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https://issues.apache.org/jira/browse/SPARK-32176?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-32176.
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Resolution: Cannot Reproduce
> Automatic type promotion to ArrayType in defined schema in from_json is broken
> ------------------------------------------------------------------------------
>
> Key: SPARK-32176
> URL: https://issues.apache.org/jira/browse/SPARK-32176
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.0.0
> Reporter: Abhishek Adhikari
> Priority: Major
>
>
> In spark 2.4, I'm able to read data where I have data in mixed types, by
> defining col "stats" as StringType and later parse the inner data
>
> stats_def =
> StructType().add("hour",IntegerType(),True).add("hits",IntegerType(),True)
> df2 =
> df.select(f.col("stats"),f.from_json(f.col("stats"),ArrayType(stats_def)).alias("stats_array"))
> df2.show(5,False)
> df2.printSchema
>
> ||stats||stats_array||
> |[\{"hour":3,"hits":1},\{"hour":4,"hits":1}]|[[3, 1], [4, 1]]|
> |{"hits":20}|[[, 20]]|
> <bound method DataFrame.printSchema of DataFrame[*stats: string, stats_array:
> array<struct<hour:int,hits:int>>*]>
>
> In spark 3.0.0 it throws error -
> java.lang.ClassCastException: java.lang.Integer cannot be cast to
> org.apache.spark.sql.catalyst.util.ArrayData
>
> I think it was an important feature and should be supported, maybe with the
> help of from_json options.
>
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