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Azure / azure-sqldb-spark

This project provides a client library that allows Azure SQL DB or SQL Server to act as an input source or output sink for Spark jobs.
MIT License
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Error "SQLServerException: The connection is closed" on writing Spark Dataframe to Azure SQL #57

Closed prapanw closed 4 years ago

prapanw commented 4 years ago

Hi, I am using [com.microsoft.azure:azure-sqldb-spark:1.0.2] to write a Spark Dataframe (50K+ rows, 6 columns) to my Azure SQL database.

I am using following method: dataDF.write.mode(SaveMode.Append).sqlDB(config) with query Timeout set to a high value (6000s).

Any ideas of why it might be failing? Below is the stack trace.

Exception: org.apache.spark.SparkException: Job aborted due to stage failure: Task 242 in stage 84850.0 failed 4 times, most recent failure: Lost task 242.3 in stage 84850.0 (TID 12743887, 10.139.64.24, executor 344): com.microsoft.sqlserver.jdbc.SQLServerException: The connection is closed. at com.microsoft.sqlserver.jdbc.SQLServerException.makeFromDriverError(SQLServerException.java:227) at com.microsoft.sqlserver.jdbc.SQLServerConnection.checkClosed(SQLServerConnection.java:796) at com.microsoft.sqlserver.jdbc.SQLServerConnection.rollback(SQLServerConnection.java:2698) at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.savePartition(JdbcUtils.scala:713) at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:839) at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$saveTable$1.apply(JdbcUtils.scala:839) at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:987) at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:987) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2321) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2321) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.doRunTask(Task.scala:140) at org.apache.spark.scheduler.Task.run(Task.scala:113) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$13.apply(Executor.scala:533) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1541) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:539) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)

arvindshmicrosoft commented 4 years ago

I'm sorry, this kind of issue is typically very context-specific and needs to be debugged and isolated first. I'm not sure if it is specific to the connector. Also, note that this connector itself is not being actively maintained. I recommend you switch to using the newer one.

p.s. feel free to reach out internally at MSFT if you'd like to troubleshoot this together.