By Alan Frederick Gates
This consultant is a perfect studying device and reference for Apache Pig, the open resource engine for executing parallel info flows on Hadoop. With Pig, you could batch-process facts with no need to create a full-fledged application—making it effortless so that you can test with new datasets.
Programming Pig introduces new clients to Pig, and gives skilled clients with entire insurance on key good points comparable to the Pig Latin scripting language, the Grunt shell, and consumer outlined services (UDFs) for extending Pig. if you would like to research terabytes of information, this booklet indicates you the way to do it successfully with Pig.
- Delve into Pig’s info version, together with scalar and intricate info types
- Write Pig Latin scripts to style, crew, sign up for, venture, and clear out your data
- Use Grunt to paintings with the Hadoop dispensed dossier process (HDFS)
- Build complicated information processing pipelines with Pig’s macros and modularity features
- Embed Pig Latin in Python for iterative processing and different complex tasks
- Create your personal load and shop features to deal with facts codecs and garage mechanisms
- Get functionality assistance for operating scripts on Hadoop clusters in much less time
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Extra resources for Programming Pig
To exit Grunt you can type quit or enter Ctrl-D. * According to Ben Reed, one of the researchers at Yahoo! ” 19 Entering Pig Latin Scripts in Grunt One of the main uses of Grunt is to enter Pig Latin in an interactive session. This can be particularly useful for quickly sampling your data and for prototyping new Pig Latin scripts. You can enter Pig Latin directly into Grunt. Pig will not start executing the Pig Latin you enter until it sees either a store or dump. However, it will do basic syntax and semantic checking to help you catch errors quickly.
As it is, Pig will output records with one field typed as a double. Pig will make a guess and then do its best to massage the data into the types it guessed. The downside here is that users coming from weakly typed languages are surprised, and perhaps frustrated, when their data comes out as a type they did not anticipate. However, on the upside, by looking at a Pig Latin script it is possible to know what the output data type will be in these cases without knowing the input data. 32 | Chapter 4: Pig’s Data Model CHAPTER 5 Introduction to Pig Latin It is time to dig into Pig Latin.
To enter Grunt, invoke Pig with no script or command to run. Typing: pig -x local will result in the prompt: grunt> This gives you a Grunt shell to interact with your local filesystem. If you omit the -x local and have a cluster configuration set in PIG_CLASSPATH, this will put you in a Grunt shell that will interact with HDFS on your cluster. As you would expect with a shell, Grunt provides command-line history and editing, as well as Tab completion. It does not provide filename completion via the Tab key.