Introduction to MapReduce
and Hadoop

What is MapReduce?
• Data-parallel programming model for
clusters of commodity machines
• Pioneered by Google
– Processes 20 PB of data per day
• Popularized by open-source Hadoop project
– Used by Yahoo!, Facebook, Amazon, …
What is MapReduce used for?
• At Google:
– Index building for Google Search
– Article clustering for Google News
– Statistical machine translation
• At Yahoo!:
– Index building for Yahoo! Search
– Spam detection for Yahoo! Mail
• At Facebook:
– Data mining
– Ad optimization
– Spam detection
Example: Facebook Lexicon
www.facebook.com/lexicon
Example: Facebook Lexicon
www.facebook.com/lexicon
What is MapReduce used for?
• In research:
– Analyzing Wikipedia conflicts (PARC)
– Natural language processing (CMU)
– Bioinformatics (Maryland)
– Particle physics (Nebraska)
– Ocean climate simulation (Washington)
–
Outline
• MapReduce architecture
• Sample applications
• Getting started with Hadoop
• Higher-level queries with Pig & Hive
• Current research
MapReduce Goals
1. Scalability to large data volumes:
– Scan 100 TB on 1 node @ 50 MB/s = 24 days
– Scan on 1000-node cluster = 35 minutes
2. Cost-efficiency:
– Commodity nodes (cheap, but unreliable)
– Commodity network
– Automatic fault-tolerance (fewer admins)
– Easy to use (fewer programmers)
Typical Hadoop Cluster
Aggregation switch
Rack switch
• 40 nodes/rack, 1000-4000 nodes in cluster
• 1 GBps bandwidth in rack, 8 GBps out of rack
• Node specs (Yahoo! terasort):
8 x 2.0 GHz cores, 8 GB RAM, 4 disks (= 4 TB?)
Typical Hadoop Cluster
Image from wiki.apache.org/hadoop-data/attachments/HadoopPresentations/attachments/aw-apachecon-eu-2009.pdf
Challenges
• Cheap nodes fail, especially if you have many
– Mean time between failures for 1 node = 3 years
– MTBF for 1000 nodes = 1 day
– Solution: Build fault-tolerance into system
• Commodity network = low bandwidth
– Solution: Push computation to the data
• Programming distributed systems is hard
– Solution: Users write data-parallel “map” and “reduce”
functions, system handles work distribution and faults
Hadoop Components
• Distributed file system (HDFS)
– Single namespace for entire cluster
– Replicates data 3x for fault-tolerance
• MapReduce framework
– Executes user jobs specified as “map” and
“reduce” functions
– Manages work distribution & fault-tolerance
Hadoop Distributed File System
• Files split into 128MB blocks
• Blocks replicated across
several datanodes (usually 3)
• Namenode stores metadata
(file names, locations, etc)
• Optimized for large files,
sequential reads
• Files are append-only
Namenode
Datanodes
1
2
3
4
1
2
4
2
1
3
1
4
3
3
2
4
File1
MapReduce Programming Model
• Data type: key-value records
• Map function:
(Kin, Vin)  list(Kinter, Vinter)
• Reduce function:
(Kinter, list(Vinter))  list(Kout, Vout)
Example: Word Count
def$mapper(line):,
,,foreach,word,in$line.split():,
,,,,output(word,,1),
def$reducer(key,,values):,
,,output(key,,sum(values)),
Word Count Execution
the quick
brown fox
the fox ate
the mouse
how now
brown cow
Map
Map
Map
Reduce
Reduce
brown, 2
fox, 2
how, 1
now, 1
the, 3
ate, 1
cow, 1
mouse, 1
quick, 1
the, 1
brown, 1
fox, 1
quick, 1
the, 1
fox, 1
the, 1
how, 1
now, 1
brown, 1
ate, 1
mouse, 1
cow, 1
Input Map Shuffle & Sort Reduce Output
An Optimization: The Combiner
def$combiner(key,,values):,
,,output(key,,sum(values)),
• Local aggregation function for repeated
keys produced by same map
• For associative ops. like sum, count, max
• Decreases size of intermediate data
• Example: local counting for Word Count:
Word Count with Combiner
Input Map & Combine Shuffle & Sort Reduce Output
the quick
brown fox
the fox ate
the mouse
how now
brown cow
Map
Map
Map
Reduce
Reduce
brown, 2
fox, 2
how, 1
now, 1
the, 3
ate, 1
cow, 1
mouse, 1
quick, 1
the, 1
brown, 1
fox, 1
quick, 1
the, 2
fox, 1
how, 1
now, 1
brown, 1
ate, 1
mouse, 1
cow, 1
MapReduce Execution Details
• Mappers preferentially placed on same node
or same rack as their input block
– Push computation to data, minimize network use
