Create a sample test.txt file:

echo -e "alice miller\t49\t3.15" > student.txt

Upload the new data file to HDFS:

su $HDFS_USER hadoop fs -mkdir -p /user/test/studenthadoop fs -copyFromLocal student.txt /user/test/student

Open the Hive command-line shell:

su $HDFS_USER hive

Create a table named student in Hive:

hive> CREATE EXTERNAL TABLE student(name string, age int, gpa double) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'STORED AS TEXTFILE LOCATION '/user/test/student';

Execute the following query in Hive:

hive> SELECT COUNT(*) FROM student;

If Hive-on-Tez is configured properly, this query should return successful results similar to the following:

hive> SELECT COUNT(*) FROM student;
Query ID = hdfs_20140604161313_544c4455-dfb3-4119-8b08-b70b46fee512
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
 set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
 set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
 set mapreduce.job.reduces=<number>
Starting Job = job_1401734196960_0007, Tracking URL = http://c6401.ambari.apache.org:8088/proxy/application_1401734196960_0007/
Kill Command = /usr/lib/hadoop/bin/hadoop job -kill job_1401734196960_0007
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2014-06-04 16:13:24,116 Stage-1 map = 0%, reduce = 0%
2014-06-04 16:13:30,670 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.82 sec
2014-06-04 16:13:39,065 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 1.97 sec
MapReduce Total cumulative CPU time: 1 seconds 970 msec
Ended Job = job_1401734196960_0007
MapReduce Jobs Launched: 
Job 0: Map: 1 Reduce: 1 Cumulative CPU: 1.97 sec HDFS Read: 240 HDFS Write: 2 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 970 msec
OK
1
Time taken: 28.47 seconds, Fetched: 1 row(s)
hive>