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[SPARK-35929][PYTHON] Support to infer nested dict as a struct when creating a DataFrame

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### What changes were proposed in this pull request?

Currently, inferring nested structs is always using `MapType`.

This behavior causes an issue because it infers the schema with a value type of the first field of the struct as below:

```python
data = [{"inside_struct": {"payment": 100.5, "name": "Lee"}}]
df = spark.createDataFrame(data)
df.show(truncate=False)
+--------------------------------+
|inside_struct                   |
+--------------------------------+
|{name -> null, payment -> 100.5}|
+--------------------------------+
```

The "name" became `null`, but it should've been `"Lee"`.

In this case, we need to be able to infer the schema with a `StructType` instead of a `MapType`.

Therefore, this PR proposes adding an new configuration `spark.sql.pyspark.inferNestedDictAsStruct.enabled` to handle which type is used for inferring nested structs.
- When `spark.sql.pyspark.inferNestedDictAsStruct.enabled` is `false` (by default), inferring nested structs by `MapType`
- When `spark.sql.pyspark.inferNestedDictAsStruct.enabled` is `true`, inferring nested structs by `StructType`

### Why are the changes needed?

Because always inferring the nested structs by `MapType` doesn't work properly for some cases.

### Does this PR introduce _any_ user-facing change?

New configuration `spark.sql.pyspark.inferNestedDictAsStruct.enabled` is added.

### How was this patch tested?

Added an unit test

Closes #33214 from itholic/SPARK-35929.

Lead-authored-by: itholic <haejoon.lee@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
This commit is contained in:
itholic 2021-07-07 15:14:18 +09:00 committed by Hyukjin Kwon
parent ddc5cb9051
commit 2537fe8cba
4 changed files with 50 additions and 13 deletions
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13
python/pyspark/sql/session.py
View file

@ -436,7 +436,9 @@ class SparkSession(SparkConversionMixin):
"""
if not data:
raise ValueError("can not infer schema from empty dataset")
schema = reduce(_merge_type, (_infer_schema(row, names) for row in data))
infer_dict_as_struct = self._wrapped._conf.inferDictAsStruct()
schema = reduce(_merge_type, (_infer_schema(row, names, infer_dict_as_struct)
for row in data))
if _has_nulltype(schema):
raise ValueError("Some of types cannot be determined after inferring")
return schema
@ -462,11 +464,13 @@ class SparkSession(SparkConversionMixin):
raise ValueError("The first row in RDD is empty, "
"can not infer schema")
infer_dict_as_struct = self._wrapped._conf.inferDictAsStruct()
if samplingRatio is None:
schema = _infer_schema(first, names=names)
schema = _infer_schema(first, names=names, infer_dict_as_struct=infer_dict_as_struct)
if _has_nulltype(schema):
for row in rdd.take(100)[1:]:
schema = _merge_type(schema, _infer_schema(row, names=names))
schema = _merge_type(schema, _infer_schema(
row, names=names, infer_dict_as_struct=infer_dict_as_struct))
if not _has_nulltype(schema):
break
else:
@ -475,7 +479,8 @@ class SparkSession(SparkConversionMixin):
else:
if samplingRatio < 0.99:
rdd = rdd.sample(False, float(samplingRatio))
schema = rdd.map(lambda row: _infer_schema(row, names)).reduce(_merge_type)
schema = rdd.map(lambda row: _infer_schema(
row, names, infer_dict_as_struct=infer_dict_as_struct)).reduce(_merge_type)
return schema
def _createFromRDD(self, rdd, schema, samplingRatio):

