Top 38 Hive Interview Questions and Answers for 2023

Preparing for the interview for a job that utilizes Hive software? This article covers a list of the most important and commonly asked Hive interview questions, which will help you get an in-depth understanding of the subject to ace your next Hadoop interview or any other job interview related to Hive. This blog will help you prepare clear and effective responses for each Apache Hive interview question that may be asked in your upcoming interview so that you can confidently demonstrate your experience.

Apache Hive – Brief Introduction

Apache Hive is a data warehouse infrastructure tool built to process structured data in Hadoop. It accelerates data summarization, analyzing datasets, and ad-hoc queries. Apache Hive provides an easy way to structure an abundance of unstructured data and execute SQL-like queries with the given data. It can easily merge with traditional data centre technologies with the help of the JDBC/ODBC interface.

In Hive, data is separated by the bucketing process. It is designed to manage and query the structured data stored in the table. The hive framework has features like UDFs, and it can increase the performance of the database through effective query optimization.

The SQL-inspired queries of Hive diminish the complexity of map-reduce programming and also decrease the familiar concept of a relational database such as a row, column, schema, and table for making learning easy.

Hive can use the directory to partition data because Hadoop’s programming works on flat flies. It improves the performance of the database queries.

Also Read: Top SQL Interview Questions & Answers

Top Hive Interview Questions and Answers for 2022

Here are some of the most common Apache Hive interview questions for freshers and experienced candidates that you can expect:

Q1. Explain the difference between Apache Pig and Hive.

Ans. This is one of the most important hive interview questions for freshers. The following are the differences:

Sure! Here are the top and important differences between Apache Pig and Hive in a tabular form:

Aspect	Apache Pig	Hive
Language	Pig Latin, a scripting language	Hive Query Language (HQL), similar to SQL
Data Processing Paradigm	Procedural	Declarative
Data Transformation	Sequential processing	Parallel processing
Schema Enforcement	Schema is optional	Schema is enforced
Optimization	Limited optimization capabilities	Advanced optimization techniques
User Interface	Command-line interface and scripting	Command-line interface and web-based interface
Use Cases	Data processing and ETL operations	Data warehousing and ad hoc querying
Data Manipulation	Focuses on transforming and analyzing data	Focuses on querying and managing structured data
Extensibility	UDFs (User-Defined Functions) and streaming support	UDFs, custom SerDe (Serializer/Deserializer) support
Performance	Suitable for small to medium-sized datasets	Suitable for large-scale datasets

Q2. What is the Hive variable? How do we set a variable?

Ans. Hive variables are similar to the variables in other programming languages. They are developed in the Hive environment that is assigned by the scripting language.

By using the keyword set

set foo=bar;

set system:foo=bar

Similarly, you can set the variable in command line for hiveconf namespace:

    beeline –hiveconf foo=bar

Q3. What are the different modes of Hive?

Ans. There are three modes:

• Embedded Metastore
• Local Metastore
• Remote Metastore

Q4. Explain the difference between HBase and Hive.

Ans. Following are the differences between HBase and Hive:

HBase	Hive
It does not allow execution of SQL Query HBase is a NoSQL database This runs on top of HDFS HBase is free from the schema model It follows real-time processing	It allows execution of most SQL queries Hive is a data warehouse Framework It runs on top of Hadoop MapReduce Hive has the schema model It does not follow real-time processing

 Q5. What is the use of partition in Hive?

Ans. Hive partitions are defined as the division of similar types of data based on partition keys, Hive design tables into partitions.

Partition is only helpful when it has partition keys. These are the basic keys that determine the data storage in the table. It is the subdirectory in the table directory.

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Q6. What are the data types supported by Hive?

Ans. Type of data types:

Primitive data types

• Numeric data type
• String data type
• Date/Time data type
• Miscellaneous data type

Complex data types

• Arrays
• Maps
• Structs
• Union

Also Read: Understanding SQL Server Data Types 

Q7. What is the precedence order in Hive configuration?

