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Uploaded bychristkv
KEY, PPTX4,203 views

Mongodb intro

Christian Kvalheim gave an introduction to NoSQL and MongoDB. Some key points: 1) MongoDB is a scalable, high-performance, open source NoSQL database that uses a document-oriented model. 2) It supports indexing, replication, auto-sharding for horizontal scaling, and querying. 3) Documents are stored in JSON-like records which can contain various data types including nested objects and arrays.

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Introduction Christian Kvalheim - christkv@10gen.com @christkv
Today's Talk • Quick introduction to NoSQL • Some Background about mongoDB • Using mongoDB • Deploying mongoDB
Database Landscape memcached key/value scalability & performance RDBMS depth of functionality
What is NoSQL? Key / Value Column Graph Document
Key-Value Stores • A mapping from a key to a value • The store doesn't know anything about the the key or value • The store doesn't know anything about the insides of the value • Operations • Set, get, or delete a key-value pair
Column-Oriented Stores • Like a relational store, but flipped around: all data for a column is kept together • An index provides a means to get a column value for a record • Operations: • Get, insert, delete records; updating fields • Streaming column data in and out of Hadoop
Graph Databases • Stores vertex-to-vertex edges • Operations: • Getting and setting edges • Sometimes possible to annotate vertices or edges • Query languages support finding paths between vertices, subject to various constraints
Document Stores • The store is a container for documents • Documents are made up of named fields • Fields may or may not have type definitions • e.g. XSDs for XML stores, vs. schema-less JSON stores • Can create "secondary indexes" • These provide the ability to query on any document field(s) • Operations: • Insert and delete documents • Update fields within documents
What is mongoDB? MongoDB is a scalable, high-performance, open source NoSQL database. • Document-oriented storage • Full Index Support • Replication & High Availability • Auto-Sharding • Querying • Fast In-Place Updates • Map/Reduce • GridFS
• Company behind mongoDB – (A)GPL license, own copyrights, engineering team – support, consulting, commercial license • Management – Google/DoubleClick, Oracle, Apple, NetApp – Funding: Sequoia, Union Square, Flybridge – Offices in NYC, Palo Alto, London, Dublin – 100+ employees
Where can you use it? MongoDB is Implemented in C++ • Platforms 32/64 bit Windows, Linux, Mac OS-X, FreeBSD, Solaris Drivers are available in many languages 10gen supported • C, C# (.Net), C++, Erlang, Haskell, Java, JavaScript, Perl, PHP, Python, Ruby, Scala, Node.JS Community supported • Clojure, ColdFusion, F#, Go, Groovy, Lua, R ... http://www.mongodb.org/display/DOCS/Drivers
History • First release – February 2009 • v1.0 - August 2009 • v1.2 - December 2009 – MapReduce, ++ • v1.4 - March 2010 – Concurrency, Geo • V1.6 - August 2010 – Sharding, Replica Sets • V1.8 – March 2011 – Journaling, Geosphere • V2.0 - Sep 2011 – V1 Indexes, Concurrency • V2.2 - Soon - Aggregation, Concurrency
Terminology RDBMS MongoDB Table Collection Row(s) JSON Document Index Index Join Embedding & Linking Partition Shard Partition Key Shard Key
Documents Blog Post Document > p = { author: "Chris", date: new ISODate(), text: "About MongoDB...", tags: ["tech", "databases"]} > db.posts.save(p)
Querying > db.posts.find() { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "Chris", date : ISODate("2012-02-02T11:52:27.442Z"), text : "About MongoDB...", tags : [ "tech", "databases" ] } Notes: _id is unique, but can be anything you'd like
Introducing BSON JSON has powerful, but limited set of datatypes • arrays, objects, strings, numbers and null BSON is a binary representation of JSON • Adds extra dataypes with Date, Int types, Id, … • Optimized for performance and navigational abilities • And compression MongoDB sends and stores data in BSON • bsonspec.org
Secondary Indexes Create index on any Field in Document // 1 means ascending, -1 means descending > db.posts.ensureIndex({author: 1}) > db.posts.findOne({author: 'Chris'}) { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author: "Chris", ... }