• Mappers save outputs to local disk before
serving to reducers
– Allows having more reducers than nodes
– Allows recovery if a reducer crashes
Fault Tolerance in MapReduce
1. If a task crashes:
– Retry on another node
• OK for a map because it had no dependencies
• OK for reduce because map outputs are on disk
– If the same task repeatedly fails, fail the job or
ignore that input block
Note: For fault tolerance to work, your map
and reduce tasks must be side-effect-free
Fault Tolerance in MapReduce
2. If a node crashes:
– Relaunch its current tasks on other nodes
– Relaunch any maps the node previously ran
• Necessary because their output files were lost
along with the crashed node
Fault Tolerance in MapReduce
3. If a task is going slowly (straggler):
– Launch second copy of task on another node
– Take the output of whichever copy finishes
first, and kill the other one
• Critical for performance in large clusters
(“everything that can go wrong will”)
Takeaways
• By providing a data-parallel programming
model, MapReduce can control job
execution under the hood in useful ways:
– Automatic division of job into tasks
– Placement of computation near data
– Load balancing
– Recovery from failures & stragglers
Outline
• MapReduce architecture
• Sample applications
• Getting started with Hadoop
• Higher-level queries with Pig & Hive
• Current research
1. Search
• Input: (lineNumber, line) records
• Output: lines matching a given pattern
• Map:
,,if(line,matches,pattern):,
,,,,,output(line),
• Reduce: identify function
– Alternative: no reducer (map-only job)
pig
sheep
yak
zebra
aardvark
ant
bee
cow
elephant
2. Sort
• Input: (key, value) records
• Output: same records, sorted by key
• Map: identity function
• Reduce: identify function
• Trick: Pick partitioning
function h such that
k
1 h(k1) Map
Map
Map
Reduce
Reduce
ant, bee
zebra
aardvark,
elephant
cow
pig
sheep, yak
[A-M]
[N-Z]
3. Inverted Index
• Input: (filename, text) records
• Output: list of files containing each word
• Map:
,,,foreach,word,in$text.split():,
,,,,,,output(word,,filename),
• Combine: uniquify filenames for each word
• Reduce:
def reduce(word,,filenames):,,,
,,,output(word,,sort(filenames))
Inverted Index Example
to be or
not to be
afraid, (12th.txt)
be, (12th.txt, hamlet.txt)
greatness, (12th.txt)
not, (12th.txt, hamlet.txt)
of, (12th.txt)
or, (hamlet.txt)
to, (hamlet.txt)
hamlet.txt
be not
afraid of
greatness
12th.txt
to, hamlet.txt
be, hamlet.txt
or, hamlet.txt
not, hamlet.txt
be, 12th.txt
not, 12th.txt
afraid, 12th.txt
of, 12th.txt
greatness, 12th.txt
4. Most Popular Words
• Input: (filename, text) records
• Output: the 100 words occurring in most files
• Two-stage solution:
– Job 1:
• Create inverted index, giving (word, list(file)) records
– Job 2:
• Map each (word, list(file)) to (count, word)
• Sort these records by count as in sort job
• Optimizations:
– Map to (word, 1) instead of (word, file) in Job 1
– Estimate count distribution in advance by sampling
5. Numerical Integration
• Input: (start, end) records for sub-ranges to integrate
– Doable using custom InputFormat
• Output: integral of f(x) dx over entire range
• Map:
,,,,,def map(start,,end):,
,,,,,,,,sum,=,0,
,,,,,,,,for(x,=,start;,x,<,end;,x,+=,step):,
,,,,,,,,,,,sum,+=,f(x),*,step,
,,,,,,,,output(“”,,sum)
• Reduce:
def reduce(key,,values):,,,
,,,,output(key,,sum(values))
Outline
• MapReduce architecture
• Sample applications
• Getting started with Hadoop
• Higher-level queries with Pig & Hive
• Current research
Getting Started with Hadoop
• Download from hadoop.apache.org
• To install locally, unzip and set JAVA_HOME,
• Guide: hadoop.apache.org/common/docs/current/quickstart.html
• Three ways to write jobs:
– Java API
– Hadoop Streaming (for Python, Perl, etc)
– Pipes API (C++)
Word Count in Java
public static class MapClass extends MapReduceBase
,,,,implements Mapper,{,
,,,,private final static IntWritable ONE,=,new IntWritable(1);,
,,,,public void map(LongWritable,key,,Text,value,,,
