gatorsmile commented on a change in pull request #23534: [SPARK-26610][PYTHON]
Fix inconsistency between toJSON Method in Python and Scala.
URL: https://github.com/apache/spark/pull/23534#discussion_r248056739
##########
File path: python/pyspark/sql/dataframe.py
##########
@@ -109,15 +109,18 @@ def stat(self):
@ignore_unicode_prefix
@since(1.3)
def toJSON(self, use_unicode=True):
- """Converts a :class:`DataFrame` into a :class:`RDD` of string.
+ """Converts a :class:`DataFrame` into a :class:`DataFrame` of JSON
string.
- Each row is turned into a JSON document as one element in the returned
RDD.
+ Each row is turned into a JSON document as one element in the returned
DataFrame.
>>> df.toJSON().first()
- u'{"age":2,"name":"Alice"}'
+ Row(value=u'{"age":2,"name":"Alice"}')
"""
- rdd = self._jdf.toJSON()
- return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode))
+ jdf = self._jdf.toJSON()
+ if self.sql_ctx._conf.pysparkDataFrameToJSONShouldReturnDataFrame():
+ return DataFrame(jdf, self.sql_ctx)
+ else:
+ return RDD(jdf.toJavaRDD(), self._sc,
UTF8Deserializer(use_unicode))
Review comment:
We are trying to convince more end users to learn and use our high-level
APIs [DataFrame/Dataset] instead of low-level APIs [RDD]. Returning RDDs
encourages our end users to choose RDD APIs, like the example you pointed out
```
spark.range(1).toJSON().map(lambda value: value + "abc")
```
Using RDD APIs will be a blackbox and an barrier for our Spark SQL
optimizer. Basically, we are unable to optimize user application in a more
holistic way.
For better perf, I think we should return a DataFrame instead of a RDD. cc
@srowen @HyukjinKwon
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