Intro to HDFS and MapReduce

Introduction to HDFS
and MapReduce

Copyright © 2012-2013, Think Big Analytics, All
Rights Reserved
Thursday, January 10, 13

Who Am I
- Ryan Tabora
- Data Developer at Think
Big Analytics

- Big Data Consulting
- Experience working with
Hadoop, HBase, Hive,
Solr, Cassandra, etc.

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Think Big is the leading professional services ﬁrm that’s purpose built
for Big Data.
• One of Silicon Valley’s Fastest Growing Big Data start ups
• 100% Focus on Big Data consulting & Data Science solution services
• Management Background:
Cambridge Technology, C-bridge, Oracle, Sun Microsystems, Quantcast,
Accenture
C-bridge Internet Solutions (CBIS) founder 1996 & executives, IPO 1999
• Clients: 40+
• North America Locations
• US East: Boston, New York, Washington D.C.
• US Central: Chicago, Austin
• US West: HQ Mountain View, San Diego, Salt Lake City
• EMEA & APAC

Conﬁdential Think Big Analytics
3

Think Big Recognized as a Top Pure-Play Big Data Vendor

Source: Forbes February
2012

Conﬁdential Think Big Analytics
01/04/13
4

Agenda
- Big Data
- Hadoop Ecosystem
- HDFS
- MapReduce in Hadoop
- The Hadoop Java API
- Conclusions

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Big Data

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A Data Shift...

Source: EMC Digital Universe Study*
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Motivation

“Simple algorithms and lots
of data trump complex
models. ”
Halevy, Norvig, and Pereira
(Google), IEEE Intelligent Systems

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Pioneers
• Google and Yahoo:
- Index 850+ million websites, over one
trillion URLs.

• Facebook ad targeting:
- 840+ million users, > 50% of whom are
active daily.

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Hadoop
Ecosystem

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Common Tool?
• Hadoop
- Cluster: distributed computing
platform.

- Commodity*, server-class hardware.
- Extensible Platform.

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Hadoop Origins
• MapReduce and Google File System (GFS)
pioneered at Google.

• Hadoop is the commercially-supported
open-source equivalent.

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What Is Hadoop?
• Hadoop is a platform.

• Distributes and replicates data.

• Manages parallel tasks created by users.

• Runs as several processes on a cluster.

• The term Hadoop generally refers to a toolset, not a
single tool.

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Why Hadoop?
• Handles unstructured to semi-structured to
structured data.

• Handles enormous data volumes.
• Flexible data analysis and machine learning
tools.

• Cost-effective scalability.

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The Hadoop Ecosystem
• HDFS - Hadoop Distributed File System.
• Map/Reduce - A distributed framework for
executing work in parallel.

• Hive - A SQL like syntax with a meta store to
allow SQL manipulation of data stored on HDFS.

• Pig - A top down scripting language to
manipulate.

• HBase - A NoSQL, non-sequential data store.

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HDFS

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What Is HDFS?
• Hadoop Distributed File System.
• Stores ﬁles in blocks across many nodes in a
cluster.

• Replicates the blocks across nodes for
durability.

• Master/Slave architecture.

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HDFS Traits
• Not fully POSIX compliant.
• No ﬁle updates.
• Write once, read many times.
• Large blocks, sequential read patterns.
• Designed for batch processing.

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HDFS Master
• NameNode
- Runs on a single node as a master process
‣ Holds ﬁle metadata (which blocks are where)
‣ Directs client access to ﬁles in HDFS
• SecondaryNameNode
- Not a hot failover
- Maintains a copy of the NameNode metadata
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HDFS Slaves
• DataNode
- Generally runs on all nodes in the cluster
‣ Block creation/replication/deletion/reads
‣ Takes orders from the NameNode

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HDFS Illustrated
NameNode

Put File

File

DataNode 1 DataNode 2 DataNode 3


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HDFS Illustrated
NameNode
1
Put File 2
3



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HDFS Illustrated
NameNode
1,4,6
Put File 2
3



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HDFS Illustrated
NameNode
1,4,6
Put File 2 ,5,3
3



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HDFS Illustrated
NameNode
1,4,6
Put File 2 ,5,3
3,2,6



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Power of Hadoop
NameNode
1,4,6
Read File 2 ,5,3
3 ,2,6



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Power of Hadoop
NameNode
,4,6
Read File 2 ,5,3
3 ,2,6

