HyukjinKwon commented on a change in pull request #27565:
[SPARK-30791][SQL][PYTHON] Add 'sameSemantics' and 'sementicHash' methods in
Dataset
URL: https://github.com/apache/spark/pull/27565#discussion_r379992810
##########
File path: python/pyspark/sql/dataframe.py
##########
@@ -2153,6 +2153,58 @@ def transform(self, func):
"should have been DataFrame." %
type(result)
return result
+ @since(3.1)
+ def sameSemantics(self, other):
+ """
+ Returns `True` when the logical query plans inside both
:class:`DataFrame`\\s are equal and
+ therefore return same results.
+
+ .. note:: The equality comparison here is simplified by tolerating the
cosmetic differences
+ such as attribute names.
+
+ .. note:: This API can compare both :class:`DataFrame`\\s very fast
but can still return
+ `False` on the :class:`DataFrame` that return the same results,
for instance, from
+ different plans. Such false negative semantic can be useful when
caching as an example.
+
+ >>> df1 = spark.range(100)
+ >>> df2 = spark.range(100)
+ >>> df3 = spark.range(100)
+ >>> df4 = spark.range(100)
+ >>> df1.withColumn("col1", df1.id *
2).sameSemantics(df2.withColumn("col1", df2.id * 2))
+ True
+ >>> df1.withColumn("col1", df1.id *
2).sameSemantics(df3.withColumn("col1", df3.id + 2))
+ False
+ >>> df1.withColumn("col1", df1.id *
2).sameSemantics(df4.withColumn("col0", df4.id * 2))
+ 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 hash code of the logical query plan against this
:class:`DataFrame`.
+
+ .. note:: Unlike the standard hash code, the hash is calculated
against the query plan
+ simplified by tolerating the cosmetic differences such as
attribute names.
+
+ >>> df1 = spark.range(100)
+ >>> df2 = spark.range(100)
+ >>> df3 = spark.range(100)
+ >>> df4 = spark.range(100)
+ >>> df1.withColumn("col1", df1.id * 2).semanticHash() == \
+ df2.withColumn("col1", df2.id * 2).semanticHash()
+ True
+ >>> # df1.withColumn("col1", df1.id * 2).semanticHash() == \
+ df3.withColumn("col1", df3.id + 2).semanticHash() # False
+ >>> df1.withColumn("col1", df1.id * 2).semanticHash() == \
+ df4.withColumn("col0", df4.id * 2).semanticHash()
+ True
Review comment:
nit:
I think you can just show a couple of examples:
```python
>>> spark.range(10).selectExpr("id as col0").semanticHash() # doctest: +SKIP
1855039936
>>> spark.range(10).selectExpr("id as col1").semanticHash() # doctest: +SKIP
1855039936
```
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