In our previously published guides, we talked about installing Hadoop, installing Pig on own server. We also talked about free IBM Analytics Demo Cloud with needed softwares pre-installed, ready to use. Here is basic example with commands to show how to process server log in Hadoop, Pig can be done using free IBM Analytics Demo Cloud. This guide can be followed by even who never used Hadoop or Pig. We collected a script for the easy work. We only can not supply our server log to you as IP address collected by our server is sensitive data and may be prohibited to share in EU countries to protect privacy of our readers. You need to collect Apache web server access log from somewhere to really follow this guide.
How To Process Server Log in Hadoop, Pig
First, SSH to IBM Analytics Demo Cloud. Then wget your access log file in your desired location, for us location is ~ aka $HOME aka where you’ll be after login via SSH. In our example, the access log file’s name is access.log.1.
In case you are on SSH of your server, then use CLI FTP commands to upload (commands are under the sub header “Transfer FTP Files Directly From One Server to Another : Major Methods With Command Line”) the log to IBM Analytics Demo Cloud instance.
First we are feeding the data to Hadoop with this command :
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hadoop fs -put access.log.1 |
If you face error, append location where diskX is disk number like disk2, USERNAME is like admin1234 (which IBM Analytics Demo Cloud provided you to SSH) :
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hadoop fs -put access.log.1 /diskX/home/USERNAME/ |
If you run this command :
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hadoop fs -ls -R |
You’ll get output like this where you’ll find your thing listed :
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drwx------+ - USERNAME USERNAME 0 2017-07-20 17:04 .staging -rw-rw----+ 3 USERNAME USERNAME 12183708 2017-07-20 16:33 access.log.1 |
Now we will create a script named script.pig at the same location with this content :
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DEFINE ApacheCommonLogLoader org.apache.pig.piggybank.storage.apachelog.CommonLogLoader(); logs = LOAD '/path/to/USERNAMER/access.log.1' USING ApacheCommonLogLoader AS (addr: chararray, logname: chararray, user: chararray, time: chararray, method: chararray, uri: chararray, proto: chararray, status: int, bytes: int); addrs = GROUP logs BY addr; counts = FOREACH addrs GENERATE flatten($0), COUNT($1) as count; DUMP counts; |
Change /path/to/USERNAMER/access.log.1 in the above example to real path and save the file. Now run this command :
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locate piggybank.jar |
You will get a list of paths. At the bottom of the list, there will be entries like :
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... /usr/ibmpacks/bigsheets/5.4/libext/piggybank.jar /usr/iop/4.1.0.0/hive/lib/piggybank.jar /usr/iop/4.1.0.0/pig/piggybank.jar /usr/iop/4.1.0.0/pig/lib/piggybank.jar |
We will use that /usr/iop/4.1.0.0/pig/piggybank.jar path. Run :
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pig |
You will get MySQL like interface :
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grunt> |
Run this command there :
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REGISTER '/usr/iop/4.1.0.0/pig/piggybank.jar'; |
To exit the interface, run :
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quit |
Now run :
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pig -x local script.pig |
With the script, you’ll get output of list of IP like this :
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... (97.88.119.105,1) (103.73.224.177,2) (112.134.38.209,12) (112.215.240.56,1) (112.76.230.153,1) (115.64.142.113,1) (117.202.230.80,1) (118.163.69.237,1) (118.71.254.244,1) (120.188.87.203,2) ... |
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Notice the above syntax – IP followed by number of times separated by comma. That is what was intended to show you a basic demo.
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