Introduction: Ansible is a configuration management and provisioning tool, similar to Chef, Puppet or Salt.Configuration management systems are designed for controlling large numbers of servers easy for administrators and operations teams. They allow you to control many different systems in an automated way from one central location.

There are many popular configuration management systems available for Linux systems, such as Chef and Puppet, these are often more complex than many people want or need. Ansible is written in Python and uses SSH to execute commands on different machines. Ansible uses YML to describe work.

Install And Configure Ansible on Ubuntu: Run the below command to install and configure ansible on Ubuntu.

sudo yum install ansible

We’ll assume you are using SSH keys for authentication. To set up SSH agent to avoid retyping passwords, you can run the below command.

ssh-keygen
ssh-agent bash
ssh-add ~/.ssh/id_rsa

Configuring Ansible Hosts: Ansible keeps track of all of the servers that it knows about through a “hosts” file. We need to set up this file first before we can begin to communicate with our other computers.Open the file with root privileges like this:

sudo vi /etc/ansible/hosts

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Reason For Writing This Blog is That  I tried to use Vectorized Reader In Hive But Faced some problem with its documentation,thats why decided to write this block

Introduction

Vectorized query execution is a Hive feature that greatly reduces the CPU usage for typical query operations like scans, filters, aggregates, and joins. A standard query execution system processes one row at a time. This involves long code paths and significant metadata interpretation in the inner loop of execution. Vectorized query execution streamlines operations by processing a block of 1024 rows at a time. Within the block, each column is stored as a vector (an array of a primitive data type). Simple operations like arithmetic and comparisons are done by quickly iterating through the vectors in a tight loop, with no or very few function calls or conditional branches inside the loop

Enabling vectorized execution

To use vectorized query execution, you must store your data in ORC format Plus

set hive.vectorized.execution.enabled = true ;

How To Query

To use vectorized query execution, you must store your data in ORC format,

just follow the below steps

• • start hive cli and create orc table with some data

hive> create table vectortable(id int) stored as orc;
                OK
Time taken: 0.487 seconds
hive>set hive.vectorized.execution.enabled = true;

hive> insert into vectortable values(1);

Query ID = hduser_20170713203731_09db3954-246b-4b23-8d34-1d9d7b62965c

Total jobs = 1 Launching Job 1 out of 1 Number of reduce tasks is set to 0 since there's no reduce operator Job running in-process (local Hadoop)

2017-07-13 20:37:33,237 Stage-1 map = 100%, reduce = 0% Ended Job = job_local722393542_0002

Stage-4 is selected by condition resolver.

Stage-3 is filtered out by condition resolver.

Stage-5 is filtered out by condition resolver. Moving data to: hdfs://localhost:54310/user/hive/warehouse/vectortable/.hive-staging_hive_2017-07-13_20-37-31_172_3262390557269287245-1/-ext-10000

Loading data to table default.vectortable Table default.vectortable stats: [numFiles=1, numRows=1, totalSize=199, rawDataSize=4]

MapReduce Jobs Launched: Stage-Stage-1: HDFS Read: 321 HDFS Write: 545 SUCCESS Total MapReduce CPU Time Spent: 0 msec OK Time taken: 2.672 seconds

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