Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

Yes, it sounds like it. So the broadcast DF size seems to be between 1 and
4GB. So I suggest that you leave it as it is.

I have not used the standalone mode since spark-2.4.3 so I may be missing a
fair bit of context here.  I am sure there are others like you that are
still using it!

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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, 17 Aug 2023 at 23:33, Patrick Tucci  wrote:

> No, the driver memory was not set explicitly. So it was likely the default
> value, which appears to be 1GB.
>
> On Thu, Aug 17, 2023, 16:49 Mich Talebzadeh 
> wrote:
>
>> One question, what was the driver memory before setting it to 4G? Did you
>> have it set at all before?
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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, 17 Aug 2023 at 21:01, Patrick Tucci 
>> wrote:
>>
>>> Hi Mich,
>>>
>>> Here are my config values from spark-defaults.conf:
>>>
>>> spark.eventLog.enabled true
>>> spark.eventLog.dir hdfs://10.0.50.1:8020/spark-logs
>>> spark.history.provider org.apache.spark.deploy.history.FsHistoryProvider
>>> spark.history.fs.logDirectory hdfs://10.0.50.1:8020/spark-logs
>>> spark.history.fs.update.interval 10s
>>> spark.history.ui.port 18080
>>> spark.sql.warehouse.dir hdfs://10.0.50.1:8020/user/spark/warehouse
>>> spark.executor.cores 4
>>> spark.executor.memory 16000M
>>> spark.sql.legacy.createHiveTableByDefault false
>>> spark.driver.host 10.0.50.1
>>> spark.scheduler.mode FAIR
>>> spark.driver.memory 4g #added 2023-08-17
>>>
>>> The only application that runs on the cluster is the Spark Thrift
>>> server, which I launch like so:
>>>
>>> ~/spark/sbin/start-thriftserver.sh --master spark://10.0.50.1:7077
>>>
>>> The cluster runs in standalone mode and does not use Yarn for resource
>>> management. As a result, the Spark Thrift server acquires all available
>>> cluster resources when it starts. This is okay; as of right now, I am the
>>> only user of the cluster. If I add more users, they will also be SQL users,
>>> submitting queries through the Thrift server.
>>>
>>> Let me know if you have any other questions or thoughts.
>>>
>>> Thanks,
>>>
>>> Patrick
>>>
>>> On Thu, Aug 17, 2023 at 3:09 PM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 Hello Paatrick,

 As a matter of interest what parameters and their respective values do
 you use in spark-submit. I assume it is running in YARN mode.

 HTH

 Mich Talebzadeh,
 Solutions Architect/Engineering Lead
 London
 United Kingdom


view my Linkedin profile
 


  https://en.everybodywiki.com/Mich_Talebzadeh



 *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, 17 Aug 2023 at 19:36, Patrick Tucci 
 wrote:

> Hi Mich,
>
> Yes, that's the sequence of events. I think the big breakthrough is
> that (for now at least) Spark is throwing errors instead of the queries
> hanging. Which is a big step forward. I can at least troubleshoot issues 
> if
> I know what they are.
>
> When I reflect on the issues I faced and the solutions, my issue may
> have been driver memory all along. I just couldn't determine that was the
> issue because I never saw any errors. In one case, converting a LEFT JOIN
> to an inner JOIN caused the query to run. In another case, replacing a 
> text
> field with an int ID and JOINing on the ID column worked. Per your advice,
> changing file formats from ORC to Parquet solved one issue. These
> interventions could have changed the 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

No, the driver memory was not set explicitly. So it was likely the default
value, which appears to be 1GB.

On Thu, Aug 17, 2023, 16:49 Mich Talebzadeh 
wrote:

> One question, what was the driver memory before setting it to 4G? Did you
> have it set at all before?
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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, 17 Aug 2023 at 21:01, Patrick Tucci 
> wrote:
>
>> Hi Mich,
>>
>> Here are my config values from spark-defaults.conf:
>>
>> spark.eventLog.enabled true
>> spark.eventLog.dir hdfs://10.0.50.1:8020/spark-logs
>> spark.history.provider org.apache.spark.deploy.history.FsHistoryProvider
>> spark.history.fs.logDirectory hdfs://10.0.50.1:8020/spark-logs
>> spark.history.fs.update.interval 10s
>> spark.history.ui.port 18080
>> spark.sql.warehouse.dir hdfs://10.0.50.1:8020/user/spark/warehouse
>> spark.executor.cores 4
>> spark.executor.memory 16000M
>> spark.sql.legacy.createHiveTableByDefault false
>> spark.driver.host 10.0.50.1
>> spark.scheduler.mode FAIR
>> spark.driver.memory 4g #added 2023-08-17
>>
>> The only application that runs on the cluster is the Spark Thrift server,
>> which I launch like so:
>>
>> ~/spark/sbin/start-thriftserver.sh --master spark://10.0.50.1:7077
>>
>> The cluster runs in standalone mode and does not use Yarn for resource
>> management. As a result, the Spark Thrift server acquires all available
>> cluster resources when it starts. This is okay; as of right now, I am the
>> only user of the cluster. If I add more users, they will also be SQL users,
>> submitting queries through the Thrift server.
>>
>> Let me know if you have any other questions or thoughts.
>>
>> Thanks,
>>
>> Patrick
>>
>> On Thu, Aug 17, 2023 at 3:09 PM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> Hello Paatrick,
>>>
>>> As a matter of interest what parameters and their respective values do
>>> you use in spark-submit. I assume it is running in YARN mode.
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *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, 17 Aug 2023 at 19:36, Patrick Tucci 
>>> wrote:
>>>
 Hi Mich,

 Yes, that's the sequence of events. I think the big breakthrough is
 that (for now at least) Spark is throwing errors instead of the queries
 hanging. Which is a big step forward. I can at least troubleshoot issues if
 I know what they are.

 When I reflect on the issues I faced and the solutions, my issue may
 have been driver memory all along. I just couldn't determine that was the
 issue because I never saw any errors. In one case, converting a LEFT JOIN
 to an inner JOIN caused the query to run. In another case, replacing a text
 field with an int ID and JOINing on the ID column worked. Per your advice,
 changing file formats from ORC to Parquet solved one issue. These
 interventions could have changed the way Spark needed to broadcast data to
 execute the query, thereby reducing demand on the memory-constrained 
 driver.

 Fingers crossed this is the solution. I will reply to this thread if
 the issue comes up again (hopefully it doesn't!).

 Thanks again,

 Patrick

 On Thu, Aug 17, 2023 at 1:54 PM Mich Talebzadeh <
 mich.talebza...@gmail.com> wrote:

> Hi Patrik,
>
> glad that you have managed to sort this problem out. Hopefully it will
> go away for good.
>
> Still we are in the dark about how this problem is going away and
> coming back :( As I recall the chronology of events were as follows:
>
>
>1. The Issue with hanging Spark job reported
>2. concurrency on Hive metastore (single threaded Derby DB) was
>identified as a possible cause
>3. You changed the underlying Hive table formats from ORC to
>Parquet and somehow it worked
>4. The issue was reported again
>5. You upgraded 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

One question, what was the driver memory before setting it to 4G? Did you
have it set at all before?

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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, 17 Aug 2023 at 21:01, Patrick Tucci  wrote:

> Hi Mich,
>
> Here are my config values from spark-defaults.conf:
>
> spark.eventLog.enabled true
> spark.eventLog.dir hdfs://10.0.50.1:8020/spark-logs
> spark.history.provider org.apache.spark.deploy.history.FsHistoryProvider
> spark.history.fs.logDirectory hdfs://10.0.50.1:8020/spark-logs
> spark.history.fs.update.interval 10s
> spark.history.ui.port 18080
> spark.sql.warehouse.dir hdfs://10.0.50.1:8020/user/spark/warehouse
> spark.executor.cores 4
> spark.executor.memory 16000M
> spark.sql.legacy.createHiveTableByDefault false
> spark.driver.host 10.0.50.1
> spark.scheduler.mode FAIR
> spark.driver.memory 4g #added 2023-08-17
>
> The only application that runs on the cluster is the Spark Thrift server,
> which I launch like so:
>
> ~/spark/sbin/start-thriftserver.sh --master spark://10.0.50.1:7077
>
> The cluster runs in standalone mode and does not use Yarn for resource
> management. As a result, the Spark Thrift server acquires all available
> cluster resources when it starts. This is okay; as of right now, I am the
> only user of the cluster. If I add more users, they will also be SQL users,
> submitting queries through the Thrift server.
>
> Let me know if you have any other questions or thoughts.
>
> Thanks,
>
> Patrick
>
> On Thu, Aug 17, 2023 at 3:09 PM Mich Talebzadeh 
> wrote:
>
>> Hello Paatrick,
>>
>> As a matter of interest what parameters and their respective values do
>> you use in spark-submit. I assume it is running in YARN mode.
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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, 17 Aug 2023 at 19:36, Patrick Tucci 
>> wrote:
>>
>>> Hi Mich,
>>>
>>> Yes, that's the sequence of events. I think the big breakthrough is that
>>> (for now at least) Spark is throwing errors instead of the queries hanging.
>>> Which is a big step forward. I can at least troubleshoot issues if I know
>>> what they are.
>>>
>>> When I reflect on the issues I faced and the solutions, my issue may
>>> have been driver memory all along. I just couldn't determine that was the
>>> issue because I never saw any errors. In one case, converting a LEFT JOIN
>>> to an inner JOIN caused the query to run. In another case, replacing a text
>>> field with an int ID and JOINing on the ID column worked. Per your advice,
>>> changing file formats from ORC to Parquet solved one issue. These
>>> interventions could have changed the way Spark needed to broadcast data to
>>> execute the query, thereby reducing demand on the memory-constrained driver.
>>>
>>> Fingers crossed this is the solution. I will reply to this thread if the
>>> issue comes up again (hopefully it doesn't!).
>>>
>>> Thanks again,
>>>
>>> Patrick
>>>
>>> On Thu, Aug 17, 2023 at 1:54 PM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 Hi Patrik,

 glad that you have managed to sort this problem out. Hopefully it will
 go away for good.

