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https://issues.apache.org/jira/browse/SPARK-28505?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-28505.
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Resolution: Invalid
> Add data source option for omitting partitioned columns when saving to file
> ---------------------------------------------------------------------------
>
> Key: SPARK-28505
> URL: https://issues.apache.org/jira/browse/SPARK-28505
> Project: Spark
> Issue Type: Wish
> Components: Input/Output, Spark Core
> Affects Versions: 2.4.4, 3.0.0
> Reporter: Juarez Rudsatz
> Priority: Minor
>
> It is very useful to have a option for omiting the columns used in
> partitioning from the output while writing to a file data source like csv,
> avro, parquet, orc or excel.
> Consider the following code:
> {{Dataset<Row> myDF = spark.createDataFrame(myRDD, MyClass.class);}}
> {{myDF.select("value1", "value2", "year","month","day")}}
> {{.write().format("csv")}}
> {{.option("header", "true")}}
> {{.partionBy("year","month","day")}}
> {{.save("hdfs://user/spark/warehouse/csv_output_dir");}}
> This will output many files in separated folders in a structure like:
> {{csv_output_dir/_SUCCESS}}
>
> {{csv_output_dir/year=2019/month=7/day=10/part-00000-ac09671e-5ee3-4479-ae83-5301aa7f424b.c000.csv}}
>
> {{csv_output_dir/year=2019/month=7/day=11/part-00000-ac09671e-5ee3-4479-ae83-5301aa7f424b.c000.csv}}
> {{...}}
> And the output will be something like:
> {{┌──────┬──────┬──────┬───────┬─────┐}}
> {{│ val1 │ val2 │ year │ month │ day │}}
> {{├──────┼──────┼──────┼───────┼─────┤}}
> {{│ 3673 │ 2345 │ 2019 │ 7 │ 10 │}}
> {{│ 2345 │ 3423 │ 2019 │ 7 │ 10 │}}
> {{│ 8765 │ 2423 │ 2019 │ 7 │ 10 │}}
> {{└──────┴──────┴──────┴───────┴─────┘}}
> When using partitioning in HIVE, the output from same source data will be
> something like:
> {{┌──────┬──────┐}}
> {{│ val1 │ val2 │}}
> {{├──────┼──────┤}}
> {{│ 3673 │ 2345 │}}
> {{│ 2345 │ 3423 │}}
> {{│ 8765 │ 2423 │}}
> {{└──────┴──────┘}}
> In this case the columns of the partitioning are not present in the CSV
> files. However output files follows the same folder/path structure as
> existing today.
> Please considere adding a opt-in config for DataFrameWriter for leaving out
> the partitioning columns as in the second example.
> The code could be something like:
> {{Dataset<Row> myDF = spark.createDataFrame(myRDD, MyClass.class);}}
> {{myDF.select("value1", "value2", "year","month","day")}}
> {{.write().format("csv")}}
> {{.option("header", "true")}}
> *{{.option("partition.omit.cols", "true")}}*
> {{.partionBy("year","month","day")}}
> {{.save("hdfs://user/spark/warehouse/csv_output_dir");}}
> Thanks.
>
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