What are important Hive Interview Questions with Answers?

In my previous article I have given the detailed interview questions for Hadoop with multiple examples. In this article I would like to give more information about Hive Interview Questions with answers. Currently Hive is using one of the most used big data language and most of the big It industries are using this language in day to day endeavor. In this article I would like to exclusively work on Hive Interview questions with answers for professionals.

Hive Interview Questions and Answers :

Question 1 : What application does HIVE support ? (80% asked Hive Interview Questions)

Answer :
Java, PHP, Python, C, and Ruby-based client applications are supported by Hive.

Question 2 : What different Hive modes are there?

Answer :
Hive can operate in either the Local or MapReduce modes. Based on the size of the Hadoop data nodes, they are categorized into two type.

1.Local
2.Map Reduce

Question 3 : When and where to use Hive modes?

Answer :
When working with tiny datasets that might originate from a single workstation, we employ the local mode. In some cases, Hadoop is set up in a pseudo mode with only one data node. In these circumstances, we employ Hive’s local mode. When there are numerous data nodes in Hadoop, MapReduce mode is employed. You can do concurrent queries on substantial datasets using the
MapReduce approach. It provides better performance.

Question 4 : Explain about Hive. (100% asked Hive Interview Questions )

Answer :
An information warehouse tool called Hive is used to promote querying and manage big data sets that are stored in scattered storage. The HiveQL programming language from Hadoop is almost identical to SQL. In cases where it would be inconvenient or wasteful to execute the logic in HiveQL, Hive also allows standard map-to-reduce projects to configure mappers and reducers (User Defined Functions UDFS).

Question 5 : How are Hbase and Hive different from one another?

Answer :
Hbase and Hive can both be utilized in various Hadoop-based technologies. HBase is a NoSQL database, whereas Hive is a Hadoop architecture data warehouse. the Hadoop-based key-value storage themselves. When Hbase will also support 4 of the operations, such as put, get, scan, and delete, Hive will also assist users who are familiar with SQL in running a few jobs in MapReduce. Hive, on the other hand, is appropriate for querying data that is analytical and has been gathered over time. Hbase happens to be good for data querying.

Question 6 : If Hive is embedded, several users may utilize the same metastore? True or
False?

Answer :
False. Metastore cannot be utilized in sharing mode. Utilizing a standalone “real” database like MySQL or PostgreSQL is advised.

Question 7 : Describe about Hive Metastore?

Answer :
The central repository for metadata about Hive tables and partitions is called Metastore. A relational database houses the metadata. An embedded Derby
database instance that should have the local disc as its backup is the default
database offered by Apache Hive for single-user storage. For storing shared
metadata or metadata from numerous users, MySQL is used.

Question 8 : Explain about the types of tables available in Hive.

Answer :
Hive have two types of table
1.Managed Table
2.External Table
1.Managed Table: Another name for a managed table is an internal table. In Hive, this is the default table. Naturally, we will obtain a Managed table when we create a table in Hive without designating it as an external table. A managed table will be created in a specific location in HDFS if it is created this way.
2.Outside table: The external table is created for use when the data is used outside of Hive. When we want to maintain the information in the table as it appears to be while erasing the meta data for the table, we use an external table. The external table only eliminates the table’s pattern.

Question 9: In the Hive, what do partitions mean?

Answer :
Hive divides the tables into partitions. The way the data is stored in the tables is determined by a partition key. Based on these keys, the partition separates the table into various sections. When a table has more than one partition key, it is quite beneficial.

Question 10 :What is the term “Bucketing”?

Answer :
Partitions are used to organize the tables. It is possible to divide these partitions even further into buckets. The table’s column’s hash function is used to base the division.

Question 11 :Difference between local and remote meta store?

Answer :
Local Meta-store: In a local meta-store design, the meta-store service continues to run in the same JVM as the Hive service and connects to a database that is operating in a different JVM, either on the same machine or a distant machine.
Remote Meta-store: The meta-store service continues to run independently of the Hive benefit JVM in the remote meta-store design. Using Thrift Network APIs, several processes can communicate with the meta-store server. For this circumstance, you can have at least one meta-store server to provide broader
accessibility.

Question 12 : State the difference between bucketing from partitioning.

Answer :
A bucket is like a file, whereas a partition is like a directory. Data within a partition is organized into many files using bucketing. It facilitates the merging of many columns. A table with several partition keys will automatically partition. Bucketing is not always carried out. The creation of numerous minor partitions is a
possibility because partitioning is a default operation. The quantity of buckets can be restricted. While most people are familiar with the terms partitioning and bucketing, only a skilled interviewee will be able to respond to questions on Hive about how they differ in actual use.

Question 13 : What is the difference between Structured and Unstructured data?

Answer :
Structured data is information that can be kept in conventional database systems as rows and columns, such as online purchase transactions. Semi-structured data is data that can only be partially saved in conventional database systems, such as data in XML records. Unstructured data is any unprocessed, raw data that cannot be classified as semi-structured or structured data. Unstructured data
examples include Facebook updates, tweets on Twitter, reviews, weblogs, and more.

Question 14 :What is the term “Explode Hive” means?

Answer :
Using the explode function, an array can be split into several rows. returns a row-set with one row for each element in the array and one column (col). Developers using Hadoop take the exhibit as input and transform it into a separate table row. Hive essentially uses detonate to convert data types into desired table formats.
Question 15 : Can many tables be created in Hive for the same data?( Most asked Hive Interview Questions)

Answer :
As hive builds schema on top of an already-existing data file. One data file may contain numerous schema; the schema is saved in the hive’s metastore, and the data in the supplied schema is not processed or serialized to disc. Schema will be utilized when we attempt to retrieve data. For instance, if the data file in the hive metastore has 5 columns (name, job, dob, id, salary), we can create different schema by selecting any number of columns from the list above. (Table with three, five, or six columns.)

Question 16 : Explain REPEAT and REVERSE function in Hive.

Answer :
The REPEAT function will repeat the provided input string n times. Characters in a string will be reversed by the REVERSE function.

Question 17 :What is LOWER or LCASE and UPPER or UCASE in Hive?

Answer :
The input string will be converted to lower case characters using the LOWER or LCASE function.The string will be converted to upper case characters using the UPPER or UCASE function.

Question 18 :Explain the function of Object Inspector?

Answer:
Utilizing the Object Inspector functionality in Hive, the study of the internal structure of the segments, columns, and complicated items is complete. The inner fields found inside the objects are accessible because to the Question Inspector capability.

Question 19 :What features does Apache Hive’s Query Processor offer?

Answer :
This component puts into practice the processing framework for transforming SQL into a graph of map/reduce jobs as well as the execution time framework for running those jobs in the order of dependencies.

Question 20 : How can I update the external table in the hive?

Answer :
When using a select statement to query an external table, regardless of how the external table is created or how much data has been loaded into it, we are unable to access the data. The Hive External table needs to be refreshed using the MSCK Repair [External Table] command if we want to see the data in it.

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