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,

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


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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.


On Thu, Aug 8, 2019 at 1:24 PM Gourav Sengupta <gourav.sengupta@gmail.com> wrote:

Just out of curiosity did you start the SPARK session using enableHiveSupport() ?

Or are you creating the table using SPARK?


On Wed, Aug 7, 2019 at 3:28 PM Hao Ren <invkrh@gmail.com> wrote:
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.

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

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