[ https://issues.apache.org/jira/browse/SPARK-22291?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wenchen Fan updated SPARK-22291: -------------------------------- Fix Version/s: 2.2.1 > Postgresql UUID[] to Cassandra: Conversion Error > ------------------------------------------------ > > Key: SPARK-22291 > URL: https://issues.apache.org/jira/browse/SPARK-22291 > Project: Spark > Issue Type: Bug > Components: Spark Core, SQL > Affects Versions: 2.2.0 > Environment: Debian Linux, Scala 2.11, Spark 2.2.0, PostgreSQL 9.6, > Cassandra 3 > Reporter: Fabio J. Walter > Assignee: Jen-Ming Chung > Labels: patch, postgresql, sql > Fix For: 2.2.1, 2.3.0 > > Attachments: > org_apache_spark_sql_execution_datasources_jdbc_JdbcUtil.png > > > My job reads data from a PostgreSQL table that contains columns of user_ids > uuid[] type, so that I'm getting the error above when I'm trying to save data > on Cassandra. > However, the creation of this same table on Cassandra works fine! user_ids > list<text>. > I can't change the type on the source table, because I'm reading data from a > legacy system. > I've been looking at point printed on log, on class > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils.scala > Stacktrace on Spark: > {noformat} > Caused by: java.lang.ClassCastException: [Ljava.util.UUID; cannot be cast to > [Ljava.lang.String; > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:443) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$14.apply(JdbcUtils.scala:442) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13$$anonfun$18.apply(JdbcUtils.scala:472) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13$$anonfun$18.apply(JdbcUtils.scala:472) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$nullSafeConvert(JdbcUtils.scala:482) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13.apply(JdbcUtils.scala:470) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$13.apply(JdbcUtils.scala:469) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:330) > at > org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:312) > at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73) > at > org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) > at > org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395) > at > org.apache.spark.sql.execution.columnar.InMemoryRelation$$anonfun$1$$anon$1.hasNext(InMemoryRelation.scala:133) > at > org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215) > at > org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038) > at > org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029) > at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969) > at > org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029) > at > org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760) > at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:285) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) > at org.apache.spark.scheduler.Task.run(Task.scala:108) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:748) > {noformat} > Proposed solution: > At this specific point > spark-sql_2.11-2.2.0-sources.jar!/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala:443 > {code:scala} > //My suggestion is change the line 443 from > ```array.asInstanceOf[Array[java.lang.String]] > .map(UTF8String.fromString)``` > //to > ```array.map(UTF8String.fromString(_.toString))``` > {code} -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: 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