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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2020/02/14 10:40:37 UTC

[GitHub] [spark] liangz1 commented on a change in pull request #27565: [WIP][SPARK-30791][SQL][PYTHON] Add 'sameSemantics' and 'sementicHash' methods in Dataset

liangz1 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_r379362550
 
 

 ##########
 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:
   Currently, this test would fail. I don't have a clue why only the same object would have the same hash and all other cases seem to have a different hash. Scala side tests all pass.

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