DataNode 2 DataNode 3


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Power of Hadoop
NameNode
5,4,6
Read File 2 ,5,3
3 ,2,6

DataNode 2 DataNode 3


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Power of Hadoop
NameNode
5,4,6
Read File 2 ,5,3
3 ,2,6

Read time
=
Transfer DataNode 2 DataNode 3

Rate x
Number of
Machines*

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Power of Hadoop
NameNode
5,4,6
Read File 2 ,5,3
3 ,2,6

Read time
100 MB/s
=
x
Transfer DataNode 2 DataNode 3

3
Rate x
=
Number of
300MB/s
Machines*

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HDFS Shell
• Easy to use command line interface.
• Create, copy, move, and delete files.
• Administrative duties - chmod, chown, chgrp.
• Set replication factor for a file.
• Head, tail, cat to view files.

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manipulate.


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MapReduce
in
Hadoop

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MapReduce Basics
• Logical functions: Mappers and Reducers.
• Developers write map and reduce functions,
then submit a jar to the Hadoop cluster.

• Hadoop handles distributing the Map and
Reduce tasks across the cluster.

• Typically batch oriented.

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MapReduce
Daemons
•JobTracker (Master)
- Manages MapReduce jobs, giving tasks to
different nodes, managing task failure

•TaskTracker (Slave)
- Creates individual map and reduce tasks
- Reports task status to JobTracker

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MapReduce in
Hadoop

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MapReduce in
Hadoop
Let’s look at how MapReduce
actually works in Hadoop,
using WordCount.

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Input Mappers Sort, Reducers Output
Shufﬂe

Hadoop uses (hadoop, 1)
MapReduce
a2
(mapreduce, 1) hadoop 1
is 2
(uses, 1)
(is, 1), (a, 1)
There is a
Map phase
(map, 1),(phase,1)
(there, 1) map 1
mapreduce 1
phase 2

(phase,1)
(is, 1), (a, 1) reduce 1
(there, 1), there 2
There is a
Reduce phase (reduce 1) uses 1

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Shufﬂe

MapReduce
a2
(mapreduce, 1) hadoop 1
is 2
(uses, 1)

We need to convert
(is, 1), (a, 1)
There is a
Map phase
(map, 1),(phase,1)

the Input
(there, 1) map 1
mapreduce 1
phase 2

into the Output.
(phase,1)
(is, 1), (a, 1) reduce 1
(there, 1), there 2
There is a
Reduce phase (reduce 1) uses 1

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Shufﬂe

Hadoop uses
MapReduce
a2
hadoop 1
is 2

There is a
Map phase
map 1
mapreduce 1
phase 2

reduce 1
there 2
There is a
Reduce phase uses 1

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Input Mappers

Hadoop uses
MapReduce
(doc1, "…")

There is a
Map phase
(doc2, "…")

(doc3, "")

There is a
Reduce phase
(doc4, "…")

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Input Mappers

(hadoop, 1)
Hadoop uses
MapReduce
(doc1, "…") (uses, 1)
(mapreduce, 1)

(there, 1)
(is, 1)
There is a
Map phase
(doc2, "…") (a, 1)
(map, 1)
(phase, 1)

(doc3, "")

(there, 1)
(is, 1)
There is a
Reduce phase
(doc4, "…") (a, 1)
(reduce, 1)
(phase, 1)

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Input Mappers Sort, Reducers
Shufﬂe
0-9, a-l
MapReduce
(doc1, "…")
(mapreduce, 1)
(uses, 1)
(is, 1), (a, 1)
There is a
Map phase
(doc2, "…") m-q
(map, 1),(phase,1)
(there, 1)

(doc3, "")
(phase,1) r-z
(is, 1), (a, 1)
(there, 1),
There is a
Reduce phase
(doc4, "…") (reduce 1)

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Input Mappers Sort, Reducers
Shufﬂe
0-9, a-l
MapReduce
(doc1, "…") (a, [1,1]),
(mapreduce, 1) (hadoop, [1]),
(is, [1,1])
(uses, 1)
(is, 1), (a, 1)
There is a
Map phase
(doc2, "…") m-q
(map, 1),(phase,1)
(there, 1) (map, [1]),
(mapreduce, [1]),
(phase, [1,1])
(doc3, "")
(phase,1) r-z
(is, 1), (a, 1) (reduce, [1]),
(there, 1), (there, [1,1]),
There is a
Reduce phase
(doc4, "…") (reduce 1) (uses, 1)

