Rich set of operators<\/strong><\/li>\n<\/ul>\nIn order to perform several operations, Pig offers many operators, for example, join, sort, filer and many more.<\/span><\/p>\n\n- Ease of programming<\/strong><\/li>\n<\/ul>\n
Since you are good at SQL, it is easy to write a Pig script. Because of Pig Latin as same as SQL.<\/span><\/p>\n\n- Optimization opportunities<\/strong><\/li>\n<\/ul>\n
In Apache Pig, all the tasks optimize their execution automatically. As a result, the programmers need to focus only on the semantics of the language.<\/span><\/p>\n\n- Extensibility<\/strong><\/li>\n<\/ul>\n
Through Pig, it is easy to read, process, and write data. It is possible by using the existing operators. Also, users can develop their own functions.<\/span><\/p>\n\n- UDF\u2019s<\/strong><\/li>\n<\/ul>\n
By using Pig, we can create User-defined Functions in other programming languages. Like Java. Also, can invoke or embed them in Pig Scripts.<\/span>
\n<\/span><\/p>\nQue 8. What is Pig Storage?<\/strong><\/p>\nAns. In Pig, there is a default load function, that is Pig Storage. Also, we can use pig storage, whenever we want to load data from a file system into the pig. <\/span><\/p>\nWe can also specify the delimiter of the data while loading data using pig storage (how the fields in the record are separated). Also, we can specify the schema of the data along with the type of the data.<\/span><\/p>\nQue 9. While writing evaluate UDF, which method has to be overridden?<\/strong><\/p>\nAns. We have to override the method exec() while writing UDF in the Pig. Whereas the base class can be different while writing filter UDF, we will have <\/span>to extend FilterFunc and for evaluate UDF, we will have to extend the EvalFunc. EvaluFunc is parameterized and must provide the return type also.<\/span><\/p>\nQue 10. What are the different UDF\u2019s in Pig?<\/strong><\/p>\nAns. On the basis of the number of rows, UDF can be processed. They are of two types:<\/span><\/p>\n\n- UDF that takes one record at a time, for example, Filter and Eval.<\/span><\/li>\n<\/ul>\n
\n- UDFs that take multiple records at a time, for example, Avg and Sum.<\/span><\/li>\n<\/ul>\n
Also, pig gives you the facility to write your own UDF\u2019s for load\/store the data.<\/span><\/p>\nApache Pig Interview Questions and Answers For Freshers. Q- 1,2,4,7<\/strong><\/p>\nApache Pig Interview Questions and Answers For Experience. Q- 3,5,6,8,9,10<\/strong><\/p>\nQue 11. What are the Optimizations a developer can use during joins?<\/strong><\/p>\nAns. We use replicated join, to perform join between a small dataset with a large dataset. Moreover, in the replicated join, the small dataset will be copied to all the machines where the mapper is running and the large dataset is divided across all the nodes. Also, it gives us the advantage of Map-side joins.<\/span><\/p>\nIf your dataset is skewed i.e. if a particular data is repeated multiple times even if you use reduce side join, the particular reducer will be overloaded and it will take a lot of time. Pig itself, calculates skewed join and the skewed key.<\/span><\/p>\nAnd, if you have datasets where the records are sorted in the same field, you can go for sorted join, this also happens in map phase and is very efficient and fast.<\/span><\/p>\nQue 12. What is a skewed join?<\/strong><\/p>\nAns. While we want to perform a join with a skewed dataset, that means a particular value will be repeated many times, is a skewed join.<\/span><\/p>\nQue 13. What is Flatten?<\/strong><\/p>\nAns. An operator in pig that removes the level of nesting, is Flatten. Sometimes, we have data in a bag or a tuple and we want to remove the level of nesting so that the data structured should become even, we use Flatten.
\n<\/span><\/p>\nIn addition, each Flatten produces a cross product of every record in the bag with all of the other expressions in the general statement.<\/span><\/p>\nQue 14. What are the complex data types in pig?<\/strong><\/p>\nAns. The following are the complex data types in Pig:<\/span><\/p>\n
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