1. Running MapReduce
Programs in Clouds
-Anshul Aggarwal
Cisco Systems

2. Cloud Computing….Mapreduce
…..Hadoop…..

3. What is MapReduce?
• Simple data-parallel programming model designed for
scalability and fault-tolerance
• Pioneered by Google
• Processes 20 petabytes of data per day
• Popularized by open-source Hadoop project
• Used at Yahoo!, Facebook, Amazon, …

4. Why MapReduce Optimization

5. Outline
• Cloud And MapReduce
• MapReduce architecture
• Example applications
• Getting started with Hadoop
• Tuning MapReduce

6. Cloud Computing
• The emergence of cloud computing
has made a tremendous impact on
the Information Technology (IT) industry
• Cloud computing moved away from personal computers and
the individual enterprise application server to services
provided by the cloud of computers
• The resources like CPU and storage are provided as general
utilities to the users on-demand based through internet
• Cloud computing is in initial stages, with many issues still to
be addressed.

7. CLOUD COMPUTING SERVICES

8. Outline
• Cloud And MapReduce
• MapReduce architecture
• Example applications
• Getting started with Hadoop
• Tuning MapReduce

9. Mapreduce
Framework

10. MapReduce History
• Historically, data processing was completely done using
database technologies. Most of the data had a well-defined
structure and was often stored in relational databases
• Data soon reached terabytes and then petabytes
• Google developed a new programming model called
MapReduce to handle large-scale data analysis,and later they
introduced the model through their seminal paper
MapReduce: Simplified Data Processing on Large Clusters.

11. What the paper says

12. Example: Facebook Lexicon
www.facebook.com/lexicon

13. What is MapReduce used for?
• At Google:
• Index construction for Google Search
• Article clustering for Google News
• Statistical machine translation
• At Yahoo!:
• “Web map” powering Yahoo! Search
• Spam detection for Yahoo! Mail
• At Facebook:
• Data mining
• Ad optimization
• Spam detection

14. MapReduce Framework
• computing paradigm for processing data that resides on hundreds of
computers
• popularized recently by Google, Hadoop, and many others
• more of a framework
• makes problem solving easier and harder
• inter-cluster network utilization
• performance of a job that will be distributed
• published by Google without any actual source code

15. MapReduce Terminology

16. Outline
• Cloud And MapReduce
• MapReduce Basics
• Example applications
• Getting started with Hadoop
• Tuning MapReduce

17. Word Count -"Hello World" of
MapReduce world.
• The word count job accepts an input directory, a mapper
function, and a reducer function as inputs.
• We use the mapper function to process the data in parallel,
and we use the reducer function to collect results of the
mapper and produce the final results.
• Mapper sends its results to reducer using a key-value based
model.
• $bin/hadoop -cp hadoop-microbook.jar
microbook.wordcount. WordCount amazon-meta.txt
wordcount-output1

18. WorkFlow

19. Example : Word Count
19Map
Tasks
Reduce
Tasks
• Job: Count the occurrences of each word in a data set

20. Outline
• Cloud And MapReduce
• MapReduce Basics
• Example applications
• Mapreduce Architecture
• Getting started with Hadoop
• Tuning MapReduce

21. How Mapreduce Works
At the highest level, there are four independent entities:
• The client, which submits the MapReduce job.
• The jobtracker, which coordinates the job run. The jobtracker
is a Java application whose main class is JobTracker.
• The tasktrackers, which run the tasks that the job has been
split into.
• The distributed filesystem (normally HDFS), which is used
for sharing job files between the other entities.

22. Anatomy of a Mapreduce Job

23. Developing a MapReduce Application
• The Configuration API
Configuration conf = new Configuration();
conf.addResource("configuration-1.xml");
conf.addResource("configuration-2.xml");
• GenericOptionsParser, Tool, and ToolRunner
• Writing a Unit Test
• Testing the Driver
• Launching a Job
% hadoop jar hadoop-examples.jar v3.MaxTemperatureDriver -
conf conf/hadoop-cluster.xml Input/ncdc/all max-temp
• Retrieving the Results

