![]() ![]() Therefore, if you run analysis on only the "US" data for, you can run that query only on the relevant partition of the table, thereby speeding up the analysis significantly. For example, all "US" data from "" is a partition of the page_views table. Each unique value of the partition keys defines a partition of the Table. Partitions -apart from being storage units -also allow the user to efficiently identify the rows that satisfy a specified criteria for example, a date_partition of type STRING and country_partition of type STRING. Partitions: Each Table can have one or more partition Keys which determines how the data is stored.IP -which is of STRING type that captures the IP address from where the page request was made. ![]() referer_url -which is of STRING that captures the location of the page from where the user arrived at the current page.page_url -which is of STRING type that captures the location of the page.userid -which is of BIGINT type that identifies the user who viewed the page.timestamp -which is of INT type that corresponds to a UNIX timestamp of when the page was viewed.An example of a table could be page_views table, where each row could comprise of the following columns (schema): Tables: Homogeneous units of data which have the same schema.Databases can also be used to enforce security for a user or group of users. Databases: Namespaces function to avoid naming conflicts for tables, views, partitions, columns, and so on.In the order of granularity - Hive data is organized into: We start by describing the concepts of data types, tables, and partitions (which are very similar to what you would find in a traditional relational DBMS) and then illustrate the capabilities of Hive with the help of some examples. In the following sections we provide a tutorial on the capabilities of the system. Getting Startedįor details on setting up Hive, HiveServer2, and Beeline, please refer to the GettingStarted guide.īooks about Hive lists some books that may also be helpful for getting started with Hive. It is best used for traditional data warehousing tasks. Hive is not designed for online transaction processing. At the same time, Hive's SQL gives users multiple places to integrate their own functionality to do custom analysis, such as User Defined Functions (UDFs). It provides SQL which enables users to do ad-hoc querying, summarization and data analysis easily. Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing on commodity hardware. Hive is a data warehousing infrastructure based on Apache Hadoop. ![]()
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