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Shufﬂe
0-9, a-l
MapReduce
(doc1, "…") (a, [1,1]), a2
(mapreduce, 1) (hadoop, [1]), hadoop 1
(is, [1,1]) is 2
(uses, 1)
(is, 1), (a, 1)
There is a
Map phase
(doc2, "…") m-q
(map, 1),(phase,1)
(there, 1) (map, [1]), map 1
(mapreduce, [1]), mapreduce 1
(phase, [1,1]) phase 2
(doc3, "")
(phase,1) r-z
(is, 1), (a, 1) (reduce, [1]), reduce 1
(there, 1), (there, [1,1]), there 2
There is a
Reduce phase
(doc4, "…") (reduce 1) (uses, 1) uses 1

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Shufﬂe
0-9, a-l
MapReduce (doc1, "…") (a, [1,1]), a2
(is, [1,1]) is 2
(uses, 1)
(is, 1), (a, 1)
There is a
Map phase
(doc2, "…") m-q
(map, 1),(phase,1)
(there, 1) (map, [1]), map 1
(doc3, "")
(phase,1) r-z
(is, 1), (a, 1) (reduce, [1]),
(there, 1), (there, [1,1]),

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Shufﬂe
0-9, a-l
MapReduce (doc1, "…") (a, [1,1]), a2
(is, [1,1]) is 2
(uses, 1)
(is, 1), (a, 1)
There is a
Map phase
(doc2, "…") m-q
(map, 1),(phase,1)
(there, 1) (map, [1]), map 1
Map: (doc3, "")

•
(phase,1) r-z
Transform one input 1), (a, 1)
(is,
to 0-N
(reduce, [1]),
outputs. (there, 1), (there, [1,1]),

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Shufﬂe
0-9, a-l
MapReduce (doc1, "…") (a, [1,1]), a2
(is, [1,1]) is 2
(uses, 1)
(is, 1), (a, 1)
There is a
Map phase
(doc2, "…") m-q
(map, 1),(phase,1)
(there, 1) (map, [1]), map 1
Map: (doc3, "") Reduce:

• •
(phase,1) r-z
Transform one input 1), (a, 1)
(is,
to 0-N Collect multiple inputs into
(reduce, [1]),
outputs. (there, 1), one output.
(there, [1,1]),

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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar
TaskTracker TaskTracker TaskTracker

DataNode DataNode DataNode

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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

M M M


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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

Map Phase M M M


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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

* Intermediate Data Is
Map Phase k,v M k,v k,v M k,v M k,v
Stored Locally


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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

Map Phase k,v k,v k,v k,v k,v


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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

k,v k,v k,v k,v k,v

Shufﬂe/Sort


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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

k,v R k,v k,v R k,v R k,v

Reduce Phase

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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

R R R

Reduce Phase

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Cluster View of
MapReduce
NameNode

M R
JobTracker

jar

Job Complete! DataNode DataNode DataNode

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The
Hadoop
Java API

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MapReduce in Java

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MapReduce in Java

Let’s look at WordCount
written in the
MapReduce Java API.

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Map Code
public class SimpleWordCountMapper
extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {

static final Text word = new Text();
static final IntWritable one = new IntWritable(1);

@Override
public void map(LongWritable key, Text documentContents,
OutputCollector<Text, IntWritable> collector, Reporter reporter)
throws IOException {
String[] tokens = documentContents.toString().split("s+");
for (String wordString : tokens) {
if (wordString.length() > 0) {
word.set(wordString.toLowerCase());
collector.collect(word, one);
}
}
}
}

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Map Code


@Override
} Let’s drill into this code...
}
}
}

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Map Code


@Override
}
}
}
}

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Map Code
public class SimpleWordCountMapper Mapper class with 4
extends MapReduceBase implements type parameters for the
Mapper<LongWritable, Text, Text, IntWritable> { input key-value types and
output types.

@Override
}
}
}
}

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Map Code

static final Text word = new Text(); Output key-value objects
static final IntWritable one = new IntWritable(1); we’ll reuse.

