Hive famous interview Questions and Answers? (Part 2)

Commonly asked Hive famous interview questions and answers.

1. What is Hive ORC?
   • Hive ORC (Optimized Row Columnar) is a file format designed for storing Hive tables in a more optimized manner for query processing. ORC uses a columnar storage format, which allows Hive to process only the columns that are required for a query, rather than reading the entire row.
   • This can result in faster query processing times and improved storage efficiency.
2. What is Hive JDBC driver?
   • Hive JDBC driver is a Java-based driver that allows applications to connect to Hive and execute SQL-like queries using the JDBC API.
   • The Hive JDBC driver can be used with any programming language that supports JDBC, including Java, Python, and Scala.
   • It allows applications to interact with Hive and access data stored in Hadoop, and it provides a standardized way to interact with Hive from external applications.
3. What is the difference between a managed table and an external table in Hive?
   • In Hive, a managed table is one in which Hive manages the lifecycle of the data stored in the table, including creation, deletion, and data storage. Data is stored in a default location in HDFS, and when the table is deleted, the data is also deleted. On the other hand, an external table is one in which data is stored outside of the default Hive data directory, and the lifecycle of the data is managed outside of Hive. External tables can be used to access data stored in other systems, such as HBase or S3.
   • A managed table in Hive is a table that is managed by the Hive metastore, which means that Hive is responsible for the data storage and management. When you create a managed table, Hive creates a directory in HDFS to store the data files for the table. In contrast, an external table in Hive is a table that is not managed by the Hive metastore. When you create an external table, you specify the location of the data files in HDFS, and Hive reads the data from that location. The data files for an external table can be located outside of the Hive warehouse directory, and can be managed by other Hadoop applications or tools.
4. What is the difference between the LIKE operator and the RLIKE operator in Hive?
   • In Hive, the LIKE operator is used to match patterns in a string using wildcards. The RLIKE (or REGEXP) operator is used to match patterns using regular expressions.
   • The difference between the two is that LIKE matches exact strings using wildcards, while RLIKE matches patterns using regular expressions.
5. What is the difference between a static partition and a dynamic partition in Hive?
   • In Hive, a static partition is one in which the partition values are explicitly specified when data is loaded into the table.
   • For example, if a table is partitioned by year and month, a static partition would be created for each year and month value.
   • A dynamic partition, on the other hand, is one in which the partition values are determined based on the data being loaded into the table.
   • This allows for more flexible partitioning, as new partitions can be created on-the-fly based on the data being loaded.
6. What is the difference between a local and a distributed cache in Hive?
   • In Hive, a local cache is a cache that is stored on the local machine where Hive is running, and is used to speed up the processing of data.
   • A distributed cache, on the other hand, is a cache that is shared across multiple nodes in a Hadoop cluster, and is used to speed up data processing across the entire cluster.
   • Distributed caching is typically used for large datasets that cannot be processed on a single machine.
7. What is the difference between a JOIN and a UNION in Hive?
   • In Hive, a JOIN is used to combine data from two or more tables based on a common key. The resulting output contains columns from both tables. A UNION, on the other hand, is used to combine the results of two or more queries into a single result set.
   • The resulting output contains only the columns that are present in both queries, and duplicates are automatically removed.
8. What is the difference between a view and a table in Hive?
   • In Hive, a table is a storage unit that contains data, whereas a view is a virtual table that is created by running a query against one or more tables.
   • The data in a view is not stored physically, but is instead generated on-the-fly when the view is queried. Views are used to simplify complex queries and to restrict access to sensitive data.
9. What is a subquery in Hive?
   • A subquery in Hive is a query that is embedded within another query. Subqueries are used to retrieve data from one or more tables and use that data in a larger query.
   • Subqueries can be used in the WHERE clause, the HAVING clause, or in a JOIN statement.
10. What is the difference between a left join and a right join in Hive?
    • In Hive, a left join (or left outer join) returns all the rows from the left table and the matching rows from the right table.
    • If there are no matching rows in the right table, the result will contain NULL values for the missing data.
    • A right join (or right outer join) is similar, but returns all the rows from the right table and the matching rows from the left table.
11. What is the difference between a delimiter and a separator in Hive?
    • In Hive, a delimiter is a character used to separate fields in a file or string.
    • For example, in a CSV file, the delimiter might be a comma. A separator, on the other hand, is a character used to separate records or rows in a file or string.
