Definitely agree with you. Have created https://issues.apache.org/jira/browse/FLINK-22297 <https://issues.apache.org/jira/browse/FLINK-22297> as a following up.
> 2021年4月16日 上午7:10，Yik San Chan <[email protected]> 写道： > > Hi Dian, > > I wonder if we can improve the error tracing and message so that it becomes > more obvious where the problem is? To me, a NPE really says very little. > > Best, > Yik San > > On Thu, Apr 15, 2021 at 11:07 AM Dian Fu <[email protected] > <mailto:[email protected]>> wrote: > Great! Thanks for letting me know~ > >> 2021年4月15日 上午11:01，Yik San Chan <[email protected] >> <mailto:[email protected]>> 写道： >> >> Hi Dian, >> >> Thanks for the reminder. Yes, the original udf implementation does not >> qualify the input and output type requirement. After adding a unit test, I >> was able to find what's wrong, and fix my UDF implementation. Here is the >> new implementation FYI. >> >> @udf(result_type=DataTypes.DOUBLE(), func_type="pandas") >> def predict(users, items): >> n_users, n_items = 943, 1682 >> model = MatrixFactorization(n_users, n_items) >> model.load_state_dict(torch.load("model.pth")) >> return pd.Series(model(users, items).detach().numpy()) >> >> And here is the unit test. >> >> def test_predict(): >> f = predict._func >> users = pd.Series([1, 2, 3]) >> items = pd.Series([1, 4, 9]) >> preds = f(users, items) >> assert isinstance(preds, pd.Series) >> assert len(preds) == 3 >> >> Thank you so much! >> >> Best, >> Yik San >> >> On Wed, Apr 14, 2021 at 11:03 PM Dian Fu <[email protected] >> <mailto:[email protected]>> wrote: >> Hi Yik San, >> >> 1) There are two kinds of Python UDFs in PyFlink: >> - General Python UDFs which process input elements at row basis. That is, it >> will process one row at a time. >> - Pandas UDFs which process input elements at batch basis. >> So you are correct that you need to use Pandas UDF for your requirements. >> >> 2) For Pandas UDF, the input type for each input argument is Pandas.Series >> and the result type should also be a Pandas.Series. Besides, the length of >> the result should be the same as the inputs. Could you check if this is the >> case for your Pandas UDF implementation? >> >> Regards, >> Dian >> >> >> On Wed, Apr 14, 2021 at 9:44 PM Yik San Chan <[email protected] >> <mailto:[email protected]>> wrote: >> The question is cross-posted on Stack Overflow >> https://stackoverflow.com/questions/67092978/pyflink-vectorized-udf-throws-nullpointerexception >> >> <https://stackoverflow.com/questions/67092978/pyflink-vectorized-udf-throws-nullpointerexception>. >> >> I have a ML model that takes two numpy.ndarray - `users` and `items` - and >> returns an numpy.ndarray `predictions`. In normal Python code, I would do: >> >> ```python >> model = load_model() >> >> df = load_data() # the DataFrame includes 4 columns, namely, user_id, >> movie_id, rating, and timestamp >> >> users = df.user_id.values >> items = df.movie_id.values >> >> predictions = model(users, items) >> ``` >> >> I am looking into porting this code into Flink to leverage its distributed >> nature. My assumption is: by distributing the prediction workload on >> multiple Flink nodes, I should be able to run the whole prediction faster. >> >> So I compose a PyFlink job. Note I implement an UDF called `predict` to run >> the prediction. >> >> ```python >> # batch_prediction.py >> >> model = load_model() >> >> settings = EnvironmentSettings.new_instance().use_blink_planner().build() >> exec_env = StreamExecutionEnvironment.get_execution_environment() >> t_env = StreamTableEnvironment.create(exec_env, >> environment_settings=settings) >> >> SOURCE_DDL = """ >> CREATE TABLE source ( >> user_id INT, >> movie_id INT, >> rating TINYINT, >> event_ms BIGINT >> ) WITH ( >> 'connector' = 'filesystem', >> 'format' = 'csv', >> 'csv.field-delimiter' = '\t', >> 'path' = 'ml-100k/u1.test' >> ) >> """ >> >> SINK_DDL = """ >> CREATE TABLE sink ( >> prediction DOUBLE >> ) WITH ( >> 'connector' = 'print' >> ) >> """ >> >> t_env.execute_sql(SOURCE_DDL) >> t_env.execute_sql(SINK_DDL) >> t_env.execute_sql( >> "INSERT INTO sink SELECT PREDICT(user_id, movie_id) FROM source" >> ).wait() >> ``` >> >> Here is the UDF. >> >> ```python >> # batch_prediction.py (cont) >> >> @udf(result_type=DataTypes.DOUBLE()) >> def predict(user, item): >> return model([user], [item]).item() >> >> t_env.create_temporary_function("predict", predict) >> ``` >> >> The job runs