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我想使用 Dataflow 将数据从 Pub/Sub 移动到 GCS。所以基本上我希望 Dataflow 在固定时间(例如 15 分钟)内积累一些消息，然后在该时间过去后将这些数据作为文本文件写入 GCS。
我的最终目标是创建一个自定义管道，所以“Pub/Sub to Cloud Storage”模板对我来说还不够，而且我根本不了解 Java，这让我开始调整 Python。
这是我目前得到的(Apache Beam Python SDK 2.10.0):
import apache_beam as beam
TOPIC_PATH="projects/<my-project>/topics/<my-topic>"
def CombineFn(e):
return "\n".join(e)
o = beam.options.pipeline_options.PipelineOptions()
p = beam.Pipeline(options=o)
data = ( p | "Read From Pub/Sub" >> beam.io.ReadFromPubSub(topic=TOPIC_PATH)
| "Window" >> beam.WindowInto(beam.window.FixedWindows(30))
| "Combine" >> beam.transforms.core.CombineGlobally(CombineFn).without_defaults()
| "Output" >> beam.io.WriteToText("<GCS path or local path>"))
res = p.run()
res.wait_until_finish()
我在本地环境中运行这个程序没有问题。
python main.py
它在本地运行，但每次 30 秒过后都会从指定的 Pub/Sub 主题读取并写入指定的 GCS 路径，正如预期的那样。
但是，现在的问题是，当我在 Google Cloud Platform(即 Cloud Dataflow)上运行它时，它会不断发出神秘的异常。
java.util.concurrent.ExecutionException: java.lang.RuntimeException: Error received from SDK harness for instruction -1096: Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 148, in _execute
response = task()
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 183, in <lambda>
self._execute(lambda: worker.do_instruction(work), work)
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 256, in do_instruction
request.instruction_id)
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 272, in process_bundle
bundle_processor.process_bundle(instruction_id)
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 494, in process_bundle
op.finish()
File "apache_beam/runners/worker/operations.py", line 506, in apache_beam.runners.worker.operations.DoOperation.finish
def finish(self):
File "apache_beam/runners/worker/operations.py", line 507, in apache_beam.runners.worker.operations.DoOperation.finish
with self.scoped_finish_state:
File "apache_beam/runners/worker/operations.py", line 508, in apache_beam.runners.worker.operations.DoOperation.finish
self.dofn_runner.finish()
File "apache_beam/runners/common.py", line 703, in apache_beam.runners.common.DoFnRunner.finish
self._invoke_bundle_method(self.do_fn_invoker.invoke_finish_bundle)
File "apache_beam/runners/common.py", line 697, in apache_beam.runners.common.DoFnRunner._invoke_bundle_method
self._reraise_augmented(exn)
File "apache_beam/runners/common.py", line 722, in apache_beam.runners.common.DoFnRunner._reraise_augmented
raise_with_traceback(new_exn)
File "apache_beam/runners/common.py", line 695, in apache_beam.runners.common.DoFnRunner._invoke_bundle_method
bundle_method()
File "apache_beam/runners/common.py", line 361, in apache_beam.runners.common.DoFnInvoker.invoke_finish_bundle
def invoke_finish_bundle(self):
File "apache_beam/runners/common.py", line 364, in apache_beam.runners.common.DoFnInvoker.invoke_finish_bundle
self.output_processor.finish_bundle_outputs(
File "apache_beam/runners/common.py", line 832, in apache_beam.runners.common._OutputProcessor.finish_bundle_outputs
self.main_receivers.receive(windowed_value)
File "apache_beam/runners/worker/operations.py", line 87, in apache_beam.runners.worker.operations.ConsumerSet.receive
self.update_counters_start(windowed_value)
File "apache_beam/runners/worker/operations.py", line 93, in apache_beam.runners.worker.operations.ConsumerSet.update_counters_start
self.opcounter.update_from(windowed_value)
File "apache_beam/runners/worker/opcounters.py", line 195, in apache_beam.runners.worker.opcounters.OperationCounters.update_from
self.do_sample(windowed_value)
