Hive is an open source, peta-byte scale date warehousing framework based on Hadoop that was developed by the Data Infrastructure Team at Facebook that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.
Hive organizes data in tables and partitions. A good partitioning scheme allows Hive to prune data while processing a query and that has a direct impact on how fast a result of the query can be produced. Behind the scenes, Hive stores partitions and tables into directories in Hadoop File System (HDFS).
Hive comprises of the following major components:
-Metastore: To store the meta data.
-Query compiler and execution engine: To convert SQL queries to a sequence of map/reduce jobs that are then executed on Hadoop.
- SerDe and ObjectInspectors: Programmable interfaces and implementations of common data formats and types.
-UDF and UDAF: Programmable interfaces and implementations for user defined functions (scalar and aggregate functions).
-Clients: Command line client similar to Mysql command line and a web UI.
Data Flow into Hadoop Cloud:
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