24. This is where the Magic Happens
public class MaxTemperatureDriver extends Configured implements Tool {
@Override
Job job = new Job(getConf(), "Max temperature");
job.setJarByClass(getClass());
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(MaxTemperatureMapper.class);
job.setCombinerClass(MaxTemperatureReducer.class);
job.setReducerClass(MaxTemperatureReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new MaxTemperatureDriver(), args);
System.exit(exitCode);
}
}

25. Configuring Map Reduce params
• <configuration>
• <property>
• <name>mapred.job.tracker</name>
• <value>MASTER_NODE:9001</value>
• </property>
• <property>
• <name>mapred.local.dir</name>
• <value>HADOOP_DATA_DIR/local</value>
• </property>
• <property>
• <name>mapred.tasktracker.map.tasks.maximum</name>
• <value>8</value>
• </property>
• </configuration>
• $bin/hadoop -cp hadoop-microbook.jar microbook.wordcount.
WordCount amazon-meta.txt wordcount-output1

26. Q & A

27. Outline
• Cloud And MapReduce
• MapReduce architecture
• Example applications
• Getting started with Hadoop
• Tuning MapReduce

28. Hadoop Clusters

29. Inpioneerdaystheyusedoxenforheavypulling,
andwhenoneoxcouldn’tbudgealog,
theydidn’ttryto growalargerox.Weshouldn’tbe
tryingforbiggercomputers,butfor
moresystemsofcomputers.
—GraceHopper

30. Why Hadoop is able to compete?
30
Scalability (petabytes of data,
thousands of machines)
Database
vs.
Flexibility in accepting all data
formats (no schema)
Commodity inexpensive hardware
Efficient and simple fault-tolerant
mechanism
Performance (tons of indexing,
tuning, data organization tech.)
Features:
- Provenance tracking
- Annotation management
- ….

31. What is Hadoop
• Hadoop is a software framework for distributed processing of large
datasets across large clusters of computers
• Large datasets  Terabytes or petabytes of data
• Large clusters  hundreds or thousands of nodes
• Hadoop is open-source implementation for Google MapReduce
• HDFS is a filesystem designed for storing very large files with
streaming data access patterns, running on clusters of commodity
hardware
31

32. What is Hadoop (Cont’d)
• Hadoop framework consists on two main layers
• Distributed file system (HDFS)
• Execution engine (MapReduce)
• Hadoop is designed as a master-slave shared-nothing architecture
32

33. Design Principles of Hadoop
• Automatic parallelization & distribution
• computation across thousands of nodes and Hidden from the end-user
• Fault tolerance and automatic recovery
• Nodes/tasks will fail and will recover automatically
• Clean and simple programming abstraction
• Users only provide two functions “map” and “reduce”
• Need to process big data
• Commodity hardware
• Large number of low-end cheap machines working in parallel to solve a
computing problem
33

34. Hardware Specs
• Memory
• RAM
• Total tasks
• No Raid required
• No Blade server
• Dedicated Switch
• Dedicated 1GB line

35. Who Uses MapReduce/Hadoop
• Google: Inventors of MapReduce computing paradigm
• Yahoo: Developing Hadoop open-source of MapReduce
• IBM, Microsoft, Oracle
• Facebook, Amazon, AOL, NetFlex
• Many others + universities and research labs
• Many enterprises are turning to Hadoop
• Especially applications generating big data
• Web applications, social networks, scientific applications
35

36. Hadoop:How it Works
• Hadoop implements Google’s MapReduce, using HDFS
• MapReduce divides applications into many small blocks of work.
• HDFS creates multiple replicas of data blocks for reliability, placing them
on compute nodes around the cluster.
• MapReduce can then process the data where it is located.
• Hadoop ‘s target is to run on clusters of the order of 10,000-nodes.
36
SathyaSaiUniversity,Prashanti
Nilayam

37. WorkFlow

38. Hadoop: Assumptions
It is written with large clusters of computers in mind and is built
around the following assumptions:
• Hardware will fail.
• Processing will be run in batches.
• Applications that run on HDFS have large data sets.
• It should provide high aggregate data bandwidth
• Applications need a write-once-read-many access model.
• Moving Computation is Cheaper than Moving Data.
• Portability is important.

