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

• now query the table with explain command to see whether hive is using vectorized execution or not
• Note:When Fetch is used in the plan instead of Map, it do not vectorize. so first set hive.fetch.task.conversion=none         (Big One To Catch)

hive> explain select id from vectortable where id>=1;
OK
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1

STAGE PLANS:
Stage: Stage-1
Map Reduce
Map Operator Tree:
TableScan
alias: vectortable
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
Filter Operator
predicate: (id >= 1) (type: boolean)
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: id (type: int)
outputColumnNames: _col0
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.TextInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Execution mode: vectorized

Stage: Stage-0
Fetch Operator
limit: -1
Processor Tree:
ListSink

Time taken: 0.081 seconds, Fetched: 33 row(s)

as you can see in explain command Execution mode: vectorized is printed it is enable for the query