Hive famous interview Questions and Answers? (Part 3)

Commonly asked Hive famous interview questions and answers.

1. What is the purpose of the Hive Web UI?
   • The Hive Web UI is a web-based user interface for interacting with Hive, which provides a graphical interface for submitting HiveQL queries and viewing the results.
   • The Hive Web UI can be accessed through a web browser, and provides a more user-friendly interface than the Hive CLI.
   • The Hive Web UI also supports advanced features such as query history, job tracking, and visual explain plans.
2. What is the purpose of the HiveServer2 in Hive?
   • The HiveServer2 is a server component in Hive that provides a JDBC/ODBC interface for external applications to interact with Hive.
   • The HiveServer2 allows external applications to submit HiveQL queries to Hive and receive the results in a structured format, such as JSON or CSV.
   • The HiveServer2 supports multiple client connections, and can be configured to run in a multi-threaded mode for improved performance.
3. What is the purpose of the Beeline in Hive?
   • Beeline is a command-line interface for interacting with Hive, which provides a more efficient and flexible interface than the Hive CLI.
   • Beeline is designed to be used with the HiveServer2, and supports advanced features such as multi-threading, authentication, and SSL encryption.
   • Beeline can be used to submit HiveQL queries and view the results, and supports multiple output formats, including CSV, TSV, and JSON.
4. What is the difference between a partitioned table and a bucketed table in Hive?
   • A partitioned table in Hive is a table that is divided into multiple partitions based on one or more partition columns.
   • Partitioning allows you to store and query large datasets more efficiently, as it enables you to filter the data based on partition values, rather than scanning the entire dataset.
   • In contrast, a bucketed table in Hive is a table that is divided into multiple buckets based on a hash function applied to one or more bucketing columns.
   • Bucketing allows you to evenly distribute the data across multiple files, which can improve query performance for certain types of queries.
5. What is the purpose of the Hive SerDe?
   • A SerDe (Serializer/Deserializer) in Hive is a component that is responsible for converting between the structured data in a table and the unstructured data in HDFS.
   • A SerDe is used to read and write data in different file formats, such as CSV, JSON, or Avro, and to parse and serialize the data according to a specified schema.
   • The Hive SerDe can be customized to support different data formats and serialization methods.
6. What is the purpose of the Hive UDF (User-Defined Function)?
   • A UDF (User-Defined Function) in Hive is a custom function that you can define and use in HiveQL queries.
   • A UDF allows you to extend the functionality of Hive by defining your own functions for specific data processing tasks.
   • A UDF can be written in a variety of programming languages, such as Java, Python, or Scala, and can be used to perform complex data transformations, aggregations, or calculations.
7. What is the purpose of the Hive UDAF (User-Defined Aggregation Function)?
   • A UDAF (User-Defined Aggregation Function) in Hive is a custom aggregation function that you can define and use in HiveQL queries.
   • A UDAF allows you to perform custom aggregation operations on a set of input values, such as calculating the average, sum, or count of a group of values.
   • A UDAF can be written in a variety of programming languages, such as Java, Python, or Scala, and can be used to perform complex aggregation operations on large datasets.
8. What is the purpose of the Hive HCatalog?
   • HCatalog is a storage abstraction layer in Hive that allows you to access data stored in different formats and storage systems, such as HDFS, HBase, or Amazon S3, using a common set of APIs.
   • HCatalog provides a metadata service that allows you to discover and access the data stored in these different systems using a unified schema.
   • This makes it easier to share data between different Hive tables and other Hadoop applications.
9. What is the difference between INNER JOIN and LEFT OUTER JOIN in Hive?
   • An INNER JOIN in Hive is a join operation that returns only the matching rows from both tables based on a common join condition.
   • In contrast, a LEFT OUTER JOIN in Hive is a join operation that returns all the rows from the left table and the matching rows from the right table, and null values for the non-matching rows from the right table.
   • In other words, a LEFT OUTER JOIN returns all the rows from the left table, along with any matching rows from the right table, and any non-matching rows from the left table are filled with null values for the right table columns.
