## Local dev environment

It is important to be able to test code on your own machine before submitting to a Hadoop cluster. Below are instructions for doing so in Eclipse.

The basic idea is to create a normal Java application but add all the Hadoop JARs to the classpath. Your own code will have the main() function, so you run it as a normal Java application.

The Hadoop libraries have been packaged, for your convenience, into a single ZIP file.

### Windows installation

• Extract the ZIP. Notice there is a hadooplibs folder as well as some others such as bin.
• Create system environment variables to point to your extracted files.
2. Type ‘env’ to search for that string, and select “Edit environment variables for your account”
3. In the window that opens up, in the top panel, click “New” and type HADOOP_HOME for the name and the path to your unzipped files for the value, e.g., C:\Users\Me\Documents\hadoop-2.6.0
4. Create another new variabled called PATH and set its value to C:\Users\Me\Documents\hadoop-2.6.0\bin (i.e., the HADOOP_HOME value plus bin). If you already have a PATH variable, edit it, add a semicolon ; to the end, and then add the Hadoop bin path.
5. Click “OK” until you are out of all the environment variable windows.
6. Close/restart Eclipse or IntelliJ so that your IDE sees these new environment variables.

### Linux/Mac OS X installation

• Extract the ZIP. Notice there is a jars folder and a sources folder.
• The files in jars will be imported as libraries in Eclipse (see Hadoop workflow notes).
• The files in sources provide source code for the Hadoop JARs in the jars folder. If Eclipse ever complains about not having the source code for an external Hadoop library, tell it to associate the corresponding JAR in sources.

For reference, I copied the JARs from these locations in a fresh Hadoop 2.6.0 distribution (from here, the non-source package), and added them to the ZIP.

• every JAR in share/hadoop/common/lib
• every JAR in share/hadoop/mapreduce
• every JAR in share/hadoop/yarn
• every JAR in share/hadoop/yarn/lib
• hadoop-common-2.6.0.jar in share/hadoop/common

Next, you can proceed to the Hadoop workflow notes.

## delenn environment

delenn is a moderately-sized server (132 GB RAM, 32 CPU cores, 15 TB disk dedicated to Hadoop) running CentOS 6.4. It runs about 10 virtual machines to simulate a Hadoop cluster. A real Hadoop cluster should be made up of physical commodity hardware (normal servers, not supercomputers). But buying lots of servers is significantly more expensive than simulating them, so we simluate them. However, performance suffers in simulated environments. See the Hadoop notes for more details about simulation vs. real hardware.

See the Hadoop notes for definitions and explanations of these nodes. Below is a summary of their roles:

resourcemanager Link Manages YARN “containers”, i.e., jobs
mrjobhistory Link Records finished MapReduce jobs

The three nodes identified above each run one daemon with the same name and purpose as described.

Each slave runs two daemons:

• datanode — Manages the slave’s HDFS contents
• nodemanager — Manages the slave’s “containers,” i.e., work jobs; example: slave8

## londo environment

londo has a local Hadoop installation as well. It is for testing purposes only, if you are unable to test on your own machine. It only runs in one thread, so it is quite slow. You would need to create a JAR package in your IDE and transfer it to londo, or compile your code on londo. Then execute the application as described in the Hadoop workflow.