Difference between revisions of "Hadoop Package"

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Execute the workflow. The workflow will halt while waiting for the job to finish. Pressing cancel will detach the running job an add it to the Job Monitor. The status of the execution can be checked by right-clicking HadoopStreaming and (for hadoop) selecting "View Standard error".
Execute the workflow. The workflow will halt while waiting for the job to finish. Pressing cancel will detach the running job an add it to the Job Monitor. The status of the execution can be checked by right-clicking HadoopStreaming and (for hadoop) selecting "View Standard error".
The job can be resumed by re-executing the workflow in vistrails. Once it completes the spreadsheet will list info about the 20 lines processed by the mapper. (usually the same)
The job can be resumed by re-executing the workflow in vistrails. Once it completes the spreadsheet will list info about the 20 lines processed by the mapper. (usually the same)
===== Using the cluster at gray02.poly.edu =====
If you are outside the poly network you need to use a ssh tunnel to get through the firewall.
Add this to ~/.ssh/config:
Host vgctunnel
HostName vgchead.poly.edu
LocalForward 8101 gray02.poly.edu:22
Host gray02
HostName localhost
Port 8101
Set up a tunnel to gray02 by running:
ssh vgctunnel


== Using Amazon AWS ==
== Using Amazon AWS ==

Revision as of 15:37, 25 February 2014

This page describes how to use the hadoop package in VisTrails. This package works on Mac and Linux.

Installation

Mac

This binary version of vistrails has the hadoop package preinstalled:

http://vgc.poly.edu/files/tommy/vistrails-mac-10.6-master-2014-02-25.dmg

Linux

Install vistrails from source:

git clone http://vistrails.org/git/vistrails.git
git clone https://github.org/rexissimus/BatchQ-PBS -b remoteq
cp -r BatchQ-PBS/remoteq vistrails/
cd vistrails
python vistrails/run.py

The first time vistrails is started it will download and install all the dependencies.

Windows

The BatchQ library used by the RemoteQ package does not support Windows. But you should be able to run Linux in a virtual machine and install vistrails there.

Modules used with the hadoop package

Machine

Represents a remote machine running SSH.

  • server - the server url
  • username - the remote server username, default is your local username
  • password - your password, connect a PasswordDialog to here
  • port - the remote ssh port, set to 0 if using an ssh tunnel

HadoopStreaming

Runs a hadoop job on a remote cluster.

  • CacheArchive - Jar files to upload
  • CacheFiles - Other files to upload
  • Combiner - combiner file to use after mapper. Can be same as reducer.
  • Environment - Environment variables
  • Identifier - A unique string identifying each new job. The job files on the server will be called ~/.vistrails-hadoop/.batchq.%Identifier%.*
  • Input - The input file/directory to process
  • Mapper - The mapper program (required)
  • Output - The output directory name
  • Reducer - The reducer program (optional)
  • Workdir - The server workdir (Default is ~/.vistrails-hadoop)

HDFSEnsureNew

Deletes file/directory from remote HDFS storage

HDFSGet

Retrieve file/directory from remote HDFS storage. Used to get the results.

  • Local File - Destination file/directory
  • Remote Location - Source file/directory in HDFS storage

HDFSPut

Upload file/directory to remote HDFS storage. Used to upload mappers, reducers and data files.

  • Local File - Source file/directory
  • Remote Location - Destination file/directory in HDFS storage

PythonSourceToFile

PythonSource that is written to a file. Used to create mapper/reducer files.

URICreator

Creates links to locations in HDFS storage for input data and other files

Deleting a job

To make sure a job can be executed from the beginning:

  • Clear the vistrails cache
  • Delete the job in the job monitor by selecting it an pressing "Del"

Example

Lets try using gray02.poly.edu to run basic example with a mapper that returns info about the machine it was executed on.


You will need an account on vgchead. In a terminal run:

ssh vgctunnel

Enter your password and keep the window open. Open vistrails-hadoop/example_nodeinfo.vt. It contains a working hadoop workflow.

Enter the machine info by going to Preferences->Module Packages, select RemoteQ and click "configure...". Enter this in the configuration:

server gray02
username <yourusername>
port 0
password True
defaultFS hdfs://gray02.poly.edu:8020/user/<yourusername>/
uris hdfs:///user/tommy/wikitext-big-notitle.csv#wikitext-big-notitle.csv

Hadoop workflow.png

Execute the workflow. The workflow will halt while waiting for the job to finish. Pressing cancel will detach the running job an add it to the Job Monitor. The status of the execution can be checked by right-clicking HadoopStreaming and (for hadoop) selecting "View Standard error". The job can be resumed by re-executing the workflow in vistrails. Once it completes the spreadsheet will list info about the 20 lines processed by the mapper. (usually the same)

Using the cluster at gray02.poly.edu

If you are outside the poly network you need to use a ssh tunnel to get through the firewall.

Add this to ~/.ssh/config:

Host vgctunnel
HostName vgchead.poly.edu
LocalForward 8101 gray02.poly.edu:22

Host gray02
HostName localhost
Port 8101

Set up a tunnel to gray02 by running:

ssh vgctunnel


Using Amazon AWS

First do the AWS_Setup.

AWS uses "*.pem" key files for access. Make sure you have one, then edit ~/.ssh/config and add

Host aws
HostName ec2-54-201-233-14.us-west-2.compute.amazonaws.com
IdentityFile ~/.ssh/<yourusername>.pem 

after replacing the host name and path to your key file. Enter the machine info by going to Preferences->Module Packages, select RemoteQ, click "configure...", and enter this in the configuration:

server aws
username hadoop
port 0
password False
defaultFS s3n://<yourusername>/
uris s3://cs9223/wikitext-big-notitle.csv#wikitext-big-notitle.csv

Change defaultFS to your s3 bucket

An example file is available at vistrails-hadoop/aws.vt. It contains a working hadoop workflow. Change the S3 bucket instances to point to your bucket and execute. When it finishes you should see the same result as in the Example above.