When we are loading a pyspark dataframe to a Hive table which contains few columns which are having the integer value greater than the Hive Int limit(Overflows the Hive Int value), it is observed that the value is getting rounded to the limit of Integer value and the rest of the cell value is getting abondeded. For that reason, it is decided to split the rows, which are having the overflown value, into multiple rows so that the total amount will not be lost.
can anyone please let me know how can we achieve this using pyspark
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I am trying to write a variety of columns to redshift from a dynamic frame using the DynamicFrameWriter.from_jdbc_conf method, but all DECIMAL fields end up as a column of NULLs.
The ETL pulls in from some redshift views, and eventually writes back to a redshift table using the aforementioned method. In general this works for other datatypes, but adding a statement as simple as SELECT CAST(12.34 AS DECIMAL(4, 2)) AS decimal_test results in a column full of NULLs. In pyspark, I can print out the column and see the decimal values immediately before they are written as NULLs to the redshift table. When I look at the schema on redshift, I can see the column decimal_test has a type of NUMERIC(4, 2). What could be causing this failure?
Have a use case to get the row from Cassandra Table which has max size.
Is there any way to do it
How to fetch the hive table partition min and max value in pyspark/beeline?
show partitions table shows all the partitions.
There is another post on same question but it uses a bash approach. Do we have any solution on pyspark?
I have a table partitioned by range in Postgres 10.6. Is there a way to tell one of its partitions to accept NULL for the column used as partition key?
The reason I need this is: my table size is 200GB and it's actually not yet partitioned. I want to partition it going forward, so I thought I would create an initial partition including all of the current rows, and then at the start of each month I would create another partition for that month's data.
The issue is, currently this table doesn't have the column I'll use for partitioning, so I want to add the column (initially null) and then tell that initial partition to hold all rows that have null in the partitioning key.
Another option would be to not add the column as null but to set an initial date value, but that would be time and space consuming because of the size of that table.
I would upgrade to v11 and initially define the partitioned table with just a default partition that contains all the NULL values.
Then you can add other partitions and gradually move the data by updating the NULL values.
I'm reading a file column by column and making it as a row and I will have to insert that into a table for which applying the schema to convert to a df. I have an integer value 2146411835(within int range) when I try to apply the schema with Integer, I get scala.MatchError: 2146411835 (of class java.lang.Integer). Any inputs?
Thanks,
Ash