Hadoop/MapReduce Workshop - McGill University · Hadoop/MapReduce Workshop...

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Hadoop/MapReduce Workshop

[email protected] 14, 2015

Dan [email protected]

Simon [email protected]

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Outline

● Hadoop introduction and motivation

● Python review

● HDFS - The Hadoop Filesystem

● MapReduce examples and exercises

– Wordcount

– Distributed grep

– Distributed sort

– Maximum

– Mean and standard deviation

● Combiners

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Exercise 0: Login and Setup

● Log-in to Guillimin

– $ ssh -X class##@hadoop.hpc.mcgill.ca

– Use the account number and password from the slip you received

● Copy workshop files

– $ cp -R /software/workshop/hadoop .

● Load Hadoop Module

– $ module show hadoop

– $ module load hadoop python

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Exercise 1: First MapReduce job

● To make sure your environment is set up correctly

● Launch an example MapReduce job

– $ hadoop jar $HADOOP_EXAMPLES pi 100 100– $HADOOP_EXAMPLES=/usr/hdp/2.2.0.0-2041/hadoop-mapreduce/hadoop-

mapreduce-examples.jar

● Final output

– Job Finished in 16.983 seconds

– Estimated value of Pi is 3.14080000000000000000

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Hadoop

● What is Hadoop?

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Hadoop

● What is Hadoop?

– A collection of related software (“software framework”, “software stack”, “software ecosystem”)

– Data-intensive computing (“Big Data”)

– Scales with data size

– Fault-tolerant

– Parallel

– Analysis of unstructured, semi-structured data

– Cluster● Commodity hardware

– Open Source

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MapReduce

● What is MapReduce?

– Parallel, distributed programming model

– Large-scale data processing

– Map()● Filter, sort, embarrassingly parallel● e.g. sort participants by first name

– Reduce()● Summary● e.g. count participants whose first name starts with each letter

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Hadoop Ecosystem

● Apache Hadoop core

– HDFS - Hadoop Distributed File System

– Yarn - Resource management, job scheduling

● Pig - high-level scripting for MapReduce programs

● Hive - Data warehouse with SQL-like queries

● HCatalog - Abstracts data storage filenames and formats

● HBase - Database

● Zookeeper - Maintains and synchronizes configuration

● Oozie - Workflow scheduling

● Sqoop - Transfer data to/from relational databases

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Hadoop Motivation

● For “big data”, hard disk input/output (I/O) is a bottleneck

● We have seen huge technology improvements in both CPU speeds and storage capacity

● I/O performance has not improved as dramatically

● We will see that Hadoop solves this problem by parallelizing I/O operations across many disks

● The genius of Hadoop is in how easy this parallelization is from the developer's perspective

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Hadoop Motivation

● Big Data Challenges - The “V”-words

– Volume● Amount of data (terabytes, petabytes)● Want to distribute storage across many nodes and analyze it in place

– Variety● Data comes in many formats, not always in a relational database● Want to store data in original format

– Velocity● Rate at which size of data grows, or speed with which it must be processed● Want to expand storage and analysis capacity as data grows

● Data is “big data” if its volume, variety, or velocity are too great to manage easily with traditional tools

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How Does Hadoop Solve These Problems?

● Distributed file system (HDFS)

– Scalable with redundancy

● Parallel computations on data nodes

– Batch (scheduled) processing

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Hadoop vs. Competition

● Hadoop works well for loosely coupled (embarrassingly) parallel problems

Database

Parallelism

Proc

ess

coup

ling

MPI

Hadoop

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● Map or reduce tasks are automatically re-run if they fail

● Data is stored redundantly and reproduced automatically if a drive fails

Database

Parallelism

Faul

t Tol

eran

ceMPI

Hadoop

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● Hadoop makes certain problems very easy to solve

● Hardest parts of parallel programming are abstracted away

● Today we will write several practical codes that could scale to 1000s of nodes with just a few lines of code

Database

Parallelism

Dev

elop

er P

rodu

ctiv

ity

MPI

Hadoop

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Hadoop vs. and Competition

● Hadoop, MPI, and databases are all improving their weaknesses

– All are becoming fault-tolerant platforms for tightly-coupled, massively parallel problems

