Top 20 Hive Interview Questions and Answers

Internal and external tables in Hive are two types of tables used to store data in Hive.

• Internal tables store data in a default location managed by Hive

• External tables store data in a location specified by the user

• Internal tables are deleted when the table is dropped, while external tables retain data in the specified location

• Example: CREATE TABLE internal_table (col1 INT, col2 STRING) STORED AS ORC;

• Example: CREATE EXTERNAL TABLE external_table (col1 INT, col2 STRING) LOCATIO...read more

Null values in HIVE can be eliminated using the COALESCE function.

• Use the COALESCE function to replace null values with a default value.

• Syntax: COALESCE(column_name, default_value)

• Example: SELECT COALESCE(name, 'Unknown') FROM table_name;

Hive is a data warehousing tool for Hadoop while SQL is a language used to manage relational databases.

• Hive is used for big data processing while SQL is used for relational databases.

• Hive uses Hadoop Distributed File System (HDFS) while SQL uses traditional storage systems.

• Hive queries are written in Hive Query Language (HQL) while SQL queries are written in Structured Query Language.

• Hive is optimized for batch processing while SQL is optimized for transaction processing.

• Hive...read more

Partition in Hive is a way to organize data in a table into multiple directories based on the values of one or more columns.

• Partitions help in improving query performance by allowing Hive to only read the relevant data directories.

• Partitions are defined when creating a table in Hive using the PARTITIONED BY clause.

• Example: CREATE TABLE table_name (column1 INT, column2 STRING) PARTITIONED BY (column3 STRING);

ACID properties ensure data consistency and reliability in HIVE tables.

• ACID stands for Atomicity, Consistency, Isolation, and Durability.

• Atomicity ensures that a transaction is treated as a single unit of work.

• Consistency ensures that the data remains in a valid state before and after the transaction.

• Isolation ensures that concurrent transactions do not interfere with each other.

• Durability ensures that once a transaction is committed, it is permanent and can survive system fa...read more

To troubleshoot hive slowness, check for resource contention, optimize queries, and monitor system performance.

• Check for resource contention such as CPU, memory, and disk usage

• Optimize queries by reducing data scanned and avoiding unnecessary joins

• Monitor system performance using tools like Ganglia or Ambari

• Consider partitioning tables to improve query performance

• Use compression to reduce disk I/O and network traffic

To add data into a partitioned hive table, you can use the INSERT INTO statement with the PARTITION clause.

• Use INSERT INTO statement to add data into the table.

• Specify the partition column values using the PARTITION clause.

• Example: INSERT INTO table_name PARTITION (partition_column=value) VALUES (data);

Hive meta store stores metadata about Hive tables, partitions, columns, and storage location.

• Hive meta store is a central repository that stores metadata information about Hive tables, partitions, columns, and storage location.

• It stores this metadata in a relational database like MySQL, Derby, or PostgreSQL.

• The metadata includes information such as table names, column names, data types, file formats, and storage location.

• Hive uses the meta store to retrieve metadata informati...read more

Hive has two types of tables - Managed and External. Managed tables are managed by Hive, while External tables are managed outside of Hive.

• Managed tables are created using 'CREATE TABLE' command and data is stored in Hive's warehouse directory

• External tables are created using 'CREATE EXTERNAL TABLE' command and data is stored outside of Hive's warehouse directory

• Managed tables are deleted when the table is dropped, while External tables are not

• Managed tables have full control...read more

Hive tables store data in HDFS while external tables reference data stored outside HDFS.

• Hive tables store data in HDFS, while external tables reference data stored outside HDFS.

• External tables are useful when data needs to be shared across different Hive instances or when data is already stored in a different location.

• Hive tables are managed by Hive and are deleted when the table is dropped, while external tables are not deleted when the table is dropped.

• Example: Creating a H...read more

Hive table loads can be done incrementally by using partitioning or by using timestamp columns.

• Partition the table based on a column like date or timestamp to load data incrementally

• Use dynamic partition inserts to add new data to specific partitions

• Utilize timestamp columns to filter and load only new data

• Consider using tools like Apache NiFi or Apache Sqoop for efficient incremental data loading

Use DISTINCT keyword in SELECT query to eliminate duplicate records in a Hive table

• Use SELECT DISTINCT * FROM table_name to retrieve unique records

• Consider using GROUP BY clause with appropriate columns to eliminate duplicates

• Utilize the ROW_NUMBER() window function to assign a unique row number to each record and filter out duplicates

External and internal tables in Hive are two types of tables used to store data in Hive.

• External tables store data outside of Hive's warehouse directory, while internal tables store data within the warehouse directory

• External tables do not delete data when the table is dropped, while internal tables delete data

• External tables are useful for sharing data between different Hive instances or with external systems

• Internal tables are managed by Hive, while external tables are mana...read more

Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

• Hive uses a SQL-like language called HiveQL to query and manage large datasets stored in Hadoop

• It allows users to write complex queries to analyze and process data

• Hive organizes data into tables, partitions, and buckets for efficient querying

• It is commonly used for data warehousing, data analysis, and data processing tasks

Hive tables are used to store structured data in Hive, similar to tables in a traditional database.

• Hive tables are created using the CREATE TABLE statement.

• Tables can be partitioned based on one or more columns.

• External tables in Hive store data outside of the default location in HDFS.

• Managed tables store data in the default location in HDFS.

• Tables can be queried using SQL-like syntax in HiveQL.

Hive architecture, table types, file formats, and dynamic partitioning.

• Hive architecture consists of metastore, driver, compiler, and execution engine.

• Hive tables can be of two types: managed tables and external tables.

• File formats supported by Hive include text, sequence, ORC, and Parquet.

• Dynamic partitioning allows automatic creation of partitions based on data.

Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

• Advantages: SQL-like query language for querying large datasets, optimized for OLAP workloads, supports partitioning and bucketing for efficient queries.

• Disadvantages: Slower performance compared to traditional databases for OLTP workloads, limited support for complex queries and transactions.

• Example: Hive can be used to analyze large volumes of log data to ext...read more