Upgrade gsutil to 3.4

third_party/gsutil/20110627/boto/docs/source/emr_tut.rst

Index: third_party/gsutil/20110627/boto/docs/source/emr_tut.rst
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-.. _emr_tut:
-
-=====================================================
-An Introduction to boto's Elastic Mapreduce interface
-=====================================================
-
-This tutorial focuses on the boto interface to Elastic Mapreduce from
-Amazon Web Services.  This tutorial assumes that you have already
-downloaded and installed boto.
-
-Creating a Connection
----------------------
-The first step in accessing Elastic Mapreduce is to create a connection
-to the service.  There are two ways to do this in boto.  The first is:
-
->>> from boto.emr.connection import EmrConnection
->>> conn = EmrConnection('<aws access key>', '<aws secret key>')
-
-At this point the variable conn will point to an EmrConnection object.
-In this example, the AWS access key and AWS secret key are passed in to
-the method explicitly.  Alternatively, you can set the environment variables:
-
-AWS_ACCESS_KEY_ID - Your AWS Access Key ID \
-AWS_SECRET_ACCESS_KEY - Your AWS Secret Access Key
-
-and then call the constructor without any arguments, like this:
-
->>> conn = EmrConnection()
-
-There is also a shortcut function in the boto package called connect_emr
-that may provide a slightly easier means of creating a connection:
-
->>> import boto
->>> conn = boto.connect_emr()
-
-In either case, conn points to an EmrConnection object which we will use
-throughout the remainder of this tutorial.
-
-Creating Streaming JobFlow Steps
---------------------------------
-Upon creating a connection to Elastic Mapreduce you will next 
-want to create one or more jobflow steps.  There are two types of steps, streaming
-and custom jar, both of which have a class in the boto Elastic Mapreduce implementation.
-
-Creating a streaming step that runs the AWS wordcount example, itself written in Python, can be accomplished by:
-
->>> from boto.emr.step import StreamingStep
->>> step = StreamingStep(name='My wordcount example',
-...                      mapper='s3n://elasticmapreduce/samples/wordcount/wordSplitter.py',
-...                      reducer='aggregate',
-...                      input='s3n://elasticmapreduce/samples/wordcount/input',
-...                      output='s3n://<my output bucket>/output/wordcount_output')
-
-where <my output bucket> is a bucket you have created in S3.
-
-Note that this statement does not run the step, that is accomplished later when we create a jobflow.  
-
-Additional arguments of note to the streaming jobflow step are cache_files, cache_archive and step_args.  The options cache_files and cache_archive enable you to use the Hadoops distributed cache to share files amongst the instances that run the step.  The argument step_args allows one to pass additional arguments to Hadoop streaming, for example modifications to the Hadoop job configuration.
-
-Creating Custom Jar Job Flow Steps
-----------------------------------
-
-The second type of jobflow step executes tasks written with a custom jar.  Creating a custom jar step for the AWS CloudBurst example can be accomplished by:
-
->>> from boto.emr.step import JarStep
->>> step = JarStep(name='Coudburst example',
-...                jar='s3n://elasticmapreduce/samples/cloudburst/cloudburst.jar',
-...                step_args=['s3n://elasticmapreduce/samples/cloudburst/input/s_suis.br',
-...                           's3n://elasticmapreduce/samples/cloudburst/input/100k.br',
-...                           's3n://<my output bucket>/output/cloudfront_output',
-...                            36, 3, 0, 1, 240, 48, 24, 24, 128, 16])
-
-Note that this statement does not actually run the step, that is accomplished later when we create a jobflow.  Also note that this JarStep does not include a main_class argument since the jar MANIFEST.MF has a Main-Class entry.
-
-Creating JobFlows
------------------
-Once you have created one or more jobflow steps, you will next want to create and run a jobflow.  Creating a jobflow that executes either of the steps we created above can be accomplished by:
-
->>> import boto
->>> conn = boto.connect_emr()
->>> jobid = conn.run_jobflow(name='My jobflow', 
-...                          log_uri='s3://<my log uri>/jobflow_logs', 
-...                          steps=[step])
-
-The method will not block for the completion of the jobflow, but will immediately return.  The status of the jobflow can be determined by:
-
->>> status = conn.describe_jobflow(jobid)
->>> status.state
-u'STARTING'
-
-One can then use this state to block for a jobflow to complete.  Valid jobflow states currently defined in the AWS API are COMPLETED, FAILED, TERMINATED, RUNNING, SHUTTING_DOWN, STARTING and WAITING.
-
-In some cases you may not have built all of the steps prior to running the jobflow.  In these cases additional steps can be added to a jobflow by running:
-
->>> conn.add_jobflow_steps(jobid, [second_step])
-
-If you wish to add additional steps to a running jobflow you may want to set the keep_alive parameter to True in run_jobflow so that the jobflow does not automatically terminate when the first step completes.
-
-The run_jobflow method has a number of important parameters that are worth investigating.  They include parameters to change the number and type of EC2 instances on which the jobflow is executed, set a SSH key for manual debugging and enable AWS console debugging.
-
-Terminating JobFlows
---------------------
-By default when all the steps of a jobflow have finished or failed the jobflow terminates.  However, if you set the keep_alive parameter to True or just want to halt the execution of a jobflow early you can terminate a jobflow by:
-
->>> import boto
->>> conn = boto.connect_emr()
->>> conn.terminate_jobflow('<jobflow id>') 
-