mapPartitions mapPartitionsWithIndex With care, you can use these and maintain the iteration order within partitions. Beware, though, that any reduce functions need to be associative and commutative.
On Tue, Oct 22, 2013 at 12:28 PM, Matt Cheah <[email protected]> wrote: > Hi everyone, > > I have a driver holding a reference to an RDD. The driver would like to > "visit" each item in the RDD in order, say with a visitor object that > invokes visit(item) to modify that visitor's internal state. The visiting > is not commutative (e.g. Visiting item A then B makes a different internal > state from visiting item B then item A). Items in the RDD also are not > necessarily distinct. > > I've looked into accumulators which don't work because they require the > operation to be commutative. Collect() will not work because the RDD is too > large; in general, bringing the whole RDD into one partition won't work > since the RDD is too large. > > Is it possible to iterate over the items in an RDD in order without > bringing the entire dataset into a single JVM at a time, and/or obtain > chunks of the RDD in order on the driver? We've tried using the internal > iterator() method. In some cases, we get a stack trace (running locally > with 3 threads). I've included the stack trace below. > > Thanks, > > -Matt Cheah > > org.apache.spark.SparkException: Error communicating with > MapOutputTracker > at org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:84) > at > org.apache.spark.MapOutputTracker.getServerStatuses(MapOutputTracker.scala:170) > at > org.apache.spark.BlockStoreShuffleFetcher.fetch(BlockStoreShuffleFetcher.scala:39) > at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:59) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:237) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:226) > at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:36) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:237) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:226) > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:29) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:237) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:226) > at > org.apache.spark.api.java.JavaRDDLike$class.iterator(JavaRDDLike.scala:60) > at org.apache.spark.api.java.JavaRDD.iterator(JavaRDD.scala:25) > at > com.palantir.finance.server.service.datatable.SparkRawDataTableProvider.compute(SparkRawDataTableProvider.java:76) > at > com.palantir.finance.server.datatable.spark.SparkDataTable.visit(SparkDataTable.java:83) > at > com.palantir.finance.server.datatable.DataTableImplementationParityTests.runDataTableTest(DataTableImplementationParityTests.java:129) > at > com.palantir.finance.server.datatable.DataTableImplementationParityTests.testParityOnSort(DataTableImplementationParityTests.java:102) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) > at > org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:47) > at > org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12) > at > org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:44) > at > org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17) > at org.junit.rules.ExternalResource$1.evaluate(ExternalResource.java:48) > at org.junit.rules.RunRules.evaluate(RunRules.java:20) > at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:271) > at > org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:70) > at > org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:50) > at org.junit.runners.ParentRunner$3.run(ParentRunner.java:238) > at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:63) > at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:236) > at org.junit.runners.ParentRunner.access$000(ParentRunner.java:53) > at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:229) > at > org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:26) > at org.junit.rules.ExternalResource$1.evaluate(ExternalResource.java:48) > at > com.palantir.finance.commons.service.ServiceThreadContainerRule$1.evaluate(ServiceThreadContainerRule.java:28) > at org.junit.rules.RunRules.evaluate(RunRules.java:20) > at org.junit.runners.ParentRunner.run(ParentRunner.java:309) > at > org.eclipse.jdt.internal.junit4.runner.JUnit4TestReference.run(JUnit4TestReference.java:50) > at > org.eclipse.jdt.internal.junit.runner.TestExecution.run(TestExecution.java:38) > at > org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:467) > at > org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.runTests(RemoteTestRunner.java:683) > at > org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.run(RemoteTestRunner.java:390) > at > org.eclipse.jdt.internal.junit.runner.RemoteTestRunner.main(RemoteTestRunner.java:197) > Caused by: java.util.concurrent.TimeoutException: Futures timed out after > [10000] milliseconds > at > org.apache.spark.internal.akka.dispatch.DefaultPromise.ready(Future.scala:870) > at > org.apache.spark.internal.akka.dispatch.DefaultPromise.result(Future.scala:874) > at org.apache.spark.internal.akka.dispatch.Await$.result(Future.scala:74) > at org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:81) > ... 46 more > >
