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Posted to issues@spark.apache.org by "Yu Xiang (Jira)" <ji...@apache.org> on 2021/03/17 09:44:00 UTC
[jira] [Commented] (SPARK-20590) Map default input data source
formats to inlined classes
[ https://issues.apache.org/jira/browse/SPARK-20590?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17303237#comment-17303237 ]
Yu Xiang commented on SPARK-20590:
----------------------------------
[~cloud_fan], I tried to use the full name as, it does not work. Any idea? (more detailed explanation of the problem is here: https://stackoverflow.com/questions/66664181/spark-multiple-sources-found-for-text)
{code:java}
DataFrameReader read = spark.read()；
JavaRDD<String> stringJavaRDD = read.format("org.apache.spark.sql.execution.datasources.text.TextFileFormat").textFile(inputPath).javaRDD();
{code}
> 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
> Assignee: Hyukjin Kwon
> Priority: Major
> Fix For: 2.2.0
>
>
> 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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