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https://issues.apache.org/jira/browse/SPARK-20590?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15995799#comment-15995799
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Apache Spark commented on SPARK-20590:
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User 'sameeragarwal' has created a pull request for this issue:
https://github.com/apache/spark/pull/17847
> Map default input data source formats to inlined classes
> --------------------------------------------------------
>
> Key: SPARK-20590
> URL: https://issues.apache.org/jira/browse/SPARK-20590
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Sameer Agarwal
>
> One of the common usability problems around reading data in spark
> (particularly CSV) is that there can often be a conflict between different
> readers in the classpath.
> As an example, if someone launches a 2.x spark shell with the spark-csv
> package in the classpath, Spark currently fails in an extremely unfriendly way
> {code}
> ./bin/spark-shell --packages com.databricks:spark-csv_2.11:1.5.0
> scala> val df = spark.read.csv("/foo/bar.csv")
> java.lang.RuntimeException: Multiple sources found for csv
> (org.apache.spark.sql.execution.datasources.csv.CSVFileFormat,
> com.databricks.spark.csv.DefaultSource15), please specify the fully qualified
> class name.
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.execution.datasources.DataSource$.lookupDataSource(DataSource.scala:574)
> at
> org.apache.spark.sql.execution.datasources.DataSource.providingClass$lzycompute(DataSource.scala:85)
> at
> org.apache.spark.sql.execution.datasources.DataSource.providingClass(DataSource.scala:85)
> at
> org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:295)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
> at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:533)
> at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:412)
> ... 48 elided
> {code}
> This JIRA proposes a simple way of fixing this error by always mapping
> default input data source formats to inlined classes (that exist in Spark).
> {code}
> ./bin/spark-shell --packages com.databricks:spark-csv_2.11:1.5.0
> scala> val df = spark.read.csv("/foo/bar.csv")
> df: org.apache.spark.sql.DataFrame = [_c0: string]
> {code}
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