Apache Pig Interview Questions and Answers
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Best Apache Pig Interview Questions and Answers
Are you preparing for the Apache Pig interview and looking for the best material to gain the required knowledge? Then you have come to the right place. To give your best performance and to nail the Pig interview in the very first attempt you need to go through these frequently asked Pig Interview Questions and Answers for freshers and experienced candidates.
To simplify your interview preparation process and to help you crack your interview we have gathered a list of top Pig interview questions and answers based on the industry experts’ opinion. Whether you are a fresher or experienced candidate preparing these frequently asked Apache Pig Interview Questions and Answers will definitely help you.
Apache Pig is an advanced platform that helps in creating programs that run on Apache Hadoop. The Apache Pig Platform uses a language called Pig Latin. It can easily execute all its Hadoop jobs in Apache Spark, MapReduce, or Apache Tez. Pig works similar to SQL and it can also be extended by using user-defined functions that a user can write in Python, Java, Ruby, JavaScript, Groovy. By the end of this Pig Interview Questions and Answers blog, you will gain all the knowledge to crack your interview.
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Frequently asked Apache Pig Interview Questions
The following are the commonly asked Pig interview questions and answers from the basic to advanced level.
The IT nowadays is highly relying on DevOps which is actually collaboration or an effective communication between the software developers and the deployment or operational team. DevOps is used to deliver software quickly with low failure rates.
Apache Pig is an advanced technology compared to MapReduce. Apache MapReduce is a low-level Platform and associated with complex implementations. It becomes a homogenous task to perform data and does not provide nested data types. All these issues have contributed to the development of Apache Pig.
Apache Pig is a specialized platform used for ad-hoc processing. Apache Pig platform is generally used in the below situations:
- To analyze the data of search engine platforms. Let us consider one example, Yahoo engine uses Apache Pig to conduct analysis on the Yahoo news feed and data delivered from the Yahoo search engine.
- To process vast amounts of data sources such as streaming online data, Weblogs, etc.
- Analyze the customer behavior of E-commerce and other websites using data.
Following are the roles performed by MapReduce in Apache Pig:
- All the Apache Pig programs are written in Pig Latin query language and it is the same as the SQL query language. The queries written in Apache Pig require an engine for the execution of these queries. Pig engine converts the queries into MapReduce jobs and MapReduce executes the programs as per the requirement.
- Pig generally never sends any output to Hadoop, instead it transforms inputs and data locations to map-reduce.
- There are some standard data-processing operations in Pig and these operations are Mapped to the map-reduce tasks.
Pig is an advanced high-level platform that makes various Apache Hadoop data analysis issues can be solved easily, and the execution can be done in a proper manner. A program written in Pig Latin is a data flow language, which needs an execution engine to execute the query. So, when a given program is written in a Pig Latin language then the Pig compiler converts the given program into different MapReduce jobs.
The following are the major components of the Pig execution environment:
- Pig Scripts: All the Pig Scripts that are written in Pig Latin using built-in operators are transferred to the Apache Pig Environment.
- Parser: The Parser performs the type checking function and checks the syntax of a script. It gives an output called DAG (directed acyclic graph).
- Optimizer: The Optimizer component has been designed to perform various types of optimization tasks such as transform, split, merge, recorder operations, etc. This optimizer adds an automatic optimization feature to Apache Pig.
You can execute the Pig scripts in three ways:
- Grunt Shell: It is an interactive shell that acts as an environment to execute every Pig script.
- Embedded Script: There are times where you find unavailability of built-in operators, In such cases, we can create user-defined functions (UDF) to get back the specific functionality using other programming languages like Python, Java, Ruby, etc. Using this technique you can execute a specific script file.
- Script File: This acts as a place to write all the Pig commands in a file and executes the pig script.
Pig Latin can handle atomic data types like long, double, int, float, etc, and also complex data types such as tuple, bag, and map.
Atomic data types are also called scalar data types. The atomic data types are basic data types and are used in different languages such as Long, Int, String, Double, Flot, char[], byte[].
The following are the complex Data types supported by Pig Latin:
- Tuple: Tuple contains an organized set of fields and each filed may contain different data types.
- Bag: A Bag contains a set of collected Tuples and these tuples represent the subsets of a row or complete rows of a table.
Map: A Map is defined as a key-value pair that helps in representing data elements. Each key should be unique like a column name and must be a chararray []. This enables the key to index easily and the values associated can be accessed very easily.
The Bag is one of the many data models available in Pig. It contains the tuples that are unorganized ones with possible duplicates. In general, the bags are used to store the tuples while grouping. The size of a bag is limited, it is almost equal to a local disk. The bags are represented with “{}”. There is no compulsion that the bag should fit into memory.
There are two types of Bags in Pig and that are Inner and Outer bags:
- An inner bag contains a bag inside a tuple
- The outer bag is nothing but a bag of tuples.
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Apache Pig is capable enough to handle both schema and schema-less data.
- If the schema consists of only field names, and available data types are treated as a byte array.
- If you give a name to the field, and the field is accessed by two ways one is with field name and the other is with positional notation. If in case the field name is missing, you can still access it with notation followed by the index number.
- If you execute an operation that is a combination of many relations and if there is any missing in relation then the resulting relation becomes a null schema.
- If in case the schema is null, then the Pig treats it as a byte array and the datatype of a field can be determined dynamically.