File "apache_beam/runners/worker/opcounters.py", line 213, in apache_beam.runners.worker.opcounters.OperationCounters.do_sample
self.coder_impl.get_estimated_size_and_observables(windowed_value))
File "apache_beam/coders/coder_impl.py", line 953, in apache_beam.coders.coder_impl.WindowedValueCoderImpl.get_estimated_size_and_observables
def get_estimated_size_and_observables(self, value, nested=False):
File "apache_beam/coders/coder_impl.py", line 969, in apache_beam.coders.coder_impl.WindowedValueCoderImpl.get_estimated_size_and_observables
self._windows_coder.estimate_size(value.windows, nested=True))
File "apache_beam/coders/coder_impl.py", line 758, in apache_beam.coders.coder_impl.SequenceCoderImpl.estimate_size
self.get_estimated_size_and_observables(value))
File "apache_beam/coders/coder_impl.py", line 772, in apache_beam.coders.coder_impl.SequenceCoderImpl.get_estimated_size_and_observables
self._elem_coder.get_estimated_size_and_observables(
File "apache_beam/coders/coder_impl.py", line 134, in apache_beam.coders.coder_impl.CoderImpl.get_estimated_size_and_observables
return self.estimate_size(value, nested), []
File "apache_beam/coders/coder_impl.py", line 458, in apache_beam.coders.coder_impl.IntervalWindowCoderImpl.estimate_size
typed_value = value
TypeError: Cannot convert GlobalWindow to apache_beam.utils.windowed_value._IntervalWindowBase [while running 'generatedPtransform-1090']
java.util.concurrent.CompletableFuture.reportGet(CompletableFuture.java:357)
java.util.concurrent.CompletableFuture.get(CompletableFuture.java:1895)
org.apache.beam.sdk.util.MoreFutures.get(MoreFutures.java:57)
org.apache.beam.runners.dataflow.worker.fn.control.RegisterAndProcessBundleOperation.finish(RegisterAndProcessBundleOperation.java:280)
org.apache.beam.runners.dataflow.worker.util.common.worker.MapTaskExecutor.execute(MapTaskExecutor.java:84)
org.apache.beam.runners.dataflow.worker.fn.control.BeamFnMapTaskExecutor.execute(BeamFnMapTaskExecutor.java:130)
org.apache.beam.runners.dataflow.worker.StreamingDataflowWorker.process(StreamingDataflowWorker.java:1233)
org.apache.beam.runners.dataflow.worker.StreamingDataflowWorker.access$1000(StreamingDataflowWorker.java:144)
org.apache.beam.runners.dataflow.worker.StreamingDataflowWorker$6.run(StreamingDataflowWorker.java:972)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.RuntimeException: Error received from SDK harness for instruction -1096: Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 148, in _execute
response = task()
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 183, in <lambda>
self._execute(lambda: worker.do_instruction(work), work)
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 256, in do_instruction
request.instruction_id)
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/sdk_worker.py", line 272, in process_bundle
bundle_processor.process_bundle(instruction_id)
File "/usr/local/lib/python2.7/dist-packages/apache_beam/runners/worker/bundle_processor.py", line 494, in process_bundle
op.finish()
File "apache_beam/runners/worker/operations.py", line 506, in apache_beam.runners.worker.operations.DoOperation.finish
def finish(self):
File "apache_beam/runners/worker/operations.py", line 507, in apache_beam.runners.worker.operations.DoOperation.finish
with self.scoped_finish_state:
File "apache_beam/runners/worker/operations.py", line 508, in apache_beam.runners.worker.operations.DoOperation.finish
self.dofn_runner.finish()
File "apache_beam/runners/common.py", line 703, in apache_beam.runners.common.DoFnRunner.finish
self._invoke_bundle_method(self.do_fn_invoker.invoke_finish_bundle)
