-in tunable High-Availability Data scale-out (Sharding) Processing scale-out (Map-Reduce, Fan-Out, tunable consistency) Flexible Data Models Integrate with BigData Analytics (e.g., Hadoop)Many Relational Database Systems are incorporating these learning! 21. Provides Data Partitioning/Sharding at the Data Platform Enables applications to build elastic scale-out applications Provides non-blocking SPLIT/DROP for shards (MERGE tocome later) Auto-connect to right shard based on sharding keyvalue Provides SPLIT resilient query mode 22. Flexible data is good, but: Procedural Scale-Out processing is good, but: Eventual Consistency is good, but: Simple Queries are good, but:Many NoSQL Database Systems are starting to incorporate these learnings! 23. Attract IndividualConsumers:- Provide interestingservice- Provide mobilityOnline- Provide socialMonetize the Social: Business - Improve individualMonetize Individual:experience- Upsell service - VIPApplication - Re-sell Aggregate Data(e.g., Advertisers) - Speed - Extra Capabilities 24. ReadableReplica PrimaryCopyShard ReadableOLTP WorkloadsReplicaTraditional OLAP WorkloadsHighly Availableknown schemaHigh Scale Readable Data warehouse, Star joinsReplicaHigh Flexibility PrimaryShard Dynamic OLAP Workloadsmostly touching 1Readableto low number ofReplica3Vs (Volume, Velocity, Variety)shardsExploratory ReadableReplicaScale-out queries, often using PrimaryShard Query eventual consistent scale-out Readable frameworks like HadoopReplicaSQL or NoSQL Store 25. 32 26. http://www.windowsazure.comPresentationSpeaker Date and TimeDo We Have the Tools We Need to Navigate theDave Campbell 2/29 9:00am PSTNew World of Data?Onsite Interview *Tim OReilly, Dave Campbell 2/29 10:15am PSTUnleash Insights on All Data With Microsoft Big Alexander Stojanovic 2/29 11:30am PSTDataOffice Hours (Q&A session)Dave Campbell 2/29 1:30pm PSTHadoop + Javascript: What We Learned Asad Khan2/29 2:20pm PSTDemocratizing BI at Microsoft: 40,000 UsersKirkland Barrett3/1 10:40am PSTand CountingData Marketplaces For Your ExtendedPiyush Lumba 3/1 2:20pm PSTEnterprise 33 27. NoSQL and the Windows Azure Platform http://download.microsoft.com/download/9/E/9/9E9F240D-0EB6-472E-B4DE- 6D9FCBB505DD/Windows%20Azure%20No%20SQL%20White%20Paper.pdf http://blogs.msdn.com/b/cbiyikoglu/archive/2011/03/03/nosql-genes-in-sql-azure- federations.aspx