Introduction to MongoDB and Hadoop

#MongoDB

Introduction to MongoDB
& MongoDB + Hadoop
Steve Francia
Chief Evangelist, 10gen

MongoDB is a ___________
database
• Document
• Open source
• High performance
• Horizontally scalable
• Full featured

Document Database
• Not for .PDF & .DOC files
• A document is essentially an associative array
• Document == JSON object
• Document == PHP Array
• Document == Python Dict
• Document == Ruby Hash
• etc

Open Source
• MongoDB is an open source project
• On GitHub
• Licensed under the AGPL
• Started & sponsored by 10gen
• Commercial licenses available
• Contributions welcome

High Performance
• Written in C++
• Extensive use of memory-mapped files
i.e. read-through write-through memory caching.
• Runs nearly everywhere
• Data serialized as BSON (fast parsing)
• Full support for primary & secondary indexes
• Document model = less work

Full Featured
• Ad Hoc queries
• Real time aggregation
• Rich query capabilities
• Traditionally consistent
• Geospatial features
• Support for most programming languages
• Flexible schema

http://www.mongodb.org/download
s

RDBMS MongoDB
Table, View ➜ Collection
Row ➜ Document
Index ➜ Index
Join ➜ Embedded Document
Foreign Key ➜ Reference
Partition ➜ Shard

Terminology

Creating an author
> db.author.insert({
first_name: 'j.r.r.',
last_name: 'tolkien',
bio: 'J.R.R. Tolkien (1892.1973), beloved throughout the
world as the creator of The Hobbit and The Lord of the Rings, was a
professor of Anglo-Saxon at Oxford, a fellow of Pembroke
College, and a fellow of Merton College until his retirement in 1959.
His chief interest was the linguistic aspects of the early English
written tradition, but even as he studied these classics he was
creating a set of his own.'
})

Querying for our author
> db.author.findOne( { last_name : 'tolkien' } )
{
"_id" : ObjectId("507ffbb1d94ccab2da652597"),
"first_name" : "j.r.r.",
"last_name" : "tolkien",
"bio" : "J.R.R. Tolkien (1892.1973), beloved throughout the world
as the creator of The Hobbit and The Lord of the Rings, was a
professor of Anglo-Saxon at Oxford, a fellow of Pembroke
College, and a fellow of Merton College until his retirement in 1959.
His chief interest was the linguistic aspects of the early English
written tradition, but even as he studied these classics he was
creating a set of his own."
}

Creating a Book
> db.books.insert({
title: 'fellowship of the ring, the',
author: ObjectId("507ffbb1d94ccab2da652597"),
language: 'english',
genre: ['fantasy', 'adventure'],
publication: {
name: 'george allen & unwin',
location: 'London',
date: new Date('21 July 1954'),
}
})

http://society6.com/PastaSoup/The-Fellowship-of-the-Ring-ZZc_Print/

Multiple values per key
> db.books.findOne({language: 'english'}, {genre: 1})
{
"_id" : ObjectId("50804391d94ccab2da652598"),
"genre" : [
"fantasy",
"adventure"
]
}

Querying for key with
multiple values
> db.books.findOne({genre: 'fantasy'}, {title: 1})
{
"title" : "fellowship of the ring, the"
}

Query key with single value or
multiple values the same way.

Nested Values
> db.books.findOne({}, {publication: 1})
{
"_id" : ObjectId("50804ec7d94ccab2da65259a"),
"publication" : {
"name" : "george allen & unwin",
"location" : "London",
"date" : ISODate("1954-07-21T04:00:00Z")
}
}

Reach into nested values
using dot notation
> db.books.findOne(
{'publication.date' :
{ $lt : new Date('21 June 1960')}
}
)
{
"title" : "fellowship of the ring, the",
"author" : ObjectId("507ffbb1d94ccab2da652597"),
"language" : "english",
"genre" : [ "fantasy", "adventure" ],
"publication" : {
"date" : ISODate("1954-07-21T04:00:00Z")
}
}

Update books
> db.books.update(
{"_id" : ObjectId("50804391d94ccab2da652598")},
{ $set : {
isbn: '0547928211',
pages: 432
}
})

True agile development .
Simply change how you work with
the data and the database follows