10. What is a Hive metastore schema and how can you customize it?
    • A Hive metastore schema is the database schema that is used by the Hive metastore to store metadata information about Hive tables and partitions.
    • The default schema is defined by Hive and includes tables such as TBLS, SDS, and PARTITIONS.
    • However, you can customize the metastore schema by modifying the schema definition in the metastore configuration file.
    • You can add, modify, or delete tables, columns, or constraints in the metastore schema to suit your specific needs.
11. What is Hive on Tez and how does it differ from Hive on MapReduce?
    • Hive on Tez is a Hive execution engine that is designed to run Hive queries on the Apache Tez execution framework.
    • Tez is a next-generation data processing framework that is designed to provide high-performance, low-latency data processing for Hadoop applications.
    • Hive on Tez is designed to provide faster query performance than Hive on MapReduce by optimizing the query execution plan and leveraging the performance benefits of Tez.
    • Hive on MapReduce is the default execution engine in Hive and is based on the MapReduce framework.
12. What is the purpose of the Hive bucketing feature?
    • Bucketing is a data organization technique in Hive that is used to divide large tables into smaller, more manageable chunks.
    • Bucketing is similar to partitioning, but it is based on a hash function applied to one or more columns in the table.
    • The hash function is used to assign each row in the table to a specific bucket.
    • Bucketing can be used to optimize certain types of queries, such as join operations, by evenly distributing the data across multiple files and reducing the amount of data that needs to be read from disk.
13. What is the Hive ACID feature and how does it work?
    • ACID is a set of properties that ensure data consistency, durability, and isolation in database transactions.
    • Hive ACID is a feature in Hive that allows you to perform atomic, consistent, isolated, and durable transactions on Hive tables. Hive ACID is implemented using a combination of HDFS snapshots and an append-only delta file.
    • When you perform an ACID operation, Hive creates a new snapshot of the table and writes the changes to the delta file.
    • This ensures that the original data is preserved and that the changes are applied atomically and consistently.
14. What is a Hive UDF and how do you create one?
    • A Hive UDF (user-defined function) is a custom function that you can create and use in Hive queries.
    • UDFs can be used to perform custom calculations, transformations, or other operations on data in Hive.
    • To create a Hive UDF, you need to write a Java or Scala function that implements the UDF interface and then compile and package the function into a JAR file.
    • You can then add the JAR file to the Hive classpath and register the UDF using the CREATE FUNCTION statement.
15. What is the difference between a Hive external table and a managed table?
    • A Hive external table is a table that is created in Hive but references data stored outside of Hive, such as in HDFS or another storage system.
    • External tables are typically used to access data that is generated or managed outside of Hive, such as log files or data generated by other Hadoop applications.
    • In contrast, a managed table is a table that is created in Hive and has data stored directly in the Hive warehouse directory.
    • Managed tables are typically used for data that is managed exclusively by Hive.
16. What is the Hive view feature and how does it work?
    • A Hive view is a virtual table that is created by defining a SQL query on one or more existing Hive tables.
    • Views can be used to simplify complex queries or to provide a simplified view of data to users who do not have access to the underlying tables.
    • When you create a Hive view, Hive creates a metadata entry for the view and stores the SQL query definition.
    • When you query the view, Hive executes the underlying SQL query and returns the results as if they were stored in a regular table.
17. What is the difference between a Hive metastore and a Hive server?
    • A Hive metastore is a component of Hive that stores metadata information about Hive tables and partitions, such as schema information, table properties, and partition locations.
    • The Hive metastore is typically implemented as a relational database, such as MySQL or PostgreSQL.
    • In contrast, a Hive server is a component of Hive that provides a client-server interface for executing Hive queries.
    • The Hive server is responsible for processing queries, managing connections, and returning results to the client.
    • The Hive server can be accessed using a variety of client interfaces, such as the Hive CLI, Beeline, or JDBC/ODBC.
