| 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
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?
I guess MySql driver may work. Just try these drivers.
You can either add the jar file to $SPARK_HOME/jars or to $SPARK_HOME/conf/spark-defaults.conf
spark.driver.extraClassPath /data6/hduser/mariaDB_specific.jar
HTH
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Many thanks for the reply, Mich. I’m running Databricks on Azure. Databricks runtime version: 8.1 ML (includes Apache Spark 3.1.1, Scala 2.12)
The UUID columns, I believe, comes from Azure IoT. It is generally 36 characters, like '934c1f58-ed11-4e48-b157-aab869d9b325’, although I note some are shorter, possibly test data. The column is defined as VARCHAR(255).
If I run the SQL outside of Spark I get exactly what I expect, the uuids in the first column, gender as ‘M’, ‘F’ or whatever in the second, and zeroes and nulls in the other columns. That is the puzzling part: that every variation of SQL I have tried works perfectly well in itself (in DataGrip eg), but returns junk when run in Spark. For the last four columns, and possibly the second, I could understand the nulls confusing it, but the bit I really don’t understand is why it says ‘uuid’ and ‘gender’, ie the column names, for all the rows of the first two columns.
As for just downloading the tables and doing the join in spark data frames, I tried that but I hit the other issue I mention:
SQLException: Out of range value for column 'id' : value id is not in Long range
Even though ‘SELECT max(id) from devices’ returns 16091, and there are no nulls. Which is why I was doing the join in the DB. The IDs, as I mentioned, are all BigInt(20).
Hi,
Which version of Spark are you using?
UUID is generated by DB through OS low level call and it is 36 characters
UUID=$(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
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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.
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
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Alun ap Rhisiart (0EEFB724) – Public.asc