Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/17626
After I rethinking about it, it really depends on how we define
`deterministic`.
I checked the definition of Hive `UDFType`.
```
/**
* Certain optimizations should not
Github user viirya commented on the issue:
https://github.com/apache/spark/pull/17626
`udf(x, y) = 1` is deterministic no matter whether x or y is deterministic
or not, is because x, y are not used, in other words they don't affect the
result of the udf.
The result of
Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/17626
The deterministic of our [Scala udaf has exactly the same
Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/17626
A simple example, `udf(x, y) = 1` is deterministic no matter whether x or y
is deterministic or not.
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Github user viirya commented on the issue:
https://github.com/apache/spark/pull/17626
udf(x, y) = x + y looks like a deterministic UDF function. Is this
udf(rand(), rand()) deterministic?
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Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/17626
Even if the input expressions are not deterministic, the output could be
still deterministic.
If we already make an assumption that ScalaUDF is deterministic, we should
make it behave
Github user viirya commented on the issue:
https://github.com/apache/spark/pull/17626
Hmm, I think it is a bit different between the deterministic assumption on
UDF functions and the `deterministic` of `ScalaUDF`.
Even your UDF functions are deterministic, if the input
Github user AmplabJenkins commented on the issue:
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Github user gatorsmile commented on the issue:
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cc @cloud-fan @dongjoon-hyun
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