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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Spark SQL reads all leaf directories on a partitioned Hive table
Date Thu, 08 Aug 2019 19:05:33 GMT
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



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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
>

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