Ans. There is a precedence hierarchy for setting properties:

• The Hive SET command
• The command line -hiveconf option
• hive-site.xml
• hive-default.xml
• hadoop-site.xml (or, equivalently, hdfs-site.xml, core-site.xml, and mapred-site.xml)
• hadoop-default.xml (or, equivalently, hdfs-default.xml, core-default.xml, and mapred-default.xml)

Q8. Is it possible to change the default location of a managed table?

Ans. Yes, changing the default location of a managed table is possible. We can change the location by using – LOCATION ‘<hdfs_path>’.

Q9. Explain the mechanism for connecting applications when we run Hive as a server.

Ans. The mechanism is done by following the below steps:

• Thrift client: By using thrift client, we can call Hive commands from different programming languages such as Java, Python, C++, Ruby
• JDBC driver: It enables accessing data and supports Type 4 JDBC driver
• ODBC driver: ODBC API Standards apply for the Hive DBMS. It supports ODBC protocols.

Q10. How to remove header rows from a table?

Ans. Using the TBLPROPERTIES clause, we can remove the N number of rows from the top or bottom of a text file without using the Hive file. TBLPROPERTIES clause can provide multiple features we can set per our needs. It can be used when files are generated with additional header or footer records.

Following are the header records in a table:

System=…

Version=…

Sub-version=…

To skip the header lines in a Hive file, we can use the following table property:

CREATE EXTERNAL TABLE employee (

name STRING,

job STRING,

dob STRING,

Also Read: Top Hadoop Interview Questions and Answers

Q11. Explain the need for buckets in Apache Hive.

Ans. The concept of bucketing provides a way of differentiating Hive table data into various files or directories. It provides effective results only if –

• There are a limited number of partitions
• Partitions are of almost similar sizes

To solve the problem of partitioning, Hive provides a bucketing concept. It is an effective way to decompose tables into manageable parts.

Q12. How can you recursively access subdirectories?

Ans. We can access subdirectories recursively by using the following command:

hive> Set mapred.input.dir.recursive=true;

hive> Set hive.mapred.supports.subdirectories=true;

The Hive tables can be directed to the higher level directory, and it is suitable for the directory structure:

/data/country/state/city/

Q13. How Hive distributes rows into buckets?

Ans. Rows can be divided into buckets by using:

hash_function (bucketing_column) modulo (num_of_buckets)

Here, Hive lead the bucket number in the table

Function used for column data type:

hash_function

Function used for integer data type:

hash_function (int_type_column)= value of int_type_column

Q14. What are the commonly used Hive services?

Ans. This is one of the most important Hive interview questions. The following are the commonly used Hive services:

• Command Line Interface (cli)
• Printing the contents of an RC file with the use of rcfilecat tool
• HiveServer (hiveserver)
• Hive Web Interface (hwi)
• Metastore
• Jar

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Q15. Is it possible to change the settings within the Hive session?

Ans. Yes, changing the settings in the Hive session by using the SET command is possible. It helps with the change in Hive job settings for an exact query.

Following command shows the occupied buckets in the table:

hive> SET hive.enforce.bucketing=true;

Using SET command, we can see the present value of any property

hive> SET hive.enforce.bucketing;

hive.enforce.bucketing=true

We cannot target the defaults of Hadoop with the above command so that we can use –

 SET -v

Let’s move on to more Hive interview questions for freshers and experienced candidates.

Q16. Mention the components used in the Hive query processor.

Ans. The following are the components:

• Parse and Semantic Analysis (ql/parse)
• Map/Reduce Execution Engine (ql/exec)
• Optimizer (ql/optimizer)
• Sessions (ql/session)
• Hive Function Framework (ql/udf)
• Plan Components (ql/plan)
• Type Interfaces (ql/typeinfo)
• Metadata Layer (ql/metadata)
• Tools (ql/tools)

Q17. What are the Trim and reverse functions?

Ans. The trim function removes the spaces related to the strings.