● Hadoop integrates easily into a workflow that includes MPI and/or databases

– Sqoop, HBase, etc. for working with databases

– Hadoop for post MPI data analysis

– Hadoop for pre MPI data processing

– Hadoop 2 introduced a scheduler, Yarn, that can schedule MPI, MapReduce, and other types of workloads

● New tools for tightly-coupled problems

– Apache Spark

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Python

● Hadoop is implemented in Java

● Developers can work with any programming language

● For a workshop, it is important to have a common language

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Python - for loops

● Can loop over individual instances of 'iterable' objects (lists, tuples, dictionaries)

● Looped sections use an indented block

– Be consistent: use a tab or 4 spaces, not both

● Do not forget the colon

myList = ['one', 'two', 'three']for item in myList: print(item)

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Python standard input/output

#!/usr/bin/pythonimport sysimport csv

reader = csv.reader(sys.stdin, delimiter=',')

for line in reader: data0 = line[0] data1 = line[1]

Load modules for systemand comma separated value file functions

Loop over lines in reader

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Dictionaries

● Unordered set of key/value pairs

– keys are unique, can be used as an index>>> dict = {'key':'value', 'apple':'the round fruit of a tree'}>>> print dict['key']value>>> print dict['apple']the round fruit of a tree

dict

'key'

'apple'

'value'

'the round fruit of a tree'

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Hadoop Distributed File System (HDFS)

● Key Concepts:

– Data is read and written in minimum units (“blocks”)

– A master node (“namenode”) manages the filesystem tree and the metadata for each file

– Data is stored on a group of worker nodes (“datanodes”)

● The same blocks are replicated across multiple datanodes (default replication = 3)

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HDFS Blocks

2: 64 MB

3: 22 MB

1: 64 MB

150 MB

datanode datanode

datanode datanode

datanode datanode

myFile.txt

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HDFS Blocks

2: 64 MB

3: 22 MB

1: 64 MB

150 MB

datanode datanode

datanode datanode

datanode datanode

myFile.txt

1: 64 MB 2: 64 MB

3: 22 MB

Data is distributed block-by-blockto multiple nodes

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HDFS Blocks

2: 64 MB

3: 22 MB

1: 64 MB

150 MB

datanode datanode

datanode datanode

datanode datanode

myFile.txt

1: 64 MB

2: 64 MB

3: 22 MB 1: 64 MB

2: 64 MB

1: 64 MB

2: 64 MB

3: 22 MB 3: 22 MB

Data redundancydefault = 3x

If we lose a node, data is availableon 2 other nodes and the namenodearranges to create a 3rd copy on anothernode

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Exercise 2: Using HDFS

● Put a file into HDFS

– $ hdfs dfs -put titanic.txt

● List files in HDFS

– $ hdfs dfs -ls

● Output the file contents

– $ hdfs dfs -cat titanic.txt

– $ hdfs dfs -tail titanic.txt

● Get help

– $ hdfs dfs -help

● Put the workshop data sets into HDFS

– $ hdfs dfs -put usask_access_logs

– $ hdfs dfs -put household_power_consumption.txt

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MapReduce

● Roman census approach:

– Want to count (and tax) all people in the Roman empire

– Better to go to where the people are (decentralized) than try to bring them to you (centralized)

– Bring back information from each village (map phase)

– Summarize the global picture (reduce phase)

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Roman Census: Mapping

Capital

Village

287 men293 women104 children854 sheep...

Village

Village

Village

Village

Village

mapper

mapper mapper

mapper

Note: These mappers are also “combiners” in Hadoop language.We will discuss what this means.

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Roman Census: Reducing

854 sheep

34 sheep

1032 sheep

206 sheep

91 sheep

545 sheep

854 sheep

854 sheep854 sheep854 sheep854 sheep287 men

854 sheep854 sheep854 sheep854 sheep854 sheep293 women

854 sheep854 sheep854 sheep854 sheep854 sheep104 children

2762 sheep

reducer

reducer

reducer

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MapReduce

Data

mapperskey,

value pairs

key, value pairs

key, value pairs

key, value pairs

key, value pairs

key, value pairs

key, value pairs

key, value pairs

key, value pairs

sort and shuffle

key, all

values

key, all

values

key, all

values

key, all

valuesreducers

results

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Mapper

● Takes specified data as input

● Works with a fraction of the data

● Works in parallel

● Outputs intermediate records

– key, value pairs● Recall hash tables or python dictionaries

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Reducer

● Takes a key or set of keys with all associated values as input

● Works with all data for that key

● Outputs the final results

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MapReduce Word Counting

● Want to count the frequency of words in a document

● What are the key, value pairs for our mapper output?

