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TL;DR; currently, users call UDFs through df.select(scalar_functions(“sqrt”, vec![col(“a”)], DataType::Float64)) Proposal: let f = df.registry();
df.select(f.udf(“sqrt”, vec![col(“a”)])?) so that they do not have to remember the UDFs return type when using it. This API will in the future allow to declare the UDF as part of the planning, like spark, instead of having to register it in the registry before using it (we just need to check if the UDF is registered or not before doing so). See complete proposal here: https://docs.google.com/document/d/1Kzz642ScizeKXmVE1bBlbLvR663BKQaGqVIyy9cAscY/edit?usp=sharing
Reporter: Jorge Leitão / @jorgecarleitao Assignee: Jorge Leitão / @jorgecarleitao
Note: This issue was originally created as ARROW-9836. Please see the migration documentation for further details.
The text was updated successfully, but these errors were encountered:
Andy Grove / @andygrove: Issue resolved by pull request 8032 #8032
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jorgecarleitao
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TL;DR; currently, users call UDFs through
df.select(scalar_functions(“sqrt”, vec![col(“a”)], DataType::Float64))
Proposal:
let f = df.registry();
df.select(f.udf(“sqrt”, vec![col(“a”)])?)
so that they do not have to remember the UDFs return type when using it.
This API will in the future allow to declare the UDF as part of the planning, like spark, instead of having to register it in the registry before using it (we just need to check if the UDF is registered or not before doing so).
See complete proposal here: https://docs.google.com/document/d/1Kzz642ScizeKXmVE1bBlbLvR663BKQaGqVIyy9cAscY/edit?usp=sharing
Reporter: Jorge Leitão / @jorgecarleitao
Assignee: Jorge Leitão / @jorgecarleitao
Related issues:
PRs and other links:
Note: This issue was originally created as ARROW-9836. Please see the migration documentation for further details.
The text was updated successfully, but these errors were encountered: