Ok This is my test
1) create table in Hive and populate it with two rows
hive> create table testme (col1 int, col2 string);
OK
hive> insert into testme values (1,'London');
Query ID = hduser_20160821212812_2a8384af-23f1-4f28-9395-a99a5f4c1a4a
OK
hive> insert into testme values (2,'NY');
Query ID = hduser_20160821212812_2a8384af-23f1-4f28-9395-a99a5f4c1a4a
OK
hive> select * from testme;
OK
1 London
2 NY
So the rows are there
Now use Spark to create two more rows
scala> case class columns (col1: Int, col2: String)
defined class columns
scala> val df =sc.parallelize(Array((3,"California"),(4,"Dehli"))).map(p =>
columns(p._1.toString.toInt, p._2.toString)).toDF()
df: org.apache.spark.sql.DataFrame = [col1: int, col2: string]
scala> df.show
+----+----------+
|col1| col2|
+----+----------+
| 3|California|
| 4| Dehli|
+----+----------+
// register it as tempTable
scala> df.registerTempTable("tmp")
scala> sql("insert into test.testme select * from tmp")
res9: org.apache.spark.sql.DataFrame = []
scala> sql("select * from testme").show
+----+----------+
|col1| col2|
+----+----------+
| 1| London|
| 2| NY|
| 3|California|
| 4| Dehli|
+----+----------+
So the rows are there.
Let me go to Hive again now
hive> select * from testme;
OK
1 London
2 NY
3 California
4 Dehli
hive> analyze table testme compute statistics for columns;
So is there any issue here?
Thanks
Dr Mich Talebzadeh
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On 22 August 2016 at 11:51, Nitin Kumar <nk94.nitinku...@gmail.com> wrote:
> Hi Furcy,
>
> If I execute the command "ANALYZE TABLE TEST_ORC COMPUTE STATISTICS"
> before checking the count from hive, Hive returns the correct count albeit
> it does not spawn a map-reduce job for computing the count.
>
> I'm running a HDP 2.4 Cluster with Hive 1.2.1.2.4 and Spark 1.6.1
>
> If others can concur we can go ahead and report it as a bug.
>
> Regards,
> Nitin
>
>
>
> On Mon, Aug 22, 2016 at 4:15 PM, Furcy Pin <furcy....@flaminem.com> wrote:
>
>> Hi Nitin,
>>
>> I confirm that there is something odd here.
>>
>> I did the following test :
>>
>> create table test_orc (id int, name string, dept string) stored as ORC;
>> insert into table test_orc values (1, 'abc', 'xyz');
>> insert into table test_orc values (2, 'def', 'xyz');
>> insert into table test_orc values (3, 'pqr', 'xyz');
>> insert into table test_orc values (4, 'ghi', 'xyz');
>>
>>
>> I ended up with 4 files on hdfs:
>>
>> 000000_0
>> 000000_0_copy_1
>> 000000_0_copy_2
>> 000000_0_copy_3
>>
>>
>> Then I renamed 000000_0_copy_2 to part-00000, and I still got COUNT(*) =
>> 4 with hive.
>> So this is not a file name issue.
>>
>> I then removed one of the files, and I got this :
>>
>> > SELECT COUNT(1) FROM test_orc ;
>> +------+--+
>> | _c0 |
>> +------+--+
>> | 4 |
>> +------+--+
>>
>> > SELECT * FROM test_orc ;
>> +--------------+----------------+----------------+--+
>> | test_orc.id | test_orc.name | test_orc.dept |
>> +--------------+----------------+----------------+--+
>> | 1 | abc | xyz |
>> | 2 | def | xyz |
>> | 4 | ghi | xyz |
>> +--------------+----------------+----------------+--+
>> 3 rows selected (0.162 seconds)
>>
>> So, my guess is that when Hive inserts data, it must keep somewhere in
>> the metastore the number of rows in the table.
>> However, if the files are modified by someone else than Hive itself,
>> (either manually or with Spark), you end up with an inconsistency.
>>
>> So I guess we can call it a bug:
>>
>> Hive should detect that the files changed and invalidate its
>> pre-calculated count.
>> Optionally, Spark should be nice with Hive and update the the count when
>> inserting.
>>
>> I don't know if this bug has already been reported, and I tested on Hive
>> 1.1.0, so perhaps it is already solved in later releases.
>>
>> Regards,
>>
>> Furcy
>>
>>
>> On Mon, Aug 22, 2016 at 9:34 AM, Nitin Kumar <nk94.nitinku...@gmail.com>
>> wrote:
>>
>>> Hi!
>>>
>>> I've noticed that hive has problems in registering new data records if
>>> the same table is written to using both the hive terminal and spark sql.
>>> The problem is demonstrated through the commands listed below
>>>
>>> ====================================================================
>>> hive> use default;
>>> hive> create table test_orc (id int, name string, dept string) stored as
>>> ORC;
>>> hive> insert into table test_orc values (1, 'abc', 'xyz');
>>> hive> insert into table test_orc values (2, 'def', 'xyz');
>>> hive> select count(*) from test_orc;
>>> OK
>>> 2
>>> hive> select distinct(name) from test_orc;
>>> OK
>>> abc
>>> def
>>>
>>> *** files in hdfs path in warehouse for the created table ***
>>>
>>>
>>>
>>>
>>> >>> data_points = [(3, 'pqr', 'xyz'), (4, 'ghi', 'xyz')]
>>> >>> column_names = ['identity_id', 'emp_name', 'dept_name']
>>> >>> data_df = sqlContext.createDataFrame(data_points, column_names)
>>> >>> data_df.show()
>>>
>>> +-----------+--------+---------+
>>> |identity_id|emp_name|dept_name|
>>> +-----------+--------+---------+
>>> | 3| pqr| xyz|
>>> | 4| ghi| xyz|
>>> +-----------+--------+---------+
>>>
>>> >>> data_df.registerTempTable('temp_table')
>>> >>> sqlContext.sql('insert into table default.test_orc select * from
>>> temp_table')
>>>
>>> *** files in hdfs path in warehouse for the created table ***
>>>
>>>
>>> hive> select count(*) from test_orc; (Does not launch map-reduce job)
>>> OK
>>> 2
>>> hive> select distinct(name) from test_orc; (Launches map-reduce job)
>>> abc
>>> def
>>> ghi
>>> pqr
>>>
>>> hive> create table test_orc_new like test_orc stored as ORC;
>>> hive> insert into table test_orc_new select * from test_orc;
>>> hive> select count(*) from test_orc_new;
>>> OK
>>> 4
>>> ==================================================================
>>>
>>> Even if I restart the hive services I cannot get the proper count output
>>> from hive. This problem only occurs if the table is written to using both
>>> hive and spark. If only spark is used to insert records into the table
>>> multiple times, the count query in the hive terminal works perfectly fine.
>>>
>>> This problem occurs for tables stored with different storage formats as
>>> well (textFile etc.)
>>>
>>> Is this because of the different naming conventions used by hive and
>>> spark to write records to hdfs? Or maybe it is not a recommended practice
>>> to write tables using different services?
>>>
>>> Your thoughts and comments on this matter would be highly appreciated!
>>>
>>> Thanks!
>>> Nitin
>>>
>>>
>>>
>>
>