,,,,,,,,,,,,,,,,,,,,OutputCollector,output,,,
,,,,,,,,,,,,,,,,,,,,Reporter,reporter),throws IOException,{,
,,,,,,String,line,=,value.toString();,
,,,,,,StringTokenizer itr,=,new StringTokenizer(line);,
,,,,,,while (itr.hasMoreTokens()),{,
,,,,,,,,output.collect(new text(itr.nextToken()),,ONE);,
,,,,,,},
,,,,},
,,},
Word Count in Java
public static class$Reduce$extends MapReduceBase
,,,,implements,Reducer,{,
,,,,public void reduce(Text,key,,Iterator,values,,
,,,,,,,,,,,,,,,,,,,,,,,OutputCollector,output,,
,,,,,,,,,,,,,,,,,,,,,,,Reporter,reporter),throws IOException,{,
,,,,,,int,sum,=,0;,
,,,,,,while (values.hasNext()),{,
,,,,,,,,sum,+=,values.next().get();,
,,,,,,},
,,,,,,output.collect(key,,new IntWritable(sum));,
,,,,},
,,},
Word Count in Java
public static void main(String[],args),throws Exception,{,
,,,,JobConf,conf,=,new JobConf(WordCount.class);,
,,,,conf.setJobName("wordcount");,
,,,,conf.setMapperClass(MapClass.class);$$$$$$$$$
,,,,conf.setCombinerClass(Reduce.class);$
,,,,conf.setReducerClass(Reduce.class);$
,,,,FileInputFormat.setInputPaths(conf,,args[0]);,
,,,,FileOutputFormat.setOutputPath(conf,,new Path(args[1]));,
,,,,conf.setOutputKeyClass(Text.class); //,out,keys,are,words,(strings)
,,,,conf.setOutputValueClass(IntWritable.class); //,values,are,counts
,,,,JobClient.runJob(conf);,
,,},
Word Count in Python with
Hadoop Streaming
import$sys,
for$line,in$sys.stdin:,
,,for$word,in$line.split():,
,,,,print(word.lower(),+,"\t",+,1),
import$sys,
counts,=,{},
for$line,in$sys.stdin:,
,,word,,count,=,line.split("\t"),
,,,,dict[word],=,dict.get(word,,0),+,int(count),
for$word,,count,in$counts:,
,,print(word.lower(),+,"\t",+,1)
Mapper.py:
Reducer.py:
Amazon Elastic MapReduce
• Web interface and command-line tools for
running Hadoop jobs on EC2
• Data stored in Amazon S3
• Monitors job and shuts machines after use
• If you want more control, you can launch a
Hadoop cluster manually using scripts in
src/contrib/ec2,
Elastic MapReduce UI
Elastic MapReduce UI
Elastic MapReduce UI
Outline
• MapReduce architecture
• Sample applications
• Getting started with Hadoop
• Higher-level queries with Pig & Hive
• Current research
Motivation
• MapReduce is great, as many algorithms
can be expressed by a series of MR jobs
• But it’s low-level: must think about keys,
values, partitioning, etc
• Can we capture common “job patterns”?
Pig
• Started at Yahoo! Research
• Runs about 30% of Yahoo!’s jobs
• Features:
– Expresses sequences of MapReduce jobs
– Data model: nested “bags” of items
– Provides relational (SQL) operators
(JOIN, GROUP BY, etc)
– Easy to plug in Java functions
An Example Problem
Suppose you have
user data in one
file, website data in
another, and you
need to find the top
5 most visited
pages by users
aged 18 - 25.
Load Users Load Pages
Filter by age
Join on name
Group on url
Count clicks
Order by clicks
Take top 5
Example from wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
In MapReduce
Example from wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
Users,,,,=,load ‘users’ as,(name,,age);,
Filtered,=,filter,Users,by,,
,,,,,,,,,,,,,,,,,,age,>=,18,and,age,<=,25;,,
Pages,,,,=,load,‘pages’,as,(user,,url);,
Joined,,,=,join,Filtered,by,name,,Pages,by,user;,
Grouped,,=,group,Joined,by url;,
Summed,,,=,foreach,Grouped,generate,group,,
,,,,,,,,,,,,,,,,,,,count(Joined),as,clicks;,
Sorted,,,=,order,Summed,by,clicks,desc;,
Top5,,,,,=,limit,Sorted,5;,
store,Top5,into ‘top5sites’;,
In Pig Latin
Example from wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
Translation to MapReduce
Notice how naturally the components of the job translate into Pig Latin.
Load Users Load Pages
Filter by age
Join on name
Group on url
Count clicks
Order by clicks
Take top 5
Users,=,load,…,
Fltrd,=,filter,…,,
Pages,=,load,…,
Joined,=,join,…,
Grouped,=,group,…,
Summed,=,…,count()…,
Sorted,=,order,…,
Top5,=,limit,…
Example from wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
Translation to MapReduce
Notice how naturally the components of the job translate into Pig Latin.