Example:

TRIM(‘ NAUKRI ‘);

Output:

NAUKRI

To remove the leading space:

LTRIM(‘NAUKRI’);

To remove the trailing space:

RTRIM(‘NAUKRI ‘);

The reverse function will reverse the characters into strings.

Example:

REVERSE(‘NAUKRI’);

Output:

IRKUAN

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Q18. Explain the default metastore.

Ans. It provides an embedded Derby database instance that can only be supported by one user where it can store metadata. If you run your Hive query by using the default derby database. Then, you will get a default subdirectory in your current directory with the name metastore_db . It will also create the metastore if it does not already exist. Here, the property of interest is javax.jdo.option.ConnectionURL.

And the default value is jdbc:derby:;databaseName=metastore_db;create=true.  This value identifies that you are using embedded derby as your Hive metastore, and its location is metastore_db.

Q19. Is multiline comment supported?

Ans. No, Hive can only support single-line comments.

Q20. What is the possible way to improve the performance with ORC format tables?

Ans. We can improve the performance by using the ORC file format by storing data highly efficiently. The performance can also be improved by using ORC files while writing, reading, and processing data.

Set hive.compute.query.using.stats-true;

Set hive.stats.dbclass-fs;

CREATE TABLE orc_table (

idint,

name string)

ROW FORMAT DELIMITED

FIELDS TERMINATED BY ‘\:’

LINES TERMINATED BY ‘\n’

STORES AS ORC;

Q21. Why does the Hive not store metadata information in HDFS?

Ans. The Hive does not store the metadata information in HDFS but instead in RDBMS. This is done to attain low latency because HDFS read/write operations are time-consuming. HDFS is not meant for regular updates. Thus, RDBMS is used as it provides low latency and random updates.

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Q22. What is the difference between local and remote metastore?

Ans. The differences between local and remote metastore are: 

Local Meta-store: In this, the meta-store service runs in the same JVM in which the Hive service runs. It associates with a database running in a different JVM, either on a similar or remote machine.

Remote Meta-store: In this, the meta-store service runs alone, separating JVM and not in the Hive benefit JVM. Thus, different procedures communicate with the metastore server utilizing Thrift Network APIs. Having at least one meta-store server for this situation will provide greater accessibility.

Q23. When would you use SORT BY instead of ORDER BY?

Ans. Let’s look at the difference between order by and sort by in hive. SORT BY clause should be used instead of ORDER BY when one has to sort huge datasets. SORT BY sorts the data using multiple reducers, while ORDER BY sorts all data using a single reducer. Thus, if ORDER BY is used against many inputs, then the execution will be time-consuming.

Q24. Explain dynamic partitioning.

Ans. In Hive Dynamic Partitioning, the data is inserted dynamically into the respective partition without you explicitly creating the partitions in the table. The values for partition columns are known in the runtime. Typically, the data is loaded from the non-partitioned table in the dynamic partition loads and takes more time to load data compared to the static partition.

Q25. What is indexing? Explain its use.

Ans. Hive index is a Hive query optimization technique to access a column or set of columns in a Hive database. We use Hive indexing as it improves the speed of query lookup on certain table columns as the database system does not need to read all rows in the table. 

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Q26. Explain the use of Hcatalog.

Ans. HCatalog is a table storage management layer for Hadoop that shares data structures with external systems. It provides access to the Hive metastore to users of other Hadoop or data processing tools, such as Pig and MapReduce, so they can easily read and write data on Hive’s data warehouse.

Q27. Explain the components of a Hive architecture.

Ans. This is one of the frequently asked Hive interview questions.

The different components of Hive architecture are: 

• User Interface: It provides an interface between the user and the hive. User Interface allows users to submit queries to the system. It creates a session handle for the query and sends it to the compiler to generate an execution plan. It supports Hive web UI and Hive command line. 
• Compiler: It generates the execution plan. 
• Execute Engine: It manages the dependencies for submitting each stage to the relevant component.
• Metastore: It sends the metadata to the compiler for the execution of the query on receiving the sent metadata request.

Q28. What is ObjectInspector functionality?

Ans. The ObjectInspector helps analyse the structure of individual columns’ structure and the row objects’ internal structure in the Hive. It offers access to complex objects that can be stored in multiple formats in the memory. The ObjectInspector describes the object’s structure and the ways to access the internal fields inside the object.

Q29. What are the different types of join in Hive?

Ans. The different types of join in Hive are: 

• Join: It gives the cross product of both the table’s data as output. It is similar to the Outer Join in SQL.
• Full Outer Join: It fulfils the join condition and gives the cross product of the left and right outer tables without match condition.
• Left Outer Join: It returns all the rows from the left table even if there are no matches in the right table.
• Right Outer Join: It returns all the rows from the right table even if there are no matches in the left table.

Q30. Which classes are used to read and write HDFS files in Hive?

Ans. The following classes are used: 

• TextInputFormat: Reads data in plain text file format.
• HiveIgnoreKeyTextOutputFormat: Writes data in plain text file format.
• SequenceFileInputFormat: Reads data in Hadoop SequenceFile format.
• SequenceFileOutputFormat: Writes data in Hadoop SequenceFile format.

Q31. What is HiveServer2 (HS2)?

Ans. The HiveServer2 (HS2) server interface allows remote clients to execute queries against the Hive. HS2 retrieves the results of the mentioned queries. It supports multi-client concurrency and authentication and aims to provide better support for open API clients like JDBC and ODBC. 

Q32. Which interface is used for accessing the Hive metastore?

Ans. WebHCat API web interface can be used for Hive commands. It is a REST API that allows applications to make HTTP requests to access the Hive metastore (HCatalog DDL). It also enables users to create and queue Hive queries and commands.

Q33. Can we use Hive in OLTP systems?

Ans. We cannot use Hive in OLTP systems as Hive does not support row-level data insertion.

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Q34. What is Hive composed of?

Ans. The Hive consists of 3 components: 

• Clients
• Services
• Storage and Computing

Q35. What are the different types of tables in Hive?

Ans. There are two types of tables available in Hive: 

• Managed Table: Both the data and schema are controlled by the Hive.
• External Table: Only the schema is under the control of the Hive.

Q36. What do you mean by describe and describe extended?

Ans. The Describe command/query displays the name of the database, the root location on the file system and comments.

The Describe extended command/query provides the details of the database or schema in a detailed manner. It displays detailed information about the table, such as the list of columns, the data type of the columns, table type, table size, and more.

Q37. How to change a column data type in Hive?

Ans. We can change a column data type in Hive by using the following command:

ALTER TABLE table_name CHANGE column_name column_name new_datatype;

Q38. How can you optimize Hive performance?

Ans. Hive performance can be optimized to run queries faster in the following ways (Hive optimization techniques):

• Using vectorization
• Using ORCFILE
• Cost-based query optimization.
• Enable Compression 
• Enable Tez Execution Engine
• Optimize LIMIT operator
• Using Parallel Execution
• Enable Mapreduce Strict Mode

39. Explain the Trim and Reverse functions in Hive with examples.

Trim Function:
The Trim function removes leading and trailing spaces (or other specified characters) from a string. It is commonly used to clean up input data by eliminating unwanted whitespace.
Syntax:

BOTH: Removes leading and trailing occurrences of trim_character.
LEADING: Removes leading occurrences of trim_character.
TRAILING: Removes trailing occurrences of trim_character.
trim_character: The character or characters to be trimmed from the string. If not specified, it defaults to removing whitespace.

Example:

Output:

Hello World

In the example above, the Trim function removes the leading and trailing spaces from the string ‘ Hello World ‘.

Reverse Function:
The Reverse function is used to reverse the characters in a string. It is often useful for tasks such as reversing a word or reversing the order of characters in a string.

Syntax:

Example:

Output:

dlroW olleH

In the example above, the Reverse function reverses the characters in the ‘Hello World’ string, resulting in ‘dlroW olleH’.

Conclusion

This brings us to the end of our blog on Hive interview questions. We hope these Hive interview questions will help you successfully clear your upcoming Hive interview.