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[FEATURE] Spark-Expectations for Data Pipelines written using Scala/Java Spark SDK #30
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This means we'd also have access to all APIs including expression encoders and ASTs. I'm a go for this, we'd need to make a decision on supported Java versions (Databricks is still using 8 internally, Java 11 and 17 are fairly well supported in the OSS project), and keep a close watch on changes coming to Spark 4.0 and what supported Scala versions (2.12.1x, 2.13,3.x) will be maintained moving forwards. |
I support this 100%. This would allow us to tap into the |
Thanks @newfront . I will work on requirements and we can all discuss the ideas on implementation and architecture. |
The decorators pattern could be replaced with builders for the Scala api. Then when migrating the decorators for pyspark, we'd be able to call into the Scala api, and the decorators could be wired into the builders options closing the loop. If we use the _gateway on py4j from pyspark this change could be done transparent (aside from requiring the underlying jar) |
So, we write |
Is your feature request related to a problem? Please describe.
Support
spark-expectations
for the pipelines written in Scala/Java Spark SDKDescribe the solution you'd like
TBD
Describe alternatives you've considered
Additional context
Make
spark-expectations
work for other language spark SDK'sThe text was updated successfully, but these errors were encountered: