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From "Takeshi Yamamuro (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-24540) Support for multiple delimiter in Spark CSV read
Date Sat, 16 Jun 2018 05:28:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-24540?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16514684#comment-16514684
] 

Takeshi Yamamuro commented on SPARK-24540:
------------------------------------------

Probably, this is a restriction of univocity parser. cc; [~hyukjin.kwon]

btw, why do you set 'is this blocked by SPARK-17967'?

> Support for multiple delimiter in Spark CSV read
> ------------------------------------------------
>
>                 Key: SPARK-24540
>                 URL: https://issues.apache.org/jira/browse/SPARK-24540
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.3.1
>            Reporter: Ashwin K
>            Priority: Major
>
> Currently, the delimiter option Spark 2.0 to read and split CSV files/data only support
a single character delimiter. If we try to provide multiple delimiters, we observer the following
error message.
> eg: Dataset<Row> df = spark.read().option("inferSchema", "true")
>                                                         
 .option("header", "false")
>                                                          .option("delimiter",
", ")
>                                                         
 .csv("C:\test.txt");
> Exception in thread "main" java.lang.IllegalArgumentException: Delimiter cannot be more
than one character: , 
> at org.apache.spark.sql.execution.datasources.csv.CSVUtils$.toChar(CSVUtils.scala:111)
>  at org.apache.spark.sql.execution.datasources.csv.CSVOptions.<init>(CSVOptions.scala:83)
>  at org.apache.spark.sql.execution.datasources.csv.CSVOptions.<init>(CSVOptions.scala:39)
>  at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.inferSchema(CSVFileFormat.scala:55)
>  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$8.apply(DataSource.scala:202)
>  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$8.apply(DataSource.scala:202)
>  at scala.Option.orElse(Option.scala:289)
>  at org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:201)
>  at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:392)
>  at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)
>  at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)
>  at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:596)
>  at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:473)
>  
> Generally, the data to be processed contains multiple delimiters and presently we need
to do a manual data clean up on the source/input file, which doesn't work well in large applications
which consumes numerous files.
> There seems to be work-around like reading data as text and using the split option, but
this in my opinion defeats the purpose, advantage and efficiency of a direct read from CSV
file.
>  



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