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Posted to reviews@spark.apache.org by "HyukjinKwon (via GitHub)" <gi...@apache.org> on 2023/10/05 05:22:12 UTC
[PR] [SPARK-45420][SQL][PYTHON][CONNECT] Add DataType.fromDDL into PySpark [spark]
HyukjinKwon opened a new pull request, #43226:
URL: https://github.com/apache/spark/pull/43226
### What changes were proposed in this pull request?
This PR implements `DataType.fromDDL` as the parity to Scala API:
https://github.com/apache/spark/blob/350b8d8388c9ad15303d39f22b249b8c73785695/sql/api/src/main/scala/org/apache/spark/sql/types/DataType.scala#L121-L126
One difference is that Python API also supports the legacy format inside `struct<...>`, e.g., `a: int, b: int`.
### Why are the changes needed?
In order for the end users to parse the DDL formatted type easily.
### Does this PR introduce _any_ user-facing change?
Yes, this PR adds a new user-facing API: `DataType.fromDDL`.
### How was this patch tested?
Unittests were added, and manually tested them too.
### Was this patch authored or co-authored using generative AI tooling?
No.
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Re: [PR] [SPARK-45420][SQL][PYTHON][CONNECT] Add DataType.fromDDL into PySpark [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #43226:
URL: https://github.com/apache/spark/pull/43226#discussion_r1346887264
##########
python/pyspark/sql/types.py:
##########
@@ -139,6 +139,54 @@ def fromInternal(self, obj: Any) -> Any:
"""
return obj
+ @classmethod
+ def fromDDL(cls, ddl: str) -> "DataType":
+ """
+ Creates :class:`DataType` for a given DDL-formatted string.
+
+ .. versionadded:: 4.0.0
+
+ Parameters
+ ----------
+ ddl : str
+ DDL-formatted string representation of types, e.g.
+ :class:`pyspark.sql.types.DataType.simpleString`, except that top level struct
+ type can omit the ``struct<>`` for the compatibility reason with
+ ``spark.createDataFrame`` and Python UDFs.
+
+ Returns
+ -------
+ :class:`DataType`
+
+ Examples
+ --------
+ Create a StructType by the corresponding DDL formatted string.
+
+ >>> from pyspark.sql.types import DataType
+ >>> DataType.fromDDL("b string, a int")
+ StructType([StructField('b', StringType(), True), StructField('a', IntegerType(), True)])
+
+ Create a single DataType by the corresponding DDL formatted string.
+
+ >>> DataType.fromDDL("decimal(10,10)")
+ DecimalType(10,10)
+
+ Create a StructType by the legacy string format.
+
+ >>> DataType.fromDDL("b: string, a: int")
+ StructType([StructField('b', StringType(), True), StructField('a', IntegerType(), True)])
+ """
+ from pyspark.sql import SparkSession
+ from pyspark.sql.functions import udf
+
+ # Intentionally uses SparkSession so one implementation can be shared with/without
+ # Spark Connect.
+ schema = (
+ SparkSession.active().range(0).select(udf(lambda x: x, returnType=ddl)("id")).schema
+ )
+ assert len(schema) == 1
+ return schema[0].dataType
Review Comment:
Good point but let's probably avoid having dependencies for now since we should likely touch how we package this.
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Re: [PR] [SPARK-45420][SQL][PYTHON][CONNECT] Add DataType.fromDDL into PySpark [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon closed pull request #43226: [SPARK-45420][SQL][PYTHON][CONNECT] Add DataType.fromDDL into PySpark
URL: https://github.com/apache/spark/pull/43226
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Re: [PR] [SPARK-45420][SQL][PYTHON][CONNECT] Add DataType.fromDDL into PySpark [spark]
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on PR #43226:
URL: https://github.com/apache/spark/pull/43226#issuecomment-1748441778
Merged to master.
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Re: [PR] [SPARK-45420][SQL][PYTHON][CONNECT] Add DataType.fromDDL into PySpark [spark]
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #43226:
URL: https://github.com/apache/spark/pull/43226#discussion_r1346850718
##########
python/pyspark/sql/types.py:
##########
@@ -139,6 +139,54 @@ def fromInternal(self, obj: Any) -> Any:
"""
return obj
+ @classmethod
+ def fromDDL(cls, ddl: str) -> "DataType":
+ """
+ Creates :class:`DataType` for a given DDL-formatted string.
+
+ .. versionadded:: 4.0.0
+
+ Parameters
+ ----------
+ ddl : str
+ DDL-formatted string representation of types, e.g.
+ :class:`pyspark.sql.types.DataType.simpleString`, except that top level struct
+ type can omit the ``struct<>`` for the compatibility reason with
+ ``spark.createDataFrame`` and Python UDFs.
+
+ Returns
+ -------
+ :class:`DataType`
+
+ Examples
+ --------
+ Create a StructType by the corresponding DDL formatted string.
+
+ >>> from pyspark.sql.types import DataType
+ >>> DataType.fromDDL("b string, a int")
+ StructType([StructField('b', StringType(), True), StructField('a', IntegerType(), True)])
+
+ Create a single DataType by the corresponding DDL formatted string.
+
+ >>> DataType.fromDDL("decimal(10,10)")
+ DecimalType(10,10)
+
+ Create a StructType by the legacy string format.
+
+ >>> DataType.fromDDL("b: string, a: int")
+ StructType([StructField('b', StringType(), True), StructField('a', IntegerType(), True)])
+ """
+ from pyspark.sql import SparkSession
+ from pyspark.sql.functions import udf
+
+ # Intentionally uses SparkSession so one implementation can be shared with/without
+ # Spark Connect.
+ schema = (
+ SparkSession.active().range(0).select(udf(lambda x: x, returnType=ddl)("id")).schema
+ )
+ assert len(schema) == 1
+ return schema[0].dataType
Review Comment:
This PR LGTM, but I feel it would be better if we leverage py4j/[ddl_parse](https://github.com/apache/spark/blob/dc6b43d84781c0e9372dd665e19233ea535fbbcf/python/pyspark/sql/connect/session.py#L358-L360) here, and then we can use this function in other places (createDF/udf/etc)
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