 Still we are in the dark about how this problem is going away and
 coming back :( As I recall the chronology of events were as follows:


1. The Issue with hanging Spark job reported
2. concurrency on Hive metastore (single threaded Derby DB) was
identified as a possible cause
3. You changed the underlying Hive table formats from ORC to
Parquet and somehow it worked
4. The issue was reported again
5. You upgraded the spark version from 3.4.0 to 3.4.1 (as a
possible underlying issue) and encountered driver memory limitation.
6. you allocated more memory to the driver and it is running ok for
now,
7. It appears that you are doing some join between a large dataset
and a smaller dataset. Spark decides to do broadcast join by 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Hi Mich,

Here are my config values from spark-defaults.conf:

spark.eventLog.enabled true
spark.eventLog.dir hdfs://10.0.50.1:8020/spark-logs
spark.history.provider org.apache.spark.deploy.history.FsHistoryProvider
spark.history.fs.logDirectory hdfs://10.0.50.1:8020/spark-logs
spark.history.fs.update.interval 10s
spark.history.ui.port 18080
spark.sql.warehouse.dir hdfs://10.0.50.1:8020/user/spark/warehouse
spark.executor.cores 4
spark.executor.memory 16000M
spark.sql.legacy.createHiveTableByDefault false
spark.driver.host 10.0.50.1
spark.scheduler.mode FAIR
spark.driver.memory 4g #added 2023-08-17

The only application that runs on the cluster is the Spark Thrift server,
which I launch like so:

~/spark/sbin/start-thriftserver.sh --master spark://10.0.50.1:7077

The cluster runs in standalone mode and does not use Yarn for resource
management. As a result, the Spark Thrift server acquires all available
cluster resources when it starts. This is okay; as of right now, I am the
only user of the cluster. If I add more users, they will also be SQL users,
submitting queries through the Thrift server.

Let me know if you have any other questions or thoughts.

Thanks,

Patrick

On Thu, Aug 17, 2023 at 3:09 PM Mich Talebzadeh 
wrote:

> Hello Paatrick,
>
> As a matter of interest what parameters and their respective values do
> you use in spark-submit. I assume it is running in YARN mode.
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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, 17 Aug 2023 at 19:36, Patrick Tucci 
> wrote:
>
>> Hi Mich,
>>
>> Yes, that's the sequence of events. I think the big breakthrough is that
>> (for now at least) Spark is throwing errors instead of the queries hanging.
>> Which is a big step forward. I can at least troubleshoot issues if I know
>> what they are.
>>
>> When I reflect on the issues I faced and the solutions, my issue may have
>> been driver memory all along. I just couldn't determine that was the issue
>> because I never saw any errors. In one case, converting a LEFT JOIN to an
>> inner JOIN caused the query to run. In another case, replacing a text field
>> with an int ID and JOINing on the ID column worked. Per your advice,
>> changing file formats from ORC to Parquet solved one issue. These
>> interventions could have changed the way Spark needed to broadcast data to
>> execute the query, thereby reducing demand on the memory-constrained driver.
>>
>> Fingers crossed this is the solution. I will reply to this thread if the
>> issue comes up again (hopefully it doesn't!).
>>
>> Thanks again,
>>
>> Patrick
>>
>> On Thu, Aug 17, 2023 at 1:54 PM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> Hi Patrik,
>>>
>>> glad that you have managed to sort this problem out. Hopefully it will
>>> go away for good.
>>>
>>> Still we are in the dark about how this problem is going away and coming
>>> back :( As I recall the chronology of events were as follows:
>>>
>>>
>>>1. The Issue with hanging Spark job reported
>>>2. concurrency on Hive metastore (single threaded Derby DB) was
>>>identified as a possible cause
>>>3. You changed the underlying Hive table formats from ORC to Parquet
>>>and somehow it worked
>>>4. The issue was reported again
>>>5. You upgraded the spark version from 3.4.0 to 3.4.1 (as a possible
>>>underlying issue) and encountered driver memory limitation.
>>>6. you allocated more memory to the driver and it is running ok for
>>>now,
>>>7. It appears that you are doing some join between a large dataset
>>>and a smaller dataset. Spark decides to do broadcast join by taking the
>>>smaller dataset, fit it into the driver memory and broadcasting it to all
>>>executors.  That is where you had this issue with the memory limit on the
>>>driver. In the absence of Broadcast join, spark needs to perform a 
>>> shuffle
>>>which is an expensive process.
>>>   1. you can increase the broadcast join memory setting the conf.
>>>   parameter "spark.sql.autoBroadcastJoinThreshold" in bytes (check the 
>>> manual)
>>>   2. You can also disable the broadcast join by setting
>>>   "spark.sql.autoBroadcastJoinThreshold", -1 to see what is happening.
>>>
>>>
>>> So you still need to find a resolution to this issue. Maybe 3.4.1 has
>>> managed to fix some underlying issues.
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

Hello Paatrick,

As a matter of interest what parameters and their respective values do
you use in spark-submit. I assume it is running in YARN mode.

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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, 17 Aug 2023 at 19:36, Patrick Tucci  wrote:

> Hi Mich,
>
> Yes, that's the sequence of events. I think the big breakthrough is that
> (for now at least) Spark is throwing errors instead of the queries hanging.
> Which is a big step forward. I can at least troubleshoot issues if I know
> what they are.
>
> When I reflect on the issues I faced and the solutions, my issue may have
> been driver memory all along. I just couldn't determine that was the issue
> because I never saw any errors. In one case, converting a LEFT JOIN to an
> inner JOIN caused the query to run. In another case, replacing a text field
> with an int ID and JOINing on the ID column worked. Per your advice,
> changing file formats from ORC to Parquet solved one issue. These
> interventions could have changed the way Spark needed to broadcast data to
> execute the query, thereby reducing demand on the memory-constrained driver.
>
> Fingers crossed this is the solution. I will reply to this thread if the
> issue comes up again (hopefully it doesn't!).
>
> Thanks again,
>
> Patrick
>
> On Thu, Aug 17, 2023 at 1:54 PM Mich Talebzadeh 
> wrote:
>
>> Hi Patrik,
>>
>> glad that you have managed to sort this problem out. Hopefully it will go
>> away for good.
>>
>> Still we are in the dark about how this problem is going away and coming
>> back :( As I recall the chronology of events were as follows:
>>
>>
>>1. The Issue with hanging Spark job reported
>>2. concurrency on Hive metastore (single threaded Derby DB) was
>>identified as a possible cause
>>3. You changed the underlying Hive table formats from ORC to Parquet
>>and somehow it worked
>>4. The issue was reported again
>>5. You upgraded the spark version from 3.4.0 to 3.4.1 (as a possible
>>underlying issue) and encountered driver memory limitation.
>>6. you allocated more memory to the driver and it is running ok for
>>now,
>>7. It appears that you are doing some join between a large dataset
>>and a smaller dataset. Spark decides to do broadcast join by taking the
>>smaller dataset, fit it into the driver memory and broadcasting it to all
>>executors.  That is where you had this issue with the memory limit on the
>>driver. In the absence of Broadcast join, spark needs to perform a shuffle
>>which is an expensive process.
>>   1. you can increase the broadcast join memory setting the conf.
>>   parameter "spark.sql.autoBroadcastJoinThreshold" in bytes (check the 
>> manual)
>>   2. You can also disable the broadcast join by setting
>>   "spark.sql.autoBroadcastJoinThreshold", -1 to see what is happening.
>>
>>
>> So you still need to find a resolution to this issue. Maybe 3.4.1 has
>> managed to fix some underlying issues.
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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, 17 Aug 2023 at 17:17, Patrick Tucci 
>> wrote:
>>
>>> Hi Everyone,
>>>
>>> I just wanted to follow up on this issue. This issue has continued since
>>> our last correspondence. Today I had a query hang and couldn't resolve the
>>> issue. I decided to upgrade my Spark install from 3.4.0 to 3.4.1. After
>>> doing so, instead of the query hanging, I got an error message that the
>>> driver didn't have enough memory to broadcast objects. After increasing the
>>> driver memory, the query runs without issue.
>>>
>>> I hope this can be helpful to someone else in the future. Thanks again
>>> for the support,
>>>
>>> Patrick
>>>
>>> On Sun, Aug 13, 2023 at 7:52 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 OK I use Hive 3.1.1

 My suggestion is to put your hive issues to u...@hive.apache.org and
 for JAVA version compatibility

 They will 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Hi Mich,

Yes, that's the sequence of events. I think the big breakthrough is that
(for now at least) Spark is throwing errors instead of the queries hanging.
Which is a big step forward. I can at least troubleshoot issues if I know
what they are.

When I reflect on the issues I faced and the solutions, my issue may have
been driver memory all along. I just couldn't determine that was the issue
because I never saw any errors. In one case, converting a LEFT JOIN to an
inner JOIN caused the query to run. In another case, replacing a text field
with an int ID and JOINing on the ID column worked. Per your advice,
changing file formats from ORC to Parquet solved one issue. These
interventions could have changed the way Spark needed to broadcast data to
execute the query, thereby reducing demand on the memory-constrained driver.

Fingers crossed this is the solution. I will reply to this thread if the
issue comes up again (hopefully it doesn't!).

