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Showing posts from January, 2014

Hadoop Research Tips

Those who are interested to work on Hadoop, One commonly asked question that I got from these people  is what Hadoop feature can I work on?
Here are some items that I have in mind that are good topics for students to attempt if they want to work in Hadoop. Ability to make Hadoop scheduler resource aware, especially CPU, memory and IO resources. The current implementation is based on statically configured slots.Abilty to make a map-reduce job take new input splits even after a map-reduce job has already started.Ability to dynamically increase replicas of data in HDFS based on access patterns. This is needed to handle hot-spots of data.Ability to extend the map-reduce framework to be able to process data that resides partly in memory. One assumption of the current implementation is that the map-reduce framework is used to scan data that resides on disk devices. But memory on commodity machines is becoming larger and larger. A cluster of 3000 machines with 64 GB each can keep about 200TB o…

Hadoop Installation on Single Machine

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To Download and Install Hadoop, the prerequisites are 1. Linux based OS 64-bit OS like             Ubuntu             CentOS             Fedora ... etc I preferred to use Ubuntu 12.04LTS, later 14.04 LTS(upcomming version)
2. JAVA 1.6 or 1.7 JDK
Go to Downloads folder > cd Downloads
Un-zip the hadoop tar file >sudo tar xzf hadoop-1.1.2.tar.gz
I created a folder in /home/hduser/ >mkdir Installations
Move the Hadoop Un-Zip folder to Installations Directory, pointing as Hadoop >sudo mv /home/hduser/Downloads/hadoop-1.2.1 hadoop
Giving some permissions to hadoop folder >sudo addgroup hadoop >sudo chown -R hduser:hadoop hadoop
Restart the terminal inorder to get .bashrc file with some content


JAVA Installation in Ubuntu version

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I have a 64 bit version of Ubuntu 12.04 LTS installed, so the instructions below only apply to this OS.
Download the Java JDK from
http://www.oracle.com/technetwork/java/javasebusiness/downloads/java-archive-downloads-javase6-419409.html#jdk-6u45-oth-JPR.
1.Click Accept License Agreement 2.Click jdk-6u45-linux-x64.bin 3.Login to Oracle.com with your Oracle account 4.Download the JDK to your ~/Downloads directory 5.After downloading, open a terminal, then enter the following commands.
cd ~/Downloads chmod +x jdk-6u45-linux-x64.bin ./jdk-6u45-linux-x64.bin Note:
The jvm directory is used to organize all JDK/JVM versions in a single parent directory.
sudo mkdir /usr/lib/jvm sudo mv jdk1.6.0_45 /usr/lib/jvm The next 3 commands are split across 2 lines per command due to width limits in the blog’s theme.

Splunk Hadoop Connect 1.1 – Opening the door to MapR; now available on all Hadoop distributions

I am happy to announce that Splunk Hadoop Connect 1.1 is now available. This version of Hadoop Connect rounds out Splunk’s integration with the Hadoop distributions by becoming certified on MapRCloudera,Hortonworks, and Apache Hadoop distributions also have the ability to benefit from the power of Splunk. Splunk Hadoop Connect provides bi-directional integration to easily and reliably move data between Splunk and Hadoop. It provides Hadoop users the ability to gain real-time analysis, visualization and role based access control for a stream of machine-generated data. It delivers three core capacities: Export data from Splunk to Hadoop, Explore Hadoop directories and Import data from Hadoop to Splunk. The most significant new feature added to version 1.1 is the ability to select whether you want to map to a remote HDFS cluster or to a mounted file system. The option to map to a mounted file system enables Splunk to integrate with MapR Hadoop distribution. MapR allows users to mount Had…

Apache Tez - Beyond Hadoop (MR)

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Thanks to Hadoop Tips
Apache Pig and Hive are higher level abstracts on top of MR (MapReduce). PigLatin scripts of Pig and HiveQL queries of Hive are converted into a DAG of MR jobs, with the first MR job (5) reading from the input the last MR job (2) writing to the output. One of the problem with this approach is that the temporary data between the MR jobs (as in the case of 11 and 9) is written to HDFS (by 11) and read from HDFS (by 9) which leads to inefficiency. Not only this, multiple MR jobs will also lead to initialization over head. With the ClickStream analysis mentioned in the blog earlier, to find out the `top 3 urls visited by users whose age is less than 16` Hive results in a DAG of 3 MR jobs, while Pig results in a DAG of 5 MR jobs.