A brief, hands-on introduction to Hadoop & Pig

A brief, hands-on introduction to Hadoop & Pig Erik Eldridge Yahoo! Developer Network OSCAMP 2009 Photo credit: http://www.flickr.com/photos/mckaysavage/1059144105/sizes/l/

Preamble

Intention: approach hadoop from a tool-user's perspective, specifically, a web dev's perspective

Intended audience: anyone with a desire to begin using Hadoop

Requirements

VMWare

Hadoop will be demonstrated using a VMWare virtual machine

I’ve found the use of a virtual machine to be the easiest way to get started with Hadoop

Setup VM

Get hadoop vm from: http://developer.yahoo.com/hadoop/tutorial/module3.html#vm-setup

Note:

user name: hadoop-user

password: hadoop

Launch vm

Log in

Note ip of machine

Start Hadoop

Run the util to launch hadoop: $ ~/start-hadoop

If it's already running, we'll get an error like "172.16.83.132: datanode running as process 6752. Stop it first. 172.16.83.132: secondarynamenode running as process 6845. Stop it first. ..."

Saying “hi” to Hadoop

Call hadoop command line util: $ hadoop

Hadoop command line options are listed here: http://hadoop.apache.org/common/docs/r0.17.0/hdfs_shell.html

Hadoop should have been launched on boot. verify this is the case: $ hadoop dfs -ls /

Saying “hi” to Hadoop

If hadoop has not been started, you'll see something like: "09/07/22 10:01:24 INFO ipc.Client: Retrying connect to server: /172.16.83.132:9000. Already tried 0 time(s). 09/07/22 10:01:24 INFO ipc.Client: Retrying connect to server: /172.16.83.132:9000. Already tried 1 time(s)...”

If hadoop has been launched, the dfs -ls command should show the contents of hdfs

Before continuing, view all the hadoop utilities and sample files: $ ls

Install Apache

Why? In the interest of creating a relevant example, I'm going to work on Apache access logs

Update apt-get so it can find apache2: $ sudo apt-get update

Install apache2 so we can generate access log data: $ sudo apt-get install apache2

Generate data

Jump into the directory containing the apache logs: $ cd /var/log/apache2

Show the top n lines of the access log: $ tail -f -n 10 access.log

Generate data

Put this script, or something similar, in an executable file on your local machine:

#!/bin/bash

url='http://{VM IP address}:’

for i in {1..1000}

do

curl $url

done

Edit the IP address to that of your VM

Generate data

Set executable permissions on the file: $ chmod +x generate.sh

Run the file: $ ./generate.sh

Note log data in tail output in VM

Exploring HDFS

Ref: http://hadoop.apache.org/common/docs/r0.18.3/hdfs_shell.html

Show home dir structure:

$ hadoop dfs -ls /user

$ hadoop dfs -ls /user/hadoop-user

Create a directory: $ hadoop dfs -mkdir /user/hadoop-user/foo

Show new dir: $ hadoop dfs -ls /user/hadoop-user/

Exploring HDFS

Attempt to re-create new dir and note error: $ hadoop dfs -mkdir /user/hadoop-user/foo

Create a destination directory using implicit path: $ hadoop dfs -mkdir bar

Auto-create nested destination directories: $ hadoop dfs -mkdir dir1/dir2/dir3

Remove dir: $ hadoop dfs -rmr /user/hadoop-user/foo

Remove dir: $ hadoop dfs -rmr bar dir1

Try to re-remove dir and note error: $ hadoop dfs -rmr bar

Browse HDFS using web UI

Open http://{VM IP address}:500750 in browser

More info: http://hadoop.apache.org/common/docs/r0.18.3/hdfs_user_guide.html#Web+Interface

Import access log data

Load access log into hdfs: $ hadoop dfs -put /var/log/apache2/access.log input/access.log

Verify it's in there: $ hadoop dfs -ls input/access.log

View the contents: $ hadoop dfs -cat input/access.log

Do something w/ the data

Ref: http://www.michael-noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_%28Single-Node_Cluster%29

Credit: http://www.michael-noll.com/wiki/Writing_An_Hadoop_MapReduce_Program_In_Python

Save the mapper and reducer code in two separate files, e.g., mapper.py and reducer.py

Stream data through these two files, saving the output back to HDFS: #!/bin/bash $HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/contrib/streaming/hadoop-0.18.0-streaming.jar -mapper /home/hadoop-user/wordcount/mapper.py -reducer /home/hadoop-user/wordcount/reducer.py -input /user/hadoop-user/input/access.log -output /user/hadoop-user/output/mapReduceOut

View output files: $ hadoop dfs -ls output/mapReduceOut

Note multiple output files ("part-00000", "part-00001", etc)

View output file contents: $ hadoop dfs -cat output/mapReduceOut/part-00000

Pig

Pig is a higher-level interface for hadoop

Interactive shell Grunt

Declarative, SQL-like language, Pig Latin

Pig engine compiles Pig Latin into MapReduce

Extensible via Java files

"writing mapreduce routines, is like coding in assembly”

Pig, Hive, etc.

Exploring Pig

Ref: http://wiki.apache.org/pig/PigTutorial

Pig is already on the VM

Launch pig w/ connection to cluster: $ java -cp pig/pig.jar:$HADOOPSITEPATH org.apache.pig.Main

View contents of HDFS from grunt: > ls

Perform word count w/ Pig

Save this script in a file, e.g, wordcount.pig: myinput = LOAD 'input/access.log' USING TextLoader(); words = FOREACH myinput GENERATE FLATTEN(TOKENIZE($0)); grouped = GROUP words BY $0; counts = FOREACH grouped GENERATE group, COUNT(words); ordered = ORDER counts BY $0; STORE ordered INTO 'output/pigOut' USING PigStorage();

Perform word count w/ Pig