Major Description
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
./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
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).
./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]
