{"action":"opened","issue":{"url":"https://api.github.com/repos/databricks/spark-redshift/issues/180","repository_url":"https://api.github.com/repos/databricks/spark-redshift","labels_url":"https://api.github.com/repos/databricks/spark-redshift/issues/180/labels{/name}","comments_url":"https://api.github.com/repos/databricks/spark-redshift/issues/180/comments","events_url":"https://api.github.com/repos/databricks/spark-redshift/issues/180/events","html_url":"https://github.com/databricks/spark-redshift/issues/180","id":133927490,"number":180,"title":"Error while using spark compiled with hadoop 1.0.4","user":{"login":"cdriesch","id":2863404,"avatar_url":"https://avatars.githubusercontent.com/u/2863404?v=3","gravatar_id":"","url":"https://api.github.com/users/cdriesch","html_url":"https://github.com/cdriesch","followers_url":"https://api.github.com/users/cdriesch/followers","following_url":"https://api.github.com/users/cdriesch/following{/other_user}","gists_url":"https://api.github.com/users/cdriesch/gists{/gist_id}","starred_url":"https://api.github.com/users/cdriesch/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cdriesch/subscriptions","organizations_url":"https://api.github.com/users/cdriesch/orgs","repos_url":"https://api.github.com/users/cdriesch/repos","events_url":"https://api.github.com/users/cdriesch/events{/privacy}","received_events_url":"https://api.github.com/users/cdriesch/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2016-02-16T09:23:36Z","updated_at":"2016-02-16T09:23:36Z","closed_at":null,"body":"We are using a DataStax Enterprise node, that it is based on hadoop 1.0.4. So far, there is no possibility to upgrade to hadoop 2. We are using version 0.5.0 of spark-redshift. When we want to write a dataframe to redshift, an error occurs since spark-redshift is compiled with hadoop 2. Is there a solution to this problem? \r\n\r\nStacktrace: \r\n\r\n**java.lang.IncompatibleClassChangeError: Found class org.apache.hadoop.mapreduce.TaskAttemptContext, but interface was expected\r\n\tat org.apache.avro.mapreduce.AvroKeyOutputFormat.getRecordWriter(AvroKeyOutputFormat.java:88)\r\n\tat com.databricks.spark.avro.AvroOutputWriter.<init>(AvroOutputWriter.scala:82)\r\n\tat com.databricks.spark.avro.AvroOutputWriterFactory.newInstance(AvroOutputWriterFactory.scala:31)\r\n\tat org.apache.spark.sql.sources.DefaultWriterContainer.initWriters(commands.scala:470)\r\n\tat org.apache.spark.sql.sources.BaseWriterContainer.executorSideSetup(commands.scala:360)\r\n\tat org.apache.spark.sql.sources.InsertIntoHadoopFsRelation.org$apache$spark$sql$sources$InsertIntoHadoopFsRelation$$writeRows$1(commands.scala:172)\r\n\tat org.apache.spark.sql.sources.InsertIntoHadoopFsRelation$$anonfun$insert$1.apply(commands.scala:160)\r\n\tat org.apache.spark.sql.sources.InsertIntoHadoopFsRelation$$anonfun$insert$1.apply(commands.scala:160)\r\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)\r\n\tat org.apache.spark.scheduler.Task.run(Task.scala:70)\r\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)\r\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\r\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\r\n\tat java.lang.Thread.run(Thread.java:745)\r\n"}}