The document discusses MongoDB, a document-oriented NoSQL database. It covers some key features of MongoDB including rich document queries, indexing for performance, replication for availability, auto-sharding for scalability, and geospatial indexing. It also provides MongoDB equivalents to SQL concepts and provides examples of CRUD operations and queries using aggregation, indexing, map-reduce, and replication sets.

1. Athira Mukundan
Solution Consultant @Sahaj
athira_tech@yahoo.in

2. THE CAP THEOREM

3. What’s the big deal?
Scalable High-Performance Open-source,Document-oriented database
Built for Speed
Rich Document based queries for Easy readability
Full Index Support for High Performance
Replication and Failover for High Availability
Auto Sharding for Easy Scalability
Geo-spatial Indexes for Geo-spatial queries
Map / Reduce for Aggregation

4. MongoDB in the RDBMS terms
SQL Terms/Concepts MongoDB Terms/Concepts
Database Database
Table Collection
Row Document
Column Field
Table Joins Embedding and Linking

5. Let’s do some Crud
Create:
Update:
Delete:
db.users.insert({name: “Alec”, place: “Bangalore”})
db.users.update({name: “Alec”}, place: “Mumbai”})
db.users.remove({name: “Alec”})

6. Mastering Find
Field Selection
Ordering
Paging
Count
db.users.find({}, {name: 1})
db.users.find().sort({name: 1})
db.users.find().sort({salary: -1}).limit(2).skip(1)
db.users.count({salary: {$gt: 50}})

7. Mastering Selectors
$lt, $lte, $gt, $gte, $ne
$exist
$in
$or
db.users.find({name: “Alec”, weight:{$gt: 70}})
db.users.find({weight: {$exists: false}})
db.users.find({managers: {$in: [‘Sam’, ‘Philip’]}})
db.users.find(weight:{$gt: 70}, $or: {height: {$gt: 170}})

8. Indexing and Aggregation
Indexing
Aggregates: $group, $sort, $sum, $avg, $min, $max
db.testdb.ensureIndex({"age" : 1, "account" : 1})
db.testdb.dropIndex(“index_name”)
db.testdb.aggregate([{$group : {_id : "$author", num_recipes : {$sum :
1}}}])
db.testdb.aggregate([{$group: {_id: "$author", num_likes: {$min:
"$likes"}}}])

9. Map Reduce
A data processing paradigm for condensing large volumes of data into useful
aggregated results
MapReduce is generally used for processing large data sets.
db.collection.mapReduce(
function() {emit(key,value);}, //map function
function(key,values) {return reduceFunction}, //reduce function
{
out: collection, query: document, sort: document, limit: number
})

10. Replication Sets

11. Sharding