– A) key=1, value='word'

– B) key='word', value=1

– C) key=[number in hdfs block], value='word'

– D) key='word', value=[number in hdfs block]

– E) Something else

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● What are the key, value pairs for our mapper output?



– C) key=[number in hdfs block], value='word'

– D) key='word', value=[number in hdfs block]

– E) Something else Explanation: We want to sort according to the words, so that is the key. We can generate a pair for each word, we don't need the mapper to keep track of frequencies

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● If our mapper input is “hello world, hello!”, what will our reducer input look like?

B) hello 1world 1hello 1

A) hello world hello

C) hello 1hello 1world 1

D) hello 2world 1

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● If our mapper input is “hello world, hello!”, what will our reducer input look like?

B) hello 1world 1hello 1

A) hello world hello

C) hello 1hello 1world 1

D) hello 2world 1

Explanation: The reducerreceives SORTED key,value pairs. The sortingis done automatically by Hadoop.D is also possible, we will learn about combinerslater.

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● What are the key, value pairs for our reducer output?



– C) key=[count in document], value='word'

– D) key='word', value=[count in document]


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● What are the key, value pairs for our reducer output?



– C) key=[count in document], value='word'

– D) key='word', value=[count in document]


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Hadoop Streaming

● Streaming lets developers use any programming language for mapping and reducing

● Use standard input and standard output

● The first tab character delimits between key and value

● Similar to Bash pipes

– $ cat file.txt | ./mapper | sort | ./reducer● HADOOP_STREAM=/usr/hdp/2.2.0.0-2041/hadoop-mapreduce/hadoop-streaming.jar

● hadoop jar $HADOOP_STREAM -files <mapper script>,<reducer script> -input <input dir> -output <output dir> -mapper <mapper script> -reducer <reducer script>

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mapper.py

#!/usr/bin/env python

import sys

for line in sys.stdin: # split the line into words words = line.split() for word in words: print word, '\t', 1

Scripts require a “hash bang” line

Import the sys module for stdin

Loop over standard input

Loop over words

Print tab-separated key, value pairs

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Reducer: Checking for key changes

● Often reducers will have to detect when the key changes in the sorted mapper output

prevKey = Nonefor line in inputReader:

key = line[0]#If the current key is the same as previous keyif key == prevKey:

value = ... # update for current line# Else we have started a new group of keyselse:

# if not first line of inputif not prevKey == None:

# Completed entire key, print valueprint prevKey, “\t”, value

value = ... # set for first entry of new key# Set prevKey to the current keyprevKey = key

#Output final key, value pairif prevKey:

print prevKey, '\t', value

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reducer.py#!/usr/bin/env python

import sys

prevWord = NonewordCount = 0word = None

for line in sys.stdin: word, count = line.split('\t', 1) count = int(count)

if word == prevWord: wordCount += count else: if prevWord: print prevWord, '\t', wordCount wordCount = count prevWord = word

if prevWord: print prevWord, '\t', wordCount

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Testing map and reduce scripts

● It is useful to test your scripts with a small amount of data in serial to check for syntax errors

– $ head -100 myData.txt | ./mapper.py | sort | ./reducer.py

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Exercise 3: Word count

● Place the directory montgomery into HDFS

– $ hdfs dfs -put montgomery

● Submit a MapReduce job with your *tested* scripts to count the word frequencies in Lucy Maud Montgomery's books

–$ hadoop jar $HADOOP_STREAM \-files mapper_wordcount.py,reducer_wordcount.py\ -mapper mapper_wordcount.py \-reducer reducer_wordcount.py \ -input montgomery \-output wordCount

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Exercise 3: Word count

● View the output directory

– $ hdfs dfs -ls wordCount

● View your results

– $hdfs dfs -cat wordCount/part-00000

● View your (sorted) results

– $ hdfs dfs -cat wordCount/part-00000 | sort -k 2 -n

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Storage

A Hadoop cluster has 10 nodes with 300GB of storage per node with the default HDFS setup (replication factor 3, 64MB blocks). Alice wants to upload two 400GB files and run WordCount on them both. What will happen?

