Top 50 Crucial Hive interview questions and answers

Big Data interviews can be general or focused on a particular system or method. This article will concentrate on the widely used big data tool, Apache Hive. After reading this article on Apache Hive interview questions, you will have a thorough understanding of the queries asked during Big Data interviews by employers.
An open-source framework called Hadoop was created to make it easier to store and process large amounts of data. In the Hadoop ecosystem, Hive is a data warehouse tool that processes and summarises data to make it more usable. After learning about Hive's role in the Hadoop ecosystem, continue reading to learn about the most typical Hive interview questions. 

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Table of Content
Introduction to Apache Hive
Job Trends for Apache Hive:
Questions for Hive Interviews
 

Introduction to Apache Hive

A well-liked data warehouse system is Apache Hive. It is heavily utilized for the analysis of structured and semi-structured data and is built on top of Hadoop. It offers a simple and dependable way to add structure to the data and execute SQL-like queries written in the Hive Query Language.
 

Job Trends for Apache Hive:

Most businesses now use Apache Hive as their primary tool for analytics on sizable data sets. It is quite popular among professionals with backgrounds other than programming who are interested in working on the Hadoop MapReduce framework because it also supports SQL-like query statements.

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Questions for Hive Interviews

Any interview involving data science is almost always required to include hive-related questions. Being ready to confidently respond to these questions will help you establish a positive impression in the interviewer's eyes and pave the way for a prosperous career. The questions in the list of Hive Interview Questions below have been specifically chosen to help you familiarise yourself with the types of questions you might encounter during the interview. If you are just starting, the interviewer will be checking to see how solid your foundation is and may ask you questions about fundamental ideas. The questions become more challenging as you gain experience and become more technical and application-focused.
 

Questions for Hive Interviews:

The complete list of the most typical Hive interview questions and their responses can be found below. Direct or application-based interview questions on Hive are both possible.

1. Which programs does Hive support?
PHP, Python, Java, Ruby-based client applications, and C are supported by Hive.

2. What varieties of tables does Hive offer?
In Hive, there are two different kinds of tables: managed and external.

3. What distinguishes managed from external tables?
Managed tables give Hive both schema and data control in contrast to external tables, which only give Hive access to data.

4. Where are the contents of a Hive table kept?
By default, the HDFS directory /user/hive/warehouse is where the Hive table is stored. You can change it by specifying the desired directory in the hive.metastore.warehouse.dir configuration parameter in the hive-site.xml file.

5. Can OLTP systems use Hive?
Hive is not appropriate for OLTP systems because it does not support row-level data insertion.

6. In Hive, can a table name be changed?
In Hive, a table's name can be changed. Alter Table table_name RENAME TO new_name can be used to rename a table.

7. Where is the data from Hive tables kept?
User/hive/warehouse is the default HDFS directory where Hive table data is kept. This could be changed.

8. In Hive, is it possible to modify the managed table's default location?
Yes, using the LOCATION " clause in Hive, the managed table's default location can be changed.

9. Describe the Hive Metastore.
A relational database called a "Metastore" is used to store the metadata for Hive partitions, tables, databases, and other objects.

10. What different kinds of meta-stores exist?
The two different types of Hive meta stores are local and remote.

11. What distinguishes local from remote meta stores?
While remote meta stores operate on a different, distinct JVM from local meta stores, local meta stores run on the same Java Virtual Machine (JVM) as the Hive service.

12. What database by default does Apache Hive use for its metastore?
The embedded Derby database offered by Hive, which is backed by the local disc, is the default database for the metastore.

13. Can several users access the same metastore?
No, Hive does not support metastore sharing.

14. What are the three operating modes available for Hive?
Hive can be used in three different operating modes: local, distributed, and pseudo-distributed.

15. Does Hive have a data type for storing date information?
All data information is stored in java.sql.timestamp format in Hive using the TIMESTAMP data type.

16. Why does Hive use partitioning?
Hive utilizes partitioning because it can lower query latency. Only pertinent partitions and associated datasets are scanned rather than whole tables.

17. What types of data are in the Hive collection?
The three data types for Hive collections are ARRAY, MAP, and STRUCT.

18. Can UNIX shell commands be executed in Hive?
Yes, one can execute shell commands in Hive by prefixing the command with a '!'.

19. Can Hive queries be executed from a script file?
Yes, this is possible with the aid of the source command. Hive> source, for instance, /path/queryfile.hql

20. What is an a.hiverc file?
It is a file that contains a list of instructions that must be executed when Command Line Input is started.

21. How can you determine whether a particular partition is present?
Use the command as follows: (partitioned column='partition value') SHOW PARTITIONS table name PARTITION

22. How would you go about listing every database that started with the letter "c"?
By utilizing the command: DISPLAY DATABASES SUCH AS "c.*"

23. In Hive, can DBPROPERTY be deleted?
The DBPROPERTY cannot be deleted, sorry.

24. Which Java class manages the input record encoding into the files that house Hive tables?
the class is called "org. apache. Hadoop. mapred.TextInputFormat."

