Hive famous interview Questions and Answers? (Part 1)

Commonly asked Hive famous interview questions and answers.

1. What is Hive?
   • Hive is an open-source data warehousing and SQL-like query tool that allows users to analyze and query large datasets stored in Hadoop Distributed File System (HDFS). It provides an interface to write queries in a SQL-like language called HiveQL or HQL.
2. What is the difference between Hive and Hadoop?
   • Hadoop is a distributed computing system that allows users to store and process large datasets in a distributed manner. Hive is a data warehousing tool that provides a SQL-like interface to analyze and query these large datasets stored in Hadoop.
3. What are the different types of tables in Hive?
   • There are two types of tables in Hive: Managed tables and External tables. Managed tables are stored in HDFS in a Hive-specific format, while external tables are stored outside of Hive, in a location specified by the user.
4. What is the difference between a partition and a bucket in Hive?
   • A partition is a way of dividing a table into smaller, more manageable parts based on a specific column value. It helps in organizing and querying large datasets efficiently. On the other hand, a bucket is a way of dividing data within a partition into even smaller parts based on a hash function. It helps in improving query performance by reducing the amount of data scanned.
5. What is a metastore in Hive?
   • The metastore is a central repository that stores metadata information about Hive tables, partitions, and databases. It helps in managing and tracking the schema and data of the Hive tables.
6. What is the purpose of the Hive query optimizer?
   • The Hive query optimizer helps in optimizing HiveQL queries by identifying the most efficient execution plan based on statistics and metadata available in the metastore. It helps in improving query performance by reducing the amount of data scanned.
7. What is the use of the Hive HCatalog?
   • HCatalog is a table and storage management layer for Hadoop that provides a unified interface for accessing data stored in different formats and systems. It allows users to access Hive tables and metadata from other Hadoop applications, such as Pig and MapReduce.
8. What are some best practices for optimizing Hive performance?
   • Some best practices for optimizing Hive performance include:
     • Using partitioning and bucketing to organize and optimize data
     • Choosing the appropriate file format based on the use case
     • Using vectorization to improve query performance
     • Avoiding complex joins and subqueries
     • Using appropriate hardware configurations and optimizing system parameters.
9. What are some limitations of Hive?
   • Some limitations of Hive include:
     • Slow query execution speed compared to traditional databases
     • Limited support for transactions and real-time updates
     • Limited support for complex data types
     • Limitations in data processing capabilities compared to MapReduce and Spark.
10. How does Hive support data modeling and schema evolution?
    • Hive supports data modeling and schema evolution by allowing users to define schema for tables using HiveQL. Users can also modify the schema of existing tables using the ALTER TABLE command. Additionally, Hive supports a flexible schema definition using the SerDe (Serializer/Deserializer) interface, which allows users to define custom data formats.
11. What is a Hive UDF?
    • A Hive UDF (User-Defined Function) is a function written by a user in a programming language such as Java, Python, or Scala, and registered in Hive to be used in HiveQL queries. UDFs can be used to perform complex data processing tasks and can be used in queries just like built-in Hive functions.
    • A Hive UDF (User-Defined Function) is a custom function created by a user to perform specific operations on Hive data. UDFs can be written in Java or any other programming language supported by Hive, and can be used to perform operations such as data cleansing, data transformation, or data aggregation. Hive UDFs are loaded into the Hive environment at runtime and can be used in Hive queries like built-in functions.
12. What is a Hive view?
    • A Hive view is a virtual table that is defined based on the results of a HiveQL query. It does not store data in itself but instead references the underlying data stored in Hive tables. Hive views provide a way to simplify complex queries and provide a layer of abstraction to the underlying data.
    • A Hive view is a virtual table that is created by combining data from one or more existing tables in Hive. A view provides a way to simplify complex queries by presenting a simplified and abstracted view of the data. Views can be used to hide sensitive data, simplify queries, or provide a consistent view of the data across multiple applications.
13. What is the role of the Hive metastore service in Hive architecture?
    • The Hive metastore service is a key component in Hive architecture, as it manages metadata information about Hive tables, partitions, and databases. It provides a central repository for storing and retrieving metadata information, which is used by Hive to optimize queries, manage data, and maintain data consistency.
14. What is Hive-on-Tez and how does it differ from Hive-on-MR?
    • Hive-on-Tez is an alternative execution engine for Hive that uses the Tez framework to execute queries, while Hive-on-MR uses the MapReduce framework. Hive-on-Tez offers better query performance than Hive-on-MR by reducing the overhead of MapReduce, providing more efficient data processing, and supporting more complex queries.
15. What is the difference between an inner join and an outer join in Hive?
    • In Hive, an inner join returns only the rows from both tables that have matching values in the join condition. On the other hand, an outer join returns all rows from both tables, along with matching rows from the other table if they exist, and NULL values if they don’t. Outer joins can be left, right, or full, depending on which table’s data is kept in the result set.
    • In Hive, an inner join is a type of join that returns only the rows that have matching values in both tables being joined. An outer join, on the other hand, returns all the rows from one table and the matching rows from the other table. If there are no matching rows in the other table, the result will contain NULL values for the missing data.
