Data Units

Data Units

In the order of granularity - Hive data is organized into:

数据库、表、分区、桶

• Databases: Namespaces that separate tables and other data units from naming confliction.
• Tables: Homogeneous units of data which have the same schema. An example of a table could be page_views table, where each row could comprise of the following columns (schema):
  • timestamp - which is of INT type that corresponds to a unix timestamp of when the page was viewed.
  • userid - which is of BIGINT type that identifies the user who viewed the page.
  • page_url - which is of STRING type that captures the location of the page.
  • referer_url - which is of STRING that captures the location of the page from where the user arrived at the current page.
  • IP - which is of STRING type that captures the IP address from where the page request was made.
• Partitions: Each Table can have one or more partition Keys which determines how the data is stored. Partitions - apart from being storage units - also allow the user to efficiently identify the rows that satisfy a certain criteria. For example, a date_partition of type STRING and country_partition of type STRING. Each unique value of the partition keys defines a partition of the Table. For example all "US" data from "2009-12-23" is a partition of the page_views table. Therefore, if you run analysis on only the "US" data for 2009-12-23, you can run that query only on the relevant partition of the table thereby speeding up the analysis significantly. Note however, that just because a partition is named 2009-12-23 does not mean that it contains all or only data from that date; partitions are named after dates for convenience but it is the user‘s job to guarantee the relationship between partition name and data content!). Partition columns are virtual columns, they are not part of the data itself but are derived on load.（分区列是虚拟列，他们不是数据本身的一部分，是在执行load时候加载的得到的）
• Buckets (or Clusters): Data in each partition may in turn be divided into Buckets based on the value of a hash function of some column of the Table. For example the page_views table may be bucketed by userid, which is one of the columns, other than the partitions columns, of the page_view table. These can be used to efficiently sample the data. 表中的具有的数据列（非分区列），可以基于数据值的Hash方法切分成桶中。如此，可以高效的取样数据。

Note that it is not necessary for tables to be partitioned or bucketed, but these abstractions allow the system to prune large quantities of data during query processing, resulting in faster query execution.表的分区和桶，并不一定是必须，但是这种抽象可以使得系统在进行数据查询，结果集返回时候减少数据的量，提高执行效率。