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 <TABLE_NAME> COMPUTE STATISTICS FOR COLUMNS
>
> HTH
>
> Mich Talebzadeh,
> Solutions Architect/Engineering Lead
> London
> United Kingdom
>
>
> view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
> https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
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>
> On Fri, 11 Aug 2023 at 11:26, Patrick Tucci <patrick.tu...@gmail.com>
> 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://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>
>>>
>>> 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 <s...@infomedia.com.au.invalid>
>>> 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 <patrick.tu...@gmail.com>
>>>> 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 <http://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.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
>>>>
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>>>
