Download as KEY, PPTX
![Documents
Blog Post Document
> p = { author: "Chris",
date: new ISODate(),
text: "About MongoDB...",
tags: ["tech", "databases"]}
> db.posts.save(p)](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-14-320.jpg)
![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](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-15-320.jpg)
![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", ...}]](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-18-320.jpg)
![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"
]
]
}
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-20-320.jpg)
![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
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-22-320.jpg)
![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
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-23-320.jpg)
![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" :
{ $near : [22, 42] } )](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-25-320.jpg)
![Map Reduce
The caller provides map and reduce functions written
in JavaScript
// Emit each tag
> map = "this['tags'].forEach(
function(item) {emit(item, 1);}
);"
// Calculate totals
> reduce = "function(key, values) {
var total = 0;
var valuesSize = values.length;
for (var i=0; i < valuesSize; i++) {
total += parseInt(values[i], 10);
}
return total;
};](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-26-320.jpg)
![Map Reduce
// run the map reduce
> db.posts.mapReduce(map, reduce, {"out": { inline : 1}});
{
"results" : [
{"_id" : "databases", "value" : 1},
{"_id" : "tech", "value" : 1 }
],
"timeMillis" : 1,
"counts" : {
"input" : 1,
"emit" : 2,
"reduce" : 0,
"output" : 2
},
"ok" : 1,
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-27-320.jpg)
![Aggregation - coming in 2.2
// Count tags
> agg = db.posts.aggregate(
{$unwind: "$tags"},
{$group : {_id : "$tags",
count : {$sum: 1}}}
)
> agg.result
[{"_id": "databases", "count": 1},
{"_id": "tech", "count": 1}]](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-28-320.jpg)
![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
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/85/Mongodb-intro-31-320.jpg)
![Documents
Blog Post Document
> p = { author: "Chris",
date: new ISODate(),
text: "About MongoDB...",
tags: ["tech", "databases"]}
> db.posts.save(p)](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-14-2048.jpg)
![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](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-15-2048.jpg)
![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", ...}]](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-18-2048.jpg)
![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"
]
]
}
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-20-2048.jpg)
![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
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-22-2048.jpg)
![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
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-23-2048.jpg)
![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" :
{ $near : [22, 42] } )](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-25-2048.jpg)
![Map Reduce
The caller provides map and reduce functions written
in JavaScript
// Emit each tag
> map = "this['tags'].forEach(
function(item) {emit(item, 1);}
);"
// Calculate totals
> reduce = "function(key, values) {
var total = 0;
var valuesSize = values.length;
for (var i=0; i < valuesSize; i++) {
total += parseInt(values[i], 10);
}
return total;
};](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-26-2048.jpg)
![Map Reduce
// run the map reduce
> db.posts.mapReduce(map, reduce, {"out": { inline : 1}});
{
"results" : [
{"_id" : "databases", "value" : 1},
{"_id" : "tech", "value" : 1 }
],
"timeMillis" : 1,
"counts" : {
"input" : 1,
"emit" : 2,
"reduce" : 0,
"output" : 2
},
"ok" : 1,
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-27-2048.jpg)
![Aggregation - coming in 2.2
// Count tags
> agg = db.posts.aggregate(
{$unwind: "$tags"},
{$group : {_id : "$tags",
count : {$sum: 1}}}
)
> agg.result
[{"_id": "databases", "count": 1},
{"_id": "tech", "count": 1}]](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-28-2048.jpg)
![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
}](https://image.slidesharecdn.com/mongodb-intro-120413052917-phpapp02/75/Mongodb-intro-31-2048.jpg)
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Mongodb intro
- 1. Introduction Christian Kvalheim -christkv@10gen.com @christkv
- 2. Today's Talk • Quickintroduction 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 • Amapping 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 • Likea 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 • Storesvertex-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 • Thestore 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? MongoDBis 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 behindmongoDB – (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 youuse 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 BlogPost 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 haspowerful, 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 indexon 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 indexon 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 queryplan > 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 // Indexnested 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" : { $near : [22, 42] } )
- 26. Map Reduce The caller provides map and reduce functions written in JavaScript // Emit each tag > map = "this['tags'].forEach( function(item) {emit(item, 1);} );" // Calculate totals > reduce = "function(key, values) { var total = 0; var valuesSize = values.length; for (var i=0; i < valuesSize; i++) { total += parseInt(values[i], 10); } return total; };
- 27. Map Reduce // runthe map reduce > db.posts.mapReduce(map, reduce, {"out": { inline : 1}}); { "results" : [ {"_id" : "databases", "value" : 1}, {"_id" : "tech", "value" : 1 } ], "timeMillis" : 1, "counts" : { "input" : 1, "emit" : 2, "reduce" : 0, "output" : 2 }, "ok" : 1, }
- 28. Aggregation - comingin 2.2 // Count tags > agg = db.posts.aggregate( {$unwind: "$tags"}, {$group : {_id : "$tags", count : {$sum: 1}}} ) > agg.result [{"_id": "databases", "count": 1}, {"_id": "tech", "count": 1}]
- 29. GridFS Save filesin 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.
- 37. download at mongodb.org conferences, appearances, and meetups http://www.10gen.com/events Facebook | Twitter | LinkedIn http://bit.ly/mongofb @mongodb http://linkd.in/joinmongo support, training, and this talk brought to you by
Editor's Notes
- #2 \n
- #3 \n
- #4 \n
- #5 * memcache, redis, membase\n* mongodb, couch\n* cassandra, riak\n* neo4j, flockdb\n\n
- #6 \n
- #7 \n
- #8 \n
- #9 \n
- #10 * JSON-style documents with dynamic schemas offer simplicity and power.\n* Index on any attribute, just like you're used to.\n* Mirror across LANs and WANs for scale and peace of mind.\n* Scale horizontally without compromising functionality.\n* Rich, document-based queries.\n* Atomic modifiers for contention-free performance.\n* Flexible aggregation and data processing.\n* Store files of any size without complicating your stack.\n
- #11 \n
- #12 \n
- #13 \n
- #14 * No joins for scalability - Doing joins across shards in SQL highly inefficient and difficult to perform.\n* MongoDB is geared for easy scaling - going from a single node to a distributed cluster is easy.\n* Little or no application code changes are needed to scale from a single node to a sharded cluster.\n
- #15 \n
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- #28 \n
- #29 \n
- #30 * If document is always presented as a whole - a single doc gives performance benefits\n* A single doc is not a panacea - as we'll see\n
- #31 * Summarise using mongodb\n
- #32 \n
- #33 * Single Master..\n
- #34 \n
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