Thanks again,

Patrick

On Thu, Aug 17, 2023 at 1:54 PM Mich Talebzadeh 
wrote:

> Hi Patrik,
>
> glad that you have managed to sort this problem out. Hopefully it will go
> away for good.
>
> Still we are in the dark about how this problem is going away and coming
> back :( As I recall the chronology of events were as follows:
>
>
>1. The Issue with hanging Spark job reported
>2. concurrency on Hive metastore (single threaded Derby DB) was
>identified as a possible cause
>3. You changed the underlying Hive table formats from ORC to Parquet
>and somehow it worked
>4. The issue was reported again
>5. You upgraded the spark version from 3.4.0 to 3.4.1 (as a possible
>underlying issue) and encountered driver memory limitation.
>6. you allocated more memory to the driver and it is running ok for
>now,
>7. It appears that you are doing some join between a large dataset and
>a smaller dataset. Spark decides to do broadcast join by taking the smaller
>dataset, fit it into the driver memory and broadcasting it to all
>executors.  That is where you had this issue with the memory limit on the
>driver. In the absence of Broadcast join, spark needs to perform a shuffle
>which is an expensive process.
>   1. you can increase the broadcast join memory setting the conf.
>   parameter "spark.sql.autoBroadcastJoinThreshold" in bytes (check the 
> manual)
>   2. You can also disable the broadcast join by setting
>   "spark.sql.autoBroadcastJoinThreshold", -1 to see what is happening.
>
>
> So you still need to find a resolution to this issue. Maybe 3.4.1 has
> managed to fix some underlying issues.
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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, 17 Aug 2023 at 17:17, Patrick Tucci 
> wrote:
>
>> Hi Everyone,
>>
>> I just wanted to follow up on this issue. This issue has continued since
>> our last correspondence. Today I had a query hang and couldn't resolve the
>> issue. I decided to upgrade my Spark install from 3.4.0 to 3.4.1. After
>> doing so, instead of the query hanging, I got an error message that the
>> driver didn't have enough memory to broadcast objects. After increasing the
>> driver memory, the query runs without issue.
>>
>> I hope this can be helpful to someone else in the future. Thanks again
>> for the support,
>>
>> Patrick
>>
>> On Sun, Aug 13, 2023 at 7:52 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> OK I use Hive 3.1.1
>>>
>>> My suggestion is to put your hive issues to u...@hive.apache.org and
>>> for JAVA version compatibility
>>>
>>> They will give you better info.
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *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 Sun, 13 Aug 2023 at 11:48, Patrick Tucci 
>>> wrote:
>>>
 I attempted to install Hive yesterday. The experience was similar to
 other attempts at installing Hive: it took a few hours and at the end of
 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

Hi Patrik,

glad that you have managed to sort this problem out. Hopefully it will go
away for good.

Still we are in the dark about how this problem is going away and coming
back :( As I recall the chronology of events were as follows:


   1. The Issue with hanging Spark job reported
   2. concurrency on Hive metastore (single threaded Derby DB) was
   identified as a possible cause
   3. You changed the underlying Hive table formats from ORC to Parquet and
   somehow it worked
   4. The issue was reported again
   5. You upgraded the spark version from 3.4.0 to 3.4.1 (as a possible
   underlying issue) and encountered driver memory limitation.
   6. you allocated more memory to the driver and it is running ok for now,
   7. It appears that you are doing some join between a large dataset and a
   smaller dataset. Spark decides to do broadcast join by taking the smaller
   dataset, fit it into the driver memory and broadcasting it to all
   executors.  That is where you had this issue with the memory limit on the
   driver. In the absence of Broadcast join, spark needs to perform a shuffle
   which is an expensive process.
  1. you can increase the broadcast join memory setting the conf.
  parameter "spark.sql.autoBroadcastJoinThreshold" in bytes (check
the manual)
  2. You can also disable the broadcast join by setting
  "spark.sql.autoBroadcastJoinThreshold", -1 to see what is happening.


So you still need to find a resolution to this issue. Maybe 3.4.1 has
managed to fix some underlying issues.

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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, 17 Aug 2023 at 17:17, Patrick Tucci  wrote:

> Hi Everyone,
>
> I just wanted to follow up on this issue. This issue has continued since
> our last correspondence. Today I had a query hang and couldn't resolve the
> issue. I decided to upgrade my Spark install from 3.4.0 to 3.4.1. After
> doing so, instead of the query hanging, I got an error message that the
> driver didn't have enough memory to broadcast objects. After increasing the
> driver memory, the query runs without issue.
>
> I hope this can be helpful to someone else in the future. Thanks again for
> the support,
>
> Patrick
>
> On Sun, Aug 13, 2023 at 7:52 AM Mich Talebzadeh 
> wrote:
>
>> OK I use Hive 3.1.1
>>
>> My suggestion is to put your hive issues to u...@hive.apache.org and for
>> JAVA version compatibility
>>
>> They will give you better info.
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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 Sun, 13 Aug 2023 at 11:48, Patrick Tucci 
>> wrote:
>>
>>> I attempted to install Hive yesterday. The experience was similar to
>>> other attempts at installing Hive: it took a few hours and at the end of
>>> the process, I didn't have a working setup. The latest stable release would
>>> not run. I never discovered the cause, but similar StackOverflow questions
>>> suggest it might be a Java incompatibility issue. Since I didn't want to
>>> downgrade or install an additional Java version, I attempted to use the
>>> latest alpha as well. This appears to have worked, although I couldn't
>>> figure out how to get it to use the metastore_db from Spark.
>>>
>>> After turning my attention back to Spark, I determined the issue. After
>>> much troubleshooting, I discovered that if I performed a COUNT(*) using
>>> the same JOINs, the problem query worked. I removed all the columns from
>>> the SELECT statement and added them one by one until I found the culprit.
>>> It's a text field on one of the tables. When the query SELECTs this column,
>>> or attempts to filter on it, the query hangs and never completes. If I
>>> remove all explicit references to this column, the query works fine. Since
>>> I need this column in the results, I went back to the ETL and extracted the
>>> values to a dimension table. I replaced the text column in the source table
>>> with an integer ID column and the query worked without issue.
>>>
>>> On 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Hi Everyone,

I just wanted to follow up on this issue. This issue has continued since
our last correspondence. Today I had a query hang and couldn't resolve the
issue. I decided to upgrade my Spark install from 3.4.0 to 3.4.1. After
doing so, instead of the query hanging, I got an error message that the
driver didn't have enough memory to broadcast objects. After increasing the
driver memory, the query runs without issue.

I hope this can be helpful to someone else in the future. Thanks again for
the support,

Patrick

On Sun, Aug 13, 2023 at 7:52 AM Mich Talebzadeh 
wrote:

> OK I use Hive 3.1.1
>
> My suggestion is to put your hive issues to u...@hive.apache.org and for
> JAVA version compatibility
>
> They will give you better info.
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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 Sun, 13 Aug 2023 at 11:48, Patrick Tucci 
> wrote:
>
>> I attempted to install Hive yesterday. The experience was similar to
>> other attempts at installing Hive: it took a few hours and at the end of
>> the process, I didn't have a working setup. The latest stable release would
>> not run. I never discovered the cause, but similar StackOverflow questions
>> suggest it might be a Java incompatibility issue. Since I didn't want to
>> downgrade or install an additional Java version, I attempted to use the
>> latest alpha as well. This appears to have worked, although I couldn't
>> figure out how to get it to use the metastore_db from Spark.
>>
>> After turning my attention back to Spark, I determined the issue. After
>> much troubleshooting, I discovered that if I performed a COUNT(*) using
>> the same JOINs, the problem query worked. I removed all the columns from
>> the SELECT statement and added them one by one until I found the culprit.
>> It's a text field on one of the tables. When the query SELECTs this column,
>> or attempts to filter on it, the query hangs and never completes. If I
>> remove all explicit references to this column, the query works fine. Since
>> I need this column in the results, I went back to the ETL and extracted the
>> values to a dimension table. I replaced the text column in the source table
>> with an integer ID column and the query worked without issue.
>>
>> On the topic of Hive, does anyone have any detailed resources for how to
>> set up Hive from scratch? Aside from the official site, since those
>> instructions didn't work for me. I'm starting to feel uneasy about building
>> my process around Spark. There really shouldn't be any instances where I
>> ask Spark to run legal ANSI SQL code and it just does nothing. In the past
>> 4 days I've run into 2 of these instances, and the solution was more voodoo
>> and magic than examining errors/logs and fixing code. I feel that I should
>> have a contingency plan in place for when I run into an issue with Spark
>> that can't be resolved.
>>
>> Thanks everyone.
>>
>>
>> On Sat, Aug 12, 2023 at 2:18 PM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> OK you would not have known unless you went through the process so to
>>> speak.
>>>
>>> Let us do something revolutionary here 
>>>
>>> Install hive and its metastore. You already have hadoop anyway
>>>
>>> https://cwiki.apache.org/confluence/display/hive/adminmanual+installation
>>>
>>> hive metastore
>>>
>>>
>>> https://data-flair.training/blogs/apache-hive-metastore/#:~:text=What%20is%20Hive%20Metastore%3F,by%20using%20metastore%20service%20API
>>> .
>>>
>>> choose one of these
>>>
>>> derby  hive  mssql  mysql  oracle  postgres
>>>
>>> Mine is an oracle. postgres is good as well.
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *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 Sat, 12 Aug 2023 at 18:31, Patrick Tucci 
>>> wrote:
>>>
 Yes, on premise.

 Unfortunately after installing Delta Lake and re-writing all tables as
 Delta tables, the issue persists.