    •  For example, in a file containing multiple lines of data, the separator might be a newline character.
12. What is the difference between a GROUP BY and a PARTITION BY in Hive?
    • In Hive, a GROUP BY clause is used to group data by one or more columns, and then perform aggregate functions on the groups.
    • The resulting output contains one row for each group.
    • A PARTITION BY clause, on the other hand, is used to divide the data into partitions based on the values in one or more columns.
    • Each partition is processed separately, and the resulting output contains one row for each input row.
13. What is a Hive UDAF?
    • A Hive UDAF (User-Defined Aggregate Function) is a custom aggregate function created by a user to perform specific operations on Hive data.
    • UDAFs can be used to perform aggregate functions such as COUNT, SUM, and AVG on user-defined data types.
    • Hive UDAFs are loaded into the Hive environment at runtime and can be used in Hive queries like built-in aggregate functions.
14. What is a Hive UDTF?
    • A Hive UDTF (User-Defined Table-Generating Function) is a custom function created by a user to generate multiple output rows from a single input row.
    • UDTFs can be used to split a single row into multiple rows or to transform a single row into multiple columns.
    • Hive UDTFs are loaded into the Hive environment at runtime and can be used in Hive queries like built-in table-generating functions.
15. What is a Hive metastore server?
    • A Hive metastore server is a service that provides remote access to the Hive metastore database.
    • The metastore server enables multiple Hive clients to share the same metastore, allowing for centralized management of Hive metadata.
    • The metastore server can be configured to use different databases and can be accessed using various protocols, such as Thrift, JDBC, and ODBC.
16. What is a Hive HCatalog?
    • HCatalog is a table and storage management layer for Hadoop that provides a unified metadata model and data access API for Hadoop components such as Hive, Pig, and MapReduce. HCatalog provides a single interface for creating, storing, and accessing data in Hadoop, regardless of the underlying data storage format or tool used to process the data.
    • Hive can use HCatalog to access data stored in Hadoop, and can use HCatalog tables as input and output for Hive queries.
17. What is dynamic partitioning in Hive?
    • Dynamic partitioning is a technique used to create partitions in Hive tables automatically based on the values in a specific column.
    • Dynamic partitioning allows Hive to create new partitions as data is inserted into the table, without the need to pre-create partition directories.
    • This can help to reduce the amount of manual effort required to manage partitions and improve query performance by allowing queries to skip irrelevant partitions.
    • Dynamic partitioning in Hive is a way of automatically creating partitions based on the values of one or more columns in a table.
    • When dynamic partitioning is enabled, Hive can create new partitions as data is inserted into the table, based on the values of the partitioning columns.
    • This can be more efficient than manually creating partitions ahead of time, as it allows Hive to create partitions only for the data that actually exists.
18. What is static partitioning in Hive?
    • Static partitioning is a technique used to create partitions in Hive tables based on the values in one or more columns.
    • Unlike dynamic partitioning, static partitioning requires the partition directories to be pre-created before data is inserted into the table.
    • This can be useful in cases where the partitioning scheme is known in advance and a fixed number of partitions are needed.
19. What is a Hive metastore listener?
    • A Hive metastore listener is a plugin that can be used to listen for events that occur within the Hive metastore, such as table creation, table deletion, or table modification.
    • The listener can be configured to perform certain actions in response to these events, such as sending a notification or executing a script.
    • Hive metastore listeners can be used to automate tasks or integrate Hive with other systems.
20. What is a Hive warehouse directory?
    • The Hive warehouse directory is the default location where Hive stores its data. By default, the Hive warehouse directory is set to /user/hive/warehouse in HDFS, but it can be changed by modifying the hive.metastore.warehouse.dir configuration parameter.
    • The Hive warehouse directory contains subdirectories for each database and table, with each subdirectory containing the data files and metadata for that table.
21. What is the difference between bucketing and partitioning in Hive?
    • Bucketing and partitioning are both techniques used to organize data in Hive tables.
    • Partitioning involves dividing data into partitions based on the values of one or more columns, while bucketing involves dividing data into buckets based on a hash function applied to one or more columns.
    • The key difference between the two is that partitioning divides data based on the actual values in the data, while bucketing divides data based on a hash function applied to the values in the data.
    • Partitioning is typically used for filtering and querying data, while bucketing is typically used for sampling and grouping data.
