Hive Interview Questions

Updated June 15, 2023

Introduction to Hive Interview Questions and Answers

In the new data era, Hive is an open-source petabyte-scale ETL and Data warehousing infrastructure tool package to store structured and unstructured data built upon Distributed File System (HDFS) for analyzing, querying, and mining giant volume data sets by enabling SQL-like language called HiveQL (HQL) and easy query execution by planning Hadoop MapReduce. There are many companies now that make use of Apache Hive for their Big Data solutions.

If you are looking for a Hive-related job, you must prepare for the 2023 Hive Interview Questions. Though every interview is different and the job scope is also different, we can help you with the top 2023 Hive Interview Questions and Answers, which will help you take the leap and succeed in your interview.

Below is the top list of Hive Interview Questions primarily asked in an interview.

Part 1 – Hive Interview Questions (Basic)

This first part covers basic Interview Questions and Answers.

1. List out the different components of Hive architecture.

Answer:

1. User Interface (UI): It acts as a communicator between users and drivers; when the user writes the queries, the UI accepts them and runs them on the driver; there are two types of interface available Command line and GUI interface.
2. Driver: It maintains the life cycle of the HiveQL query. It receives the questions from the user interface and creates the session to process the query.
3. Compiler: It receives the query plans from the driver and gets the required information from Metastore to execute the plan.
4. Metascore: It stores the information about the data like a table; it can be an internal or external table. It sends the metadata information to the compiler to execute the query.
5. Execute Engine: The hive service will execute the result in an execution engine; it runs the query in MapReduce to process the data. It is responsible for controlling each stage for all these components.

2. What are the different types of modes that Hive can operate?

Answer:

There are the common Hive Interview Questions asked in an interview. Hive can operate in two modes based on the size of the data. These modes are:

1. Map-reduce Mode
2. Local Mode

3. What are the scenarios where Hive can be used and cannot be used?

Answer:

When you’re creating Data warehouse applications, when your data is Static, when your application does not need high response time, when the data volume is enormous when the data is not changing rapidly, and when you are using queries instead of scripting, Hive supports only OLAP transactions; it is not suitable for OLTP transactions.

4. What are the file formats that Hive supports? List the type of applications that HIVE supports.

Answer:

By default, Hive supports Text File format, and it also promotes binary file formats such as Sequence files, ORC files, Parquet files, and Avro Data files.

• Sequence file: It is generally a binary format file that can be compressed and splittable.
• ORC file: Optimized Row Columnar file is a recorded column-based and column-oriented storage file.
• Parquet file: It is a column-oriented binary file it is highly efficient for large-scale queries.
• Avro Data file: It is the same as a sequence file format which is a splittable, compressible, and row-oriented file.
• The maximum size of string data type allowed in Hive is 2 GB.

Hive is a data warehouse framework suitable for Java, C++, PHP, Python, or Ruby applications.

5. What are the different types of tables that are available in Hive?

Answer:

There are two types of a table in the Hive application, they are:

1. Managed Tables: The data and schema are in control of the Hive.
2. External Tables: Only the schema is in control of the Hive.

Part 2 – Hive Interview Questions (Advanced)

Let us now have a look at the advanced Interview Questions.

1. What is a Metastore in Hive? List and explain the different types of Hive Meta stores configuration.

Answer:

Metastore in Hive stores the metadata information; it is a central repository in Hive. It allows for storing the metadata information in an external database. There are three types of Metastore configuration, they are:

1. Embedded meta store: It is a default mode; it can locally access the Hive library; all the command line operations are done in an embedded manner. The Hive service, the Megastore service, and the database run in the same JVM.
2. Local meta store: It stores data in an external database such as MySql or Oracle. The Hive service and meta store service runs in the same JVM; it connects to the database running in a separate JVM.
3. Remote meta store: It uses the remote mode to run queries; the megastore and hive services run in a separate JVM. You can have multiple meta-store servers to increase availability.

2. What are the different components of the Hive Query Processor?

Answer:

• Semantic Analyser
• UDF’s and USAF’s
• Optimizer
• Operator
• Parser
• Execution Engine
• Type Checking
• Logical Plan Generation
• Physical Plan Generation

3. What is the functionality of Object-Inspector in Hive?

Answer:

It is composed of a Hive that is used to identify the structure of the individual columns and the internal system of row objects. The complex things stored in multiple formats can be accessed using Object-Inspector in Hive.

The object-Inspector will identify the structure of an object and ways to access the internal fields inside the thing.

4. What are the different ways to connect the applications to Hive Server?

Answer:

There are three ways to connect the applications to the Hive server; they are:

1. Thrift Client: This runs all the hive commands using a different programming language, such as Java, C++, PHP, Python, or Ruby.
2. ODBC Driver: This will support the ODBC protocol
3. JDBC Driver: This will support the JDBC protocol

5. What are the default read and write classes in Hive?

Answer:

Below are the read-and-write classes available in Hive:

• TextInputFormat: This class is used to read data in plain text format.
• HiveIgnoreKeyTextOutputFormat: This class is used to write data in simple text format.
• SequenceFileInputFormat: This class reads data in the Hadoop Sequence file format.
• SequenceFileOutputFormat: This class writes data in the Hadoop Sequence file format.

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