– A) The data upload fails at the first file

– B) The data upload fails at the second file

– C) MapReduce job fails

– D) None of the above. MapReduce is successful.

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Storage

A Hadoop cluster has 10 nodes with 300GB of storage per node with the default HDFS setup (replication factor 3, 64MB blocks). Alice wants to upload two 400GB files and run WordCount on them both. What will happen?



– C) MapReduce job fails

– D) None of the above. MapReduce is successful.

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Storage

A Hadoop cluster has 10 nodes with 300GB of storage per node with the default HDFS setup (replication factor 3, 64MB blocks). Alice wants to upload three 400GB files and run WordCount on them all. What will happen?



– C) The data upload fails at the third file

– D) MapReduce job fails

– E) None of the above. MapReduce is successful.

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Storage

A Hadoop cluster has 10 nodes with 300GB of storage per node with the default HDFS setup (replication factor 3, 64MB blocks). Alice wants to upload three 400GB files and run WordCount on them all. What will happen?



– C) The data upload fails at the third file

– D) MapReduce job fails

– E) None of the above. MapReduce is successful.

Explanation: The small cluster can only store 1.0TB of data with3X replication! Alice wants to upload 1.2TB.

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Simplifying MapReduce Commands

● The native streaming commands are cumbersome

● TIP: Create simplifying aliases and functions

– e.g.

● Place these commands into ~/.bashrc so they are executed in each new bash session (each login)

● To avoid confusion, we will only use the native commands today

mapreduce_stream(){ hadoop jar $HADOOP_STREAM -files $1,$2 \ -mapper $1 \ -reducer $2 \ -input $3 -output $4}

alias mrs mapreduce_stream

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Exercise 4: Hadoop Web UI

● Hadoop includes a web-based user interface

● Launch a firefox window

– $ firefox &

● Navigate to the Hadoop Job Monitor

– http://lm-2r01-n02:8088/cluster

● Navigate to the namenode and filesystem

– http://lm-2r01-n01:50070/dfshealth.jsp

● Navigate to the job history

– http://lm-2r01-n02:19888/jobhistory

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Accessing Job Logs

● Through the web UI

● Through the command line– $ yarn logs -applicationId application_1431541293708_0051

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Example: Distributed Grep

Hadoop Command:

$ hadoop jar $HADOOP_STREAM \-D mapreduce.job.reduces=0 \-D mapred.reduce.tasks=0 \-input titanic.txt \-output grepout \-mapper “/bin/grep Williams”

View Results:

$ hdfs dfs -cat grepout/part-0000*

● Note: We don't have to write any scripts!

● Note: There is no reducer phase

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Household Power Consumption

● Dataset: household_power_consumption.txt

● From the UCI Machine Learning Repository

● 9 Columns, semicolon separated (see household_power_consumption.explain)

– 1. date: dd/mm/yyyy

– 2. time: hh:mm:ss

– 3. minute-averaged active power (kilowatts)

– 4. minute-averaged reactive power (kilowatts)

– 5. minute-averaged voltage (volts)

– 6. minute-averaged current (amps)

– 7. kitchen active energy (watt-hours)

– 8. laundry active energy (watt-hours)

– 9. water-heater and A/C active energy (watt-hours)

● $ hdfs dfs -put household_power_consumption.txt

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Problematic Input

● In the household_power_consumption data, missing values are specified by '?'

● Analysts must decide how to deal with unexpected values in unstructured data

● Today, we will ignore itfor line in sys.stdin:

try: data = float(line.split(';')[2])

except:continue

...

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On which date was the maximum minute-averaged active power?

● What should the output of our mapper be?

A) power 1

B) date 1

C) date power

D) something else

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On which date was the maximum minute-averaged active power?

● What should the output of our mapper be?

A) power 1

B) date 1

C) date power

D) something else

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Working with .csv files in python

● We can use the csv module in python to parse .csv files more easily

import sys, csv

reader = csv.reader(sys.stdin, delimiter=';')

for line in reader: data = float(line[2]) ...

