How can see the SQL statements that SPARK sends to my database? - apache-spark

Question

I have a spark cluster and a vertica database. I use
spark.read.jdbc( # etc
to load Spark dataframes into the cluster. When I do a certain groupby function
df2 = df.groupby('factor').agg(F.stddev('sum(PnL)'))
df2.show()
I then get a vertica syntax exception
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1454)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1442)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1441)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1441)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1667)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1622)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1611)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1890)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1903)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1916)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:347)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2193)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2546)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2192)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2199)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1935)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1934)
at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2576)
at org.apache.spark.sql.Dataset.head(Dataset.scala:1934)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2149)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.sql.SQLSyntaxErrorException: [Vertica][VJDBC](4856) ERROR: Syntax error at or near "Window"
at com.vertica.util.ServerErrorData.buildException(Unknown Source)
at com.vertica.io.ProtocolStream.readExpectedMessage(Unknown Source)
at com.vertica.dataengine.VDataEngine.prepareImpl(Unknown Source)
at com.vertica.dataengine.VDataEngine.prepare(Unknown Source)
at com.vertica.dataengine.VDataEngine.prepare(Unknown Source)
at com.vertica.jdbc.common.SPreparedStatement.<init>(Unknown Source)
at com.vertica.jdbc.jdbc4.S4PreparedStatement.<init>(Unknown Source)
at com.vertica.jdbc.VerticaJdbc4PreparedStatementImpl.<init>(Unknown Source)
at com.vertica.jdbc.VJDBCObjectFactory.createPreparedStatement(Unknown Source)
at com.vertica.jdbc.common.SConnection.prepareStatement(Unknown Source)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anon$1.<init>(JDBCRDD.scala:400)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD.compute(JDBCRDD.scala:379)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
Caused by: com.vertica.support.exceptions.SyntaxErrorException: [Vertica][VJDBC](4856) ERROR: Syntax error at or near "Window"
... 27 more
What I want to know is, what exactly did spark try to execute against the vertica database? Is there a trace config I can set somewhere?
Thanks!

Answer 1

You can look at the query_requests system table to see what SQL has been run against your database. You can filter on user_name and start_timestamp to try and help find the query.
Typically when you control the SQL, you would want to add in a label. But in this case you'll have to search for it.
Also be aware that the retention period of this is governed by the data collector settings.

Answer 2

Using the Spark web UI you can check the behavior and performance of your Spark application. It can also show you the SQL's in SQL tab for web UI. You can also explore the Resource Manager log for more details.
Spark web UI at http://<host ip>:4040.
You can access the SQL tab under /SQL URL, e.g. http://:4040/SQL/.