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The Basics
1. MongoDB has the same concept of a database with which you are
likely already familiar
2. A database can have zero or more collections.
3. Collections are made up of zero or more documents.
4. A document is made up of one or more fields
5. Indexes in MongoDB function mostly like their RDBMS counterparts.
6. Cursors are different than the other five concepts but they are
important enough, and often overlooked

Examples : 1
• db.unicorns.insert({name: 'Aurora’, gender: 'f', weight: 450})
• db.unicorns.find()
• db.unicorns.insert({name: 'Horny',dob: new Date(1992,2,13,7,47),
loves: ['carrot','papaya'],weight: 600,gender: 'm',vampires: 63});
The special $lt, $lte, $gt, $gte
• db.unicorns.find({gender: 'm',weight: {$gt: 700}})
• db.unicorns.find({gender: {$ne: 'f'},weight: {$gte: 701}})

Examples : 2
• db.unicorns.find({vampires: {$exists: false}})
• db.unicorns.find({loves: {$in:['apple','orange']}})
• db.unicorns.find({gender: 'f’, $or: [{loves: 'apple'},{weight: {$lt:
500}}]})
• db.unicorns.find({_id: ObjectId("TheObjectId")})

Updating Document
• db.unicorns.update({name: 'Roooooodles’}, {weight: 590})
• db.unicorns.update({weight: 590}, {$set: {name: 'Roooooodles',dob:
new Date(1979, 7, 18, 18, 44), loves: ['apple'],gender: 'm',vampires:
99}})
• db.unicorns.update({name: 'Roooooodles'},{$set: {weight: 590}})
• db.unicorns.update({name: 'Pilot'},{$inc: {vampires: -2}})
• db.unicorns.update({name: 'Aurora'},{$push: {loves: 'sugar'}})

Upserts
• db.hits.update({page: 'unicorns’}, {$inc: {hits: 1}});
• db.hits.update({page: 'unicorns’}, {$inc: {hits: 1}}, {upsert:true});
• Multiple Updates
• db.unicorns.update({},{$set: {vaccinated: true }});
• db.unicorns.update({},{$set: {vaccinated: true }},{multi:true});

Find
• db.unicorns.find({}, {name: 1});
• db.unicorns.find().sort({weight: -1})
• db.unicorns.find().sort({name: 1,vampires: -1})
• db.unicorns.find().sort({weight: -1}).limit(2).skip(1)
• db.unicorns.count({vampires: {$gt: 50}})
• db.unicorns.find({vampires: {$gt: 50}}).count()

Data Modeling
• No Joins
• The first and most fundamental difference that you’ll need to get comfortable
with is MongoDB’s lack of joins.
• joins are generally seen as non-scalable
• Embedded Documents
• db.employees.insert({_id: ObjectId("4d85c7039ab0fd70a117d734"),name:
'Ghanima',family: {mother: 'Chani',father: 'Paul',brother:
ObjectId("4d85c7039ab0fd70a117d730")}})

Denormalization
• Yet another alternative to using joins is to denormalize your data.
Historically, denormalization was reserved for performance-sensitive code,
or when data should be snapshotted (like in an audit log). However, with
the ever growing popularity of NoSQL, many of which don’t have joins,
denormalization as part of normal modeling is becoming increasingly
common.
• The traditional way to associate a specific user with a post is via a userid
column within posts. With such a model, you can’t display posts without
retrieving (joining to) users. A possible alternative is simply to store the
name as well as the userid with each post. You could even do so with an
embedded document, like user: {id: ObjectId('Something'), name: 'Leto'}.
Yes, if you let users change their name, you may have to update each
document (which is one multi-update).

Example
• db.users.insert({name: 'leto’, email: 'leto@dune.gov’, addresses:
[{street: "229 W. 43rd St", city: "New York",
state:"NY",zip:"10036"},{street: "555 University", city: "Palo Alto",
state:"CA«,zip:"94107"}]})
• Comments and blogs example

When to use mongodb
• Flexible Schema
• Growing application
• Writes
• First, you have an option to send a write command and have it return
immediately without waiting for the write to be acknowledged.
• db.createCollection('logs', {capped: true ,size: 1048576})
• When our capped collection reaches its 1MB limit, old documents are
automatically purged.
• Durability
• MongoDB enables journaling by default, which allows fast recovery of the
server in case of a crash or abrupt power loss.

Continue …
• Full Text Search
• It supports fifteen languages with stemming and stop words.
• Transations eg. findAndModify, like $inc and $set.
• Data Processing
• feature-rich and different ways to group by (which is an understatement)
• Geospatial
• find documents that are $near a set of coordinates or $within a box or
circle.
• Tools

Aggregating data
• db.unicorns.aggregate([{$match: {weight:{$lt:600}}}, {$group:
{_id:'$gender', total:{$sum:1},
avgVamp:{$avg:'$vampires'}}},{$sort:{avgVamp:-1}} ])
• db.unicorns.aggregate([{$unwind:'$loves'},{$group: {_id:'$loves',
total:{$sum:1},unicorns:{$addToSet:'$name'}}},{$sort:{total:-
1}},{$limit:1} ])
• MapReduce
• MapReduce is a two-step approach to data processing. First you map, and
then you reduce

Performance and tools
• db.unicorns.ensureIndex({name: 1});
• db.unicorns.find().explain()
• db.unicorns.find({name: 'Pilot'}).explain()
• Replication
• MongoDB replication works in some ways similarly to how relational database replication works.
All production deployments should be replica sets, which consist of ideally three or more servers
that hold the same data.
• Sharding
• Sharding is an approach to scalability which partitions your data across multiple servers or
clusters.
* While replication can help performance somewhat (by isolating long running queries to
secondaries, and reducing latency for some other types of queries), its main purpose is to
provide high availability.

Continue …
• Stats
• You can obtain statistics on a database by typing db.stats().
• Profiler
• You enable the MongoDB profiler by executing: db.setProfilingLevel(2);
• With it enabled, we can run a command: db.unicorns.find({weight: {$gt:
600}});
• And then examine the profiler: db.system.profile.find()
• Backups and Restore
• mongodump --db learn --out backup

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Mongo db

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