18. What is the difference between a Hive bucket and a partition?
    • A Hive bucket is a way of organizing data in a table by dividing it into buckets based on the values of one or more columns.
    • Buckets can be used to improve query performance by reducing the amount of data that needs to be scanned.
    •  In contrast, a partition is a way of dividing a table into smaller, more manageable parts based on the value of a partitioning column.
    • Partitions can also be used to improve query performance by reducing the amount of data that needs to be scanned.
    • The main difference between buckets and partitions is that buckets are based on the values of one or more columns, while partitions are based on the value of a single partitioning column.
19. What is the Hive skew join optimization and how does it work?
    • Hive skew join optimization is a feature in Hive that is used to optimize joins where one or more keys have a large number of duplicate values.
    • This can cause uneven data distribution and lead to slow query performance.
    • The skew join optimization works by identifying the skewed keys and creating a special bucket for them.
    • This bucket is then replicated multiple times, so that each reducer processes a smaller amount of data.
    • This helps to improve query performance by reducing the amount of data that needs to be processed by each reducer.
20. What is the Hive Dynamic Partitioning feature and how does it work?
    • Hive Dynamic Partitioning is a feature in Hive that allows you to create new partitions in a table dynamically, based on the values of a partitioning column in the data being inserted.
    • This can be useful when you have a large amount of data that needs to be partitioned and you don’t want to create all of the partitions manually.
    • To use dynamic partitioning, you need to enable the feature and specify the partitioning columns in the INSERT statement.
    • When you insert data, Hive automatically creates new partitions for each unique combination of partitioning column values that is encountered.
21. What is the difference between a Hive JOIN and a UNION?
    • A Hive JOIN is used to combine data from two or more tables based on a common column.
    • JOINs can be used to combine data from different sources or to perform more complex data transformations.
    • In contrast, a UNION is used to combine the results of two or more SELECT statements into a single result set.
    • UNIONs are typically used to combine data from different parts of a single table or to combine data from different tables with similar schemas.
22. What is the Hive HCatalog feature and how does it work?
    • Hive HCatalog is a storage and metadata management system for Hadoop that provides a unified interface for accessing data stored in Hadoop.
    • HCatalog is built on top of Hive and provides a metadata layer that abstracts the underlying storage system.
    • This allows applications to access data stored in Hadoop using a variety of tools and programming languages, including Hive, Pig, MapReduce, and Java.
    • When you use HCatalog, you can access data in Hadoop as if it were stored in a traditional relational database.
23. What is the Hive Tez execution engine and how does it work?
    • Hive Tez is an execution engine for Hive that is designed to improve query performance by optimizing the execution plan of Hive queries.
    • Tez is built on top of YARN and provides a more efficient way of executing Hive queries by reducing the amount of data that needs to be transferred between nodes.
    • When you run a Hive query using the Tez execution engine, Hive generates a logical execution plan for the query and then uses Tez to optimize and execute the plan.
    • The result is faster query execution and better resource utilization.
24. What is the difference between a Hive UDF and a Hive UDAF?
    • A Hive UDF (User-Defined Function) is a function that you can define and use in Hive queries to perform custom operations on data.
    • UDFs can be used to implement custom transformations or to perform calculations that are not provided by the built-in Hive functions.
    • A Hive UDAF (User-Defined Aggregation Function) is a function that you can define and use in Hive queries to perform custom aggregations on data.
    • UDAFs are used to implement custom aggregation functions that are not provided by the built-in Hive functions.
25. What is the Hive Streaming feature and how does it work?
    • Hive Streaming is a feature in Hive that allows you to ingest real-time data into Hive tables using external applications or services.
    • Hive Streaming works by establishing a continuous connection between the external application and the Hive server.
    • The external application can then send data to Hive using the established connection.
    • The data is processed by the Hive server and inserted into the appropriate tables.
    • This allows you to work with real-time data in Hive and perform real-time analytics on the data.
26. What is the Hive Beeline command-line interface and how does it work?
    • Hive Beeline is a command-line interface for Hive that allows you to interact with Hive using SQL commands.
