Introduction to Apache Hive and Pig
Apache Hive is a framework that sits on top of Hadoop for doing ad-hoc queries on data in Hadoop. Hive supports HiveQL which is similar to SQL, but doesn't support the complete constructs of SQL.
Hive coverts the HiveQL query into Java MapReduce program and then submits it to the Hadoop cluster. The same outcome can be achieved using HiveQL and Java MapReduce, but using Java MapReduce will required a lot of code to be written/debugged compared to HiveQL. So, it increases the developer productivity to use Hive.
To summarize, Hive through HiveQL language provides a higher level abstraction over Java MapReduce programming. As with any other high level abstraction, there is a bit of performance overhead using HiveQL when compared to Java MapReduce. But the Hive community is working to narrow down this gap for most of the commonly used scenarios.
Along the same line Pig provides a higher level abstraction over MapReduce. Pig supports PigLatin constructs, which is converted into Java MapReduce program and then submitted to the Hadoop cluster.
Hive coverts the HiveQL query into Java MapReduce program and then submits it to the Hadoop cluster. The same outcome can be achieved using HiveQL and Java MapReduce, but using Java MapReduce will required a lot of code to be written/debugged compared to HiveQL. So, it increases the developer productivity to use Hive.
To summarize, Hive through HiveQL language provides a higher level abstraction over Java MapReduce programming. As with any other high level abstraction, there is a bit of performance overhead using HiveQL when compared to Java MapReduce. But the Hive community is working to narrow down this gap for most of the commonly used scenarios.
Along the same line Pig provides a higher level abstraction over MapReduce. Pig supports PigLatin constructs, which is converted into Java MapReduce program and then submitted to the Hadoop cluster.
Harini 9505140022
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