HIVE Installation & Setup Guide

-


Pre-requisites
  1. Ubuntu / CentOS
  2. Hadoop 1.x/ 2.x , I prefer to install with 2.x
Step –> 1: Download and Install
Download the Hive from the Apache Download Mirror and i place it in /home/bigdata/Installations/ directory.
$ cd /home/bigdata/Installation
$ wget http://redrockdigimark.com/apachemirror/hive/stable/apache-hive-1.2.1-bin.tar.gz ( i preferred to download hive-1.2.1.tar.gz, as it is stable version)

$ sudo tar xzf hive-1.2.1.tar.gz

Step –> 2:

 After downloading and installation. Now we are moving to edit hive-env.sh file for Configuration. To configure hive, there I have installed and give permission to bigdata.
In $HIVE_HOME/conf/hive-env.sh
export JAVA_HOME=/opt/jdk1.80_10 
Step –> 3:
add hbase path to bashrc
$ gedit .bashrc
and add following lines to it
#HIVE
export HIVE_HOME=/home/bigdata/Installations/hive-1.2.1/
export PATH=$PATH:$HIVE_HOME/bin
Step –> 4:
Restart the terminal and start hadoop, then start hive
$ hive
>show tables;
(to test hive installation)

 Happy Hadooping !!

Comments

Post a Comment

thank you for your feedback

Popular posts from this blog

Cloudera Data Hub: Where Agility Meets Control

-
Image
Cloudera’s new Data Hub cloud service, powered by  Cloudera Data Platform , enables users to seamlessly migrate on-premises data management and analytics workloads to the cloud as well as implement new cloud workloads in pursuit of your cloud-first data management strategy. On August 22 nd ,  Cloudera demonstrated its Data Hub  service during a webinar highlighting key business benefits, use cases, and product capabilities. Below is a brief overview of the topics covered and some of the most frequently asked questions from attendees. What is Cloudera Data Hub? Cloudera Data Hub  is a powerful cloud service on Cloudera Data Platform (CDP) that makes it easier, safer, and faster to build modern, mission-critical, data-driven applications with enterprise security, governance, scale, and control. The cloud-native service is powered by a suite of integrated open source technologies that delivers the widest range of analytical workloads such as data marts and data...
See more »

3X FASTER INTERACTIVE QUERY WITH APACHE HIVE LLAP

-
Image
Thanks to Carter Shanklin & Nita Dembla from Hortonworks for valuable post. One of the most exciting new features of HDP 2.6 from Hortonworks was the general availability of Apache Hive with LLAP. If you missed DataWorks Summit you’ll want to look at some of the great LLAP experiences our users shared, including Geisinger who found that  Hive LLAP outperforms their traditional EDW  for most of their queries, and Comcast who found  Hive LLAP is faster than Presto for 75% of benchmark queries . These great results are thanks to performance and stability improvements Hortonworks made to Hive LLAP resulting in 3x faster interactive query in HDP 2.6. This blog dives into the reasons HDP 2.6 is so much faster. We’ll also take a look at the massive step forward Hive has made in SQL compliance with HDP 2.6, enabling Hive to run all 99 TPC-DS queries with only trivial modifications to the original source queries. STARTING OFF: 3X PERFORMANCE GAINS IN HDP 2.6 WITH HIVE ...
See more »

Introduction to HDFS Erasure Coding in Apache Hadoop

-
Image
Thanks to blog contributors from Cloudera Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50% compared to replication while maintaining the same durability guarantees. This post explains how it works. HDFS by default replicates each block three times. Replication provides a simple and robust form of redundancy to shield against most failure scenarios. It also eases scheduling compute tasks on locally stored data blocks by providing multiple replicas of each block to choose from. However, replication is expensive: the default 3x replication scheme incurs a 200% overhead in storage space and other resources (e.g., network bandwidth when writing the data). For datasets with relatively low I/O activity, the additional block replicas are rarely accessed during normal operations, but still consume the same amount of storage space. Therefore, a natural improvement is to use erasure coding (EC) in place of replication, which uses far less storage s...
See more »

Big Data Trendz

Loading...