[jira] [Assigned] (SPARK-21365) Deduplicate logics parsing DDL-like type definition
[
https://issues.apache.org/jira/browse/SPARK-21365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wenchen Fan reassigned SPARK-21365:
---
Assignee: Hyukjin Kwon
> Deduplicate logics parsing DDL-like type definition
> ---
>
> Key: SPARK-21365
> URL: https://issues.apache.org/jira/browse/SPARK-21365
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.2.0
>Reporter: Hyukjin Kwon
>Assignee: Hyukjin Kwon
> Fix For: 2.3.0
>
>
> It looks we duplicate
> https://github.com/apache/spark/blob/d492cc5a21cd67b3999b85d97f5c41c3734b1ba3/python/pyspark/sql/types.py#L823-L845
> logic for parsing DDL-like type definitions.
> There are also two more points here:
> - This does not support "field type" but "field: type".
> - This does not support nested schemas. For example as below:
> {code}
> >>> spark.createDataFrame([[[1]]], "struct>").show()
> ...
> ValueError: The strcut field string format is: 'field_name:field_type', but
> got: a: struct
> {code}
> {code}
> >>> spark.createDataFrame([[[1]]], "a: struct").show()
> ...
> ValueError: The strcut field string format is: 'field_name:field_type', but
> got: a: struct
> {code}
> {code}
> >>> spark.createDataFrame([[[1]]], "a int").show()
> ...
> ValueError: Could not parse datatype: a int
> {code}
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[jira] [Assigned] (SPARK-21365) Deduplicate logics parsing DDL-like type definition
[
https://issues.apache.org/jira/browse/SPARK-21365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-21365:
Assignee: (was: Apache Spark)
> Deduplicate logics parsing DDL-like type definition
> ---
>
> Key: SPARK-21365
> URL: https://issues.apache.org/jira/browse/SPARK-21365
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.2.0
>Reporter: Hyukjin Kwon
>
> It looks we duplicate
> https://github.com/apache/spark/blob/d492cc5a21cd67b3999b85d97f5c41c3734b1ba3/python/pyspark/sql/types.py#L823-L845
> logic for parsing DDL-like type definitions.
> There are also two more points here:
> - This does not support "field type" but "field: type".
> - This does not support nested schemas. For example as below:
> {code}
> >>> spark.createDataFrame([[[1]]], "struct>").show()
> ...
> ValueError: The strcut field string format is: 'field_name:field_type', but
> got: a: struct
> {code}
> {code}
> >>> spark.createDataFrame([[[1]]], "a: struct").show()
> ...
> ValueError: The strcut field string format is: 'field_name:field_type', but
> got: a: struct
> {code}
> {code}
> >>> spark.createDataFrame([[[1]]], "a int").show()
> ...
> ValueError: Could not parse datatype: a int
> {code}
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[jira] [Assigned] (SPARK-21365) Deduplicate logics parsing DDL-like type definition
[
https://issues.apache.org/jira/browse/SPARK-21365?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-21365:
Assignee: Apache Spark
> Deduplicate logics parsing DDL-like type definition
> ---
>
> Key: SPARK-21365
> URL: https://issues.apache.org/jira/browse/SPARK-21365
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
>Affects Versions: 2.2.0
>Reporter: Hyukjin Kwon
>Assignee: Apache Spark
>
> It looks we duplicate
> https://github.com/apache/spark/blob/d492cc5a21cd67b3999b85d97f5c41c3734b1ba3/python/pyspark/sql/types.py#L823-L845
> logic for parsing DDL-like type definitions.
> There are also two more points here:
> - This does not support "field type" but "field: type".
> - This does not support nested schemas. For example as below:
> {code}
> >>> spark.createDataFrame([[[1]]], "struct>").show()
> ...
> ValueError: The strcut field string format is: 'field_name:field_type', but
> got: a: struct
> {code}
> {code}
> >>> spark.createDataFrame([[[1]]], "a: struct").show()
> ...
> ValueError: The strcut field string format is: 'field_name:field_type', but
> got: a: struct
> {code}
> {code}
> >>> spark.createDataFrame([[[1]]], "a int").show()
> ...
> ValueError: Could not parse datatype: a int
> {code}
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