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Architecture of HiveHive has a wide range of components that performs various functions. We have discussed these components below:
- User interface
Hive offers interaction between the users and the Hadoop distributed file system. Some of the user interfaces supported by Hive are the Hive command line, Hive Web UI, and Windows server’s Hive HD insight.
- Meta Store
Hive stores metadata of tables, databases, etc., or schema in respective database stores. These stores are known as Meta stores.
- HiveQL Process Engine
This component works the same as the SQL for querying schema information on the Meta store. It is one of the developments that are meant to replace the traditional approach employed by a MapReduce program. HiveQL process engine allows users to write a query for a MapReduce task and process it rather than coding a MapReduce program in Java.
- Execution Engine
This is the conjunction segment of the HiveQL process engine and Hive’s MapReduce. It is tasked with processing a query and generating results similar to MapReduce results.
- HBASE or HDFS
HBASE or HDFS is a method for storing data in the file system.
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Hiring our apache hive homework helpers is the best decision you can ever make when you are stuck with a project based on Hadoop. The Hadoop ecosystem contains Hive and other sub-projects. It is an open-source framework that stores and analyzes big data in a distributed platform. Hadoop has two main modules:
Hadoop Distributed File System (HDFS)
This module supports the storage and processing of datasets. It boasts of a fault-tolerant file system that runs on commodity hardware.
This module is a parallel programming model. It processes large amounts of data on huge clusters of commodity hardware. The data processed by MapReduce can be structured, unstructured, and semi-structured. There are two main concepts that you should be familiar with in MapReduce:
1. Data flow
• Map function
• Partition function
• Shuffling and sorting
2. MapReduce API
• Mapper class
• Reducer class
• Job class
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Column types are used in the hive to create tables. The following are some of the column types that support column data types in the hive:
Integral type, INT is used to specify data of type integer. BIGINT and SMALLINT are used when the integer range is bigger and smaller respectively. Also, TINYINT is used when the range is the least.
They are usually specified using single or double-quotes. String types have two data types, CHAR and VARCHAR. C-types escape characters are supported in Hive.
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Views in apache hive are created based on the requirements of the user. Any data set result can be saved as a view. Also, the use of views is standard in relational database management systems. Besides, all DML operations can be executed in a view. We can create a view in the hive during the execution of the SELECT statement. On the other hand, an index can simply be defined as a pointer in a given column of a table. Our apache hive project help professionals can assist you with dropping views and indexes.
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