There are four MapReduce-based HLQL presented in this paper that have emerged out of the MR programing model. They also provide abstractions on the top of Hadoop framework. These abstractions are used to reduce the amount of low-level difficulties required for typical tasks and to translate queries into native MR jobs. Moreover, they allow developers to write programs using MapReduce-based HLQL abstractions that can be compiled into native MR jobs. These languages provide several operators, so developers can develop their own functions for reading, writing, and processing data. MapReduce-based HLQL are easy to be scripted, modified, and understood. Their relationship with Hadoop is shown in Fig. 1. Bigdata training center in chennai
Hive: a data warehousing over Hadoop
Hive is a data warehouse infrastructure, built on the top of Hadoop framework that is developed by Facebook . It provides a simple query language called HiveQL that supports queries expressed in a SQL-like declarative query language. Hive queries are compiled and translated into MR jobs that are executed on Hadoop. Hive provides a SQL-like, called Hive query language (HiveQL) for querying data stored in a Hadoop . Having SQL-like features, HiveQL provides several functions and operations like group by, joins, aggregation etc. In other words, it provides an easy data summarization, ad-hoc querying and analysis of large volumes of data. Bigdata training center in chennai @Dataz
Life Cycle of HiveQL
The Hive architecture presented in the Fig. 2 is mainly composed of four main components. The first component is the external interface that consists of sub-component: command line (CLI), web user interface, application-programming interface (API) shown either as JDBC or ODBC . The next one is the driver manager, the life cycle of HiveQL statements during compilation and execution that receives the queries and creates a session handle . The third component is the compiler invoked by the driver upon receiving HiveQL queries. It translates those statements for generating an execution plan. The fourth one is the metastore which is the system catalog for Hive. It performs the validation of the relational schema or query. All other components of Hive interact with the metastore .
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