Compound Indexes Create index on multiple fields in a Document // 1 means ascending, -1 means descending > db.posts.ensureIndex({author: 1, ts: -1}) > db.posts.find({author: 'Chris'}).sort({ts: -1}) [{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author: "Chris", ...}, { _id : ObjectId("4f61d325c496820ceba84124"), author: "Chris", ...}]
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: /^ro*/i }) // count posts by author > db.posts.find({author: 'Chris'}).count()
Examine the query plan > db.posts.find({"author": 'Ross'}).explain() { "cursor" : "BtreeCursor author_1", "nscanned" : 1, "nscannedObjects" : 1, "n" : 1, "millis" : 0, "indexBounds" : { "author" : [ [ "Chris", "Chris" ] ] } }
Atomic Operations $set, $unset, $inc, $push, $pushAll, $pull, $pullAll, $bit // Create a comment > new_comment = { author: "Fred", date: new Date(), text: "Best Post Ever!"} // Add to post > db.posts.update({ _id: "..." }, {"$push": {comments: new_comment}, "$inc": {comments_count: 1} });
Nested Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "Chris", date : "Thu Feb 02 2012 11:50:01", text : "About MongoDB...", tags : [ "tech", "databases" ], comments : [{ author : "Fred", date : "Fri Feb 03 2012 13:23:11", text : "Best Post Ever!" }], comment_count : 1 }
Nested Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "Chris", date : "Thu Feb 02 2012 11:50:01", text : "About MongoDB...", tags : [ "tech", "databases" ], comments : [{ author : "Fred", date : "Fri Feb 03 2012 13:23:11", text : "Best Post Ever!" }], comment_count : 1 }
Secondary Indexes // Index nested documents > db.posts.ensureIndex("comments.author": 1) > db.posts.find({"comments.author": "Fred"}) // Index on tags (multi-key index) > db.posts.ensureIndex( tags: 1) > db.posts.find( { tags: "tech" } )
Geo • Geo-spatial queries • Require a geo index • Find points near a given point • Find points within a polygon/sphere // geospatial index > db.posts.ensureIndex( "author.location": "2d" ) > db.posts.find( "author.location" : { $near : [22, 42] } )
Map Reduce The caller provides map and reduce functions written in JavaScript // Emit each tag > map = "this['tags'].forEach( function(item) {emit(item, 1);} );" // Calculate totals > reduce = "function(key, values) { var total = 0; var valuesSize = values.length; for (var i=0; i < valuesSize; i++) { total += parseInt(values[i], 10); } return total; };
Map Reduce // run the map reduce > db.posts.mapReduce(map, reduce, {"out": { inline : 1}}); { "results" : [ {"_id" : "databases", "value" : 1}, {"_id" : "tech", "value" : 1 } ], "timeMillis" : 1, "counts" : { "input" : 1, "emit" : 2, "reduce" : 0, "output" : 2 }, "ok" : 1, }
Aggregation - coming in 2.2 // Count tags > agg = db.posts.aggregate( {$unwind: "$tags"}, {$group : {_id : "$tags", count : {$sum: 1}}} ) > agg.result [{"_id": "databases", "count": 1}, {"_id": "tech", "count": 1}]
GridFS Save files in mongoDB Stream data back to the client // (Python) Create a new instance of GridFS >>> fs = gridfs.GridFS(db) // Save file to mongo >>> my_image = open('my_image.jpg', 'r') >>> file_id = fs.put(my_image) // Read file >>> fs.get(file_id).read()
Rich Documents • Intuitive • Developer friendly • Encapsulates whole objects • Performant • They are scalable
Rich Documents { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), line_items : [ { sku: 'tt-123', name: 'Coltrane: Impressions' }, { ski: 'tt-457', name: 'Davis: Kind of Blue' } ], address : { name: 'Banker', street: '111 Main', zip: 10010 }, payment: { cc: 4567, exp: Date(2012, 7, 7) }, subtotal: 2355 }
Deployment • Single server - need a strong backup plan P
Deployment • Single server - need a strong backup plan P • Replica sets - High availability P S S - Automatic failover
Deployment • Single server - need a strong backup plan P • Replica sets - High availability P S S - Automatic failover • Sharded - Horizontally scale - Auto balancing P S S P S S
MongoDB Use Cases • Archiving • Content Management • Ecommerce • Finance • Gaming • Government • Metadata Storage • News & Media • Online Advertising • Online Collaboration • Real-time stats/analytics • Social Networks • Telecommunications
In Production
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