1. Overview
Hive is a data warehouse tool, which can store data in Hadoop file system and use SQL-style query language to manipulate these data. It can easily handle structured, semi-structured and unstructured data. Hive uses a language similar to SQL to query data, which is very easy for developers who are familiar with SQL.
2. Architecture
Hive architecture has three layers: user interface, driver and execution engine. The user interface is responsible for accepting HiveQL statements, and the driver converts these statements into MapReduce tasks and returns the execution results to the user interface. The execution engine is the MapReduce framework, which performs the actual query on the data.
In Hive architecture, it also includes Metastore and Hive Server. Metastore maintains metadata information about tables, partitions and tables (such as field names, types, partition information, etc.). ), and Hive Server is responsible for interprocess communication.
3. Data type
Hive supports most SQL standard data types, such as string, integer, floating point and so on. In addition, Hive has some custom data types, such as ARRAY, MAP and STRUCT.
4.HiveQL
Hive's query language is called HiveQL, which is similar to SQL and supports most SQL standard query statements. HiveQL also supports custom functions and user-defined aggregate functions, which is very helpful for advanced data processing.
5.Hive and Hadoop ecosystem
Hive is closely integrated with Hadoop ecosystem and can be easily integrated with other tools. For example, Hive can import data from relational database into Hadoop through Sqoop, or query real-time data through HBase.