The Updated Book record
db.books.findOne()
{
"_id" : ObjectId("50804ec7d94ccab2da65259a"),
"author" : ObjectId("507ffbb1d94ccab2da652597"),
"genre" : [ "fantasy", "adventure" ],
"isbn" : "0395082544",
"language" : "english",
"pages" : 432,
"publication" : {
"date" : ISODate("1954-07-21T04:00:00Z")
},
"title" : "fellowship of the ring, the"
}

Creating indexes
> db.books.ensureIndex({title: 1})

> db.books.ensureIndex({genre : 1})

> db.books.ensureIndex({'publication.date': -1})

Finding author by book
> book = db.books.findOne(
{"title" : "return of the king, the"})

> db.author.findOne({_id: book.author})
{
"_id" : ObjectId("507ffbb1d94ccab2da652597"),
"first_name" : "j.r.r.",
"last_name" : "tolkien",
"bio" : "J.R.R. Tolkien (1892.1973), beloved throughout the world as
the creator of The Hobbit and The Lord of the Rings, was a professor of
Anglo-Saxon at Oxford, a fellow of Pembroke College, and a fellow of
Merton College until his retirement in 1959. His chief interest was the
linguistic aspects of the early English written tradition, but even as he
studied these classics he was creating a set of his own."
}

Doers & Tellers talking about
different things
http://www.slideshare.net/siliconangle/trendconnect-big-data-report-september

Doers talk a lot more about
actual solutions

They know it's a two sided
story

Storage

Processing

Take aways

• MongoDB and Hadoop
• MongoDB for storage &
operations
• Hadoop for processing &
analytics

Applications have
complex needs
• MongoDB ideal operational
database
• MongoDB ideal for BIG data

• Not a data processing engine, but
provides processing functionality

Many options for Processing
Data
• Process in MongoDB using Map Reduce

• Process in MongoDB using Aggregation
Framework

• Process outside MongoDB (using Hadoop)

MongoDB Map Reduce
• MongoDB map reduce quite capable... but with limits

• - Javascript not best language for processing map
reduce
• - Javascript limited in external data processing
libraries
• - Adds load to data store

MongoDB
Aggregation
• Most uses of MongoDB Map Reduce were for
aggregation

• Aggregation Framework optimized for aggregate
queries

• Realtime aggregation similar to SQL GroupBy

Introduction to MongoDB and Hadoop

DEMO
• Install Hadoop MongoDB Plugin
• Import tweets from twitter
• Write mapper
• Write reducer
• Call myself a data scientist

Installing Mongo-
hadoop
https://gist.github.com/1887726
hadoop_version '0.23'
hadoop_path="/usr/local/Cellar/hadoop/$hadoop_version.0/libexec/lib"

git clone git://github.com/mongodb/mongo-hadoop.git
cd mongo-hadoop
sed -i '' "s/default/$hadoop_version/g" build.sbt
cd streaming
./build.sh

Groking Twitter
curl
https://stream.twitter.com/1/statuses/sample.json
-u<login>:<password>
| mongoimport -d test -c live

... let it run for about 2 hours

Map Hashtags in Java
public class TwitterMapper
extends Mapper<Object, BSONObject, Text, IntWritable> {

@Override
public void map( final Object pKey,
final BSONObject pValue,
final Context pContext )
throws IOException, InterruptedException{

BSONObject entities = (BSONObject)pValue.get("entities");
if(entities == null) return;
BasicBSONList hashtags = (BasicBSONList)entities.get("hashtags");
if(hashtags == null) return;

for(Object o : hashtags){
String tag = (String)((BSONObject)o).get("text");
pContext.write( new Text( tag ), new IntWritable( 1 ) );
}
}
}

Reduce hashtags in
Java
public class TwitterReducer

extends Reducer<Text, IntWritable, Text, IntWritable> {
@Override

public void reduce( final Text pKey,

final Iterable<IntWritable> pValues,

final Context pContext )
throws IOException, InterruptedException{

int count = 0;
for ( final IntWritable value : pValues ){

count += value.get();

}

pContext.write( pKey, new IntWritable( count ) );

