Beginning Apache Pig Big Data Processing Made Easy
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In a MapReduce framework, programs need to be translated into a series of Map and Reduce stages. However, this is not a programming model which data analysts are familiar with.
So, in order to bridge this gap, an abstraction called Pig was built on top of Hadoop. Similar to Pigs, who eat anything, the Pig programming language is designed to work upon any kind of data. That's why the name, Pig! A Pig Latin program consists of a series of operations or transformations which are applied to the input data to produce output.
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These operations describe a data flow which is translated into an executable representation, by Pig execution environment. Underneath, results of these transformations are series of MapReduce jobs which a programmer is unaware of. So, in a way, Pig allows the programmer to focus on data rather than the nature of execution. PigLatin is a relatively stiffened language which uses familiar keywords from data processing e.
This mode is suitable only for analysis of small datasets using Pig Map Reduce mode: In this mode, queries written in Pig Latin are translated into MapReduce jobs and are run on a Hadoop cluster cluster may be pseudo or fully distributed. MapReduce mode with the fully distributed cluster is useful of running Pig on large datasets.
There is lot of development effort required to decide on how different Map and Reduce joins will take place and there could be chances that hadoop developers might not be able to map the data into the particular schema format. However, the advantage is that MapReduce provides more control for writing complex business logic when compared to Pig and Hive.
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At times, the job might require several hive queries for instance 12 levels of nested FROM clauses then it becomes difficult for Hadoop developers to write using MapReduce coding approach. Most of the jobs can be run using Pig and Hive but to make use of the advanced application programming interfaces, hadoop developers must make use of MapReduce coding approach.
If there are any large data sets that Pig and Hive cannot handle for instance key distribution then Hadoop MapReduce comes to the rescue. There are certain circumstances when hadoop developers can choose to use Hadoop MapReduce over Pig and Hive-. However, the above choices depend on various non-technical limitations like design, money, coupling decisions, time and expertise. It is an undeniable fact that Hadoop MapReduce is characteristically the best with performance. Pig and Hive are slowly and openly entering into the above list by intensifying their feature sets.
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Pig has tools for data storage, data execution and data manipulation. Pig Latin is highly promoted by Yahoo as all the data engineers at Yahoo use Pig for processing data on the biggest hadoop clusters in the world. Hive was started by Facebook to provide hadoop developers with more of a traditional data warehouse interface for MapReduce programming. Hive queries are converted to MapReduce programs in the background by the hive compiler for the jobs to be executed parallel across the Hadoop cluster. This helps hadoop developers to focus more on the business problem rather than having to focus on complex programming language logic.
The drawback to using Hive is that hadoop developers have to compromise on optimizing the queries as it depends on the Hive optimizer and hadoop developers need to train the Hive optimizer on efficient optimization of queries.
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Hive is generally used for processing structured data in the form of tables. Hive eliminates tricky coding and lots of boiler plate that would otherwise be an overhead if they were following MapReduce coding approach. To the contrary, Pig Latin has most of the general processing concepts of SQL like selecting, filtering, grouping and ordering, however the syntax of Pig Latin is somewhat different from SQL, SQL users are required to make some conceptual adjustments to learn Pig.
Apache Pig requires more verbose coding when compared to Apache Hive, however it is still a fraction when compared to what Java MapReduce programs require.
Apache Pig offers more optimization and control on the data flow than Hive. If we compare it with its equivalent Pig Latin script shown below then has only 7 lines of code thus making it faster for hadoop developers to code in Pig Latin rather than using Hadoop MapReduce programming approach.
There is no need to import any additional libraries and anyone with basic knowledge of SQL and without a Java background can easily understand it. Thus, using higher level languages like Pig Latin or Hive Query Language hadoop developers and analysts can write Hadoop MapReduce jobs with less development effort. This kind of competition is usually seen as open source at its best. There is a core collection of packages that serve like a standard to keep everyone in synchrony. Each of the groups is competing to add the right sauce that will attract customers, both paying and nonpaying.
There continues to be controversy over just how much is rolled into the central collection, as there can be in any major open source project, but the amount of experimentation is so large that it's hard to be too focused on the amount of sharing.
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To get a feel for the excitement, I took four major collections out for a test-drive. I powered up a cluster of nodes on Rackspace, installed the tools, pushed the buttons, and ran some sample jobs. It's getting to be surprisingly easy to spend a few pennies for an hour or two of machine time -- so much so that I found myself debating whether it was worth leaving my cluster idling over lunchtime. Lest anyone doubt the efficiency of cloud computing, I noticed that the rate for my cluster of relatively fat machines with 4GB of RAM was less than the cost to park a car around the corner.
The parking meters spin faster.
The not-so-good news is that these collections are far from perfect. None of the tools I tried worked exactly as promised.