building web applications with mongo db presentation
DESCRIPTION
TRANSCRIPT
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http://mongodb.orghttp://10gen.com
Building applications with MongoDB – An introduction
Roger [email protected]
@rogerb
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Today’s Talk• Developing your first Web Application with
MongoDB
• What is MongoDB, Platforms and availability• Data Modeling, queries and geospatial queries
• Location bases App• Example uses MongoDB Javascript shell
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Why MongoDB• Intrinsic support for agile development
• Super low latency access to your data–Very little CPU overhead
• No Additional caching layer required
• Built in Replication and Horizontal Scaling support
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MongoDB• Document Oriented Database
–Data is stored in documents, not tables / relations
• MongoDB is Implemented in C++ for best performance• Platforms 32/64 bit Windows Linux, Mac OS-X, FreeBSD,
Solaris
• Language drivers for:– Ruby / Ruby-on-Rails – Java– C#– JavaScript – C / C++ – Erlang Python, Perl others..... and much more ! ..
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Design• Want to build an app where users can check in
to a location
• Leave notes or comments about that location
• Iterative Approach:–Decide requirements–Design documents–Rinse, repeat :-)
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Requirements• Locations
–Need to store locations (Offices, Restaurants etc)•Want to be able to store name, address and tags
•Maybe User Generated Content, i.e. tips / small notes ?
–Want to be able to find other locations nearby
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Requirements• Locations
–Need to store locations (Offices, Restaurants etc)•Want to be able to store name, address and tags•Maybe User Generated Content, i.e. tips / small notes ?
–Want to be able to find other locations nearby
• Checkins–User should be able to ‘check in’ to a location–Want to be able to generate statistics
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TerminologyRDBMS MongoTable, View CollectionRow(s) JSON DocumentIndex IndexJoin Embedded
DocumentPartition ShardPartition Key Shard Key
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Collections
loc1, loc2, loc3
Locations
Users
User1, User2
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JSON Sample Doc { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "roger", date : "Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)", text : ”MongoSF", tags : [ ”San Francisco", ”MongoDB" ] } Notes: - _id is unique, but can be anything you’d like
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BSON
• JSON has powerful, but limited set of datatypes–Mongo extends datypes with Date, Int types, Id, …
• MongoDB stores data in BSON
• BSON is a binary representation of JSON–Optimized for performance and navigational abilities
–Also compression–See bsonspec.org
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location1= {name: "10gen East Coast”,address: ”134 5th Avenue 3rd Floor”,city: "New York”,zip: "10011”
}
Locations v1
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location1= {name: "10gen East Coast”,address: ”134 5th Avenue 3rd Floor”,city: "New York”,zip: "10011”
}
db.locations.find({zip:”10011”}).limit(10)
Places v1
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location1 = {name: "10gen East Coast”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,
tags: [“business”, “mongodb”]
}
Places v2
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location1 = {name: "10gen East Coast”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,
tags: [“business”, “mongodb”]
}
db.locations.find({zip:”10011”, tags:”business”})
Places v2
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location1 = {name: "10gen East Coast”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,
tags: [“business”, “mongodb”],
latlong: [40.0,72.0]
}
Places v3
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location1 = {name: "10gen East Coast”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,
tags: [“business”, “cool place”],
latlong: [40.0,72.0]
}
db.locations.ensureIndex({latlong:”2d”})
Places v3
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location1 = {name: "10gen HQ”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,
tags: [“business”, “cool place”],
latlong: [40.0,72.0]
}
db.locations.ensureIndex({latlong:”2d”})db.locations.find({latlong:{$near:[40,70]}})
Places v3
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location1 = {name: "10gen HQ”,address: "17 West 18th Street 8th Floor”,city: "New York”,zip: "10011”,latlong: [40.0,72.0],
tags: [“business”, “cool place”],
tips: [{user:"nosh", time:6/26/2010, tip:"stop by for office
hours on Wednesdays from 4-6pm"}, {.....},
]
}
Places v4
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Creating your indexesdb.locations.ensureIndex({tags:1})db.locations.ensureIndex({name:1})db.locations.ensureIndex({latlong:”2d”})
Finding places:db.locations.find({latlong:{$near:[40,70]}})
With regular expressions:db.locations.find({name: /^typeaheadstring/)
By tag:db.locations.find({tags: “business”})
Querying your Places
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Initial data load:db.locations.insert(place1)
Using update to Add tips:db.locations.update({name:"10gen HQ"},
{$push :{tips: {user:"nosh", time:6/26/2010,
tip:"stop by for office hours on Wednesdays from 4-6"}}}}
Inserting and updating locations
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Requirements• Locations
–Need to store locations (Offices, Restaurants etc)•Want to be able to store name, address and tags•Maybe User Generated Content, i.e. tips / small notes ?
–Want to be able to find other locations nearby
• Checkins–User should be able to ‘check in’ to a location–Want to be able to generate statistics
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user1 = {name: “nosh”email: “[email protected]”,...
checkins: [{ location: “10gen HQ”, ts: 9/20/2010 10:12:00, …}, … ]}
Users
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Simple Statsdb.users.find({‘checkins.location’: “10gen HQ”)
db.checkins.find({‘checkins.location’: “10gen HQ”}).sort({ts:-1}).limit(10)
db.checkins.find({‘checkins.location’: “10gen HQ”, ts: {$gt: midnight}}).count()
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user1 = {name: “nosh”email: “[email protected]”,...checkins: [4b97e62bf1d8c7152c9ccb74,
5a20e62bf1d8c736ab]}
checkins [] = ObjectId reference to locations collection
Alternative
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Check-in = 2 opsread location to obtain location idUpdate ($push) location id to user object
Queries: find all locations where a user checked in: checkin_array = db.users.find({..}, {checkins:true}).checkins
db.location.find({_id:{$in: checkin_array}})
User Check in
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Unsharded Deployment
Secondary
Primary•Configure as a replica set for automated failover•Async replication between nodes
•Add more secondaries to scale reads
Secondary
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Sharded Deployment
Secondary
Primary
MongoSMongoS
•Autosharding distributes data among two or more replica sets• Mongo Config Server(s) handles distribution & balancing
• Transparent to applications
config
config
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Use Cases•RDBMS replacement for high-traffic web
applications
•Content Management-type applications
•Real-time analytics
•High-speed data logging
Web 2.0, Media, SaaS, Gaming, Finance, Telecom, Healthcare