fine. However, the prediction actually runs on each and every >> row of the `source` table, which is not performant. Instead, I want to split >> the 80,000 (user_id, movie_id) pairs into, let's say, 100 batches, with each >> batch having 800 rows. The job triggers the `model(users, items)` function >> 100 times (= # of batch), where both `users` and `items` have 800 elements. >> >> I couldn't find a way to do this. By looking at the >> [docs](https://ci.apache.org/projects/flink/flink-docs-stable/dev/python/table-api-users-guide/udfs/vectorized_python_udfs.html >> >> <https://ci.apache.org/projects/flink/flink-docs-stable/dev/python/table-api-users-guide/udfs/vectorized_python_udfs.html>), >> vectorized user-defined functions may work. >> >> ```python >> # batch_prediction.py (snippet) >> >> # I add the func_type="pandas" >> @udf(result_type=DataTypes.DOUBLE(), func_type="pandas") >> def predict(user, item): >> ... >> ``` >> >> Unfortunately, it doesn't. >> >> ``` >> > python batch_prediction.py >> ... >> Traceback (most recent call last): >> File "batch_prediction.py", line 55, in <module> >> "INSERT INTO sink SELECT PREDICT(user_id, movie_id) FROM source" >> File >> "/usr/local/anaconda3/envs/flink-ml/lib/python3.7/site-packages/pyflink/table/table_result.py", >> line 76, in wait >> get_method(self._j_table_result, "await")() >> File >> "/usr/local/anaconda3/envs/flink-ml/lib/python3.7/site-packages/py4j/java_gateway.py", >> line 1286, in __call__ >> answer, self.gateway_client, self.target_id, self.name >> <http://self.name/>) >> File >> "/usr/local/anaconda3/envs/flink-ml/lib/python3.7/site-packages/pyflink/util/exceptions.py", >> line 147, in deco >> return f(*a, **kw) >> File >> "/usr/local/anaconda3/envs/flink-ml/lib/python3.7/site-packages/py4j/protocol.py", >> line 328, in get_return_value >> format(target_id, ".", name), value) >> py4j.protocol.Py4JJavaError: An error occurred while calling o51.await. >> : java.util.concurrent.ExecutionException: >> org.apache.flink.table.api.TableException: Failed to wait job finish >> at >> java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:357) >> at java.util.concurrent.CompletableFuture.get(CompletableFuture.java:1908) >> at >> org.apache.flink.table.api.internal.TableResultImpl.awaitInternal(TableResultImpl.java:119) >> at >> org.apache.flink.table.api.internal.TableResultImpl.await(TableResultImpl.java:86) >> 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 >> org.apache.flink.api.python.shaded.py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) >> at >> org.apache.flink.api.python.shaded.py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) >> at org.apache.flink.api.python.shaded.py4j.Gateway.invoke(Gateway.java:282) >> at >> org.apache.flink.api.python.shaded.py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) >> at >> org.apache.flink.api.python.shaded.py4j.commands.CallCommand.execute(CallCommand.java:79) >> at >> org.apache.flink.api.python.shaded.py4j.GatewayConnection.run(GatewayConnection.java:238) >> at java.lang.Thread.run(Thread.java:748) >> Caused by: org.apache.flink.table.api.TableException: Failed to wait job >> finish >> at >> org.apache.flink.table.api.internal.InsertResultIterator.hasNext(InsertResultIterator.java:59) >> at >> org.apache.flink.table.api.internal.TableResultImpl$CloseableRowIteratorWrapper.hasNext(TableResultImpl.java:355) >> at >> org.apache.flink.table.api.internal.TableResultImpl$CloseableRowIteratorWrapper.isFirstRowReady(TableResultImpl.java:368) >> at >> org.apache.flink.table.api.internal.TableResultImpl.lambda$awaitInternal$1(TableResultImpl.java:107) >> at >> java.util.concurrent.CompletableFuture$AsyncRun.run(CompletableFuture.java:1640) >> at >> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) >> at >> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) >> ... 1 more >> Caused by: java.util.concurrent.ExecutionException: >> org.apache.flink.runtime.client.JobExecutionException: Job execution failed. >> at >> java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:357) >> at java.util.concurrent.CompletableFuture.get(CompletableFuture.java:1908) >> at >> org.apache.flink.table.api.internal.InsertResultIterator.hasNext(InsertResultIterator.java:57) >> ... 