    • Beeline is a lightweight alternative to the Hive CLI and provides a more user-friendly interface for running Hive queries.
    • Beeline works by establishing a connection to the Hive server and allowing you to execute SQL commands against the server.
    • Beeline supports all of the standard SQL commands, as well as some Hive-specific commands.
27. What is the difference between an external table and a managed table in Hive?
    • In Hive, an external table is a table that is not managed by Hive.
    • The data for an external table is stored outside of the Hive data warehouse and is typically stored in HDFS or some other external storage system.
    • When you create an external table in Hive, you define the schema for the table and specify the location of the data.
    • In contrast, a managed table is a table that is managed by Hive.
    • The data for a managed table is stored in the Hive data warehouse and is typically stored in HDFS.
    • When you create a managed table in Hive, you define the schema for the table and Hive takes care of managing the data and metadata for the table.
28. What is the difference between a Hive ORC file and a Parquet file?
    • Both ORC and Parquet are columnar storage file formats for Hadoop that are designed to improve query performance by reducing the amount of data that needs to be scanned.
    • ORC is a file format that is optimized for Hive queries and is designed to provide high performance for Hive workloads.
    • Parquet is a file format that is designed to be used with a variety of Hadoop tools and is optimized for analytics workloads.
    • The main difference between ORC and Parquet is that ORC is optimized for Hive queries, while Parquet is more general-purpose and can be used with a variety of Hadoop tools.
29. What is the Hive ACID feature and how does it work?
    • The Hive ACID (Atomicity, Consistency, Isolation, and Durability) feature is a feature in Hive that provides support for transactions and data consistency.
    • ACID provides a way to perform insert, update, and delete operations on Hive tables in a way that guarantees data consistency and avoids data corruption.
    • ACID works by providing a set of transactional operations that can be used to modify data in Hive tables.
    • When you use the ACID feature, Hive guarantees that the transactions are executed atomically, consistently, in isolation, and with durability, ensuring that the data remains
30. What is Hive partitioning and how does it work?
    • Hive partitioning is a feature in Hive that allows you to organize data in Hive tables into partitions based on the values of one or more columns.
    • Partitioning is useful for improving query performance by reducing the amount of data that needs to be scanned.
    • When you create a partitioned table in Hive, you specify one or more partition columns, and Hive creates a separate directory in HDFS for each partition.
    • When you query the partitioned table, Hive only scans the partitions that match the query conditions, which can significantly reduce query processing time.
31. What is a Hive metastore and what is its role in Hive?
    • A Hive metastore is a database that stores metadata about the Hive tables, partitions, and other objects in a Hive data warehouse.
    • The metastore is used by Hive to manage the tables, store the schema information, and track the locations of the data files.
    • The metastore is also used to store information about the partitions and the locations of the partition files.
    • The metastore is typically implemented using a relational database management system (RDBMS) such as MySQL or PostgreSQL.
32. What is HiveQL and how is it different from SQL?
    • HiveQL is the SQL-like query language used in Hive for interacting with Hive tables.
    • HiveQL is similar to SQL in syntax and structure, but it is designed specifically for working with Hadoop and is optimized for distributed processing.
    • HiveQL includes some additional features that are not available in SQL, such as support for partitioning and support for custom User-Defined Functions (UDFs).
33. What is a Hive bucket and how does it work?
    • A Hive bucket is a way of organizing data in Hive tables to improve query performance. A bucket is a way of dividing data into smaller, more manageable units based on the values of one or more columns.
    • When you create a bucketed table in Hive, you specify one or more bucketing columns, and Hive creates a separate file for each bucket.
    • When you query the bucketed table, Hive only scans the buckets that match the query conditions, which can significantly reduce query processing time.
34. What is the Hive MapReduce framework and how does it work?
    • The Hive MapReduce framework is a component of Hive that is used for processing data stored in Hadoop using the MapReduce programming model.
    • When you run a Hive query, Hive generates a MapReduce job that processes the data in parallel across multiple nodes in the Hadoop cluster.