The UDF is abbreviated as User-defined functions. There are some situations where some functions are unavailable in built-in operators. In such situations, we can get back that functionality by creating User Defined functions (UDF) programmatically. We can use various programming languages to create UDF’s such as Python, Java, Ruby, etc and embed them into a Pig Latin Script file.
Important points to remember about UDF’s:
- LoadPush transfers the operators from the Pig run-time to loader implementations.
- LoadFunc abstract class provides three different methods for loading data.
- The load/store UDFs managers and controls the data inside and out of Pig.
- LoadCaster provides the facility to transform the byte arrays into specific arrays.
Users can interact with a shell in Apache Pig using Pig’s interactive shell or Grunt. This allows users to access local or HDFS file systems. To start the Grunt, users have to use pig –x local command.
Following is the list of Pig Diagnostic operators that you can use them to debug scripts in Pig.
- DUMP: Show the contents of a relation
- EXPLAIN: Shows the MapReduce, Physical, and Logical execution plans.
- DESCRIBE: Returns a schema of the relation
- ILLUSTRATE: Gives a clear and step-by-step explanation of the execution of statements.
No, it is not possible to run a MapReduce job with Illustrate. Illustrates does not perform any job. It is mainly intended for showing the output for each stage and not the final output.
The Illustrator operator is mainly used to analyze how data is transferred through a series of Pig Latin Statements. It also acts as the best tool to debug a script. This has occupied an important position in the Pig platform.
Illustrator plays an important role in making the Apache Pig so popular. Processing large datasets requires a huge time. To get away from this issue, developers usually run Pig scripts on sample data, but there is a probability that the sample data selected may not execute all the Pig scripts properly. For example, If the script contains join operators then there must be some records in the sample data that have the same key, if not the join operation will not return any results.
All Pig Latin operators are related to each other and function based on relations.
The following are the different relational operators in Pig:
- COGROUP: It is specialized in combining different tables and executes GROUP operation will be performed on the result of the join table.
- DISTINCT: Makes the Tuples unique by eliminating duplicates
- CROSS: This relational operator is being used to compute two or more cross-product relations.
- Filter: Filters a set of tuples based on the given condition.
- GROUP: It combines or groups the data in different relations.
- FOREACH: Iterates tuples of relation and generates a data transformation.
- JOIN: Combines multiple relations
- LIMIT: Controles the number of output tuples
- LOAD: Dump the data from the file system.
- SPLIT: Divides the single relation into multiple.
Apache Pig COGROUP selects the members of various relations, combines them with the same type of fields, and develops a bag that consists of a single instance of both relations. COGROUP combines the data sets by choosing a single data set at a time.
In Apache Pig when counting a number of elements in a bag the COUNT function does not take null values into consideration. Whereas COUNT_STAR includes all the values.
In Apache Pig both GROUP and COGROUP operators look similar. The simple differentiation is that the GROUP is used in single statement relations whereas COGROUP is being used for the statements that involve two or more relations. The Group operator collects all the records within the same key and COGROUP collects records of inputs based on a key.
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A MapFile is defined as a class and helps in the file-based map from values to keys.
A map acts as a directory and consists of two files which are a data file and a smaller index file.
The data file contains key values in the map and the Smaller index file consists of a fraction of keys. The map files are usually generated by adding entries in order.
The BloomMapFile acts as an extension to MapFile. It uses dynamic Bloom filters that give you a quick membership test for the keys. BloomMapFile is generally used in the HBase table format.
Two types of execution modes are present in Apache Pig which are:
- MapReduce Mode: It is a default execution mode that needs access to the HDFS installation and Hadoop cluster. As this is a default mode you don’t have to specify -x flag. The input, as well as output, is available on HDFS.
- Local Mode: In this local mode all the files have access to a single machine and all the files are installed and run on the localhost. . The Input as well as output is available on the local file system.
In some cases, there will be data in a bag or tuple and if we wish to erase the nesting from data you need a Flatten modifier in Pig. Flatten helps in un-nesting tuples and bags. It becomes easier to perform the flatten function for tuples but becomes a bit complex when it comes to un-nesting bags as it requires developing new tuples.
Pig Statistics is an advanced level tool or framework specially developed for gathering and storing the script level statistics for Pig Latin. When the scripts are executed Pig Latin scripts and MapReduce jobs are collected. Once the job is done, all these statistics are available.
The following are the stats classes available in Java:
- JobStats
- PigStats
- InputStats.
- OutputStats
Yes, there are some limitations available in Apache pig, which are:
- Apache Pig platform has been designed for ETL type use cases but it does not fit for the real-time scenarios.
- Apache Pig will not be suitable for targeting a single record in a huge volume of data sets.
- It is built-on MapReduce, which is suitable for batch processing.
Following are the various types of UDF’s supported in Pig:
- Eval
- Algebraic
- Filter
Following are the various types of Scalar data types available in Pig:
- Integer
- Double
- Float
- Char array
- Bytearray
- Long
We have three complex data types in Apache Pig and they are Tuple, Bag, and Map.
The following are the relational operators available in PIg Latin:
- For each
- Filters
- Order by
- Distinct
- Group
- Limit
- Join
- Continuous and quick software delivery
- Lower complex issues that has to be fixed
- Quick solution to fix the problems
We hope that this Apache Pig interview questions and answers blog helped you in gaining knowledge of all the important Pig questions. Mastering these Pig frequently asked interview questions will help you in gaining the confidence levels and lets you crack the interview at the very first attempt. We also add the latest apache Pig interview questions to this blog so, stay updated. Happy learning!
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