16. What is the difference between a Hive script and a Hive query?
    • A Hive script is a set of HiveQL commands that are saved in a file and executed using the Hive CLI (Command Line Interface). A Hive query, on the other hand, is a single HiveQL command that is executed on the command line or through an API. Hive scripts are useful for automating tasks, while Hive queries are used for ad-hoc data analysis.
17. What is a Hive metastore database?
    • A Hive metastore database is a database that is used by Hive to store metadata information about tables, partitions, and databases. By default, Hive uses an embedded Derby database as its metastore database, but it can also be configured to use other databases such as MySQL or PostgreSQL.
18. What is HiveServer2 and how does it differ from HiveServer1?
    • HiveServer2 is a more scalable and secure version of the HiveServer1, which is the original Hive server. HiveServer2 provides a Thrift API that allows remote clients to connect to Hive and execute queries, while HiveServer1 provides a JDBC interface. HiveServer2 also supports authentication and authorization mechanisms, which HiveServer1 does not.
19. What is HiveQL?
    • HiveQL is a SQL-like query language used in Hive for querying and analyzing large datasets stored in Hadoop.
    • HiveQL supports many standard SQL operations such as SELECT, JOIN, GROUP BY, and ORDER BY, as well as extensions to support Hadoop-specific operations such as partitioning and bucketing.
    • HiveQL is used to write Hive queries and can be executed using the Hive shell, HiveServer2, or other tools that support Hive.
    • HiveQL is a SQL-like language used to query and manipulate data in Hive. HiveQL is similar to SQL, but is optimized for use with Hadoop and is designed to work with large-scale datasets.
    • HiveQL is used to create tables, load data, and perform operations such as filtering, grouping, and joining.
    • HiveQL is a SQL-like query language used to interact with data stored in Apache Hive.
    • It is a declarative language that supports a wide range of SQL-like operations, including filtering, grouping, joining, and aggregating data.
    • HiveQL is similar to SQL but also includes some Hive-specific extensions and functions.
20. What is a Hive bucketing?
    • Hive bucketing is a way of dividing data within a partition into even smaller parts based on a hash function. It helps in improving query performance by reducing the amount of data scanned.
    • Bucketing is similar to partitioning, but instead of dividing data based on column values, it divides data based on a hash function applied to one or more columns.
    • Hive bucketing is a technique used to group data within a table based on the values in one or more columns. It is a way to optimize data storage and retrieval by storing data in separate files based on the values in the specified columns.
    • Bucketing can improve query performance and reduce the amount of data that needs to be read for a particular query.
21. What is a Hive table partitioning?
    • Hive table partitioning is a way of dividing a table into smaller, more manageable parts based on the values of one or more columns.
    • It helps in improving query performance by reducing the amount of data scanned. Partitioning is done based on column values such as date or country, and each partition can be treated as a separate sub-table.
22. What is Hive bucketing and partitioning?
    • Hive bucketing and partitioning are two techniques used to improve query performance in Hive.
    • Bucketing divides data within a partition into smaller parts based on a hash function, while partitioning divides a table into smaller parts based on the values of one or more columns.
    • By using both techniques together, Hive can optimize queries even further and provide faster data retrieval.
23. What is Hive on Spark?
    • Hive on Spark is an alternative execution engine for Hive that uses the Spark framework to execute queries.
    • It provides better performance than Hive on MapReduce by utilizing the in-memory processing capabilities of Spark.
    • Hive on Spark is becoming increasingly popular due to its fast query processing and the ability to integrate with other Spark-based applications.
24. What is a Hive transaction?
    • Hive supports a limited form of transactions, known as ACID (Atomicity, Consistency, Isolation, and Durability) transactions.
    • Hive transactions are only supported for certain storage formats such as ORC and Parquet, and only for specific operations such as INSERT, UPDATE, and DELETE.
    • Hive transactions help to ensure data consistency and provide a higher level of data integrity.
25. What is the difference between Hive and Pig?
    • Hive and Pig are both data processing frameworks used in Hadoop, but they have different strengths and use cases.
    • Hive is primarily used for SQL-like data analysis, while Pig is used for data processing using a scripting language called Pig Latin.
    • Hive is more suitable for ad-hoc queries and data exploration, while Pig is more suitable for data processing pipelines and ETL jobs.
26. What is the difference between Hive and Impala?
    • Hive and Impala are both SQL-like data processing frameworks used in Hadoop, but they have different strengths and use cases.
    • Hive is designed for batch processing of large datasets, while Impala is designed for interactive, real-time querying.
    • Hive uses MapReduce or Tez for query execution, while Impala uses an in-memory engine for faster query processing.
27. What is Hive UDF?
    • Hive UDF (User-Defined Function) is a way to extend the functionality of Hive by allowing users to write their own custom functions in Java or other programming languages.
    • UDFs can be used in HiveQL statements just like built-in functions, and they can perform complex computations or transformations on data.