}

}

All together
#!/bin/sh
export HADOOP_HOME="/Users/mike/hadoop/hadoop-1.0.4"
declare -a job_args
cd ..
job_args=("jar" "examples/twitter/target/twitter-example_*.jar")
job_args=(${job_args[@]} "com.mongodb.hadoop.examples.twitter.TwitterConfig ")
job_args=(${job_args[@]} "-D" "mongo.job.verbose=true")
job_args=(${job_args[@]} "-D" "mongo.job.background=false")
job_args=(${job_args[@]} "-D" "mongo.input.key=")
job_args=(${job_args[@]} "-D" "mongo.input.uri=mongodb://localhost:27017/test.live")
job_args=(${job_args[@]} "-D" "mongo.output.uri=mongodb://localhost:27017/test.twit_hashtags")
job_args=(${job_args[@]} "-D" "mongo.input.query=")
job_args=(${job_args[@]} "-D" "mongo.job.mapper=com.mongodb.hadoop.examples.twitter.TwitterMapper")
job_args=(${job_args[@]} "-D" "mongo.job.reducer=com.mongodb.hadoop.examples.twitter.TwitterReducer")
job_args=(${job_args[@]} "-D" "mongo.job.input.format=com.mongodb.hadoop.MongoInputFormat")
job_args=(${job_args[@]} "-D" "mongo.job.output.format=com.mongodb.hadoop.MongoOutputFormat")
job_args=(${job_args[@]} "-D" "mongo.job.output.key=org.apache.hadoop.io.Text")
job_args=(${job_args[@]} "-D" "mongo.job.output.value=org.apache.hadoop.io.IntWritable")
job_args=(${job_args[@]} "-D" "mongo.job.mapper.output.key=org.apache.hadoop.io.Text")
job_args=(${job_args[@]} "-D" "mongo.job.mapper.output.value=org.apache.hadoop.io.IntWritable")
job_args=(${job_args[@]} "-D" "mongo.job.combiner=com.mongodb.hadoop.examples.twitter.TwitterReducer")
job_args=(${job_args[@]} "-D" "mongo.job.partitioner=")
job_args=(${job_args[@]} "-D" "mongo.job.sort_comparator=")

#echo "${job_args[@]}"
$HADOOP_HOME/bin/hadoop "${job_args[@]}" "$1"

Popular Hash
Tags
db.twit_hashtags.find().sort( {'count' : -1 })
{ "_id" : "YouKnowYoureInLoveIf", "count" : 287 }
{ "_id" : "teamfollowback", "count" : 200 }
{ "_id" : "RT", "count" : 150 }
{ "_id" : "Arsenal", "count" : 148 }
{ "_id" : "milars", "count" : 145 }
{ "_id" : "sanremo", "count" : 145 }
{ "_id" : "LoseMyNumberIf", "count" : 139 }
{ "_id" : "RelationshipsShould", "count" : 137 }
{ "_id" : "oomf", "count" : 117 }
{ "_id" : "TeamFollowBack", "count" : 105 }
{ "_id" : "WhyDoPeopleThink", "count" : 102 }
{ "_id" : "np", "count" : 100 }

Aggregation in Mongo
2.2
db.live.aggregate(
{ $unwind : "$entities.hashtags" } ,
{ $match :
{ "entities.hashtags.text" :
{ $exists : true } } } ,
{ $group :
{ _id : "$entities.hashtags.text",
count : { $sum : 1 } } } ,
{ $sort : { count : -1 } },
{ $limit : 10 }
)

Popular Hash
Tags
db.twit_hashtags.aggregate(a){
"result" : [
{ "_id" : "YouKnowYoureInLoveIf", "count" : 287 },
{ "_id" : "teamfollowback", "count" : 200 },
{ "_id" : "RT", "count" : 150 },
{ "_id" : "Arsenal", "count" : 148 },
{ "_id" : "milars", "count" : 145 },
{ "_id" : "sanremo","count" : 145 },
{ "_id" : "LoseMyNumberIf", "count" : 139 },
{ "_id" : "RelationshipsShould", "count" : 137 },
],"ok" : 1
}

#MongoDB

Questions?
Steve Francia
Chief Evangelist, 10gen
@spf13
Spf13.com

Introduction to MongoDB and Hadoop

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