22. What is the purpose of Hive metastore in Hive?
    • The Hive metastore is a central repository that stores metadata about Hive tables, including their schema, partitioning scheme, and location in HDFS.
    • The metastore allows Hive to track and manage tables across different sessions and clients, and to enforce data schema consistency and data access control policies.
    • The metastore also provides a uniform interface for other Hadoop components, such as Pig and MapReduce, to access and process data stored in Hive.
23. What is the difference between an external table and a managed table in Hive?
    • An external table in Hive is a table that points to data that is stored outside of Hive, such as in HDFS or in an external database.
    • The data is not managed by Hive and can be modified or deleted independently of Hive.
    • In contrast, a managed table in Hive is a table that has its data stored in HDFS and is managed by Hive, including the metadata about the table’s schema, partitioning, and location.
24. What is the purpose of the HiveServer2 service?
    • HiveServer2 is a service that provides a Thrift interface for Hive, allowing external applications to interact with Hive using the HiveQL language.
    • The service provides a client-server architecture, with multiple clients able to connect to a single HiveServer2 instance.
    • HiveServer2 supports authentication, authorization, and multi-tenancy, and can be used to run queries, perform administrative tasks, and manage connections to the Hive metastore.
25. What is the difference between a Hive function and a Hive script?
    • A Hive function is a piece of code that performs a specific operation on Hive data, such as data cleansing or data transformation.
    • Hive functions can be used in Hive queries and are executed within the Hive environment.
    • In contrast, a Hive script is a set of commands or queries written in a file that is executed using the Hive command-line interface or through a scheduling tool such as Oozie.
    • Hive scripts can be used to automate tasks or to run complex queries that require multiple steps.
26. What is Hive on Tez and how does it differ from traditional MapReduce execution engine in Hive?
    • Hive on Tez is a execution engine in Hive that uses Apache Tez to process Hive queries.
    • Apache Tez is a data processing framework that is designed to improve the performance of complex data processing tasks by providing a more efficient execution engine than MapReduce.
    • Hive on Tez differs from traditional MapReduce execution engine in Hive in that it uses a directed acyclic graph (DAG) to process queries, rather than the traditional MapReduce job flow.
27. What is the difference between a Hive table and a Hive view?
    • A Hive table is a structured collection of data that is stored in HDFS and managed by Hive.
    • It has a defined schema and can be partitioned and bucketed. A Hive view, on the other hand, is a virtual table that is defined by a SQL-like SELECT statement.
    • It does not have its own data, but instead retrieves data from one or more underlying tables.
    • Hive views can be used to simplify complex queries, to enforce data access policies, or to provide a unified interface to data stored in multiple tables.
28. What is the role of a SerDe in Hive?
    • A SerDe (Serializer/Deserializer) in Hive is a plugin that is used to serialize data into a format that can be stored in HDFS and to deserialize data back into a structured format that can be queried using Hive.
    • A SerDe is responsible for translating between Hive’s internal data representation and the external format of the data, such as a delimited text file or a binary data file.
    • Hive provides a number of built-in SerDes, but custom SerDes can also be developed and used as needed.
29. What is the purpose of the Hive CLI tool?
    • The Hive CLI (Command Line Interface) tool is a command-line interface for Hive that allows users to interact with Hive using HiveQL commands.
    • The CLI tool provides a way to execute queries, perform administrative tasks, and manage metadata for Hive tables.
    • It can be used for debugging and testing queries, as well as for automating tasks through shell scripts.
30. What is the difference between a Hive metastore and a Hive server?
    • A Hive metastore is a central repository that stores metadata about Hive tables, including their schema, partitioning scheme, and location in HDFS.
    • The metastore allows Hive to track and manage tables across different sessions and clients, and to enforce data schema consistency and data access control policies. In contrast, a Hive server is a service that provides a Thrift interface for Hive, allowing external applications to interact with Hive using the HiveQL language.
    • The server provides a client-server architecture, with multiple clients able to connect to a single HiveServer2 instance.
31. What is a UDF in Hive and how is it used?
    • A UDF (User-Defined Function) in Hive is a custom function that can be defined by a user to extend the functionality of HiveQL.
    • UDFs can be written in Java or any scripting language supported by Hive and can be used to perform custom operations on data, such as string manipulation or mathematical calculations.
    • UDFs can be registered in Hive and used in HiveQL queries just like built-in functions.
