One more thing that I find useful is to use a tool like Squirre
<http://squirrel-sql.sourceforge.net/>l.client. It is basic but very
powerful to test JDBC connections.

SQuirreL SQL Client Home Page (sourceforge.net)
<http://squirrel-sql.sourceforge.net/#installation>

Just install it on a node in the cloud and try testing your connections. It
has SQL query tool as well.

HTH
<http://squirrel-sql.sourceforge.net/#installation>



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On Fri, 16 Apr 2021 at 18:28, Alun ap Rhisiart <cod...@alunaprhisiart.uk>
wrote:

> Thanks, Mich,
>
> Yes, I’m coming to the conclusion that there is a driver issue. Our ops
> guy is back next Monday, so I’ll see what he can do. I tried things like
> adding a WHERE clause so that I was sure that the row received had no
> nulls, in case that was the problem, but it was exactly the same.
>
> Regards,
>
> Alun
>
>
> On 16 Apr 2021, at 17:05, Mich Talebzadeh <mich.talebza...@gmail.com>
> wrote:
>
> thanks for the info Alun.
>
> From my experience these issues of messed up columns through JDBC are
> usually related to the JDBC driver being used.
>
> Your database is MariaDB. I have no direct experience of this database but
> it is akin to MySQL. Case in point, I had all sorts of issues connecting to
> Hive through JDBC connection from Cloud to on-premise after trying four
> different drivers., only hive_driver: com.cloudera.hive.jdbc41.HS2Driver
> worked
>
> Can you try a few other drivers that support SSL for MariaDB from
> different vendors?
>
> Powerful MySQL JDBC Driver Download | Progress DataDirect
> <https://www.progress.com/jdbc>
>
> I guess MySql driver may work. Just try these drivers.
>
> You can either add the jar file to $SPARK_HOME/jars or to
> $SPARK_HOME/conf/spark-defaults.conf
>
> spark.driver.extraClassPath        /data6/hduser/mariaDB_specific.jar
>
>
> HTH
>
>
>
>    view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
> *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, 16 Apr 2021 at 16:34, Alun ap Rhisiart <cod...@alunaprhisiart.uk>
> wrote:
>
>> Many thanks for the reply, Mich. I’m running Databricks on Azure.
>> Databricks runtime version: 8.1 ML (includes Apache Spark 3.1.1, Scala
>> 2.12)
>>
>> The UUID columns,  I believe, comes from Azure IoT. It is generally 36
>> characters, like '934c1f58-ed11-4e48-b157-aab869d9b325’, although I note
>> some are shorter, possibly test data. The column is defined as VARCHAR(255).
>>
>> If I run the SQL outside of Spark I get exactly what I expect, the uuids
>> in the first column, gender as ‘M’, ‘F’ or whatever in the second, and
>> zeroes and nulls in the other columns. That is the puzzling part: that
>> every variation of SQL I have tried works perfectly well in itself (in
>> DataGrip eg), but returns junk when run in Spark. For the last four
>> columns, and possibly the second, I could understand the nulls confusing
>> it, but the bit I really don’t understand is why it says ‘uuid’
>> and ‘gender’, ie the column names, for all the rows of the first two
>> columns.
>>
>> As for just downloading the tables and doing the join in spark data
>> frames, I tried that but I hit the other issue I mention:
>>
>> SQLException: Out of range value for column 'id' : value id is not in
>> Long range
>>
>> Even though ‘SELECT max(id) from devices’ returns 16091, and there are no
>> nulls. Which is why I was doing the join in the DB. The IDs, as I
>> mentioned, are all BigInt(20).
>>
>>
>> On 16 Apr 2021, at 15:53, Mich Talebzadeh <mich.talebza...@gmail.com>
>> wrote:
>>
>> Hi,
>>
>> Which version of Spark are you using?
>>
>> UUID is generated by DB through OS low level call and it is 36 characters
>>
>> UUID=$(uuidgen)
>> echo $UUID
>> ef080790-4c3f-4a5f-8db7-1024338d34f2
>>
>>
>> in other words string will do it or VARCHAR(36)
>>
>> When you run that SQL directly on the database itself what do you get?
>>
>> The alternative is to make two calls directly via JDBC to the underlying
>> database, get the data back into DF from those two tables and do the join
>> in Pyspark itself as a test
>>
>> Spark connection tyo and DB which allows JDBC is generic
>>
>> def loadTableFromJDBC(spark, url, tableName, user, password, driver,
>> fetchsize):
>>     try:
>>        df = spark.read. \
>>             format("jdbc"). \
>>             option("url", url). \
>>             option("dbtable", tableName). \
>>             option("user", user). \
>>             option("password", password). \
>>             option("driver", driver). \
>>             option("fetchsize", fetchsize). \
