Introduction to MongoDB

Presented at the LA NoSQL meetup on 7/12/2011

Transcript

1. Sridhar Nanjundeswaran Software Engineer, 10gen © Copyright 2010 10gen Inc.
2. Overview • Non-Relational Databases • MongoDB • Use cases and customers
3. • Why do we need them? • Type of non-relational databases Non-Relational Databases
4. Costs go up
5. Productivity goes down
6. Other issues with traditional RDBMS • Application evolution • Replication for high read loads • Sharding for write throughput
7. Non-Relational Data Models • Data model determines the kinds of items that can be stored and retrieved • What can the system store? • Opaque data, documents? • What kind of queries can you do? • E.g . SQL is based on relational algebra
8. Types of Non-Relational Data Models • Key-value stores • Document stores • Column-oriented databases • Graph databases
9. Consistency Models • Relational databases support transactions • Can only see committed changes • Commits/aborts span multiple changes • Read-only transaction flavors • Read committed, repeatable read, etc • Single vs Multi-Master
10. Single Master • All writes go to a single master and then replicated • Replication can provide read scalability • Writing becomes a bottleneck • Physical limitations (seek time) • Throughput of a single I/O subsystem
11. Single Master - Sharding • Partition the primary key space via hashing • Set up a duplicate system for each shard • The write-rate limitation now applies to each shard • Joins or aggregation across shards are problematic • Can the data be re-sharded on a live system? • Can shards be re-balanced on a live system?
12. Multi-Master • Dynamo like solutions • Writes can occur to any node • All writes are replicated everywhere • Collisions can occur • Who wins? • A collision resolution strategy is required
13. No-SQL solutions Data Model Key-Value Document Column- Oriented Consistency Model Single Master Membase MongoDB Multi- Master/Dynamo Riak CouchDB Cassandra, HBase, Hypertable
14. What is MongoDB? MongoDB‟s architecture and features Installing and running MongoDB
15. What is MongoDB • Document Store • Horizontally Scalable • High Performance
16. MongoDB vs Traditional RDBMS databases contain rows server contain tables schema joins
17. Terminology RDBMS Mongo Table, View Collection Row(s) JSON Document Index Index Join Embedded Document Partition Shard Partition Key Shard Key
18. MongoDB is a Single-Master System • All writes are to a primary (master) • Failure of the primary is detected, and a new one is elected • Application writes get an error if there is no quorum to elect a new master • Reads can continue
19. MongoDB Storage Management • Data is kept in memory-mapped files • Files are allocated as needed • Documents in a collection are kept on a list using a geographical addressing scheme • Indexes (B*-trees) point to documents using geographical addresses
20. Release History • First release – February 2009 • v1.0 - August 2009 • v1.2 - December 2009 - Map/Reduce, lots of small things • v1.4 - March 2010 - Concurrency/Geo • V1.6 - August 2010 - Sharding/Replica Sets • V1.8 – March 2011 – Journaling, Covered/Sparse indexes, Geo sphere
21. Documents Blog Post Document p = { author: “sridhar”, date: new Date(), title: “Using the C# driver with MongoDB”, tags: [“NoSQL”, “Mongo”, “MongoDB”]} > db.posts.save(p)
22. Querying >db.posts.find() { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : “sridhar", date : “Mon Jul 11 2011 19:47:11 GMT-0700 (PDT)", title: “Using the C# driver with MongoDB”, tags: [“NoSQL”, “Mongo”, “MongoDB”]}
23. Secondary Indexes Create index on any Field in Document // 1 means ascending, -1 means descending >db.posts.ensureIndex({author: 1}) >db.posts.find({author: „sridhar'}) { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : “sridhar", ... }
24. Query Operators • Conditional Operators • $all, $exists, $mod, $ne, $in, $nin, $nor, $or, $size, $type • $lt, $lte, $gt, $gte // find posts with any tags > db.posts.find( {tags: {$exists: true }} ) // find posts matching a regular expression > db.posts.find( {author: /^sri*/i } ) // count posts by author > db.posts.find( {author: „sridhar‟} ).count()
25. Atomic Operations • $set, $unset, $inc, $push, $pushAll, $pull, $pullAll, $bit > comment = { author: “fred”, date: new Date(), text: “Interesting blog post”} > db.posts.update( { _id: “...” }, $push: {comments: comment} );
26. Nested Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : “sridhar", date : “Mon Jul 11 2011 19:47:11 GMT-0700 (PDT)", text : “Using the C# driver with MongoDB", tags : [ “NoSQL", “Mongo", “MongoDB" ], comments : [ { author : "Fred", date : “Mon Jul 11 2011 20:51:03 GMT-0700 (PDT)", text : “Interesting blog post" } ]}
27. Indexes // Index nested documents > db.posts.ensureIndex( “comments.author”:1 )  db.posts.find({„comments.author‟:‟Fred‟}) // Index on tags > db.posts.ensureIndex( tags: 1) > db.posts.find( { tags: ‟Mongo‟ } ) // geospatial index > db.posts.ensureIndex( “author.location”: “2d” ) > db.posts.find( “author.location” : { $near : [22,42] } )
28. MongoDB – More • Geo-spatial queries • Require a geo index • Find points near a given point • Find points within a polygon/sphere • Built-in Map-Reduce • The caller provides map and reduce functions written in JavaScript
29. Scaling MongoDB • Replication - Read scalability • Master/Slave • Replica Sets • Sharding – Read and write scalability • Collections are sharded • Each shard is served by its own replica set • Shard key ranges are automatically balanced
30. Primary Secondary Secondary Primary Secondary Secondary Primary Secondary Secondary Primary Secondary Secondary Key Range 0..30 Key Range 31..60 Key Range 61..90 Key Range 91.. 100 MongoS MongoS MongoS Read Write MongoS
31. MongoDB Access • Drivers are available in many languages • 10gen supported • C, C# (.Net), C++, Erlang, Haskell, Java, JavaScript, Perl, PHP, Python, Ruby, Scala • Community supported • Clojure, ColdFusion, F#, Go, Groovy, Lua, R • http://www.mongodb.org/display/DOCS/Overview+- +Writing+Drivers+and+Tools
32. V2.0 • Pretty soon • Better concurrency • Faster data compaction • Faster map/reduce • TTL collections • Geospatial polygons • Hash shard key • Index 2.0 (smaller+faster)
33. Future – a short list • Full text Search • More concurrency • Online compaction • Internal compression • New aggregation framework Vote: http://jira.mongodb.org
34. MongoDB Availability • Source • https://github.com/mongodb/mongo • Server • License: AGPL • http://www.mongodb.org/downloads • Drivers • License: Apache • http://www.mongodb.org/display/DOCS/Drivers
35. • Use cases • Case studies Use cases and customers
36. Content Management
37. Gaming
38. Analytics
39. © Copyright 2010 10gen Inc. try at try.mongodb.org
40. @mongodb © Copyright 2010 10gen Inc. conferences, appearances, and meetups http://www.10gen.com/events http://bit.ly/mongofb Facebook | Twitter | LinkedIn http://linkd.in/joinmongo download at mongodb.org We’re Hiring ! sridhar@10gen.com @snanjund