15
python/pyspark/sql/tests/test_types.py
View file

@ -204,6 +204,21 @@ class TypesTests(ReusedSQLTestCase):
df = self.spark.createDataFrame(rdd)
self.assertEqual(Row(field1=1, field2=u'row1'), df.first())
def test_infer_nested_dict_as_struct(self):
# SPARK-35929: Test inferring nested dict as a struct type.
NestedRow = Row("f1", "f2")
with self.sql_conf({"spark.sql.pyspark.inferNestedDictAsStruct.enabled": True}):
data = [NestedRow([{"payment": 200.5, "name": "A"}], [1, 2]),
NestedRow([{"payment": 100.5, "name": "B"}], [2, 3])]
nestedRdd = self.sc.parallelize(data)
df = self.spark.createDataFrame(nestedRdd)
self.assertEqual(Row(f1=[Row(payment=200.5, name='A')], f2=[1, 2]), df.first())
df = self.spark.createDataFrame(data)
self.assertEqual(Row(f1=[Row(payment=200.5, name='A')], f2=[1, 2]), df.first())
def test_create_dataframe_from_dict_respects_schema(self):
df = self.spark.createDataFrame([{'a': 1}], ["b"])
self.assertEqual(df.columns, ['b'])

26
python/pyspark/sql/types.py
View file

@ -1003,7 +1003,7 @@ if sys.version_info[0] < 4:
_array_type_mappings['u'] = StringType
def _infer_type(obj):
def _infer_type(obj, infer_dict_as_struct=False):
"""Infer the DataType from obj
"""
if obj is None:
@ -1020,14 +1020,22 @@ def _infer_type(obj):
return dataType()
if isinstance(obj, dict):
for key, value in obj.items():
if key is not None and value is not None:
return MapType(_infer_type(key), _infer_type(value), True)
return MapType(NullType(), NullType(), True)
if infer_dict_as_struct:
struct = StructType()
for key, value in obj.items():
if key is not None and value is not None:
struct.add(key, _infer_type(value, infer_dict_as_struct), True)
return struct
else:
for key, value in obj.items():
if key is not None and value is not None:
return MapType(_infer_type(key, infer_dict_as_struct),
_infer_type(value, infer_dict_as_struct), True)
return MapType(NullType(), NullType(), True)
elif isinstance(obj, list):
for v in obj:
if v is not None:
return ArrayType(_infer_type(obj[0]), True)
return ArrayType(_infer_type(obj[0], infer_dict_as_struct), True)
return ArrayType(NullType(), True)
elif isinstance(obj, array):
if obj.typecode in _array_type_mappings:
@ -1036,12 +1044,12 @@ def _infer_type(obj):
raise TypeError("not supported type: array(%s)" % obj.typecode)
else:
try:
return _infer_schema(obj)
return _infer_schema(obj, infer_dict_as_struct=infer_dict_as_struct)
except TypeError:
raise TypeError("not supported type: %s" % type(obj))
def _infer_schema(row, names=None):
def _infer_schema(row, names=None, infer_dict_as_struct=False):
"""Infer the schema from dict/namedtuple/object"""
if isinstance(row, dict):
items = sorted(row.items())
@ -1067,7 +1075,7 @@ def _infer_schema(row, names=None):
fields = []
for k, v in items:
try:
fields.append(StructField(k, _infer_type(v), True))
fields.append(StructField(k, _infer_type(v, infer_dict_as_struct), True))
except TypeError as e:
raise TypeError("Unable to infer the type of the field {}.".format(k)) from e
return StructType(fields)

9
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
View file

@ -3335,6 +3335,13 @@ object SQLConf {
.intConf
.createWithDefault(0)
val INFER_NESTED_DICT_AS_STRUCT = buildConf("spark.sql.pyspark.inferNestedDictAsStruct.enabled")
.doc("PySpark's SparkSession.createDataFrame infers the nested dict as a map by default. " +
"When it set to true, it infers the nested dict as a struct.")
.version("3.3.0")
.booleanConf
.createWithDefault(false)
/**
* Holds information about keys that have been deprecated.
*
@ -4048,6 +4055,8 @@ class SQLConf extends Serializable with Logging {
def maxConcurrentOutputFileWriters: Int = getConf(SQLConf.MAX_CONCURRENT_OUTPUT_FILE_WRITERS)
def inferDictAsStruct: Boolean = getConf(SQLConf.INFER_NESTED_DICT_AS_STRUCT)
/** ********************** SQLConf functionality methods ************ */
/** Set Spark SQL configuration properties. */