Hands-on Hadoop: An intro for Web developers

Erik Eldridge

Engineer/Evangelist

Yahoo! Developer Network

Photo credit: http://www.flickr.com/photos/exfordy/429414926/sizes/l/

Goals

• Gain familiarity with Hadoop

• Approach Hadoop from a web dev's perspective

Prerequisites

• 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

• Curl installed

Setup VM

• Download VM from YDN– 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 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

• 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

• Show home dir structure: – $ hadoop dfs -ls /user

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

Exploring HDFS

• Attempt to re-create new dir and note error: – $ hadoop dfs -mkdir /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/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}:50030 in browser

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

Count words in data using Hadoop Streaming

• Hadoop Streaming refers to the ability to use an arbitrary language to define a job’s map and reduce processes

Python wordcount mapper

Credit: http://www.michael-noll.com/wiki/Writing_An_Hadoop_MapReduce_Program_In_Python#Map:_mapper.py

Python wordcount reducer

Credit: http://www.michael-noll.com/wiki/Writing_An_Hadoop_MapReduce_Program_In_Python#Map:_mapper.py

Test run mapper and reducer

• $ cat data | mapper.py \| sort | reducer.py

Run Hadoop

• Stream data through these two files, saving the output back to HDFS:$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/hadoop-streaming.jar \ -input /data/ydata/ydata-ysm-keyphrase-bid-imp-click-v1_0 \ -output /user/{username}/output \ -mapper /home/{username}/mapper.py \ -reducer /home/{username}/reducer.py

View output

• 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

• 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

Pig word count

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

• Run this script:$ java -cp pig/pig.jar:$HADOOPSITEPATH org.apache.pig.Main -f wordcount.pig

• View output$ hadoop dfs -cat output/pigOut/part-00000

Resources

• Apache Hadoop Site– hadoop.apache.org

• Apache Pig Site– hadoop.apache.org/pig/

• YDN Hadoop Tutorial – developer.yahoo.com/hadoop/tutorial/

module3.html#vm-setup

• Michael G Noll’s tutorial:– www.michael-noll.com/wiki/

Running_Hadoop_On_Ubuntu_Linux_%28Single-Node_Cluster%29

Thank you

• Follow me on Twitter:– http://twitter.com/erikeldridge

• Find these slides on Slideshare:– http://slideshare.net/erikeldridge

• Feedback? Suggestions?– http://speakerrate.com/erikeldridge