    • The MapReduce framework works by breaking the data into smaller chunks and distributing the processing of the data across multiple nodes in the cluster.
    • Each node processes its assigned portion of the data and then sends the results back to the master node, which combines the results into a single output.
35. What is Hive-on-Spark and how does it work?
    • Hive-on-Spark is a component of Hive that allows you to use the Spark execution engine to process Hive queries.
    • Hive-on-Spark works by translating Hive queries into Spark jobs and then executing those jobs on a Spark cluster.
    • Hive-on-Spark is designed to improve query performance by leveraging the in-memory processing capabilities of Spark.
36. What is a Hive bucket join and how does it work?
    • A Hive bucket join is a type of join operation in Hive that is optimized for joining bucketed tables.
    • A bucket join works by joining the buckets of the two tables that have the same bucket number.
    • This can significantly reduce the amount of data that needs to be processed during the join operation and can improve query performance.
37. How can you optimize the performance of Hive queries?
    • There are several ways to optimize the performance of Hive queries, including:
      • Partitioning the data based on the query conditions to reduce the amount of data that needs to be scanned.
      • Bucketing the data to improve the efficiency of join and aggregation operations.
      • Using compression to reduce the amount of data that needs to be transferred between nodes.
      • Using the ORC or Parquet file formats to improve query performance by storing data in a columnar format.
      • Tuning the Hive configuration settings to optimize the performance of the Hive execution engine.
38. What is Hive ACID and how does it work?
    • Hive ACID (Atomicity, Consistency, Isolation, Durability) is a feature in Hive that provides transactional support for Hive tables.
    • Hive ACID allows multiple concurrent transactions to read and write data to the same table without conflicts.
    • When a transaction is committed, the changes made by the transaction are immediately visible to other transactions.
    • If a transaction fails, the changes made by the transaction are rolled back, ensuring that the table remains in a consistent state.
39. What is Hive streaming and how does it work?
    • Hive streaming is a feature in Hive that allows you to stream data into a Hive table in real-time using a messaging system such as Apache Kafka or Apache Flume.
    • Hive streaming works by creating a temporary external table in Hive that is backed by a messaging system topic.
    • As data is streamed into the messaging system, it is automatically inserted into the temporary external table in Hive.
    • Once the data is in the temporary external table, you can use HiveQL to manipulate and analyze the data.
40. What is the difference between a local and a distributed cache in Hive?
    • In Hive, a local cache is a cache of data that is stored on the local file system of each node in the cluster.
    • A distributed cache, on the other hand, is a cache of data that is stored on the Hadoop Distributed File System (HDFS) and is available to all nodes in the cluster.
    • Local caches are useful for caching small amounts of data that are used frequently by a single node, while distributed caches are useful for caching larger amounts of data that are used by multiple nodes in the cluster.
41. What is the difference between a Hive table and an external table?
    • In Hive, a table is a collection of data that is stored in a Hive warehouse directory on the Hadoop Distributed File System (HDFS).
    • An external table, on the other hand, is a table that is defined by a schema, but the data is stored outside of the Hive warehouse directory.
    • External tables are useful for working with data that is generated by external systems or for working with data that is stored in a different format than the format used by Hive.
42. What is a Hive metastore service and how does it work?
    • A Hive metastore service is a service that provides access to the Hive metastore database.
    • The metastore service is responsible for managing the metadata about the Hive tables and other objects, and it provides an API for accessing and manipulating the metadata.
    • The metastore service can be accessed by Hive clients, such as the Hive command line interface (CLI) or the HiveServer2 service.
43. What is the role of the HiveServer2 service in Hive?
    • The HiveServer2 service is a component of Hive that provides a JDBC/ODBC interface for accessing Hive tables and executing Hive queries.
    • The HiveServer2 service can be used by third-party applications to access Hive data using standard SQL interfaces.
    • The HiveServer2 service also provides a secure, multi-user environment for running Hive queries, with support for authentication and authorization.