Hive is a data warehousing tool built on top of Hadoop, which allows users to query and analyze data stored in Hadoop Distributed File System (HDFS). Hive provides a SQL-like interface called HiveQL or HQL, which allows users to write SQL queries to analyze data stored in Hadoop.
Here are some key features of SQL in Hive:
- HiveQL: HiveQL is the SQL-like language used in Hive. It is a declarative language used to query data stored in HDFS. HiveQL supports most of the SQL standard syntax, including SELECT, FROM, WHERE, GROUP BY, and ORDER BY clauses.
- Data Types: Hive supports several data types including Boolean, TinyInt, SmallInt, Int, BigInt, Float, Double, Decimal, String, and Timestamp. In addition, Hive supports complex data types such as arrays, maps, and structs.
- Joins: Hive supports several types of joins, including inner join, left join, right join, and full outer join. Users can use the JOIN keyword to join tables based on a common field.
- Aggregations: Hive supports several aggregate functions, including SUM, COUNT, AVG, MIN, and MAX. Users can use these functions to aggregate data and get summary information.
- Partitioning: Hive supports partitioning of tables based on one or more columns. This allows users to efficiently query large datasets by only scanning a subset of data.
- Indexing: Hive supports indexing of tables, which allows users to quickly look up data based on a particular column or set of columns.
Overall, SQL in Hive provides a powerful and flexible interface for analyzing data stored in Hadoop. Its support for complex data types, partitioning, and indexing makes it well-suited for large-scale data warehousing applications.