>>             load()
>>        return df
>>     except Exception as e:
>>         print(f"""{e}, quitting""")
>>         sys.exit(1)
>>
>> HTH
>>
>>
>>
>>    view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>> *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, 16 Apr 2021 at 12:21, Alun ap Rhisiart <cod...@alunaprhisiart.uk>
>> wrote:
>>
>>> I’m just starting using PySpark (Databricks) for a education
>>> application. Part of this is monitoring children’s online behaviour to
>>> alert teachers whether there may be problems with bullying, extreme diets,
>>> suicide ideation, and so on. I have IoT data which I need to combine with
>>> information from MariaDB (this is all in Azure). I have SparkJDBC42 and
>>> mariadb_java_client_2_7_2 jars installed. The connection to the database is
>>> established, in that I can see it can retrieve the schema for tables.
>>>
>>> I have a couple of issues. The first is that I can never retrieve any id
>>> columns (which are all defined as BigInt(20)), as I get a ‘long out of
>>> range’ error. I’m currently working around that by not including the ids
>>> themselves in the return. However, the big problem is that I get the column
>>> names returned in each row instead of the values for each row, where the
>>> columns are defined as strings (VARCHAR etc). Also, for columns defined as
>>> TinyInt they are returned as booleans, but reversed (0 is returned as
>>> True). I have tried running  the SQL outside of databricks/Spark (eg in
>>> DataGrip) and it returns perfectly sensible data every time.
>>>
>>> The code at gist:412e1f3324136a574303005a0922f610
>>> <https://gist.github.com/alunap/412e1f3324136a574303005a0922f610>
>>>
>>>
>>> Returned:
>>> +----+------+----+-----------+----+--------------+ |uuid|gender|
>>> cpp|young_carer| spp|asylum_refugee|
>>> +----+------+----+-----------+----+--------------+ |uuid|gender|true|
>>> true|true| true| |uuid|gender|true| true|true| true| |uuid|gender|true|
>>> true|true| true| |uuid|gender|true| true|true| true| |uuid|gender|true|
>>> true|true| true| |uuid|gender|true| true|true| true| |uuid|gender|true|
>>> true|true| true| |uuid|gender|true| true|true| true| |uuid|gender|true|
>>> true|true| true| |uuid|gender|true| true|true| true|
>>> +----+------+----+-----------+----+--------------+ only showing top 10 rows
>>>
>>> On the database, device.uuid field is VARCHAR(255) and contains valid
>>> uuids (no nulls).
>>> children.gender is VARCHAR(255) and contains ‘M’, ‘F’, ‘MALE’, ‘FEMALE’,
>>> ‘NONE’, or null.
>>> children.cpp, young_carer, spp, and asylum_refugee are all tinyint(1) =
>>> 0. They are nearly all 0, but the first 10 rows contain some nulls.
>>>
>>> I tried enclosing the query with brackets ‘(SELECT…) t’ as I gather it
>>> is a subquery, and I tried adding a WHERE d.uuid = ‘an id’ with an id being
>>> one where there are no nulls in the column, but no difference. So,
>>> completely baffled at this point.
>>>
>>> Thanks for any suggestions,
>>>
>>> Alun ap Rhisiart
>>>
>>
>> Alun ap Rhisiart
>>
>> attached you'll find my public key
>>
>> You can use this key to encrypt and secure our messages.
>>
>> To start using it, you'll need to install an OpenPGP software on your
>> computer.  Below you'll find a list of possible solutions for your
>> operating system:
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>> macOS
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>> Linux https://ssd.eff.org/en/module/how-use-pgp-linux
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>> Android
>> https://play.google.com/store/apps/details?id=org.sufficientlysecure.keychain
>>
>> Please import the public key into your local OpenPGP Key-Manager.
>>
>>
>>
>>
>>
> Alun ap Rhisiart
>
> attached you'll find my public key
>
> You can use this key to encrypt and secure our messages.
>
> To start using it, you'll need to install an OpenPGP software on your
> computer.  Below you'll find a list of possible solutions for your
> operating system:
>
> macOS
> https://gpgtools.tenderapp.com/kb/how-to/first-steps-where-do-i-start-where-do-i-begin-setup-gpgtools-create-a-new-key-your-first-encrypted-mail
> Linux https://ssd.eff.org/en/module/how-use-pgp-linux
> Windows https://ssd.eff.org/en/module/how-use-pgp-windows-pc
> iOS https://itunes.apple.com/app/ipgmail/id430780873?mt=8
> Android
> https://play.google.com/store/apps/details?id=org.sufficientlysecure.keychain
>
> Please import the public key into your local OpenPGP Key-Manager.
>
>
>
>
>

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