 On Sat, Aug 12, 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

OK I use Hive 3.1.1

My suggestion is to put your hive issues to u...@hive.apache.org and for
JAVA version compatibility

They will give you better info.

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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 Sun, 13 Aug 2023 at 11:48, Patrick Tucci  wrote:

> I attempted to install Hive yesterday. The experience was similar to other
> attempts at installing Hive: it took a few hours and at the end of the
> process, I didn't have a working setup. The latest stable release would not
> run. I never discovered the cause, but similar StackOverflow questions
> suggest it might be a Java incompatibility issue. Since I didn't want to
> downgrade or install an additional Java version, I attempted to use the
> latest alpha as well. This appears to have worked, although I couldn't
> figure out how to get it to use the metastore_db from Spark.
>
> After turning my attention back to Spark, I determined the issue. After
> much troubleshooting, I discovered that if I performed a COUNT(*) using
> the same JOINs, the problem query worked. I removed all the columns from
> the SELECT statement and added them one by one until I found the culprit.
> It's a text field on one of the tables. When the query SELECTs this column,
> or attempts to filter on it, the query hangs and never completes. If I
> remove all explicit references to this column, the query works fine. Since
> I need this column in the results, I went back to the ETL and extracted the
> values to a dimension table. I replaced the text column in the source table
> with an integer ID column and the query worked without issue.
>
> On the topic of Hive, does anyone have any detailed resources for how to
> set up Hive from scratch? Aside from the official site, since those
> instructions didn't work for me. I'm starting to feel uneasy about building
> my process around Spark. There really shouldn't be any instances where I
> ask Spark to run legal ANSI SQL code and it just does nothing. In the past
> 4 days I've run into 2 of these instances, and the solution was more voodoo
> and magic than examining errors/logs and fixing code. I feel that I should
> have a contingency plan in place for when I run into an issue with Spark
> that can't be resolved.
>
> Thanks everyone.
>
>
> On Sat, Aug 12, 2023 at 2:18 PM Mich Talebzadeh 
> wrote:
>
>> OK you would not have known unless you went through the process so to
>> speak.
>>
>> Let us do something revolutionary here 
>>
>> Install hive and its metastore. You already have hadoop anyway
>>
>> https://cwiki.apache.org/confluence/display/hive/adminmanual+installation
>>
>> hive metastore
>>
>>
>> https://data-flair.training/blogs/apache-hive-metastore/#:~:text=What%20is%20Hive%20Metastore%3F,by%20using%20metastore%20service%20API
>> .
>>
>> choose one of these
>>
>> derby  hive  mssql  mysql  oracle  postgres
>>
>> Mine is an oracle. postgres is good as well.
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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 Sat, 12 Aug 2023 at 18:31, Patrick Tucci 
>> wrote:
>>
>>> Yes, on premise.
>>>
>>> Unfortunately after installing Delta Lake and re-writing all tables as
>>> Delta tables, the issue persists.
>>>
>>> On Sat, Aug 12, 2023 at 11:34 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 ok sure.

 Is this Delta Lake going to be on-premise?

 Mich Talebzadeh,
 Solutions Architect/Engineering Lead
 London
 United Kingdom


view my Linkedin profile
 


  https://en.everybodywiki.com/Mich_Talebzadeh



 *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, 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

I attempted to install Hive yesterday. The experience was similar to other
attempts at installing Hive: it took a few hours and at the end of the
process, I didn't have a working setup. The latest stable release would not
run. I never discovered the cause, but similar StackOverflow questions
suggest it might be a Java incompatibility issue. Since I didn't want to
downgrade or install an additional Java version, I attempted to use the
latest alpha as well. This appears to have worked, although I couldn't
figure out how to get it to use the metastore_db from Spark.

After turning my attention back to Spark, I determined the issue. After
much troubleshooting, I discovered that if I performed a COUNT(*) using the
same JOINs, the problem query worked. I removed all the columns from the
SELECT statement and added them one by one until I found the culprit. It's
a text field on one of the tables. When the query SELECTs this column, or
attempts to filter on it, the query hangs and never completes. If I remove
all explicit references to this column, the query works fine. Since I need
this column in the results, I went back to the ETL and extracted the values
to a dimension table. I replaced the text column in the source table with
an integer ID column and the query worked without issue.

On the topic of Hive, does anyone have any detailed resources for how to
set up Hive from scratch? Aside from the official site, since those
instructions didn't work for me. I'm starting to feel uneasy about building
my process around Spark. There really shouldn't be any instances where I
ask Spark to run legal ANSI SQL code and it just does nothing. In the past
4 days I've run into 2 of these instances, and the solution was more voodoo
and magic than examining errors/logs and fixing code. I feel that I should
have a contingency plan in place for when I run into an issue with Spark
that can't be resolved.

Thanks everyone.


On Sat, Aug 12, 2023 at 2:18 PM Mich Talebzadeh 
wrote:

> OK you would not have known unless you went through the process so to
> speak.
>
> Let us do something revolutionary here 
>
> Install hive and its metastore. You already have hadoop anyway
>
> https://cwiki.apache.org/confluence/display/hive/adminmanual+installation
>
> hive metastore
>
>
> https://data-flair.training/blogs/apache-hive-metastore/#:~:text=What%20is%20Hive%20Metastore%3F,by%20using%20metastore%20service%20API
> .
>
> choose one of these
>
> derby  hive  mssql  mysql  oracle  postgres
>
> Mine is an oracle. postgres is good as well.
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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 Sat, 12 Aug 2023 at 18:31, Patrick Tucci 
> wrote:
>
>> Yes, on premise.
>>
>> Unfortunately after installing Delta Lake and re-writing all tables as
>> Delta tables, the issue persists.
>>
>> On Sat, Aug 12, 2023 at 11:34 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> ok sure.
>>>
>>> Is this Delta Lake going to be on-premise?
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *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 Sat, 12 Aug 2023 at 12:03, Patrick Tucci 
>>> wrote:
>>>
 Hi Mich,

 Thanks for the feedback. My original intention after reading your
 response was to stick to Hive for managing tables. Unfortunately, I'm
 running into another case of SQL scripts hanging. Since all tables are
 already Parquet, I'm out of troubleshooting options. I'm going to migrate
 to Delta Lake and see if that solves the issue.

 Thanks again for your feedback.

 Patrick

 On Fri, Aug 11, 2023 at 10:09 AM Mich Talebzadeh <
 mich.talebza...@gmail.com> wrote:

> Hi Patrick,
>
> There is not anything wrong with Hive On-premise it is the best data
> warehouse there is
>
> Hive handles both ORC and Parquet formal well. They are both columnar
> implementations of relational model. What you are 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

OK you would not have known unless you went through the process so to speak.

Let us do something revolutionary here 

Install hive and its metastore. You already have hadoop anyway

https://cwiki.apache.org/confluence/display/hive/adminmanual+installation

hive metastore

https://data-flair.training/blogs/apache-hive-metastore/#:~:text=What%20is%20Hive%20Metastore%3F,by%20using%20metastore%20service%20API
.

choose one of these

derby  hive  mssql  mysql  oracle  postgres

Mine is an oracle. postgres is good as well.

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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 Sat, 12 Aug 2023 at 18:31, Patrick Tucci  wrote:

> Yes, on premise.
>
> Unfortunately after installing Delta Lake and re-writing all tables as
> Delta tables, the issue persists.
>
> On Sat, Aug 12, 2023 at 11:34 AM Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
>> ok sure.
>>
>> Is this Delta Lake going to be on-premise?
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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 Sat, 12 Aug 2023 at 12:03, Patrick Tucci 
>> wrote:
>>
>>> Hi Mich,
>>>
>>> Thanks for the feedback. My original intention after reading your
>>> response was to stick to Hive for managing tables. Unfortunately, I'm
>>> running into another case of SQL scripts hanging. Since all tables are
>>> already Parquet, I'm out of troubleshooting options. I'm going to migrate
>>> to Delta Lake and see if that solves the issue.
>>>
>>> Thanks again for your feedback.
>>>
>>> Patrick
>>>
>>> On Fri, Aug 11, 2023 at 10:09 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 Hi Patrick,

 There is not anything wrong with Hive On-premise it is the best data
 warehouse there is

 Hive handles both ORC and Parquet formal well. They are both columnar
 implementations of relational model. What you are seeing is the Spark API
 to Hive which prefers Parquet. I found out a few years ago.

 From your point of view I suggest you stick to parquet format with Hive
 specific to Spark. As far as I know you don't have a fully independent Hive
 DB as yet.

 Anyway stick to Hive for now as you never know what issues you may be
 facing using moving to Delta Lake.

 You can also use compression

 STORED AS PARQUET
 TBLPROPERTIES ("parquet.compression"="SNAPPY")

 ALSO

 ANALYZE TABLE  COMPUTE STATISTICS FOR COLUMNS

 HTH

 Mich Talebzadeh,
 Solutions Architect/Engineering Lead
 London
 United Kingdom


view my Linkedin profile
 


  https://en.everybodywiki.com/Mich_Talebzadeh



 *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 Fri, 11 Aug 2023 at 11:26, Patrick Tucci 
 wrote:

> Thanks for the reply Stephen and Mich.
>
> Stephen, you're right, it feels like Spark is waiting for something,
> but I'm not sure what. I'm the only user on the cluster and there are
> plenty of resources (+60 cores, +250GB RAM). I even tried restarting
> Hadoop, Spark and the host servers to make sure nothing was lingering in
> the background.
>
> Mich, thank you so much, your suggestion worked. Storing the tables as
> Parquet solves the issue.
>
> Interestingly, I found that only the MemberEnrollment table needs to
> be Parquet. The ID field in MemberEnrollment is an int calculated during
> load by a ROW_NUMBER() function. Further testing found that if I hard code
> a 0 as MemberEnrollment.ID instead of using the ROW_NUMBER() function, 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Yes, on premise.