28. What is Hive UDAF?
    • Hive UDAF (User-Defined Aggregate Function) is a way to extend the functionality of Hive by allowing users to write their own custom aggregate functions in Java or other programming languages.
    • UDAFs can be used in HiveQL statements to perform complex computations on grouped data, such as calculating the average or median of a group of values.
29. What is Hive Tez?
    • Hive Tez is an alternative execution engine for Hive that uses Apache Tez to execute queries.
    • Tez provides faster query processing than MapReduce by using a directed acyclic graph (DAG) of tasks to execute queries in a more efficient manner.
    • Hive Tez is becoming increasingly popular due to its faster query processing and the ability to optimize complex queries.
30. What is a Hive metastore?
    • A Hive metastore is a central repository that stores metadata information about tables, databases, and partitions in Hive.
    • The metastore keeps track of the schema of each table, the location of data files, and other information necessary for processing data in Hive.
    • A Hive metastore is a database that stores metadata about Hive tables, including their schema, partitioning information, and storage location.
    • The metastore is used to manage the lifecycle of the tables and to keep track of the data stored within them.
    • The metastore can be configured to use different databases, including MySQL, PostgreSQL, and Oracle.
31. What is Hive dynamic partitioning?
    • Hive dynamic partitioning is a way to create partitions automatically based on the values of columns in a table.
    • Instead of manually specifying the partition values, dynamic partitioning can be used to create new partitions on the fly as new data is inserted into the table.
    • This can help to improve query performance by reducing the need for manual partition creation.
32. What is Hive ACID?
    • Hive ACID is a set of features that provide transactional support in Hive for data updates, deletes, and inserts.
    • ACID stands for Atomicity, Consistency, Isolation, and Durability, and it provides a way to ensure data consistency and integrity in Hive.
    • Hive ACID is supported for specific storage formats such as ORC and Parquet, and it allows users to perform complex data manipulations on large datasets.
33. What is the difference between Hive and HBase?
    • Hive and HBase are both data processing frameworks used in Hadoop, but they have different strengths and use cases.
    • Hive is designed for batch processing of large datasets and provides SQL-like query capabilities, while HBase is a NoSQL database designed for real-time, random access to large amounts of structured and unstructured data.
    • Hive is more suitable for ad-hoc queries and data exploration, while HBase is more suitable for real-time data processing and low-latency access to data.
34. What is the difference between Hive and Spark SQL?
    • Hive and Spark SQL are both SQL-like query engines used in Hadoop, but they have different strengths and use cases.
    • Hive is designed for batch processing of large datasets and supports SQL-like query syntax, while Spark SQL is designed for in-memory processing of data using the Spark framework.
    • Hive uses MapReduce or Tez for query execution, while Spark SQL uses Spark’s in-memory engine for faster query processing.
35. What is Hive ACID merge?
    • Hive ACID merge is a way to combine two tables in Hive based on a common column.
    • It provides a way to merge two ACID tables while preserving their transactional guarantees.
    • Hive ACID merge is supported in specific storage formats such as ORC and Parquet, and it allows users to perform complex data manipulations on large datasets.
36. What is Hive LLAP?
    • Hive LLAP (Live Long and Process) is a query execution engine for Hive that provides interactive, low-latency querying capabilities.
    • LLAP uses in-memory caching and data compression techniques to speed up query processing and reduce network traffic.
    • Hive LLAP is designed for ad-hoc querying and data exploration, and it provides a fast, responsive query experience for users.
37. What is Hive bucketing?
    • Hive bucketing is a way to partition data within a table based on a specified set of columns.
    • It is a way of dividing data into more manageable portions for faster querying. By bucketing data on certain columns, Hive can ensure that related data is stored in the same bucket and can be processed together, which can improve query performance.
38. What is Hive serde?
    • Hive serde (serializer-deserializer) is a way to specify how Hive should serialize and deserialize data when reading from or writing to files. SerDe allows Hive to read and write data in various formats, including CSV, TSV, JSON, and Avro.
    • SerDe is important for data transformation and conversion, as it allows Hive to read data in one format and write it in another.
    • A Hive SerDe (Serializer/Deserializer) is a module used to serialize and deserialize data between Hive tables and Hadoop data types. It is used to convert data between different formats, such as JSON, CSV, and Avro, and to manage the schema of the data.
    • SerDe modules can be customized and extended to handle specific data formats and requirements.
39. What is Hive skew join?
    • Hive skew join is a way to optimize queries that join two or more large tables where one of the tables has a significant skew in its data distribution.
    • By using skew join, Hive can optimize the query plan to perform the join more efficiently, by identifying and handling the skew in the data distribution.
    • This can help to improve query performance and reduce the time required to process large datasets.
40. What is Hive transactional tables?
    • Hive transactional tables are tables that support transactions in Hive, which allow multiple concurrent writers to update, insert, and delete data while preserving data consistency and integrity.
    • Transactional tables provide ACID properties (Atomicity, Consistency, Isolation, and Durability) for data updates, deletes, and inserts, and they are supported for specific storage formats such as ORC and Parquet.