The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached.

[JKR]

Getting below error

Context: Using Databricks shared interactive cluster for scheduled run multiple parallel jobs at the same time after every 5 mins. When I check Ganglia, driver node's memory reaches almost max and then restart of driver happens and the same process repeats. I'm not using any of the below operations:

• collect() operator, which brings a large amount of data to the driver.
• Conversion of a large DataFrame to Pandas DataFrame using the toPandas() function.

java.lang.OutOfMemoryError: unable to create new native thread

at java.lang.Thread.start0(Native Method)

at java.lang.Thread.start(Thread.java:719)

at java.util.concurrent.ThreadPoolExecutor.addWorker(ThreadPoolExecutor.java:957)

at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1367)

at scala.concurrent.impl.ExecutionContextImpl.execute(ExecutionContextImpl.scala:24)

at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:72)

at scala.concurrent.impl.Promise$KeptPromise$Kept.onComplete(Promise.scala:372)

at scala.concurrent.impl.Promise$KeptPromise$Kept.onComplete$(Promise.scala:371)

at scala.concurrent.impl.Promise$KeptPromise$Successful.onComplete(Promise.scala:379)

at scala.concurrent.impl.Promise.transform(Promise.scala:33)

at scala.concurrent.impl.Promise.transform$(Promise.scala:31)

at scala.concurrent.impl.Promise$KeptPromise$Successful.transform(Promise.scala:379)

at scala.concurrent.Future.map(Future.scala:292)

at scala.concurrent.Future.map$(Future.scala:292)

at scala.concurrent.impl.Promise$KeptPromise$Successful.map(Promise.scala:379)

at scala.concurrent.Future$.apply(Future.scala:659)

at com.databricks.backend.daemon.driver.JupyterKernelListener$BackgroundPollTask.start(JupyterKernelListener.scala:174)

at com.databricks.backend.daemon.driver.JupyterKernelListener.<init>(JupyterKernelListener.scala:340)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.$anonfun$startPython$1(JupyterDriverLocal.scala:708)

at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)

at scala.util.Try$.apply(Try.scala:213)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.com$databricks$backend$daemon$driver$JupyterDriverLocal$$withRetry(JupyterDriverLocal.scala:663)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.startPython(JupyterDriverLocal.scala:680)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.<init>(JupyterDriverLocal.scala:403)

at com.databricks.backend.daemon.driver.PythonDriverWrapper.instantiateDriver(DriverWrapper.scala:781)

at com.databricks.backend.daemon.driver.DriverWrapper.setupRepl(DriverWrapper.scala:350)

at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:246)

at java.lang.Thread.run(Thread.java:750)

java.lang.OutOfMemoryError: unable to create new native thread

at java.lang.Thread.start0(Native Method)

at java.lang.Thread.start(Thread.java:719)

at java.util.concurrent.ThreadPoolExecutor.addWorker(ThreadPoolExecutor.java:957)

at java.util.concurrent.ThreadPoolExecutor.execute(ThreadPoolExecutor.java:1367)

at scala.concurrent.impl.ExecutionContextImpl.execute(ExecutionContextImpl.scala:24)

at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:72)

at scala.concurrent.impl.Promise$KeptPromise$Kept.onComplete(Promise.scala:372)

at scala.concurrent.impl.Promise$KeptPromise$Kept.onComplete$(Promise.scala:371)

at scala.concurrent.impl.Promise$KeptPromise$Successful.onComplete(Promise.scala:379)

at scala.concurrent.impl.Promise.transform(Promise.scala:33)

at scala.concurrent.impl.Promise.transform$(Promise.scala:31)

at scala.concurrent.impl.Promise$KeptPromise$Successful.transform(Promise.scala:379)

at scala.concurrent.Future.map(Future.scala:292)

at scala.concurrent.Future.map$(Future.scala:292)

at scala.concurrent.impl.Promise$KeptPromise$Successful.map(Promise.scala:379)

at scala.concurrent.Future$.apply(Future.scala:659)

at com.databricks.backend.daemon.driver.JupyterKernelListener$BackgroundPollTask.start(JupyterKernelListener.scala:174)

at com.databricks.backend.daemon.driver.JupyterKernelListener.<init>(JupyterKernelListener.scala:340)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.$anonfun$startPython$1(JupyterDriverLocal.scala:708)

at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)

at scala.util.Try$.apply(Try.scala:213)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.com$databricks$backend$daemon$driver$JupyterDriverLocal$$withRetry(JupyterDriverLocal.scala:663)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.startPython(JupyterDriverLocal.scala:680)

at com.databricks.backend.daemon.driver.JupyterDriverLocal.<init>(JupyterDriverLocal.scala:403)

at com.databricks.backend.daemon.driver.PythonDriverWrapper.instantiateDriver(DriverWrapper.scala:781)

at com.databricks.backend.daemon.driver.DriverWrapper.setupRepl(DriverWrapper.scala:350)

at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:246)

at java.lang.Thread.run(Thread.java:750)

[jose_gonzalez]

please check the driver's logs, for example the log4j and the GC logs

[JKR]

@Jose Gonzalez​  Where can I find GC logs ? and what specifically I look for in log4j and GC logs ?

I want to understand why my driver is consuming that much RAM resources when jobs executed it must free the memory itself and let the other jobs use that memory.