Unfortunately after installing Delta Lake and re-writing all tables as
Delta tables, the issue persists.

On Sat, Aug 12, 2023 at 11:34 AM Mich Talebzadeh 
wrote:

> ok sure.
>
> Is this Delta Lake going to be on-premise?
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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 Sat, 12 Aug 2023 at 12:03, Patrick Tucci 
> wrote:
>
>> Hi Mich,
>>
>> Thanks for the feedback. My original intention after reading your
>> response was to stick to Hive for managing tables. Unfortunately, I'm
>> running into another case of SQL scripts hanging. Since all tables are
>> already Parquet, I'm out of troubleshooting options. I'm going to migrate
>> to Delta Lake and see if that solves the issue.
>>
>> Thanks again for your feedback.
>>
>> Patrick
>>
>> On Fri, Aug 11, 2023 at 10:09 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> Hi Patrick,
>>>
>>> There is not anything wrong with Hive On-premise it is the best data
>>> warehouse there is
>>>
>>> Hive handles both ORC and Parquet formal well. They are both columnar
>>> implementations of relational model. What you are seeing is the Spark API
>>> to Hive which prefers Parquet. I found out a few years ago.
>>>
>>> From your point of view I suggest you stick to parquet format with Hive
>>> specific to Spark. As far as I know you don't have a fully independent Hive
>>> DB as yet.
>>>
>>> Anyway stick to Hive for now as you never know what issues you may be
>>> facing using moving to Delta Lake.
>>>
>>> You can also use compression
>>>
>>> STORED AS PARQUET
>>> TBLPROPERTIES ("parquet.compression"="SNAPPY")
>>>
>>> ALSO
>>>
>>> ANALYZE TABLE  COMPUTE STATISTICS FOR COLUMNS
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *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 Fri, 11 Aug 2023 at 11:26, Patrick Tucci 
>>> wrote:
>>>
 Thanks for the reply Stephen and Mich.

 Stephen, you're right, it feels like Spark is waiting for something,
 but I'm not sure what. I'm the only user on the cluster and there are
 plenty of resources (+60 cores, +250GB RAM). I even tried restarting
 Hadoop, Spark and the host servers to make sure nothing was lingering in
 the background.

 Mich, thank you so much, your suggestion worked. Storing the tables as
 Parquet solves the issue.

 Interestingly, I found that only the MemberEnrollment table needs to be
 Parquet. The ID field in MemberEnrollment is an int calculated during load
 by a ROW_NUMBER() function. Further testing found that if I hard code a 0
 as MemberEnrollment.ID instead of using the ROW_NUMBER() function, the
 query works without issue even if both tables are ORC.

 Should I infer from this issue that the Hive components prefer Parquet
 over ORC? Furthermore, should I consider using a different table storage
 framework, like Delta Lake, instead of the Hive components? Given this
 issue and other issues I've had with Hive, I'm starting to think a
 different solution might be more robust and stable. The main condition is
 that my application operates solely through Thrift server, so I need to be
 able to connect to Spark through Thrift server and have it write tables
 using Delta Lake instead of Hive. From this StackOverflow question, it
 looks like this is possible:
 https://stackoverflow.com/questions/69862388/how-to-run-spark-sql-thrift-server-in-local-mode-and-connect-to-delta-using-jdbc

 Thanks again to everyone who replied for their help.

 Patrick


 On Fri, Aug 11, 2023 at 2:14 AM Mich Talebzadeh <
 mich.talebza...@gmail.com> wrote:

> Steve may have a valid point. You raised an issue with concurrent
> writes before, if I recall correctly. Since this limitation may be due to
> Hive metastore. By default Spark uses Apache Derby for its database
> persistence. 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

ok sure.

Is this Delta Lake going to be on-premise?

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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 Sat, 12 Aug 2023 at 12:03, Patrick Tucci  wrote:

> Hi Mich,
>
> Thanks for the feedback. My original intention after reading your response
> was to stick to Hive for managing tables. Unfortunately, I'm running into
> another case of SQL scripts hanging. Since all tables are already Parquet,
> I'm out of troubleshooting options. I'm going to migrate to Delta Lake and
> see if that solves the issue.
>
> Thanks again for your feedback.
>
> Patrick
>
> On Fri, Aug 11, 2023 at 10:09 AM Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
>> Hi Patrick,
>>
>> There is not anything wrong with Hive On-premise it is the best data
>> warehouse there is
>>
>> Hive handles both ORC and Parquet formal well. They are both columnar
>> implementations of relational model. What you are seeing is the Spark API
>> to Hive which prefers Parquet. I found out a few years ago.
>>
>> From your point of view I suggest you stick to parquet format with Hive
>> specific to Spark. As far as I know you don't have a fully independent Hive
>> DB as yet.
>>
>> Anyway stick to Hive for now as you never know what issues you may be
>> facing using moving to Delta Lake.
>>
>> You can also use compression
>>
>> STORED AS PARQUET
>> TBLPROPERTIES ("parquet.compression"="SNAPPY")
>>
>> ALSO
>>
>> ANALYZE TABLE  COMPUTE STATISTICS FOR COLUMNS
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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 Fri, 11 Aug 2023 at 11:26, Patrick Tucci 
>> wrote:
>>
>>> Thanks for the reply Stephen and Mich.
>>>
>>> Stephen, you're right, it feels like Spark is waiting for something, but
>>> I'm not sure what. I'm the only user on the cluster and there are plenty of
>>> resources (+60 cores, +250GB RAM). I even tried restarting Hadoop, Spark
>>> and the host servers to make sure nothing was lingering in the background.
>>>
>>> Mich, thank you so much, your suggestion worked. Storing the tables as
>>> Parquet solves the issue.
>>>
>>> Interestingly, I found that only the MemberEnrollment table needs to be
>>> Parquet. The ID field in MemberEnrollment is an int calculated during load
>>> by a ROW_NUMBER() function. Further testing found that if I hard code a 0
>>> as MemberEnrollment.ID instead of using the ROW_NUMBER() function, the
>>> query works without issue even if both tables are ORC.
>>>
>>> Should I infer from this issue that the Hive components prefer Parquet
>>> over ORC? Furthermore, should I consider using a different table storage
>>> framework, like Delta Lake, instead of the Hive components? Given this
>>> issue and other issues I've had with Hive, I'm starting to think a
>>> different solution might be more robust and stable. The main condition is
>>> that my application operates solely through Thrift server, so I need to be
>>> able to connect to Spark through Thrift server and have it write tables
>>> using Delta Lake instead of Hive. From this StackOverflow question, it
>>> looks like this is possible:
>>> https://stackoverflow.com/questions/69862388/how-to-run-spark-sql-thrift-server-in-local-mode-and-connect-to-delta-using-jdbc
>>>
>>> Thanks again to everyone who replied for their help.
>>>
>>> Patrick
>>>
>>>
>>> On Fri, Aug 11, 2023 at 2:14 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 Steve may have a valid point. You raised an issue with concurrent
 writes before, if I recall correctly. Since this limitation may be due to
 Hive metastore. By default Spark uses Apache Derby for its database
 persistence. *However it is limited to only one Spark session at any
 time for the purposes of metadata storage.*  That may be the cause
 here as well. Does this happen if the underlying tables are created as
 PARQUET as opposed to ORC?

 HTH

 Mich Talebzadeh,
 Solutions Architect/Engineering Lead
 London
 United 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Hi Mich,

Thanks for the feedback. My original intention after reading your response
was to stick to Hive for managing tables. Unfortunately, I'm running into
another case of SQL scripts hanging. Since all tables are already Parquet,
I'm out of troubleshooting options. I'm going to migrate to Delta Lake and
see if that solves the issue.

Thanks again for your feedback.

Patrick

On Fri, Aug 11, 2023 at 10:09 AM Mich Talebzadeh 
wrote:

> Hi Patrick,
>
> There is not anything wrong with Hive On-premise it is the best data
> warehouse there is
>
> Hive handles both ORC and Parquet formal well. They are both columnar
> implementations of relational model. What you are seeing is the Spark API
> to Hive which prefers Parquet. I found out a few years ago.
>
> From your point of view I suggest you stick to parquet format with Hive
> specific to Spark. As far as I know you don't have a fully independent Hive
> DB as yet.
>
> Anyway stick to Hive for now as you never know what issues you may be
> facing using moving to Delta Lake.
>
> You can also use compression
>
> STORED AS PARQUET
> TBLPROPERTIES ("parquet.compression"="SNAPPY")
>
> ALSO
>
> ANALYZE TABLE  COMPUTE STATISTICS FOR COLUMNS
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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 Fri, 11 Aug 2023 at 11:26, Patrick Tucci 
> wrote:
>
>> Thanks for the reply Stephen and Mich.
>>
>> Stephen, you're right, it feels like Spark is waiting for something, but
>> I'm not sure what. I'm the only user on the cluster and there are plenty of
>> resources (+60 cores, +250GB RAM). I even tried restarting Hadoop, Spark
>> and the host servers to make sure nothing was lingering in the background.
>>
>> Mich, thank you so much, your suggestion worked. Storing the tables as
>> Parquet solves the issue.
>>
>> Interestingly, I found that only the MemberEnrollment table needs to be
>> Parquet. The ID field in MemberEnrollment is an int calculated during load
>> by a ROW_NUMBER() function. Further testing found that if I hard code a 0
>> as MemberEnrollment.ID instead of using the ROW_NUMBER() function, the
>> query works without issue even if both tables are ORC.
>>
>> Should I infer from this issue that the Hive components prefer Parquet
>> over ORC? Furthermore, should I consider using a different table storage
>> framework, like Delta Lake, instead of the Hive components? Given this
>> issue and other issues I've had with Hive, I'm starting to think a
>> different solution might be more robust and stable. The main condition is
>> that my application operates solely through Thrift server, so I need to be
>> able to connect to Spark through Thrift server and have it write tables
>> using Delta Lake instead of Hive. From this StackOverflow question, it
>> looks like this is possible:
>> https://stackoverflow.com/questions/69862388/how-to-run-spark-sql-thrift-server-in-local-mode-and-connect-to-delta-using-jdbc
>>
>> Thanks again to everyone who replied for their help.
>>
>> Patrick
>>
>>
>> On Fri, Aug 11, 2023 at 2:14 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> Steve may have a valid point. You raised an issue with concurrent writes
>>> before, if I recall correctly. Since this limitation may be due to Hive
>>> metastore. By default Spark uses Apache Derby for its database
>>> persistence. *However it is limited to only one Spark session at any
>>> time for the purposes of metadata storage.*  That may be the cause here
>>> as well. Does this happen if the underlying tables are created as PARQUET
>>> as opposed to ORC?
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *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 Fri, 11 Aug 2023 at 01:33, Stephen Coy 
>>> wrote:
>>>
 Hi Patrick,

