Starting with MongoDB

Murad Kamalov

Estonia

Tallinn Tartu

Finland

Helsinki

Dunedin

DoThinger.com

• Do things together with people nearby

@DoThinger

Intro to MongoDB

• Schemaless

• Supports replication and shardling

• Indexing

• File storage

• Aggregation (Map/Reduce)

• Geospacial queries

Benefits of MongoDB

• Great choice for agile projects

– no schema = no limitations

– ease of use

• Scalability & high availability

– thanks to shardling and replication

• Failover and automatic recovery

Main Concepts

• Database is set of collections • Collection is like a table in MySQL (e.g events

collection) • Document belongs to a collection, like row in

MySQL, unlike in MySQL documents in same collection can have different structure

• Documents store fields as key-value pairs, where value can be basic data type, another document or array

• Data is stored in JSON format (serialized as BSON)

Example

• Inserting document into events collection:

> db.events.insert({ “title” : “CodeCraft Meeting”,

“description” : “CodeCraft meeting in Dunedin on Tuesday evening”,

“start_datetime” : ISODate(“2012-04-03T17:30:00”),

“category” : “social”

})

> db.events.insert({ “title” : “CodeCraft Meeting 2”,

“description” : “CodeCraft meeting in Dunedin on Tuesday evening”,

“start_datetime” : ISODate(“2012-05-03 17:30:00”),

“category” : “social”,

“participants” : 50

})

• When Inserting, indexed field _id is created automatically for each inserted document

Embedded Documents

• Linking versus Embedding – e.g instead of creating separate collection for comments,

just embed comments in events collection – MongoDB doesn’t support JOINs = use embedding

> db.events.insert({

“title” : “Title”,

“description” : “Description”,

“start_date” : ISODate(“2012-04-03T17:30:00”),

“category” : “social”,

“comments”: [

{“author”: <user_id>, “comment” :“My First Comment”},

{“author”: <user_id>, “comment”: “My Second Comment”}]

})

Querying

• Can be done in many various ways in MongoDB

> db.events.find({“title”: “CodeCraft Meeting”})

> {“_id”: ObjectId(41234351451345), “title” : “CodeCraft Meeting”, “description” : “Description”, “start_datetime” : ISODate(“2012-04-03T17:30:00”), “category” : “social”}

> db.events.find({“start_datetime” : {$gte : ISODate(“2012-04-01T00:00:00”)}})

> {“_id”: ObjectId(41234351451345), “title” : “CodeCraft Meeting”, “description” : “Description”, “start_datetime” : ISODate(“2012-04-03T17:30:00”), “category” : “social”}

{“_id”: ObjectId(41226542345234), “title” : “CodeCraft Meeting 2”, “description” : “Description”, “start_datetime” : ISODate(“2012-05-03T17:30:00”), “category” : “social”, “participants” : 50}

Updates

• Replacing the entire document

> db.events.update({“_id”: ObjectId(41234351451345)},

{“title” : “New Title”,

“description” : “Description”,

“start_date” : ISODate(“2012-04-03T17:30:00”),

“category” : “social”,

“comments”: [

{“author”: <user_id>, “comment” :“My First Comment”},

{“author”: <user_id>, “comment”: “My Second Comment”

})

• Atomic updates

> db.events.update({“_id”:ObjectId(41234351451345)},

{$set : {“title”: {“New Title”}}}

)

Updates (2)

• Pushing Elements to Arrays > db.events.update({“_id”:ObjectId(41234351451345)},

{$push : {“comments”: {“author”: <user_id>, “comment”: “My

Third Comment”}}})

> {“title” : “Title”,

“description” : “Description”,

“start_date” : ISODate(“2012-04-03T17:30:00”),

“category” : “social”,

“comments”: [

{“author”: <user_id>, “comment” :“My First Comment”},

{“author”: <user_id>, “comment”: “My Second Comment”},

{“author”: <user_id>, “comment”: “My Third Comment”}]

})

Geospatial queries

• MongoDB has build-in support for geospacial queries > db.events.insert({

“title” : “Title”,

“description” : “Description”,

“start_datetime” : ISODate(“2012-04-03T17:30:00”),

“category” : “social”,

“location” : [30, 30]

})

> db.events.ensureIndex({“location” : “2d”})

> db.events.find({“location”: {$near: [28,28],

$maxDistance: 5}})

Files (GridFS)

• In MySQL it’s a bad practice to save large binary files into the database

• In MongoDB it’s a bad practice NOT TO save large files into the database.

• MongoDB splits saved files into chunks, which allows querying of only necessary parts of the binary files

• Example(pymongo library) >>> fs = gridfs.GridFs(db_name)

>>> filename = fs.put(“Example file data”)

>>> file_data = fs.get(filename).read()

Map/Reduce

db.events.mapReduce(map_func, reduce_func,

{out: “out_collection”})

• map_func and reduce_func are written in JavaScript

• Output of reduce_func is stored in output collection

Language Drivers

• Different language drivers for MongoDB provide different levels of abstraction – PyMongo (similar to mongo shell) vs MongoEngine (ORM-like)

class Event(mongoengine.Document):

title = StringField()

description = StringField()

start_date = DateTimeField(default=datetime.now)

category = StringField()

comments = ListField(EmbeddedDocumentField(Comment))

#querying

event = Events.objects(title = “Title”)

#adding comments

event.update(push__comments = comment)

Thank You!

Questions?