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Exercise 5: Compute the maximum

● Write a mapper and a reducer to compute the maximum value of the minute-averaged active power (3rd column), as well as the date on which this power occurred

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Combiners

● To compute the max, you may have...

– Output a list of all values with a single common key

– Had a single reducer compute the maximum value in serial

● We would like to do some pre-reduction on the mapper nodes to balance the workload from the reducer to the mappers

● To find the maximum, we only need to send the maximum from each mapper through the network, not every value

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Combiners (maximum)

16/12/2006 4.21616/12/2006 5.36016/12/2006 5.37416/12/2006 5.38816/12/2006 3.666

20/12/2006 1.51620/12/2006 1.49820/12/2006 1.51820/12/2006 1.49220/12/2006 1.504

...20/12/2006;02:46:00;1.516;0.262;245.780;6.200;0.000;1.000;18.00020/12/2006;02:47:00;1.498;0.258;246.060;6.200;0.000;2.000;19.00020/12/2006;02:48:00;1.518;0.264;246.240;6.200;0.000;1.000;18.000...

20/12/2006 1.51620/12/2006 1.49820/12/2006 1.51820/12/2006 1.49220/12/2006 1.504

16/12/2006 5.388 20/12/2006 1.518

Shuffle and sort, reduce

Map

Combine

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Combiners

● To compute the maximum the reducer and the combiner can be the same script

– max() is associative and commutative

max([a,b]) = max([b,a])

max([a, max([b,c])]) = max([max([a,b]),c])

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Combiners

● To test your combiner scripts

● Hadoop sorts locally before the combiner and globally between the combiner and reducer

● Note that Hadoop does not guarantee how many times the combiner will be run

$ cat data.txt | ./mapper.py | sort | ./combiner.py | ./reducer.py

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Combiners

● What is the benefit of reducing the number of key-value pairs sent to the reducer?

A) The amount of work done in the Map phase is reduced

B) The amount of work done in the Reduce phase is reduced

C) The amount of data sent through the network is reduced

D) More than one of the above

E) None of the above

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Combiners

● What is the benefit of reducing the number of key-value pairs sent to the reducer?

A) The amount of work done in the Map phase is reduced

B) The amount of work done in the Reduce phase is reduced

C) The amount of data sent through the network is reduced

D) More than one of the above

E) None of the above

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Histograms

Mwtoews, 2008

● Histograms 'bin' continuous data into discrete ranges

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Exercise 6: Histogram

● Write a mapper that uses round(power) to 'bin' the minute-averaged active power readings (3rd column)

– Output for each reading: [power bin], 1

● Write a reducer that creates combined counts

– Input: [power bin], count

– Output: [power bin], count

– This script must also function as combiner

● Submit your tested scripts as a Hadoop job

– Use the reducer script as a combiner

– $ hadoop jar $HADOOP_STREAM ... -combiner reducer_hist.py ...

● You may generate a plot (plotting is outside the scope of the workshop)– $ hdfs dfs -cat histogram/part-00000 | solutions/plot_hist.py

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Histogram

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Mean and Standard Deviation

● We can't easily use combiners to compute the mean

– max(max(a,b), max(c,d,e)) = max(a,b,c,d,e)

– mean(mean(a,b), mean(c,d,e)) != mean(a,b,c,d,e)

● Reducer function can be used as a combiner if

– associative: (A*B)*C = A*(B*C)

– commutative: A*B = B*A

– e.g.: counting, addition, multiplication, ...

● Computing the mean and standard deviation means the reducer is stuck with a lot of math

– Combiner idea for mean● key = intermediate sum● value = number of entries

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Power consumption by day of week

● Are there days of the week when more power is consumed, on average?

● Want to know the mean and standard deviation for each week day

● Simplification: Compute average of minute-averaged powers, grouped by day of week

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Python datetime

● The datetime module is a powerful library for working with dates and times

● We can easily find the day of the week from the date

– from datetime import datetime

– weekday = datetime.strptime(date, "%d/%m/%Y").weekday()

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Exercise 7: Mean and Standard Deviation

● Write mapper and reducer code to compute the mean and standard deviation for active power (3rd column) for each of the seven days of the week

● Test your scripts using serial Bash commands

● Submit your job to Hadoop

● Tip: Wikipedia - Algorithms for calculating variance

– Python code to compute mean and variance in a single pass

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Speedup - Mean and St.Dev.

