Updated June 15, 2023
Introduction to Apache PIG Interview Questions and Answers
So you have finally found your dream job in Apache PIG, but we are wondering how to crack the 2023 Apache PIG interview and what the probable Apache PIG interview questions could be. Every Apache PIG interview and the job scope are different too. Keeping this in mind, we have designed the most common Apache PIG interview questions and answers to help you get success in your Apache PIG interview.
The following is the list of 2023 Apache PIG Interview questions that are mostly asked.
Part 1 – Apache PIG Interview Questions (Basic)
1. What are the critical differences between MapReduce and Apache Pig?
Answer:
Following are the key differences between Apache Pig and MapReduce due to which Apache Pig came into the picture:
- MapReduce is a low-level data processing model, whereas Apache Pig is a high-level data flow platform.
- Without writing the complex Java implementations in MapReduce, programmers can achieve the same performances easily using Pig Latin.
- Apache Pig provides nested data types like bags, tuples, and maps as they are missing from MapReduce.
- Pig supports the data operations like filters, joins, ordering, sorting, etc., with many built-in operators. At the same time, performing the same function in MapReduce is an immense task.
2. Explain the uses of MapReduce in Pig.
Answer:
Developers use Pig Latin, a query language similar to SQL, to write Apache Pig programs. To execute a query, there is a need for an execution engine. The Pig engine converts the questions into MapReduce jobs, and the programs require MapReduce as the execution engine to run.
3. Explain the uses of Pig.
Answer:
We can use the Pig in three categories, they are:
- ETL data pipeline: It helps to populate our data warehouse. A pig can pipeline the data to an external application; It will wait for the processed data to finish before continuing. It is the most common use case for Pig.
- Research on raw data.
- Iterative processing.
4. Compare Apache Pig and SQL.
Answer:
- Apache Pig differs from SQL’s usage for ETL, lazy evaluation, storing data at any point in the pipeline, support for pipeline splits, and explicit declaration of execution plans. Structural Query Language (SQL) focuses on queries that generate a single result. SQL has no inbuilt mechanism for splitting the data processing stream and applying different operators to each sub-stream.
- Apache Pig enables users to include their code at any stage in the pipeline. In contrast, with SQL, data must be first imported into the database before the cleaning and transformation process can begin.
Part 2 – Apache PIG Interview Questions (Advanced)
5. Explain the different complex data types in Pig
Answer:
Apache Pig supports three complex data types:
- Maps: # joins together key-value stores.
Example: [‘city’ # ‘Pune’, ‘pin’ #411045] - Tuples: Just similar to the row in a table, where a comma separates different items. Tuples can have multiple attributes.
- Bags: An unordered collection of tuples. The Bag allows multiple duplicate tuples.
Example: {(‘Mumbai’,022),(‘New Delhi’,011),(‘Kolkata’,44)}
6. Explain different Execution models available in Pig.
Answer:
Three different execution modes available in Pig they are:
- Interactive mode or Grunt mode.
Interactive or grunt mode: The interactive shell in Pig is known as a grunt shell. If no file is specified to run in Pig, it will start. - Batch mode or Script mode.
Pig executes the specified commands in the script file. - Embedded mode
We can embed Pig programs in Java and run the programs from Java.
7. Explain the execution plans (Logical & Physical plan) of a Pig Script.
Answer:
During the execution of a Pig script, both logical and physical plans are created. Pig scripts are based on interpreter checking. Semantic checking and basic parsing generate the Logical plan, and data processing does not occur during creation. The syntax check is performed for operators for each line in the Pig script, creating a logical plan. Whenever an error is encountered within the script, an exception is thrown, and the program execution ends, else for each statement in the script has its logical plan.
A logical plan contains the operators’ collection in the script but does not contain the edges between the operators.
Once the logical plan is generated, the script execution progresses to the physical plan, which describes the physical operators that Apache Pig will use to execute the Pig script. A physical plan is more or less like a series of MapReduce jobs, but then the plan does not reference how it will be executed in MapReduce. During creating a physical plan, the cogroup logical operator is converted into three physical operators: Local Rearrange, Global Rearrange, and Package. The physical plan typically resolves the load and store functions.
8. What are the debugging tools used for Apache Pig scripts?
Answer:
Describe and explain the essential debugging utilities in Apache Pig.
- Explain utility is helpful for Hadoop developers when trying to debug errors or optimize PigLatin scripts. Explain can be applied to a particular alias or the entire script in the grunt interactive shell. Explain utility produces several graphs in text format, which can be printed to a file.
- Describe debugging utility is helpful to developers when writing Pig scripts as it shows the schema of a relation in the script. Beginners learning Apache Pig can use the described utility to understand how each operator changes data. A pig script can have multiple descriptions.
9. What are some of the Apache Pig use cases you can think of?
Answer:
- Developers commonly use Apache Pig as a big data tool for iterative processing, raw data research, and traditional ETL data pipelines. Researchers widely use it because Pig can operate when the schema is unknown, inconsistent, or incomplete, allowing them to utilize the data before it is cleaned and loaded into the data warehouse.
- For instance, a website can use behavior prediction models to track visitors’ responses to various ads, images, articles, etc.
10. Highlight the difference between group and Cogroup operators in Pig.
Answer:
Both operators can work with one or more relations. Group and Cogroup operators are identical. The Group operator collects all records with the same key. Cogroup is a combination of group and join; it is a generalization of a group. Instead of collecting records of one input depending on a key, it contains descriptions of n inputs based on a key. At a time, we can Cogroup up to 127 relations.
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