Overview
HCatalog is a table and storage management layer for Hadoop that enables users with different data processing tools — Pig, MapReduce — to more easily read and write data on the grid. HCatalog’s table abstraction presents users with a relational view of data in the Hadoop distributed file system (HDFS) and ensures that users need not worry about where or in what format their data is stored — RCFile format, text files, SequenceFiles, or ORC files.
HCatalog Architecture
HCatalog is built on top of the Hive metastore and incorporates Hive's DDL. HCatalog provides read and write interfaces for Pig and MapReduce and uses Hive's command line interface for issuing data definition and metadata exploration commands.
Interfaces
| pig | HCatLoader HCatStorer |
| mapreduce | HCatInputFormat HCatOutputFormat |
Data Model
HCatalog presents a relational view of data. Data is stored in tables and these tables can be placed in databases. Tables can also be hash partitioned on one or more keys; that is, for a given value of a key (or set of keys) there will be one partition that contains all rows with that value (or set of values). For example, if a table is partitioned on date and there are three days of data in the table, there will be three partitions in the table. New partitions can be added to a table, and partitions can be dropped from a table. Partitioned tables have no partitions at create time. Unpartitioned tables effectively have one default partition that must be created at table creation time. There is no guaranteed read consistency when a partition is dropped.
Partitions contain records. Once a partition is created records cannot be added to it, removed from it, or updated in it. Partitions are multi-dimensional and not hierarchical. Records are divided into columns. Columns have a name and a datatype. HCatalog supports the same datatypes as Hive.Data Flow Example
reference:
https://cwiki.apache.org/confluence/display/Hive/HCatalog