Hi,
while working with JDBC datasource I saw that many "or" clauses with
non-equality operators causes huge performance degradation of SQL query
to database (DB2). For example:
val df = spark.read.format("jdbc").(other options to parallelize
load).load()
df.where(s"(date1 > $param1 and (date1 < $param2 or date1 is null) or x
> 100)").show() // in real application whose predicates were pushed
many many lines below, many ANDs and ORs
If I use cache() before where, there is no predicate pushdown of this
"where" clause. However, in production system caching many sources is a
waste of memory (especially is pipeline is long and I must do cache many
times).
I asked on StackOverflow for better ideas:
https://stackoverflow.com/questions/50336355/how-to-prevent-predicate-pushdown
However, there are only workarounds. I can use those workarounds, but
maybe it would be better to add such functionality directly in the API?
For example: df.withAnalysisBarrier().where(...) ?
Please let me know if I should create a JIRA or it's not a good idea for
some reasons.
Pozdrawiam / Best regards,
Tomek Gawęda
