[jira] [Commented] (SPARK-39168) Consider all values in a python list when inferring schema

["Hyukjin Kwon (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-39168?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17536385#comment-17536385 ] 

Hyukjin Kwon commented on SPARK-39168:
--------------------------------------

Sounds making sense to me. Are you interested in submitting a PR?

cc [~itholic] who wrote {{spark.sql.pyspark.inferNestedDictAsStruct.enabled}} option.

> 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
>            Priority: Major
>
> 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, 2}]|
> +----------------------+
> {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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