● ~2 million entries

● Serial Bash version

– $ cat household_power_consumption.txt | ./mapper.py | sort | ./reducer.py

– 75 seconds

● Hadoop version

– 2 mappers, 1 reducer: 50 seconds● Speedup: 1.5X

– 2 mappers, 2 reducers: 48 seconds● Speedup: 1.6X ● -D mapred.reduce.tasks=2

– 4 mappers, 4 reducers: 29 seconds● Speedup: 2.6X

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Choosing numbers of maps/reduces

● Mappers

– More mappers increases parallelism

– Too many mappers increases scheduling overhead

– Hadoop automatically sets the number of mappers according to the block size and input data size

● Reducers

– Too few reducers increases the computational load on each reducer

– Too many reducers increases shuffle and HDFS overhead

– Rule of thumb: Each reducer should process 1-10GB of data

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Iterative MapReducing

● Many tasks in scientific computing cannot be easily expressed as a single MapReduce job

● Often, we require iterating over data

● K-means clustering is an example

– We will see how it can be implemented in MapReduce

– We will not implement it, just see how it works with the MapReduce framework

– A MapReduce K-means clustering is implemented in Mahout (scalable machine learning algorithms)

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K-means clustering

● Unsupervised machine learning

● Divide a data set into k different categories based on the features of that data set

● Computational hotspot: computing distances between each cluster centroid and each data point, O(n*k)

● E.g. Clothing manufacturer: based on customer's height and weight data, divide them into 3 or more size categories

● E.g. Categorize astronomical objects into stars, galaxies, quasars, etc. based on spectral data

● E.g. Categorize gene expression profiles to study function within similar expressions

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K-means clustering

Step 1: Randomly generate K locations (circles)

Step 2: Group data points by proximity to locations

Step 3: Update locations to the centroid of each group

Iterate over steps 2 and 3

Images: I, Weston.pace

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MapReduce K-Means

Mapper● Calculate centroid distances

● Assign data points to nearest centroid

Data points Centroid locations

Reducer● Compute new centroids

Converged?

No

MapperMapper

Final Centroid locations

Yes

Mapperkey: best centroidvalue: data point

Reducerkey: old centroidvalue: new centroid

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Iterative MapReducing

● To make iterative jobs easier, the Hadoop ecosystem has tools for iterative workloads

– Twister - Iterative MapReduce framework

– HaLoop - Iterative MapReduce framework

– Mahout - Scalable implementations of machine learning algorithms on Hadoop (including k-means)

– Spark - Framework for in-memory distributed computing, in-memory data sharing between jobs

● Make use of high-level interfaces to MapReduce for more complex jobs

– Pig, Mahout, etc.

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What questions do you have?

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In the time remaining...

● Import your own data into HDFS for analysis

– Your quota is 300GB (after replication) by default

● Examine some data from Twitter

– /software/workshop/twitter_data

– 3.8 million tweets + metadata ~ 11 GB

● Continue to work with the workshop data sets

– titanic.txt

– household_power_consumption.txt

– usask_access_log

● Contact us to add your user account to the Hadoop test environment (class accounts deactivate later today)

– [email protected]

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Keep Learning...

● Contact us for access to our Hadoop test system

– [email protected]

● Download a Hadoop virtual machine

– http://hortonworks.com/products/hortonworks-sandbox

● View online training materials

– https://www.udacity.com/course/ud617

– http://cloudera.com/content/cloudera/en/training/library.html

● Calcul Quebec workshop on Apache Spark (French)

– https://wiki.calculquebec.ca/w/Formations

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Bonus Exercise: Top 10 websites

● Produce a top 10 list of websites accessed on the University of Saskatchewan website

– usask_access_logs

● Be careful

– Some lines wont conform to your expectations

– How to handle?● skip? ● exception?

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Top 10 List

● Mapper

– output: key 1

● Combiner

– returns top 10 for each mapper

– output: key count

● Reducer

– finds the global top 10

– output: key count

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