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https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-37604:
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Assignee: Apache Spark
> The parameter emptyValueInRead in CSVOptions is not designed as supposed to be
> ------------------------------------------------------------------------------
>
> Key: SPARK-37604
> URL: https://issues.apache.org/jira/browse/SPARK-37604
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0, 3.2.0
> Reporter: Guo Wei
> Assignee: Apache Spark
> Priority: Major
>
> For null values, the parameter nullValue can be set when reading or writing
> in CSVOptions:
> {code:scala}
> // For writing, convert: null(dataframe) => nullValue(csv)
> // For reading, convert: nullValue or ,,(csv) => null(dataframe)
> {code}
> For example, a column has null values, if nullValue is set to "null" string.
> {code:scala}
> Seq(("Tesla", null.asInstanceOf[String])).toDF("make",
> "comment").write.option("nullValue", "NULL").csv(path){code}
> The saved csv file is shown as:
> {noformat}
> Tesla,NULL
> {noformat}
> and if we read this csv file with nullValue set to "null" string.
> {code:java}
> spark.read.option("nullValue", "NULL").csv(path)
> {code}
> we can get the DataFrame which data is shown as:
> ||make||comment||
> |tesla|null|
> {color:#57d9a3}*We can succeed to recovery it to the original
> DataFrame.*{color}
>
> Since Spark 2.4, for empty strings, there are emptyValueInRead for reading
> and emptyValueInWrite for writing that can be set in CSVOptions:
> {code:scala}
> // For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
> // For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
> I think the read handling is not suitable, we can not convert "" or
> `{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings)
> but {color:#172b4d}emptyValueInRead's setting value{color}, it supposed to be
> as flows:
> {code:scala}
> // For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
> For example, a column has empty strings, if emptyValueInWrite is set to
> "EMPTY" string.
> {code:scala}
> Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
> "EMPTY").csv(path){code}
> The saved csv file is shown as:
> {noformat}
> Tesla,EMPTY {noformat}
> and if we read this csv file with emptyValue(emptyValueInRead) set to "EMPTY"
> string.
> {code:java}
> spark.read.option("emptyValue", "EMPTY").csv(path)
> {code}
> we actually get the DataFrame which data is shown as:
> ||make||comment||
> |tesla|EMPTY|
> but the DataFrame which data should be shown as below as expected:
> ||make||comment||
> |tesla| |
> {color:#de350b}*We can not recovery it to the original DataFrame.*{color}
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