39. Complete Overview

40. Hadoop Distributed File System (HDFS)
40
Centralized namenode
- Maintains metadata info about files
Many datanode (1000s)
- Store the actual data
- Files are divided into blocks
- Each block is replicated N times
(Default = 3)
File F 1 2 3 4 5
Blocks (64 MB)

41. Main Properties of HDFS
• Large: A HDFS instance may consist of thousands of server
machines, each storing part of the file system’s data
• Replication: Each data block is replicated many times
(default is 3)
• Failure: Failure is the norm rather than exception
• Fault Tolerance: Detection of faults and quick, automatic
recovery from them is a core architectural goal of HDFS
• Namenode is consistently checking Datanodes
41

42. Outline
• Cloud And MapReduce
• MapReduce architecture
• Example applications
• Getting started with Hadoop
• Tuning MapReduce

43. Tuning Parameters

44. Mapping workers to
Processors
• The input data (on HDFS) is stored on the local disks of the machines
in the cluster. HDFS divides each file into 64 MB blocks, and stores
several copies of each block (typically 3 copies) on different
machines.
• The MapReduce master takes the location information of the input
files into account and attempts to schedule a map task on a machine
that contains a replica of the corresponding input data. Failing that, it
attempts to schedule a map task near a replica of that task's input
data. When running large MapReduce operations on a significant
fraction of the workers in a cluster, most input data is read locally and
consumes no network bandwidth.
44
SathyaSaiUniversity,Prashanti
Nilayam

45. Task Granularity
• The map phase has M pieces and the reduce phase has R pieces.
• M and R should be much larger than the number of worker
machines.
• Having each worker perform many different tasks improves dynamic
load balancing, and also speeds up recovery when a worker fails.
• Larger the M and R, more the decisions the master must make
• R is often constrained by users because the output of each reduce task
ends up in a separate output file.
• Typically, (at Google), M = 200,000 and R = 5,000, using 2,000
worker machines.
45
SathyaSaiUniversity,Prashanti
Nilayam

46. Speculative Execution – One
approach
• Tasks may be slow for various reasons, including hardware
degradation or software mis-configuration, but the causes
may be hard to detect since the tasks still complete
• successfully, albeit after a longer time than expected. Hadoop
doesn’t try to diagnose and fix slow-running tasks;
• instead, it tries to detect when a task is running slower than
expected and launches another, equivalent, task as a backup.

47. Problem Statement
The problem at hand is defining a resource provisioning
framework for MapReduce jobs running in a cloud keeping in
mind performance goals such as
Resource utilization with
-optimal number of map and reduce slots
-improvements in execution time
-Highly scalable solution

48. References
[1] E. Bortnikov, A. Frank, E. Hillel, and S. Rao, “Predicting execution bottlenecks in map-
reduce clusters” In Proc. of the 4th USENIX conference on Hot Topics in Cloud computing,
2012.
[2] R. Buyya, S. K. Garg, and R. N. Calheiros, “SLA-Oriented Resource Provisioning for Cloud
Computing: Challenges, Architecture, and Solutions” In International Conference on Cloud and
Service Computing, 2011.
[3] S. Chaisiri, Bu-Sung Lee, and D. Niyato, “Optimization of Resource Provisioning Cost in
Cloud Computing” in Transactions On Service Computing, Vol. 5, No. 2, IEEE, April-June 2012
[4] L Cherkasova and R.H. Campbell, “Resource Provisioning Framework for MapReduce Jobs
with Performance Goals”, in Middleware 2011, LNCS 7049, pp. 165–186, 2011
[5] J. Dean, and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters”,
Communications of the ACM, Jan 2008
[6] Y. Hu, J. Wong, G. Iszlai, and M. Litoiu, “Resource Provisioning for Cloud Computing” In
Proc. of the 2009 Conference of the Center for Advanced Studies on Collaborative Research,
2009.
[7] K. Kambatla, A. Pathak, and H. Pucha, “Towards optimizing hadoop provisioning in the
cloud in Proc. of the First Workshop on Hot Topics in Cloud Computing, 2009
[8] Kuyoro S. O., Ibikunle F. and Awodele O., “Cloud Computing Security Issues and
Challenges” in International Journal of Computer Networks (IJCN), Vol. 3, Issue 5, 2011