Great we are already discussing/working to fix the issue.Happy to help if I can :-)
Any workarounds that we can use for now? Please note I am not invoking any additional packages while running spark submit on the thin jar. Thanks,Srabasti Banerjee On Thursday, 30 August, 2018, 9:02:11 PM GMT-7, Hyukjin Kwon <gurwls...@gmail.com> wrote: Yea, this is exactly what I have been worried of the recent changes (discussed in https://issues.apache.org/jira/browse/SPARK-24924)See https://github.com/apache/spark/pull/17916. This should be fine in upper Spark versions. FYI, +Wechen and DongjoonI want to add Thomas Graves and Gengliang Wang too but can't fine their email addresses. 2018년 8월 31일 (금) 오전 11:52, Srabasti Banerjee <srabast...@ymail.com.invalid>님이 작성: Hi, I am trying to run below code to read file as a dataframe onto a Stream (for Spark Streaming) developed via Eclipse IDE, defining schemas appropriately, by running thin jar on server and am getting error below. Tried out suggestions from researching on internet based on "spark.read.option.schema.csv" similar errors with no success. Am thinking this can be a bug as the changes might not have been done for readStream option? Has anybody encountered similar issue for Spark Streaming? Looking forward to hear your response(s)! ThanksSrabasti Banerjee Error Exception in thread "main" java.lang.RuntimeException: Multiple sources found for csv (com.databricks.spark.csv.DefaultSource15, org.apache.spark.sql.execution.datasources.csv.CSVFileFormat), please specify the fully qualified class name. Code: val csvdf = spark.readStream.option("sep", ",").schema(userSchema).csv("server_path") //does not resolve error val csvdf = spark.readStream.option("sep", ",").schema(userSchema).format("com.databricks.spark.csv").csv("server_path") //does not resolve error val csvdf = spark.readStream.option("sep", ",").schema(userSchema).csv("server_path") //does not resolve errorval csvdf = spark.readStream.option("sep", ",").schema(userSchema).format("org.apache.spark.sql.execution.datasources.csv").csv("server_path") //does not resolve errorval csvdf = spark.readStream.option("sep", ",").schema(userSchema).format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat").csv("server_path") //does not resolve errorval csvdf = spark.readStream.option("sep", ",").schema(userSchema).format("com.databricks.spark.csv.DefaultSource15").csv("server_path") //does not resolve error