7 more >> Caused by: org.apache.flink.runtime.client.JobExecutionException: Job >> execution failed. >> at >> org.apache.flink.runtime.jobmaster.JobResult.toJobExecutionResult(JobResult.java:147) >> at >> org.apache.flink.runtime.minicluster.MiniClusterJobClient.lambda$getJobExecutionResult$2(MiniClusterJobClient.java:119) >> at >> java.util.concurrent.CompletableFuture.uniApply(CompletableFuture.java:616) >> at >> java.util.concurrent.CompletableFuture$UniApply.tryFire(CompletableFuture.java:591) >> at >> java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:488) >> at >> java.util.concurrent.CompletableFuture.complete(CompletableFuture.java:1975) >> at >> org.apache.flink.runtime.rpc.akka.AkkaInvocationHandler.lambda$invokeRpc$0(AkkaInvocationHandler.java:229) >> at >> java.util.concurrent.CompletableFuture.uniWhenComplete(CompletableFuture.java:774) >> at >> java.util.concurrent.CompletableFuture$UniWhenComplete.tryFire(CompletableFuture.java:750) >> at >> java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:488) >> at >> java.util.concurrent.CompletableFuture.complete(CompletableFuture.java:1975) >> at >> org.apache.flink.runtime.concurrent.FutureUtils$1.onComplete(FutureUtils.java:996) >> at akka.dispatch.OnComplete.internal(Future.scala:264) >> at akka.dispatch.OnComplete.internal(Future.scala:261) >> at akka.dispatch.japi$CallbackBridge.apply(Future.scala:191) >> at akka.dispatch.japi$CallbackBridge.apply(Future.scala:188) >> at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:36) >> at >> org.apache.flink.runtime.concurrent.Executors$DirectExecutionContext.execute(Executors.java:74) >> at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:44) >> at >> scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:252) >> at akka.pattern.PromiseActorRef.$bang(AskSupport.scala:572) >> at >> akka.pattern.PipeToSupport$PipeableFuture$$anonfun$pipeTo$1.applyOrElse(PipeToSupport.scala:22) >> at >> akka.pattern.PipeToSupport$PipeableFuture$$anonfun$pipeTo$1.applyOrElse(PipeToSupport.scala:21) >> at scala.concurrent.Future$$anonfun$andThen$1.apply(Future.scala:436) >> at scala.concurrent.Future$$anonfun$andThen$1.apply(Future.scala:435) >> at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:36) >> at >> akka.dispatch.BatchingExecutor$AbstractBatch.processBatch(BatchingExecutor.scala:55) >> at >> akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply$mcV$sp(BatchingExecutor.scala:91) >> at >> akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply(BatchingExecutor.scala:91) >> at >> akka.dispatch.BatchingExecutor$BlockableBatch$$anonfun$run$1.apply(BatchingExecutor.scala:91) >> at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72) >> at >> akka.dispatch.BatchingExecutor$BlockableBatch.run(BatchingExecutor.scala:90) >> at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:40) >> at >> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(ForkJoinExecutorConfigurator.scala:44) >> at akka.dispatch.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) >> at >> akka.dispatch.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) >> at akka.dispatch.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) >> at >> akka.dispatch.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) >> Caused by: org.apache.flink.runtime.JobException: Recovery is suppressed by >> NoRestartBackoffTimeStrategy >> at >> org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.handleFailure(ExecutionFailureHandler.java:116) >> at >> org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.getFailureHandlingResult(ExecutionFailureHandler.java:78) >> at >> org.apache.flink.runtime.scheduler.DefaultScheduler.handleTaskFailure(DefaultScheduler.java:224) >> at >> org.apache.flink.runtime.scheduler.DefaultScheduler.maybeHandleTaskFailure(DefaultScheduler.java:217) >> at >> org.apache.flink.runtime.scheduler.DefaultScheduler.updateTaskExecutionStateInternal(DefaultScheduler.java:208) >> at >> org.apache.flink.runtime.scheduler.SchedulerBase.updateTaskExecutionState(SchedulerBase.java:610) >> at >> org.apache.flink.runtime.scheduler.SchedulerNG.updateTaskExecutionState(SchedulerNG.java:89) >> at >> org.apache.flink.runtime.jobmaster.JobMaster.updateTaskExecutionState(JobMaster.java:419) >> 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 >> org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcInvocation(AkkaRpcActor.java:286) >> at >> org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcMessage(AkkaRpcActor.java:201) >> at >> org.apache.flink.runtime.rpc.akka.FencedAkkaRpcActor.handleRpcMessage(FencedAkkaRpcActor.java:74) >> at >> org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleMessage(AkkaRpcActor.java:154) >> at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:26) >> at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:21) >> at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:123) >> at akka.japi.pf.UnitCaseStatement.applyOrElse(CaseStatements.scala:21) >> at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:170) >> at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:171) >> at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:171) >> at akka.actor.Actor$class.aroundReceive(Actor.scala:517) >> at akka.actor.AbstractActor.aroundReceive(AbstractActor.scala:225) >> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:592) >> at akka.actor.ActorCell.invoke(ActorCell.scala:561) >> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:258) >> at akka.dispatch.Mailbox.run(Mailbox.scala:225) >> at akka.dispatch.Mailbox.exec(Mailbox.scala:235) >> ... 4 more >> Caused by: org.apache.flink.streaming.runtime.tasks.AsynchronousException: >> Caught exception while processing timer. >> at >> org.apache.flink.streaming.runtime.tasks.StreamTask$StreamTaskAsyncExceptionHandler.handleAsyncException(StreamTask.java:1108) >> at >> org.apache.flink.streaming.runtime.tasks.StreamTask.handleAsyncException(StreamTask.java:1082) >> at >> org.apache.flink.streaming.runtime.tasks.StreamTask.invokeProcessingTimeCallback(StreamTask.java:1213) >> at >> org.apache.flink.streaming.runtime.tasks.StreamTask.lambda$null$17(StreamTask.java:1202) >> at >> org.apache.flink.streaming.runtime.tasks.StreamTaskActionExecutor$SynchronizedStreamTaskActionExecutor.runThrowing(StreamTaskActionExecutor.java:92) >> at org.apache.flink.streaming.runtime.tasks.mailbox.Mail.run(Mail.java:78) >> at >> org.apache.flink.streaming.runtime.tasks.mailbox.MailboxExecutorImpl.tryYield(MailboxExecutorImpl.java:91) >> at >> org.apache.flink.streaming.runtime.tasks.StreamOperatorWrapper.quiesceTimeServiceAndCloseOperator(StreamOperatorWrapper.java:155) >> at >> org.apache.flink.streaming.runtime.tasks.StreamOperatorWrapper.close(StreamOperatorWrapper.java:130) >> at >> org.apache.flink.streaming.runtime.tasks.OperatorChain.closeOperators(OperatorChain.java:412) >> at >> org.apache.flink.streaming.runtime.tasks.StreamTask.afterInvoke(StreamTask.java:585) >> at >> org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:547) >> at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:722) >> at org.apache.flink.runtime.taskmanager.Task.run(Task.java:547) >> at java.lang.Thread.run(Thread.java:748) >> Caused by: TimerException{java.lang.RuntimeException: Failed to close remote >> bundle} >> ... 13 more >> Caused by: java.lang.RuntimeException: Failed to close remote bundle >> at >> org.apache.flink.streaming.api.runners.python.beam.BeamPythonFunctionRunner.finishBundle(BeamPythonFunctionRunner.java:371) >> at >> org.apache.flink.streaming.api.runners.python.beam.BeamPythonFunctionRunner.flush(BeamPythonFunctionRunner.java:325) >> at >> org.apache.flink.streaming.api.operators.python.AbstractPythonFunctionOperator.invokeFinishBundle(AbstractPythonFunctionOperator.java:291) >> at >> org.apache.flink.table.runtime.operators.python.scalar.arrow.RowDataArrowPythonScalarFunctionOperator.invokeFinishBundle(RowDataArrowPythonScalarFunctionOperator.java:77) >> at >> org.apache.flink.streaming.api.operators.python.AbstractPythonFunctionOperator.checkInvokeFinishBundleByTime(AbstractPythonFunctionOperator.java:285) >> at >> org.apache.flink.streaming.api.operators.python.AbstractPythonFunctionOperator.lambda$open$0(AbstractPythonFunctionOperator.java:134) >> at >> org.apache.flink.streaming.runtime.tasks.StreamTask.invokeProcessingTimeCallback(StreamTask.java:1211) >> ... 12 more >> Caused by: java.lang.NullPointerException >> at >> org.apache.flink.streaming.api.runners.python.beam.BeamPythonFunctionRunner.finishBundle(BeamPythonFunctionRunner.java:369) >> ... 18 more >> ``` >> >> The error messages are not very helpful. Can anyone help? Thanks! >> >> Note: source code can be found >> [here](https://github.com/YikSanChan/flink-torch/tree/83ea0510172db3d7ff33db19883150f2fe5c1f43 >> >> <https://github.com/YikSanChan/flink-torch/tree/83ea0510172db3d7ff33db19883150f2fe5c1f43>). >> To run the code, you will need Anaconda locally, then: >> >> ``` >> conda env create -f environment.yml >> conda activate flink-ml >> ``` >> >> Best, >> Yik San >
