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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2020/02/14 11:08:23 UTC
[GitHub] [spark] WeichenXu123 commented on a change in pull request #27565:
[WIP][SPARK-30791][SQL][PYTHON] Add 'sameSemantics' and 'sementicHash'
methods in Dataset
WeichenXu123 commented on a change in pull request #27565: [WIP][SPARK-30791][SQL][PYTHON] Add 'sameSemantics' and 'sementicHash' methods in Dataset
URL: https://github.com/apache/spark/pull/27565#discussion_r379374685
##########
File path: python/pyspark/sql/dataframe.py
##########
@@ -2153,6 +2153,50 @@ def transform(self, func):
"should have been DataFrame." % type(result)
return result
+ @since(3.1)
+ def sameSemantics(self, other):
+ """
+ Return true when the query plan of the given :class:`DataFrame` will return the same
+ results as this :class:`DataFrame`.
+
+ >>> df1 = spark.createDataFrame([(1, 2),(4, 5)], ["col1", "col2"])
+ >>> df2 = spark.createDataFrame([(1, 2),(4, 5)], ["col0", "col2"])
+ >>> df3 = spark.createDataFrame([(0, 2),(4, 5)], ["col1", "col2"])
+ >>> df4 = spark.createDataFrame([(1, 2),(4, 5)], ["col1", "col2"])
+ >>> df1.sameSemantics(df2)
+ False
+ >>> df1.sameSemantics(df3)
+ False
+ >>> df1.sameSemantics(df4)
+ True
+ >>> df1.sameSemantics(df1)
+ True
+ """
+ if not isinstance(other, DataFrame):
+ raise ValueError("other parameter should be of DataFrame; however, got %s"
+ % type(other))
+ return self._jdf.sameSemantics(other._jdf)
+
+ @since(3.1)
+ def semanticHash(self):
+ """
+ Returns a `hashCode` for the calculation performed by the query plan of this Dataset.
+
+ >>> df1 = spark.createDataFrame([(1, 2),(4, 5)], ["col1", "col2"])
+ >>> df2 = spark.createDataFrame([(1, 2),(4, 5)], ["col0", "col2"])
+ >>> df3 = spark.createDataFrame([(0, 2),(4, 5)], ["col1", "col2"])
+ >>> df4 = spark.createDataFrame([(1, 2),(4, 5)], ["col1", "col2"])
+ >>> df1.semanticHash() == df2.semanticHash()
+ False
+ >>> df1.semanticHash() == df3.semanticHash()
+ False
+ >>> df1.semanticHash() == df4.semanticHash()
+ True
Review comment:
emm, let's change unit test. Don't test on dataframe created from in-memory list (LocalRelation), they have different implementation between scala and pyspark.
Our usecase also do not care the behavior of LocalRelation.
so, I suggested add a unit test on: spark.df.read(...).where(...)
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