 When this has happened to me in the past (admittedly via spark-submit)
 it has been because another job was still running and had already claimed
 some of the resources 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

Hi Patrick,

There is not anything wrong with Hive On-premise it is the best data
warehouse there is

Hive handles both ORC and Parquet formal well. They are both columnar
implementations of relational model. What you are seeing is the Spark API
to Hive which prefers Parquet. I found out a few years ago.

>From your point of view I suggest you stick to parquet format with Hive
specific to Spark. As far as I know you don't have a fully independent Hive
DB as yet.

Anyway stick to Hive for now as you never know what issues you may be
facing using moving to Delta Lake.

You can also use compression

STORED AS PARQUET
TBLPROPERTIES ("parquet.compression"="SNAPPY")

ALSO

ANALYZE TABLE  COMPUTE STATISTICS FOR COLUMNS

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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 Fri, 11 Aug 2023 at 11:26, Patrick Tucci  wrote:

> Thanks for the reply Stephen and Mich.
>
> Stephen, you're right, it feels like Spark is waiting for something, but
> I'm not sure what. I'm the only user on the cluster and there are plenty of
> resources (+60 cores, +250GB RAM). I even tried restarting Hadoop, Spark
> and the host servers to make sure nothing was lingering in the background.
>
> Mich, thank you so much, your suggestion worked. Storing the tables as
> Parquet solves the issue.
>
> Interestingly, I found that only the MemberEnrollment table needs to be
> Parquet. The ID field in MemberEnrollment is an int calculated during load
> by a ROW_NUMBER() function. Further testing found that if I hard code a 0
> as MemberEnrollment.ID instead of using the ROW_NUMBER() function, the
> query works without issue even if both tables are ORC.
>
> Should I infer from this issue that the Hive components prefer Parquet
> over ORC? Furthermore, should I consider using a different table storage
> framework, like Delta Lake, instead of the Hive components? Given this
> issue and other issues I've had with Hive, I'm starting to think a
> different solution might be more robust and stable. The main condition is
> that my application operates solely through Thrift server, so I need to be
> able to connect to Spark through Thrift server and have it write tables
> using Delta Lake instead of Hive. From this StackOverflow question, it
> looks like this is possible:
> https://stackoverflow.com/questions/69862388/how-to-run-spark-sql-thrift-server-in-local-mode-and-connect-to-delta-using-jdbc
>
> Thanks again to everyone who replied for their help.
>
> Patrick
>
>
> On Fri, Aug 11, 2023 at 2:14 AM Mich Talebzadeh 
> wrote:
>
>> Steve may have a valid point. You raised an issue with concurrent writes
>> before, if I recall correctly. Since this limitation may be due to Hive
>> metastore. By default Spark uses Apache Derby for its database
>> persistence. *However it is limited to only one Spark session at any
>> time for the purposes of metadata storage.*  That may be the cause here
>> as well. Does this happen if the underlying tables are created as PARQUET
>> as opposed to ORC?
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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 Fri, 11 Aug 2023 at 01:33, Stephen Coy 
>> wrote:
>>
>>> Hi Patrick,
>>>
>>> When this has happened to me in the past (admittedly via spark-submit)
>>> it has been because another job was still running and had already claimed
>>> some of the resources (cores and memory).
>>>
>>> I think this can also happen if your configuration tries to claim
>>> resources that will never be available.
>>>
>>> Cheers,
>>>
>>> SteveC
>>>
>>>
>>> On 11 Aug 2023, at 3:36 am, Patrick Tucci 
>>> wrote:
>>>
>>> Hello,
>>>
>>> I'm attempting to run a query on Spark 3.4.0 through the Spark
>>> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
>>> standalone mode using HDFS for storage.
>>>
>>> The query is as follows:
>>>
>>> SELECT ME.*, MB.BenefitID
>>> FROM MemberEnrollment ME
>>> JOIN MemberBenefits MB
>>> ON ME.ID  = MB.EnrollmentID
>>> WHERE MB.BenefitID 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Thanks for the reply Stephen and Mich.

Stephen, you're right, it feels like Spark is waiting for something, but
I'm not sure what. I'm the only user on the cluster and there are plenty of
resources (+60 cores, +250GB RAM). I even tried restarting Hadoop, Spark
and the host servers to make sure nothing was lingering in the background.

Mich, thank you so much, your suggestion worked. Storing the tables as
Parquet solves the issue.

Interestingly, I found that only the MemberEnrollment table needs to be
Parquet. The ID field in MemberEnrollment is an int calculated during load
by a ROW_NUMBER() function. Further testing found that if I hard code a 0
as MemberEnrollment.ID instead of using the ROW_NUMBER() function, the
query works without issue even if both tables are ORC.

Should I infer from this issue that the Hive components prefer Parquet over
ORC? Furthermore, should I consider using a different table storage
framework, like Delta Lake, instead of the Hive components? Given this
issue and other issues I've had with Hive, I'm starting to think a
different solution might be more robust and stable. The main condition is
that my application operates solely through Thrift server, so I need to be
able to connect to Spark through Thrift server and have it write tables
using Delta Lake instead of Hive. From this StackOverflow question, it
looks like this is possible:
https://stackoverflow.com/questions/69862388/how-to-run-spark-sql-thrift-server-in-local-mode-and-connect-to-delta-using-jdbc

Thanks again to everyone who replied for their help.

Patrick


On Fri, Aug 11, 2023 at 2:14 AM Mich Talebzadeh 
wrote:

> Steve may have a valid point. You raised an issue with concurrent writes
> before, if I recall correctly. Since this limitation may be due to Hive
> metastore. By default Spark uses Apache Derby for its database
> persistence. *However it is limited to only one Spark session at any time
> for the purposes of metadata storage.*  That may be the cause here as
> well. Does this happen if the underlying tables are created as PARQUET as
> opposed to ORC?
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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 Fri, 11 Aug 2023 at 01:33, Stephen Coy 
> wrote:
>
>> Hi Patrick,
>>
>> When this has happened to me in the past (admittedly via spark-submit) it
>> has been because another job was still running and had already claimed some
>> of the resources (cores and memory).
>>
>> I think this can also happen if your configuration tries to claim
>> resources that will never be available.
>>
>> Cheers,
>>
>> SteveC
>>
>>
>> On 11 Aug 2023, at 3:36 am, Patrick Tucci 
>> wrote:
>>
>> Hello,
>>
>> I'm attempting to run a query on Spark 3.4.0 through the Spark
>> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
>> standalone mode using HDFS for storage.
>>
>> The query is as follows:
>>
>> SELECT ME.*, MB.BenefitID
>> FROM MemberEnrollment ME
>> JOIN MemberBenefits MB
>> ON ME.ID  = MB.EnrollmentID
>> WHERE MB.BenefitID = 5
>> LIMIT 10
>>
>> The tables are defined as follows:
>>
>> -- Contains about 3M rows
>> CREATE TABLE MemberEnrollment
>> (
>> ID INT
>> , MemberID VARCHAR(50)
>> , StartDate DATE
>> , EndDate DATE
>> -- Other columns, but these are the most important
>> ) STORED AS ORC;
>>
>> -- Contains about 25m rows
>> CREATE TABLE MemberBenefits
>> (
>> EnrollmentID INT
>> , BenefitID INT
>> ) STORED AS ORC;
>>
>> When I execute the query, it runs a single broadcast exchange stage,
>> which completes after a few seconds. Then everything just hangs. The
>> JDBC/ODBC tab in the UI shows the query state as COMPILED, but no stages or
>> tasks are executing or pending:
>>
>> 
>>
>> I've let the query run for as long as 30 minutes with no additional
>> stages, progress, or errors. I'm not sure where to start troubleshooting.
>>
>> Thanks for your help,
>>
>> Patrick
>>
>>
>> This email contains confidential information of and is the copyright of
>> Infomedia. It must not be forwarded, amended or disclosed without consent
>> of the sender. If you received this message by mistake, please advise the
>> sender and delete all copies. Security of transmission on the internet
>> cannot be guaranteed, could be infected, intercepted, or corrupted and you
>> should ensure you have suitable antivirus protection in place. By sending
>> us your or any third party personal details, you consent to (or confirm you
>> have obtained 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

Steve may have a valid point. You raised an issue with concurrent writes
before, if I recall correctly. Since this limitation may be due to Hive
metastore. By default Spark uses Apache Derby for its database
persistence. *However
it is limited to only one Spark session at any time for the purposes of
metadata storage.*  That may be the cause here as well. Does this happen if
the underlying tables are created as PARQUET as opposed to ORC?