@Override
}
}
}
}

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Map Code

static final Text word = new Text(); Map method with input,
static final IntWritable one = new IntWritable(1); output “collector”, and
reporting object.
@Override
}
}
}
}

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Map Code


@Override
collector.collect(word, one); Tokenize the line,
} “collect” each
} (word, 1)
}
}

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Reduce Code
public class SimpleWordCountReducer
Reducer<Text, IntWritable, Text, IntWritable> {

@Override
public void reduce(Text key, Iterator<IntWritable> counts,
OutputCollector<Text, IntWritable> output, Reporter reporter)
int count = 0;
while (counts.hasNext()) {
count += counts.next().get();
}
output.collect(key, new IntWritable(count));
}
}

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Reduce Code

@Override
int count = 0;
}
}
}
Let’s drill into this code...

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Reduce Code

@Override
int count = 0;
}
}
}

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Reduce Code
public class SimpleWordCountReducer Reducer class with 4
extends MapReduceBase implements type parameters for the
Reducer<Text, IntWritable, Text, IntWritable> { input key-value types and
output types.
@Override
int count = 0;
}
}
}

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Reduce Code
extends MapReduceBase implements Reduce method with
Reducer<Text, IntWritable, Text, IntWritable> { input, output “collector”,
and reporting object.
@Override
int count = 0;
}
}
}

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Reduce Code

@Override
int count = 0;
Count the counts per
} word and emit
output.collect(key, new IntWritable(count)); (word, N)
}
}

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Other Options


manipulate.


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Conclusions

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Hadoop Beneﬁts

• A cost-effective, scalable way to:
- Store massive data sets.
- Perform arbitrary analyses on
those data sets.

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Hadoop Tools

• Offers a variety of tools for:
- Application development.
- Integration with other platforms
(e.g., databases).

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Hadoop
Distributions
• A rich, open-source ecosystem.
- Free to use.
- Commercially-supported
distributions.

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Thank You!
- Feel free to contact me at
‣ ryan.tabora@thinkbiganalytics.com
- Or our solutions consultant
‣ matt.mcdevitt@thinkbiganalytics.com
- As always, THINK BIG!

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Bonus
Content

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manipulate.


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Hive:
SQL for
Hadoop

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Hive

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Hive

written in Hive,
the SQL for Hadoop.

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CREATE TABLE docs (line STRING);

LOAD DATA INPATH 'docs'
OVERWRITE INTO TABLE docs;

CREATE TABLE word_counts AS
SELECT word, count(1) AS count FROM
(SELECT explode(split(line, 's')) AS word
FROM docs) w
GROUP BY word ORDER BY word;

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FROM docs) w
GROUP BY word ORDER BY word; Let’s drill into this code...

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FROM docs) w

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Create a table to hold
CREATE TABLE docs (line STRING); the raw text we’re
counting. Each line is a
“column”.

FROM docs) w

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LOAD DATA INPATH 'docs' Load the text in the
“docs” directory into the
OVERWRITE INTO TABLE docs; table.

FROM docs) w

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Create the final table
LOAD DATA INPATH 'docs' and fill it with the results
OVERWRITE INTO TABLE docs; from a nested query of
the docs table that
performs WordCount
CREATE TABLE word_counts AS on the fly.
FROM docs) w

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Hive

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Hive

Because so many Hadoop users
come from SQL backgrounds,
Hive is one of the most
essential tools in the ecosystem!!

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manipulate.


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Pig:
Data Flow
for Hadoop

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Pig

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Pig

written in Pig,
the Data Flow language
for Hadoop.

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inpt = LOAD 'docs' using TextLoader
AS (line:chararray);

words = FOREACH inpt
GENERATE flatten(TOKENIZE(line)) AS word;

grpd = GROUP words BY word;

cntd = FOREACH grpd
GENERATE group, COUNT(words);

STORE cntd INTO 'output';

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cntd = FOREACH grpd

STORE cntd INTO 'output'; Let’s drill into this code...

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cntd = FOREACH grpd


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AS (line:chararray); Like the Hive example,
load “docs” content,
each line is a “ﬁeld”.


cntd = FOREACH grpd


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AS (line:chararray); Tokenize into words (an
array) and “ﬂatten” into
separate records.


cntd = FOREACH grpd


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Collect the same words
grpd = GROUP words BY word; together.

cntd = FOREACH grpd


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cntd = FOREACH grpd
Count each word.


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cntd = FOREACH grpd
Save the results.
STORE cntd INTO 'output'; Proﬁt!

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Pig

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Pig

Pig and Hive overlap,
but Pig is popular for ETL,
e.g., data transformation,
cleansing, ingestion, etc.

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Questions?

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Intro to HDFS and MapReduce

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