Introduction to MongoDB and it's features as of 2.0.X

1. Introduction
Christian Kvalheim - christkv@10gen.com
@christkv

2. Today's Talk
• Quick introduction to NoSQL
• Some Background about mongoDB
• Using mongoDB
• Deploying mongoDB

3. Database Landscape
memcached
key/value
scalability & performance
RDBMS
depth of functionality

4. What is NoSQL?
Key / Value Column Graph Document

5. 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

6. 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

7. 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

8. 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

9. 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

10. • 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

11. 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

12. 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

13. Terminology
RDBMS MongoDB
Table Collection
Row(s) JSON Document
Index Index
Join Embedding & Linking
Partition Shard
Partition Key Shard Key

14. Documents
Blog Post Document
> p = { author: "Chris",
date: new ISODate(),
text: "About MongoDB...",
tags: ["tech", "databases"]}
> db.posts.save(p)

15. 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

16. 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

17. 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", ... }

18. 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", ...}]

19. 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()

20. 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"
]
]
}
}

21. 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}
});

22. 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
}

23. 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
}

24. 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" } )

25. 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] } )

26. 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;
};

27. 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,
}

28. 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}]

29. 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()

30. Rich Documents
• Intuitive
• Developer friendly
• Encapsulates whole objects
• Performant
• They are scalable

31. 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
}

32. Deployment
• Single server
- need a strong backup plan P

33. Deployment
• Single server
- need a strong backup plan P
• Replica sets
- High availability P S S
- Automatic failover

34. 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

35. 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

36. In Production

37. download at mongodb.org
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