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A command-separated list of files to be copied to the MapReduce cluster -mapper The command to be run as the mapper -reducer The command to be run as the reducer -input The DFS input path for the Map step -output The DFS output directory for the Reduce step mrjob mrjob is a Python MapReduce library, created by Yelp, that wraps Hadoop streaming, allowing MapReduce applications to be written in a more Pythonic manner.
Writing MapReduce applications with mrjob has many benefits: While mrjob is a great solution, it does have its drawbacks. Installation The installation of mrjob is simple; it can be installed with pip by using the following command: To run the job locally and count the frequency of words within a file named input.
Within the mrjob library, the class that inherits from MRJob contains the methods that define the steps of the MapReduce job. The steps within an mrjob application are mapper, combiner, and reducer. The class inheriting MRJob only needs to define one of these steps.
The mapper method defines the mapper for the MapReduce job. The combiner method defines the combiner for the MapReduce job. The combiner is a process that runs after the mapper and before the reducer. It receives, as input, all of the data emitted by the mapper, and the output of the combiner is sent to the reducer.
The combiner's input is a key, which was yielded by the mapper, and a value, which is a generator that yields all values yielded by one mapper that corresponds to the key. The reducer method defines the reducer for the MapReduce job.
The final component of a MapReduce job written with the mrjob library is the two lines at the end of the file: Executing mrjob Executing a MapReduce application with mrjob is similar to executing any other Python program.
The command line must contain the name of the mrjob application and the input file: Multiple files can be passed to mrjob as inputs by specifying the filenames on the command line: The dataset used is the salary information from the city of Baltimore for Hadoop Streaming and mrjob were then used to highlight how MapReduce jobs can be written in Python.
Pig and Python Pig is composed of two major parts: Compared to Java MapReduce, Pig is easier to write, understand, and maintain because it is a data transformation language that allows the processing of data to be described as a sequence of transformations.
Pig is also highly extensible through the use of the User Defined Functions UDFs which allow custom processing to be written in many languages, such as Python.
An example of a Pig application is the Extract, Transform, Load ETL process that describes how an application extracts data from a data source, transforms the data for querying and analysis purposes, and loads the result onto a target data store.
Once Pig loads the data, it can perform projections, iterations, and other transformations. UDFs enable more complex algorithms to be applied during the transformation phase. This chapter begins with an example Pig script. Pig and Pig Latin are then introduced and described in detail with examples.
The chapter concludes with an explanation of how Pig's core features can be extended through the use of Python. It assumes that a a data file, input. Once the job is complete, a success message, similar to the one below, will be displayed: Main - Pig script completed in 18 seconds and milliseconds ms The results of the wordcount.
The first statement loads data from the filesystem and stores it in the relation records: Execution Modes Pig has two execution modes: Running Pig in local mode only requires a single machine.
Pig will run on the local host and access the local filesystem. To run Pig in local mode, use the -x local flag: Running Pig in MapReduce mode requires access to a Hadoop cluster.
To run Pig in MapReduce mode, simply call Pig from the command line or use the -x mapreduce flag: Interactive Mode Pig can be run interactively in the Grunt shell.
To invoke the Grunt shell, simply call Pig from the command line and specify the desired execution mode.
The following example starts the Grunt shell in local mode: Running Pig interactively is a great way to learn Pig.Write a narrative essay on how you spend your holiday; Content rewrite asa archery; Radiology program application essay; What motivation theories may be found in each case study essay.
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|Source Code||Adam Krouskop Thanks, I did find that exact repo, and I ended up looking through it for a bit.|
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Finally, I want to measure the throughput of the driver with read/write buffer size set to 1 B, 1 KB, I MB with delay set to 0,1 ms and 1 sec.
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