32. What is the difference between a Hive partition and a bucket?
    • A Hive partition is a way of dividing a table into smaller, more manageable parts based on one or more columns in the table’s schema.
    • Partitions are created based on the values in the partitioning columns and can be used to restrict the amount of data that needs to be scanned during queries, which can improve query performance.
    • A bucket, on the other hand, is a way of physically organizing data within a partition. Data is hashed based on the values in one or more columns and assigned to a specific bucket.
    • Buckets can be used to improve the performance of certain types of queries, such as joins or aggregations, by reducing the amount of data that needs to be read from disk.
33. What is the difference between a managed table and an external table in Hive?
    • In Hive, a managed table is a table whose data is managed by Hive and stored in HDFS.
    • Hive is responsible for creating and managing the data files, and any changes to the schema or partitioning of the table are reflected in the underlying data files.
    • An external table, on the other hand, is a table that references data that is stored outside of Hive, such as in a different HDFS location or in a file system outside of Hadoop.
    • With external tables, Hive only manages metadata about the table, and any changes to the schema or partitioning do not affect the underlying data files.
34. What is the difference between a join and a union in Hive?
    • In Hive, a join is a way of combining data from two or more tables based on a common key column.
    • The resulting output contains all of the columns from both tables, but only the rows where the key columns match.
    • A union, on the other hand, is a way of combining data from two or more tables with the same schema.
    • The resulting output contains all of the rows from both tables, without removing any duplicates. In other words, a join combines columns while a union combines rows.
35. What is the purpose of a distributed cache in Hive?
    • A distributed cache in Hive is a way of distributing files or archives to the nodes in a Hadoop cluster so that they can be accessed by Hive tasks during query execution.
    • The distributed cache can be used to store shared libraries, configuration files, or data files that are needed by UDFs or other custom code used in Hive queries.
    • By distributing these files to the nodes in the cluster, Hive can reduce the amount of data that needs to be transferred over the network during query execution, which can improve query performance.
36. What is the purpose of the Hive metastore?
    • The Hive metastore is a central repository that stores metadata about Hive tables and partitions, including the schema, partitioning, and location of data files.
    • The metastore is used by Hive to manage tables and partitions, and to perform operations such as querying, data insertion, and data deletion. The metastore can be configured to use different types of databases, including MySQL, PostgreSQL, and Oracle.
    • The Hive metastore is a component in Hive that stores the metadata information for Hive tables, such as the table schema, location of the data files, and partition information.
    • The Hive metastore allows Hive to manage the data files for the tables and query them using SQL-like syntax. The Hive metastore can be configured to store metadata information in different databases, such as MySQL, PostgreSQL, or Oracle.
37. What is the purpose of the EXPLAIN statement in Hive?
    • The EXPLAIN statement in Hive is used to display the execution plan of a HiveQL query.
    • The execution plan shows how Hive will execute the query, including which tables will be read, how data will be filtered and sorted, and which joins and aggregations will be performed.
    • The execution plan can be used to identify potential performance bottlenecks in a query, and to optimize queries by making changes to the query structure or the underlying data.
38. What is a SerDe in Hive?
    • A SerDe (Serializer/Deserializer) in Hive is a component that is used to serialize data into a format that can be stored in HDFS, and to deserialize data from that format back into a usable form.
    • SerDes are used to convert between Hive data types and Hadoop’s native serialization formats, such as Avro or JSON.
    • SerDes can be customized to handle different data formats or data types, and can be used in Hive to read and write data from different data sources, such as CSV files or log files.
39. What is the purpose of the HCatalog in Hive?
    • HCatalog is a component of Hive that provides a metadata and storage abstraction layer on top of Hadoop.
    • HCatalog allows Hive tables to be accessed by other Hadoop applications, such as Pig or MapReduce, without requiring knowledge of the underlying data format or location.
    • HCatalog also provides support for partitioning and metadata management, making it easier to manage and share data across different Hadoop applications.
40. What is the purpose of the Hive CLI (Command-Line Interface)?
    • The Hive CLI is a command-line interface for interacting with Hive, which allows you to submit HiveQL queries and view the results.
    • The Hive CLI provides a simple way to interact with Hive for basic tasks, such as creating tables, loading data, and querying data.
    • However, the Hive CLI can be less efficient for complex queries or large datasets, as it runs queries in a single-threaded mode and can be slower than other Hive interfaces, such as the Hive Web UI or Beeline.