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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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Abhishek Adhikari updated SPARK-32176:
--------------------------------------
Description:
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]]|
|[\\{"hour":5,"hits":"2"},\\{"hour":6,"hits":2}]|null |
|[\\{"hour":1,"hits":8},\\{"hour":2,"hits":5}] |[[1, 8], [2, 5]]| |\{"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 an from_json options.
was:
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]]|
|[\{"hour":5,"hits":"2"},\{"hour":6,"hits":2}]|null |
|[\{"hour":1,"hits":8},\{"hour":2,"hits":5}] |[[1, 8], [2, 5]]| |\{"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 an from_json options.
> 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]]|
> |[\\{"hour":5,"hits":"2"},\\{"hour":6,"hits":2}]|null |
> |[\\{"hour":1,"hits":8},\\{"hour":2,"hits":5}] |[[1, 8], [2, 5]]|
> |\{"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 an from_json options.
>
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