ACADGILD:: HADOOP LESSON

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INTRODUCTION
Why was Hive introduced?
Few years ago, Hadoop came into existence for solving queries on huge structured, Semi-structured,
and unstructured datasets. Hadoop is considered to be the bestsolution to store and process those huge
datasets, because of its advantages like scalability, less infrastructure costs(commodity hardware), data
security(replication factor) and MapReduce, which is the best programming structure.
In the beginning big organizations like Facebook, who received huge datasets of raw data on a daily
basis, were writing Python scripts to ingest data into the Oracle databases. As the amount of data kept
increasing, it was not practical to continue writing lengthy and complex programs. So to handle huge
datasets and to process them, Facebook came out with Hive, a language similar to SQL, where there
was no need for programmers to write complex programs anymore.
Apache introduced Hive as an open-source and is currently one of the most used Hadoop tools in many
organizations.
What is Hive?
Hive is a data warehouse solution built on top of Hadoop for managing huge amount of data stored in
HDFS. It provides an SQL-like query language called HiveQL. Hive is designed in such a way that it
allows easy data summarization, ad-hoc querying, and analysis of Big Data to Process structured data
in Hadoop cluster.
Hive uses HDFS for storage, but stores all the meta information about the database and table in Derby
or MySQL database.
HIVE Vs SQL
HIVE SQL
Hive is a SQL-like scripting language built on
MapReduce
According to ANSI, SQL is the standard
language for RDMBS, used to communicate with
databases
Used for analytics
Used for transactional processing(OLTP) &
analytics
Data per query in PBs Data per query in GBs
Faster execution while performing analytics on
Huge data sets compared to SQL
Slower execution while performing analytics on
huge data sets compared to HIVE
No Normalization required Supports Normalization
https://acadgild.com/blog/hive-beginners-guide/

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How to install Hive?
To install Hive, please follow the procedure in the below link:
https://drive.google.com/open?id=0B1QaXx7tpw3SMDktdk5RbHk1MVE
Hive Shell
Hive provides a default interface, where it allows users to run Hive commands. The CLI (Command
Line Interface) is the default Hive shell service which allows users to work on Hive programs.
The following are the commands used to start the Hive shell:
Creating a Database
The purpose of creating a database before creating a table is that the database in Hive is a namespace,
where a table or collection of tables should be placed to work on Hive queries. Here the database and
the schema represents the same category.
The command to create a database are as follows:
hive>CREATE DATABASEEMPdb;
Listing Databases
The show databases command displays the list of databases in HDFS so that the user can select the
database he wants to work with.
hive> SHOW DATABASES;

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Using a Database
The command to use database is USE. The use database command allows the user to utilize the
mentioned database to work with, where he can create new tables in that database and alter particular
table contents too.
hive> USE EMPdb;
Creating a Table
The command for creating a table is ‘Create’.
Before creating a table, a user must know the following details:
•The create table command allows the user to create a new table with user input
attributes/columns.
•Row format delimited Fields terminated by ‘t’ – This line informsHive that each column in the
file is separated by a tab.
hive> Create table emp(id int, name string, sal float)
> row format delimited
> fields terminated by ‘t’ ;

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List Tables
The ‘show tables’ command displays the list of tables present in a particular database.
Hive> show Tables;
Once the table names are displayed, the user can select a particular table and work on it. The user can
also create a new table and start processing the required query.
Describe Schema of the Table
The describe command is similar to adesc command in SQL. The describe command displays table
definition/metadata of the mentioned table and the users can see column names and data types
associated to that particular column.
Hive>DESCRIBE emp_table;

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Load a File from the Local File System
We know that Hadoop is designed to work only on huge datasets instead of few selected rows/columns.
So, the user needs to loadthe entire file to HDFS and process it using Hive commands.
The command used to load file from local system to HDFS is as follows:
Hive>load data local inpath<filename> into table<tablename>
Load File from HDFS
The command used for loading file from HDFS is ‘load data’.
Hive>load data inpath<filename> into table<tablename>
Show Table Contents
Once the selected file is loaded to HDFS,the contents of the table can be checked using the select
statement.
The select * statement of a table displays all the columns which are present in that table.

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Commonly Used Alter Commands
Some of the commonly used alter commands in Hive are as follows:
Renaming the Current Table:
hive> ALTER TABLE EMP RENAME TO EMP_TABLE
Adding New Columns to an Existing Table:
hive> ALTER TABLE EMP_TABLE ADD COLUMNS (YOJ DATE)
Truncating a Table
The Truncating table command allows users to delete only the content values in the table and not the
table schema.

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After performing the Truncating command, we can see that the table schema is unaffected but the
contents of the table have been deleted. This is how you can truncate/delete table contents without
affecting the table schema.
Dropping a Database
The command to drop a database is ‘drop database’. The drop database database_name allows users to
drop a particular database from the HDFS memory.
hive> DROP DATABASE EMPdb;
You can send us your queries and feedback in the comment section below

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ACADGILD:: HADOOP LESSON

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