Load Users Load Pages
Filter by age
Join on name
Group on url
Count clicks
Order by clicks
Take top 5
Users,=,load,…,
Fltrd,=,filter,…,,
Pages,=,load,…,
Joined,=,join,…,
Grouped,=,group,…,
Summed,=,…,count()…,
Sorted,=,order,…,
Top5,=,limit,…
Job 1
Job 2
Job 3
Example from wiki.apache.org/pig-data/attachments/PigTalksPapers/attachments/ApacheConEurope09.ppt
Hive
• Developed at Facebook
• Used for most Facebook jobs
• “Relational database” built on Hadoop
– Maintains table schemas
– SQL-like query language (which can also
call Hadoop Streaming scripts)
– Supports table partitioning,
complex data types, sampling,
some optimizations
Sample Hive Queries
SELECT,p.url,,COUNT(1),as,clicks,,
FROM,users,u,JOIN,page_views p,ON,(u.name,=,p.user),
WHERE,u.age,>=,18,AND,u.age,<=,25,
GROUP,BY,p.url
ORDER,BY,clicks,
LIMIT,5;,
• Find top 5 pages visited by users aged 18-25:
• Filter page views through Python script:
SELECT,TRANSFORM(p.user,,p.date),
USING,'map_script.py',
AS,dt,,uid,CLUSTER,BY,dt
FROM,page_views p;,
Conclusions
• MapReduce’s data-parallel programming model
hides complexity of distribution and fault tolerance
• Principal philosophies:
– Make it scale, so you can throw hardware at problems
– Make it cheap, saving hardware, programmer and
administration costs (but requiring fault tolerance)
• Hive and Pig further simplify programming
• MapReduce is not suitable for all problems, but
when it works, it may save you a lot of time
Outline
• MapReduce architecture
• Sample applications
• Getting started with Hadoop
• Higher-level queries with Pig & Hive
• Current research
Cluster Computing Research
• New execution models
– Dryad (Microsoft): DAG of tasks
– Pregel (Google): bulk synchronous processes
– MapReduce Online (Berkeley): streaming
• Easier programming
– DryadLINQ (MSR): language-integrated queries
– SEJITS (Berkeley): specializing Python/Ruby
• Improving efficiency/scheduling/etc
Self-Serving Example: Spark
• Motivation: iterative jobs (common in
machine learning, optimization, etc)
• Problem: iterative jobs reuse the same data
over and over, but MapReduce / Dryad / etc
require acyclic data flows
• Solution: support “caching” data between
parallel operations.. but remain fault-tolerant
• Also experiment with language integration etc
Data Flow
MapReduce Spark
. . .
w
f(x,w) w
x f(x,w)
x
x
w
f(x,w)
Example: Logistic Regression
Goal: find best line separating 2 datasets
+
–
+
+
+
+
+
+
+
+
– –
–
–
–
–
–
–
+
target
–
random initial line
Serial Version
val data = readData(...)
var w = Vector.random(D)
for (i <- 1 to ITERATIONS) {
var gradient = Vector.zeros(D)
for (p <- data) {
val scale = (1/(1+exp(-p.y*(w dot p.x))) - 1) * p.y
gradient += scale * p.x
}
w -= gradient
}
println("Final w: " + w)
Spark Version
val data = spark.hdfsTextFile(...).map(readPoint).cache()
var w = Vector.random(D)
for (i <- 1 to ITERATIONS) {
var gradient = spark.accumulator(Vector.zeros(D))
for (p <- data) {
val scale = (1/(1+exp(-p.y*(w dot p.x))) - 1) * p.y
gradient += scale * p.x
}
w -= gradient.value
}
println("Final w: " + w)
Performance
40s$/$itera+on$
first$itera+on$60s$
further$itera+ons$2s$
Crazy Idea: Interactive Spark
• Being able to cache datasets in memory is
great for interactive analysis: extract a
working set, cache it, query it repeatedly
• Modified Scala interpreter to support
interactive use of Spark
• Result: can search Wikipedia in ~0.5s after a
~20-second initial load
• Still figuring out how this should evolve
Resources
• Hadoop: hadoop.apache.org/common
• Pig: hadoop.apache.org/pig
• Hive: hadoop.apache.org/hive
• Video tutorials: www.cloudera.com/hadoop-training
• Amazon Elastic MapReduce:
docs.amazonwebservices.com/
ElasticMapReduce/latest/GettingStartedGuide/