Brian Schaefer created SPARK-39168:
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Summary: Consider all values in a python list when inferring schema
Key: SPARK-39168
URL: https://issues.apache.org/jira/browse/SPARK-39168
Project: Spark
Issue Type: New Feature
Components: PySpark
Affects Versions: 3.2.1
Reporter: Brian Schaefer
Schema inference fails on the following case:
{code:python}
>>> data = [{"a": [1, None], "b": [None, 2]}]
>>> spark.createDataFrame(data)
ValueError: Some of types cannot be determined after inferring
{code}
This is because only the first value in the array is used to infer the element
type for the array:
[https://github.com/apache/spark/blob/b63674ea5f746306a96ab8c39c23a230a6cb9566/python/pyspark/sql/types.py#L1260].
The element type of the "b" array is inferred as {{NullType}} but I think it
makes sense to infer the element type as {{{}LongType{}}}.
One approach to address the above would be to infer the type from the first
non-null value in the array. However, consider a case with structs:
{code:python}
>>> spark.conf.set("spark.sql.pyspark.inferNestedDictAsStruct.enabled", True)
>>> data = [{"a": [{"b": 1}, {"c": 2}]}]
>>> spark.createDataFrame(data).schema
StructType([StructField('a', ArrayType(StructType([StructField('b', LongType(),
True)]), True), True)])
{code}
The element type of the "a" array is inferred as a struct with one field, "b".
However, it would be convenient to infer the element type as a struct with both
fields "b" and "c". Omitted fields from each dictionary would become null
values in each struct:
{code:java}
+----------------------+
| a|
+----------------------+
|[{1, null}, {null, 1}]|
+----------------------+
{code}
To support both of these cases, the type of each array element could be
inferred, and those types could be merged, similar to the approach
[here|https://github.com/apache/spark/blob/b63674ea5f746306a96ab8c39c23a230a6cb9566/python/pyspark/sql/session.py#L574-L576].
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