25. Which Java class manages the encoding of output records into Hive query files?
the 'HiveIgnoreKeyTextOutputFormat' class from org.apache.hadoop.hive.ql.io.

26. What occurs to the data when a Hive table partition is pointed to a new directory?
The data must be manually transferred because it is still located in the old directory.

27. Do you archive Hive tables to free up space in the HDFS?
No, archiving Hive tables only serves to lessen the number of files, which facilitates simpler data management.

28. How can you prevent a query from accessing a partition?
Use the ALTER TABLE command along with the ENABLE OFFLINE clause.

29. What does Hive's table-generating function do?
Hive uses the MapReduce programming framework to split up sizable datasets into smaller chunks and process them concurrently.

30. Is it possible to avoid MapReduce on Hive?
By setting the hive.exec.mode.local.auto property to "true," you can force Hive to avoid using MapReduce to return query results.

31. Is it possible to create a Cartesian join between two Hive tables?
This is impossible because MapReduce programming does not allow for its implementation.

32. In Hive, what is a view?
Search results can be treated as tables thanks to a logical construct called a view.

33. Is it possible for a view's name to match a Hive table name?
No, the view's name must always be distinctive within the database.

34. Is it possible to view using the INSERT or LOAD commands?
No, you cannot use these commands about a view in Hive.

35. What does Hive's indexing mean?
A query optimization method called "Hive indexing" can speed up access to a column or set of columns in a Hive database.

36. Does Hive support comments with multiple lines?
No, Hive supports multi-line comments.

37. How do you access a Hive table's indexes?
By utilizing the command: LISTEN TO INDEX ON TABLE NAME

38. What does the Hive ObjectInspector function do?
It facilitates access to the sophisticated objects that are stored within the database and aids in the analysis of the structure of specific columns and rows.

39. What does bucketing mean?
The process of bucketing, which helps prevent over-partitioning, involves hashing the values in a column into several user-defined buckets.

40. What is the benefit of bucketing?
Bucketing speeds up query response time and aids in sampling process optimization.

41. Describe Hcatalog.
Hcatalog is a tool that facilitates the exchange of data structures with other external Hadoop ecosystem systems.

42. In the Hive, what is UDF?
A user-designed function, or UDF, is a function that is not included in the existing Hive functions and is developed using a Java program.

43. What is the function of /*streamtable(table name)/?
Before a query is executed, a table can be streamed into memory using a query hint.

44. What are Hive's limitations?
The following restrictions apply to Hive:

• Row-level support is absent, and real-time queries are not supported.
• Processing online transactions are not possible with Hive.

45. What is the purpose of a Hcatolog?
Hcatalog is a crucial tool for sharing Data structures with external systems. For reading and writing data in a Hive data warehouse, it offers access to the Hive metastore.

46. What are the parts of the Hive query processor?
The parts of a Hive query processor are as follows:

• Logical generation plan
• Physical Generation Plan.
• The engine of execution.
• The UDF and UDAF.
• Operators.
• Optimizer.
• Parser.
• Semantic Examiner
• Checking the type.

47. Why do we need buckets?
Here are the two primary justifications for bucketing a partition:
Data associated with a distinct join key must be present in the same partition to perform a map-side join. What about circumstances in which your partition key is different from your join key? As a result, in these circumstances, you can perform a map side join by bucketing the table using the join key.
We can reduce the query time by bucketing, which improves the sampling procedure.

48. How are the rows divided into buckets by Hive?
The bucket count for a row is determined by Hive using the formula: hash function (bucketing column) modulo (num of buckets). In this case, the hash function is based on the column's data type. The integer data type is supported by the hash function.
Value of int type column = hash function (int type column)

49. How does Hive's performance improve thanks to ORC format tables?
The ORC (Optimized Row Column) format makes it simple to store Hive Data and helps to streamline several restrictions.

50. What different parts make up a Hive architecture?
The five elements of a hive architecture are as follows:

• User interface: It makes it easier for users to perform tasks like sending queries to the Hive system. Hive Web UI, Hive Command-Line, and Hive HDInsight are all available through the user interface.
• Driver: It creates a session handle for the query and sends it to the compiler to determine the execution strategy.
• Metastore: This is a collection of arranged data and details about various tables and partitions in a warehouse.
• Compiler: It produces query expressions, performs semantic analysis on various query blocks, and creates the execution plans for the queries.
• Execution Engine: It puts into practice the execution plans that the compiler generates.

The Hive interview questions and answers above cover the majority of the crucial Hive topics, but this is by no means an exhaustive list. You might think about enrolling in an Integrated Program In Business Analytics if you're interested in breaking through in the data industry and developing as a Future Leader.