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https://issues.apache.org/jira/browse/SPARK-32176?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
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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