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From Subash Prabakar <subashpraba...@gmail.com>
Subject Re: Spark SQL reads all leaf directories on a partitioned Hive table
Date Tue, 13 Aug 2019 03:58:36 GMT
I had the similar issue reading the external parquet table . In my case I
had permission issue in one partition so I added filter to exclude that
partition but still the spark didn’t prune it. Then I read that in order
for spark to be aware of all the partitions it first read the folders and
then updated its metastore . Then the sql is applied on TOP of it. Instead
of using the existing hive SerDe and this property is only for parquet
files.

Hive metastore Parquet table conversion
<https://spark.apache.org/docs/2.3.0/sql-programming-guide.html#hive-metastore-parquet-table-conversion>

When reading from and writing to Hive metastore Parquet tables, Spark SQL
will try to use its own Parquet support instead of Hive SerDe for better
performance. This behavior is controlled by the
spark.sql.hive.convertMetastoreParquetconfiguration, and is turned on by
default.

Reference:
https://spark.apache.org/docs/2.3.0/sql-programming-guide.html

Set the above property to false . It should work.

If anyone have better explanation please let me know - I have same
question. Why only parquet has this problem ?

Thanks
Subash

On Fri, 9 Aug 2019 at 16:18, Hao Ren <invkrh@gmail.com> wrote:

> Hi Mich,
>
> Thank you for your reply.
> I need to be more clear about the environment. I am using spark-shell to
> run the query.
> Actually, the query works even without core-site, hdfs-site being under
> $SPARK_HOME/conf.
> My problem is efficiency. Because all of the partitions was scanned
> instead of the one in question during the execution of the spark sql query.
> This is why this simple query takes too much time.
> I would like to know how to improve this by just reading the specific
> partition in question.
>
> Feel free to ask more questions if I am not clear.
>
> Best regards,
> Hao
>
> On Thu, Aug 8, 2019 at 9:05 PM Mich Talebzadeh <mich.talebzadeh@gmail.com>
> wrote:
>
>> also need others as well using soft link ls -l
>>
>> cd $SPARK_HOME/conf
>>
>> hive-site.xml -> ${HIVE_HOME/conf/hive-site.xml
>> core-site.xml -> ${HADOOP_HOME}/etc/hadoop/core-site.xml
>> hdfs-site.xml -> ${HADOOP_HOME}/etc/hadoop/hdfs-site.xml
>>
>> Dr Mich Talebzadeh
>>
>>
>>
>> LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>*
>>
>>
>>
>> http://talebzadehmich.wordpress.com
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Thu, 8 Aug 2019 at 15:16, Hao Ren <invkrh@gmail.com> wrote:
>>
>>>
>>>
>>> ---------- Forwarded message ---------
>>> From: Hao Ren <invkrh@gmail.com>
>>> Date: Thu, Aug 8, 2019 at 4:15 PM
>>> Subject: Re: Spark SQL reads all leaf directories on a partitioned Hive
>>> table
>>> To: Gourav Sengupta <gourav.sengupta@gmail.com>
>>>
>>>
>>> Hi Gourva,
>>>
>>> I am using enableHiveSupport.
>>> The table was not created by Spark. The table already exists in Hive.
>>> All I did is just reading it by using SQL query in Spark.
>>> FYI, I put hive-site.xml in spark/conf/ directory to make sure that
>>> Spark can access to Hive.
>>>
>>> Hao
>>>
>>> On Thu, Aug 8, 2019 at 1:24 PM Gourav Sengupta <
>>> gourav.sengupta@gmail.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> Just out of curiosity did you start the SPARK session using
>>>> enableHiveSupport() ?
>>>>
>>>> Or are you creating the table using SPARK?
>>>>
>>>>
>>>> Regards,
>>>> Gourav
>>>>
>>>> On Wed, Aug 7, 2019 at 3:28 PM Hao Ren <invkrh@gmail.com> wrote:
>>>>
>>>>> Hi,
>>>>> I am using Spark SQL 2.3.3 to read a hive table which is partitioned
>>>>> by day, hour, platform, request_status and is_sampled. The underlying
data
>>>>> is in parquet format on HDFS.
>>>>> Here is the SQL query to read just *one partition*.
>>>>>
>>>>> ```
>>>>> spark.sql("""
>>>>> SELECT rtb_platform_id, SUM(e_cpm)
>>>>> FROM raw_logs.fact_request
>>>>> WHERE day = '2019-08-01'
>>>>> AND hour = '00'
>>>>> AND platform = 'US'
>>>>> AND request_status = '3'
>>>>> AND is_sampled = 1
>>>>> GROUP BY rtb_platform_id
>>>>> """).show
>>>>> ```
>>>>>
>>>>> However, from the Spark web UI, the stage description shows:
>>>>>
>>>>> ```
>>>>> Listing leaf files and directories for 201616 paths:
>>>>> viewfs://root/user/bilogs/logs/fact_request/day=2018-08-01/hour=11/platform=AS/request_status=0/is_sampled=0,
>>>>> ...
>>>>> ```
>>>>>
>>>>> It seems the job is reading all of the partitions of the table and the
>>>>> job takes too long for just one partition. One workaround is using
>>>>> `spark.read.parquet` API to read parquet files directly. Spark has
>>>>> partition-awareness for partitioned directories.
>>>>>
>>>>> But still, I would like to know if there is a way to leverage
>>>>> partition-awareness via Hive by using `spark.sql` API?
>>>>>
>>>>> Any help is highly appreciated!
>>>>>
>>>>> Thank you.
>>>>>
>>>>> --
>>>>> Hao Ren
>>>>>
>>>>
>>>
>>> --
>>> Hao Ren
>>>
>>> Software Engineer in Machine Learning @ Criteo
>>>
>>> Paris, France
>>>
>>>
>>> --
>>> Hao Ren
>>>
>>> Software Engineer in Machine Learning @ Criteo
>>>
>>> Paris, France
>>>
>>
>
> --
> Hao Ren
>
> Software Engineer in Machine Learning @ Criteo
>
> Paris, France
>

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