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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 Fri, 11 Aug 2023 at 01:33, Stephen Coy 
wrote:

> Hi Patrick,
>
> When this has happened to me in the past (admittedly via spark-submit) it
> has been because another job was still running and had already claimed some
> of the resources (cores and memory).
>
> I think this can also happen if your configuration tries to claim
> resources that will never be available.
>
> Cheers,
>
> SteveC
>
>
> On 11 Aug 2023, at 3:36 am, Patrick Tucci  wrote:
>
> Hello,
>
> I'm attempting to run a query on Spark 3.4.0 through the Spark
> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
> standalone mode using HDFS for storage.
>
> The query is as follows:
>
> SELECT ME.*, MB.BenefitID
> FROM MemberEnrollment ME
> JOIN MemberBenefits MB
> ON ME.ID  = MB.EnrollmentID
> WHERE MB.BenefitID = 5
> LIMIT 10
>
> The tables are defined as follows:
>
> -- Contains about 3M rows
> CREATE TABLE MemberEnrollment
> (
> ID INT
> , MemberID VARCHAR(50)
> , StartDate DATE
> , EndDate DATE
> -- Other columns, but these are the most important
> ) STORED AS ORC;
>
> -- Contains about 25m rows
> CREATE TABLE MemberBenefits
> (
> EnrollmentID INT
> , BenefitID INT
> ) STORED AS ORC;
>
> When I execute the query, it runs a single broadcast exchange stage, which
> completes after a few seconds. Then everything just hangs. The JDBC/ODBC
> tab in the UI shows the query state as COMPILED, but no stages or tasks are
> executing or pending:
>
> 
>
> I've let the query run for as long as 30 minutes with no additional
> stages, progress, or errors. I'm not sure where to start troubleshooting.
>
> Thanks for your help,
>
> Patrick
>
>
> This email contains confidential information of and is the copyright of
> Infomedia. It must not be forwarded, amended or disclosed without consent
> of the sender. If you received this message by mistake, please advise the
> sender and delete all copies. Security of transmission on the internet
> cannot be guaranteed, could be infected, intercepted, or corrupted and you
> should ensure you have suitable antivirus protection in place. By sending
> us your or any third party personal details, you consent to (or confirm you
> have obtained consent from such third parties) to Infomedia’s privacy
> policy. http://www.infomedia.com.au/privacy-policy/
>

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Stephen Coy]

Hi Patrick,

When this has happened to me in the past (admittedly via spark-submit) it has 
been because another job was still running and had already claimed some of the 
resources (cores and memory).

I think this can also happen if your configuration tries to claim resources 
that will never be available.

Cheers,

SteveC


On 11 Aug 2023, at 3:36 am, Patrick Tucci  wrote:

Hello,

I'm attempting to run a query on Spark 3.4.0 through the Spark ThriftServer. 
The cluster has 64 cores, 250GB RAM, and operates in standalone mode using HDFS 
for storage.

The query is as follows:

SELECT ME.*, MB.BenefitID
FROM MemberEnrollment ME
JOIN MemberBenefits MB
ON ME.ID = MB.EnrollmentID
WHERE MB.BenefitID = 5
LIMIT 10

The tables are defined as follows:

-- Contains about 3M rows
CREATE TABLE MemberEnrollment
(
ID INT
, MemberID VARCHAR(50)
, StartDate DATE
, EndDate DATE
-- Other columns, but these are the most important
) STORED AS ORC;

-- Contains about 25m rows
CREATE TABLE MemberBenefits
(
EnrollmentID INT
, BenefitID INT
) STORED AS ORC;

When I execute the query, it runs a single broadcast exchange stage, which 
completes after a few seconds. Then everything just hangs. The JDBC/ODBC tab in 
the UI shows the query state as COMPILED, but no stages or tasks are executing 
or pending:



I've let the query run for as long as 30 minutes with no additional stages, 
progress, or errors. I'm not sure where to start troubleshooting.

Thanks for your help,

Patrick

This email contains confidential information of and is the copyright of 
Infomedia. It must not be forwarded, amended or disclosed without consent of 
the sender. If you received this message by mistake, please advise the sender 
and delete all copies. Security of transmission on the internet cannot be 
guaranteed, could be infected, intercepted, or corrupted and you should ensure 
you have suitable antivirus protection in place. By sending us your or any 
third party personal details, you consent to (or confirm you have obtained 
consent from such third parties) to Infomedia's privacy policy. 
http://www.infomedia.com.au/privacy-policy/

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Hi Mich,

I don't believe Hive is installed. I set up this cluster from scratch. I
installed Hadoop and Spark by downloading them from their project websites.
If Hive isn't bundled with Hadoop or Spark, I don't believe I have it. I'm
running the Thrift server distributed with Spark, like so:

~/spark/sbin/start-thriftserver.sh --master spark://10.0.50.1:7077

I can look into installing Hive, but it might take some time. I tried to
set up Hive when I first started evaluating distributed data processing
solutions, but I encountered many issues. Spark was much simpler, which was
part of the reason why I chose it.

Thanks again for the reply, I truly appreciate your help.

Patrick

On Thu, Aug 10, 2023 at 3:43 PM Mich Talebzadeh 
wrote:

> sorry host is 10.0.50.1
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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, 10 Aug 2023 at 20:41, Mich Talebzadeh 
> wrote:
>
>> Hi Patrick
>>
>> That beeline on port 1 is a hive thrift server running on your hive
>> on host 10.0.50.1:1.
>>
>> if you can access that host, you should be able to log into hive by
>> typing hive. The os user is hadoop in your case and sounds like there is no
>> password!
>>
>> Once inside that host, hive logs are kept in your case
>> /tmp/hadoop/hive.log or go to /tmp and do
>>
>> /tmp> find ./ -name hive.log. It should be under /tmp/hive.log
>>
>> Try running the sql inside hive and see what it says
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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, 10 Aug 2023 at 20:02, Patrick Tucci 
>> wrote:
>>
>>> Hi Mich,
>>>
>>> Thanks for the reply. Unfortunately I don't have Hive set up on my
>>> cluster. I can explore this if there are no other ways to troubleshoot.
>>>
>>> I'm using beeline to run commands against the Thrift server. Here's the
>>> command I use:
>>>
>>> ~/spark/bin/beeline -u jdbc:hive2://10.0.50.1:1 -n hadoop -f
>>> command.sql
>>>
>>> Thanks again for your help.
>>>
>>> Patrick
>>>
>>>
>>> On Thu, Aug 10, 2023 at 2:24 PM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 Can you run this sql query through hive itself?

 Are you using this command or similar for your thrift server?

 beeline -u jdbc:hive2:///1/default
 org.apache.hive.jdbc.HiveDriver -n hadoop -p xxx

 HTH

 Mich Talebzadeh,
 Solutions Architect/Engineering Lead
 London
 United Kingdom


view my Linkedin profile
 


  https://en.everybodywiki.com/Mich_Talebzadeh



 *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, 10 Aug 2023 at 18:39, Patrick Tucci 
 wrote:

> Hello,
>
> I'm attempting to run a query on Spark 3.4.0 through the Spark
> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
> standalone mode using HDFS for storage.
>
> The query is as follows:
>
> SELECT ME.*, MB.BenefitID
> FROM MemberEnrollment ME
> JOIN MemberBenefits MB
> ON ME.ID = MB.EnrollmentID
> WHERE MB.BenefitID = 5
> LIMIT 10
>
> The tables are defined as follows:
>
> -- Contains about 3M rows
> CREATE TABLE MemberEnrollment
> (
> ID INT
> , MemberID VARCHAR(50)
> , StartDate DATE
> , EndDate DATE
> -- Other columns, but these are the most important
> ) STORED AS ORC;
>
> -- Contains about 25m rows
> CREATE TABLE MemberBenefits
> (
> EnrollmentID INT
> , BenefitID INT
> ) STORED AS ORC;
>
> 

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

sorry host is 10.0.50.1

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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, 10 Aug 2023 at 20:41, Mich Talebzadeh 
wrote:

> Hi Patrick
>
> That beeline on port 1 is a hive thrift server running on your hive on
> host 10.0.50.1:1.
>
> if you can access that host, you should be able to log into hive by typing
> hive. The os user is hadoop in your case and sounds like there is no
> password!
>
> Once inside that host, hive logs are kept in your case
> /tmp/hadoop/hive.log or go to /tmp and do
>
> /tmp> find ./ -name hive.log. It should be under /tmp/hive.log
>
> Try running the sql inside hive and see what it says
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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, 10 Aug 2023 at 20:02, Patrick Tucci 
> wrote:
>
>> Hi Mich,
>>
>> Thanks for the reply. Unfortunately I don't have Hive set up on my
>> cluster. I can explore this if there are no other ways to troubleshoot.
>>
>> I'm using beeline to run commands against the Thrift server. Here's the
>> command I use:
>>
>> ~/spark/bin/beeline -u jdbc:hive2://10.0.50.1:1 -n hadoop -f
>> command.sql
>>
>> Thanks again for your help.
>>
>> Patrick
>>
>>
>> On Thu, Aug 10, 2023 at 2:24 PM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> Can you run this sql query through hive itself?
>>>
>>> Are you using this command or similar for your thrift server?
>>>
>>> beeline -u jdbc:hive2:///1/default
>>> org.apache.hive.jdbc.HiveDriver -n hadoop -p xxx
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Solutions Architect/Engineering Lead
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *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, 10 Aug 2023 at 18:39, Patrick Tucci 
>>> wrote:
>>>
 Hello,

 I'm attempting to run a query on Spark 3.4.0 through the Spark
 ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
 standalone mode using HDFS for storage.

