HyukjinKwon commented on a change in pull request #27331:
URL: https://github.com/apache/spark/pull/27331#discussion_r445512162
##########
File path: python/pyspark/sql/readwriter.py
##########
@@ -1048,6 +1048,128 @@ def jdbc(self, url, table, mode=None, properties=None):
self.mode(mode)._jwrite.jdbc(url, table, jprop)
+class DataFrameWriterV2(object):
+ """
+ Interface used to write a class:`pyspark.sql.dataframe.DataFrame`
+ to external storage using the v2 API.
+
+ .. versionadded:: 3.1.0
+ """
+
+ def __init__(self, df, table):
+ self._df = df
+ self._spark = df.sql_ctx
+ self._jwriter = df._jdf.writeTo(table)
+
+ @since(3.1)
+ def using(self, provider):
+ """
+ Specifies a provider for the underlying output data source.
+ Spark's default catalog supports "parquet", "json", etc.
+ """
+ self._jwriter.using(provider)
+ return self
+
+ @since(3.1)
+ def option(self, key, value):
+ """
+ Add a write option.
+ """
+ self._jwriter.option(key, to_str(value))
+ return self
+
+ @since(3.1)
+ def options(self, **options):
+ """
+ Add write options.
+ """
+ options = {k: to_str(v) for k, v in options.items()}
+ self._jwriter.options(options)
+ return self
+
+ @since(3.1)
+ def partitionedBy(self, col, *cols):
Review comment:
@rdblue, @brkyvz, @cloud-fan, Should we maybe at least use a different
class for these partition column expressions such as `PartitioningColumn` like
we do for `TypedColumn`, and add `asPartitioningColumn` to `Column`?
I remember we basically want to remove these partitioning specific
expressions at [[DISCUSS] Revert and revisit the public custom expression API
for partition (a.k.a. Transform API)
](http://apache-spark-developers-list.1001551.n3.nabble.com/DISCUSS-Revert-and-revisit-the-public-custom-expression-API-for-partition-a-k-a-Transform-API-td28683.html)
if we find a better way to do it.
I suspect doing `PartitionedColumn` is a-okay as a temporary solution
because we can guard it by typing, and we can move these partitioning-specific
expressions into a separate package. I think this way make them distinguished
at least. I can work as well on it if this sounds fine to you guys.
##########
File path: python/pyspark/sql/readwriter.py
##########
@@ -1048,6 +1048,128 @@ def jdbc(self, url, table, mode=None, properties=None):
self.mode(mode)._jwrite.jdbc(url, table, jprop)
+class DataFrameWriterV2(object):
+ """
+ Interface used to write a class:`pyspark.sql.dataframe.DataFrame`
+ to external storage using the v2 API.
+
+ .. versionadded:: 3.1.0
+ """
+
+ def __init__(self, df, table):
+ self._df = df
+ self._spark = df.sql_ctx
+ self._jwriter = df._jdf.writeTo(table)
+
+ @since(3.1)
+ def using(self, provider):
+ """
+ Specifies a provider for the underlying output data source.
+ Spark's default catalog supports "parquet", "json", etc.
+ """
+ self._jwriter.using(provider)
+ return self
+
+ @since(3.1)
+ def option(self, key, value):
+ """
+ Add a write option.
+ """
+ self._jwriter.option(key, to_str(value))
+ return self
+
+ @since(3.1)
+ def options(self, **options):
+ """
+ Add write options.
+ """
+ options = {k: to_str(v) for k, v in options.items()}
+ self._jwriter.options(options)
+ return self
+
+ @since(3.1)
+ def partitionedBy(self, col, *cols):
Review comment:
@rdblue, @brkyvz, @cloud-fan, Should we maybe at least use a different
class for these partition column expressions such as `PartitionedColumn` like
we do for `TypedColumn`?
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