 The query is as follows:

 SELECT ME.*, MB.BenefitID
 FROM MemberEnrollment ME
 JOIN MemberBenefits MB
 ON ME.ID = MB.EnrollmentID
 WHERE MB.BenefitID = 5
 LIMIT 10

 The tables are defined as follows:

 -- Contains about 3M rows
 CREATE TABLE MemberEnrollment
 (
 ID INT
 , MemberID VARCHAR(50)
 , StartDate DATE
 , EndDate DATE
 -- Other columns, but these are the most important
 ) STORED AS ORC;

 -- Contains about 25m rows
 CREATE TABLE MemberBenefits
 (
 EnrollmentID INT
 , BenefitID INT
 ) STORED AS ORC;

 When I execute the query, it runs a single broadcast exchange stage,
 which completes after a few seconds. Then everything just hangs. The
 JDBC/ODBC tab in the UI shows the query state as COMPILED, but no stages or
 tasks are executing or pending:

 [image: image.png]

 I've let the query run for as long as 30 minutes with no additional
 stages, progress, or errors. I'm not sure where to start troubleshooting.

 Thanks for your help,

 Patrick

>>>

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

Hi Patrick

That beeline on port 1 is a hive thrift server running on your hive on
host 10.0.50.1:1.

if you can access that host, you should be able to log into hive by typing
hive. The os user is hadoop in your case and sounds like there is no
password!

Once inside that host, hive logs are kept in your case /tmp/hadoop/hive.log
or go to /tmp and do

/tmp> find ./ -name hive.log. It should be under /tmp/hive.log

Try running the sql inside hive and see what it says

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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, 10 Aug 2023 at 20:02, Patrick Tucci  wrote:

> Hi Mich,
>
> Thanks for the reply. Unfortunately I don't have Hive set up on my
> cluster. I can explore this if there are no other ways to troubleshoot.
>
> I'm using beeline to run commands against the Thrift server. Here's the
> command I use:
>
> ~/spark/bin/beeline -u jdbc:hive2://10.0.50.1:1 -n hadoop -f
> command.sql
>
> Thanks again for your help.
>
> Patrick
>
>
> On Thu, Aug 10, 2023 at 2:24 PM Mich Talebzadeh 
> wrote:
>
>> Can you run this sql query through hive itself?
>>
>> Are you using this command or similar for your thrift server?
>>
>> beeline -u jdbc:hive2:///1/default
>> org.apache.hive.jdbc.HiveDriver -n hadoop -p xxx
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Solutions Architect/Engineering Lead
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *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, 10 Aug 2023 at 18:39, Patrick Tucci 
>> wrote:
>>
>>> Hello,
>>>
>>> I'm attempting to run a query on Spark 3.4.0 through the Spark
>>> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
>>> standalone mode using HDFS for storage.
>>>
>>> The query is as follows:
>>>
>>> SELECT ME.*, MB.BenefitID
>>> FROM MemberEnrollment ME
>>> JOIN MemberBenefits MB
>>> ON ME.ID = MB.EnrollmentID
>>> WHERE MB.BenefitID = 5
>>> LIMIT 10
>>>
>>> The tables are defined as follows:
>>>
>>> -- Contains about 3M rows
>>> CREATE TABLE MemberEnrollment
>>> (
>>> ID INT
>>> , MemberID VARCHAR(50)
>>> , StartDate DATE
>>> , EndDate DATE
>>> -- Other columns, but these are the most important
>>> ) STORED AS ORC;
>>>
>>> -- Contains about 25m rows
>>> CREATE TABLE MemberBenefits
>>> (
>>> EnrollmentID INT
>>> , BenefitID INT
>>> ) STORED AS ORC;
>>>
>>> When I execute the query, it runs a single broadcast exchange stage,
>>> which completes after a few seconds. Then everything just hangs. The
>>> JDBC/ODBC tab in the UI shows the query state as COMPILED, but no stages or
>>> tasks are executing or pending:
>>>
>>> [image: image.png]
>>>
>>> I've let the query run for as long as 30 minutes with no additional
>>> stages, progress, or errors. I'm not sure where to start troubleshooting.
>>>
>>> Thanks for your help,
>>>
>>> Patrick
>>>
>>

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Hi Mich,

Thanks for the reply. Unfortunately I don't have Hive set up on my cluster.
I can explore this if there are no other ways to troubleshoot.

I'm using beeline to run commands against the Thrift server. Here's the
command I use:

~/spark/bin/beeline -u jdbc:hive2://10.0.50.1:1 -n hadoop -f command.sql

Thanks again for your help.

Patrick


On Thu, Aug 10, 2023 at 2:24 PM Mich Talebzadeh 
wrote:

> Can you run this sql query through hive itself?
>
> Are you using this command or similar for your thrift server?
>
> beeline -u jdbc:hive2:///1/default
> org.apache.hive.jdbc.HiveDriver -n hadoop -p xxx
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *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, 10 Aug 2023 at 18:39, Patrick Tucci 
> wrote:
>
>> Hello,
>>
>> I'm attempting to run a query on Spark 3.4.0 through the Spark
>> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
>> standalone mode using HDFS for storage.
>>
>> The query is as follows:
>>
>> SELECT ME.*, MB.BenefitID
>> FROM MemberEnrollment ME
>> JOIN MemberBenefits MB
>> ON ME.ID = MB.EnrollmentID
>> WHERE MB.BenefitID = 5
>> LIMIT 10
>>
>> The tables are defined as follows:
>>
>> -- Contains about 3M rows
>> CREATE TABLE MemberEnrollment
>> (
>> ID INT
>> , MemberID VARCHAR(50)
>> , StartDate DATE
>> , EndDate DATE
>> -- Other columns, but these are the most important
>> ) STORED AS ORC;
>>
>> -- Contains about 25m rows
>> CREATE TABLE MemberBenefits
>> (
>> EnrollmentID INT
>> , BenefitID INT
>> ) STORED AS ORC;
>>
>> When I execute the query, it runs a single broadcast exchange stage,
>> which completes after a few seconds. Then everything just hangs. The
>> JDBC/ODBC tab in the UI shows the query state as COMPILED, but no stages or
>> tasks are executing or pending:
>>
>> [image: image.png]
>>
>> I've let the query run for as long as 30 minutes with no additional
>> stages, progress, or errors. I'm not sure where to start troubleshooting.
>>
>> Thanks for your help,
>>
>> Patrick
>>
>

Re: Spark-SQL - Query Hanging, How To Troubleshoot

[Mich Talebzadeh]

Can you run this sql query through hive itself?

Are you using this command or similar for your thrift server?

beeline -u jdbc:hive2:///1/default
org.apache.hive.jdbc.HiveDriver -n hadoop -p xxx

HTH

Mich Talebzadeh,
Solutions Architect/Engineering Lead
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*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, 10 Aug 2023 at 18:39, Patrick Tucci  wrote:

> Hello,
>
> I'm attempting to run a query on Spark 3.4.0 through the Spark
> ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
> standalone mode using HDFS for storage.
>
> The query is as follows:
>
> SELECT ME.*, MB.BenefitID
> FROM MemberEnrollment ME
> JOIN MemberBenefits MB
> ON ME.ID = MB.EnrollmentID
> WHERE MB.BenefitID = 5
> LIMIT 10
>
> The tables are defined as follows:
>
> -- Contains about 3M rows
> CREATE TABLE MemberEnrollment
> (
> ID INT
> , MemberID VARCHAR(50)
> , StartDate DATE
> , EndDate DATE
> -- Other columns, but these are the most important
> ) STORED AS ORC;
>
> -- Contains about 25m rows
> CREATE TABLE MemberBenefits
> (
> EnrollmentID INT
> , BenefitID INT
> ) STORED AS ORC;
>
> When I execute the query, it runs a single broadcast exchange stage, which
> completes after a few seconds. Then everything just hangs. The JDBC/ODBC
> tab in the UI shows the query state as COMPILED, but no stages or tasks are
> executing or pending:
>
> [image: image.png]
>
> I've let the query run for as long as 30 minutes with no additional
> stages, progress, or errors. I'm not sure where to start troubleshooting.
>
> Thanks for your help,
>
> Patrick
>

Spark-SQL - Query Hanging, How To Troubleshoot

[Patrick Tucci]

Hello,

I'm attempting to run a query on Spark 3.4.0 through the Spark
ThriftServer. The cluster has 64 cores, 250GB RAM, and operates in
standalone mode using HDFS for storage.

The query is as follows:

SELECT ME.*, MB.BenefitID
FROM MemberEnrollment ME
JOIN MemberBenefits MB
ON ME.ID = MB.EnrollmentID
WHERE MB.BenefitID = 5
LIMIT 10

The tables are defined as follows:

-- Contains about 3M rows
CREATE TABLE MemberEnrollment
(
ID INT
, MemberID VARCHAR(50)
, StartDate DATE
, EndDate DATE
-- Other columns, but these are the most important
) STORED AS ORC;

-- Contains about 25m rows
CREATE TABLE MemberBenefits
(
EnrollmentID INT
, BenefitID INT
) STORED AS ORC;

When I execute the query, it runs a single broadcast exchange stage, which
completes after a few seconds. Then everything just hangs. The JDBC/ODBC
tab in the UI shows the query state as COMPILED, but no stages or tasks are
executing or pending:

[image: image.png]

I've let the query run for as long as 30 minutes with no additional stages,
progress, or errors. I'm not sure where to start troubleshooting.

Thanks for your help,

Patrick