File "apache_beam/runners/common.py", line 697, in apache_beam.runners.common.DoFnRunner._invoke_bundle_method
self._reraise_augmented(exn)
File "apache_beam/runners/common.py", line 722, in apache_beam.runners.common.DoFnRunner._reraise_augmented
raise_with_traceback(new_exn)
File "apache_beam/runners/common.py", line 695, in apache_beam.runners.common.DoFnRunner._invoke_bundle_method
bundle_method()
File "apache_beam/runners/common.py", line 361, in apache_beam.runners.common.DoFnInvoker.invoke_finish_bundle
def invoke_finish_bundle(self):
File "apache_beam/runners/common.py", line 364, in apache_beam.runners.common.DoFnInvoker.invoke_finish_bundle
self.output_processor.finish_bundle_outputs(
File "apache_beam/runners/common.py", line 832, in apache_beam.runners.common._OutputProcessor.finish_bundle_outputs
self.main_receivers.receive(windowed_value)
File "apache_beam/runners/worker/operations.py", line 87, in apache_beam.runners.worker.operations.ConsumerSet.receive
self.update_counters_start(windowed_value)
File "apache_beam/runners/worker/operations.py", line 93, in apache_beam.runners.worker.operations.ConsumerSet.update_counters_start
self.opcounter.update_from(windowed_value)
File "apache_beam/runners/worker/opcounters.py", line 195, in apache_beam.runners.worker.opcounters.OperationCounters.update_from
self.do_sample(windowed_value)
File "apache_beam/runners/worker/opcounters.py", line 213, in apache_beam.runners.worker.opcounters.OperationCounters.do_sample
self.coder_impl.get_estimated_size_and_observables(windowed_value))
File "apache_beam/coders/coder_impl.py", line 953, in apache_beam.coders.coder_impl.WindowedValueCoderImpl.get_estimated_size_and_observables
def get_estimated_size_and_observables(self, value, nested=False):
File "apache_beam/coders/coder_impl.py", line 969, in apache_beam.coders.coder_impl.WindowedValueCoderImpl.get_estimated_size_and_observables
self._windows_coder.estimate_size(value.windows, nested=True))
File "apache_beam/coders/coder_impl.py", line 758, in apache_beam.coders.coder_impl.SequenceCoderImpl.estimate_size
self.get_estimated_size_and_observables(value))
File "apache_beam/coders/coder_impl.py", line 772, in apache_beam.coders.coder_impl.SequenceCoderImpl.get_estimated_size_and_observables
self._elem_coder.get_estimated_size_and_observables(
File "apache_beam/coders/coder_impl.py", line 134, in apache_beam.coders.coder_impl.CoderImpl.get_estimated_size_and_observables
return self.estimate_size(value, nested), []
File "apache_beam/coders/coder_impl.py", line 458, in apache_beam.coders.coder_impl.IntervalWindowCoderImpl.estimate_size
typed_value = value
TypeError: Cannot convert GlobalWindow to apache_beam.utils.windowed_value._IntervalWindowBase [while running 'generatedPtransform-1090']
org.apache.beam.runners.fnexecution.control.FnApiControlClient$ResponseStreamObserver.onNext(FnApiControlClient.java:157)
org.apache.beam.runners.fnexecution.control.FnApiControlClient$ResponseStreamObserver.onNext(FnApiControlClient.java:140)
org.apache.beam.vendor.grpc.v1p13p1.io.grpc.stub.ServerCalls$StreamingServerCallHandler$StreamingServerCallListener.onMessage(ServerCalls.java:248)
org.apache.beam.vendor.grpc.v1p13p1.io.grpc.ForwardingServerCallListener.onMessage(ForwardingServerCallListener.java:33)
org.apache.beam.vendor.grpc.v1p13p1.io.grpc.Contexts$ContextualizedServerCallListener.onMessage(Contexts.java:76)
org.apache.beam.vendor.grpc.v1p13p1.io.grpc.internal.ServerCallImpl$ServerStreamListenerImpl.messagesAvailable(ServerCallImpl.java:263)
org.apache.beam.vendor.grpc.v1p13p1.io.grpc.internal.ServerImpl$JumpToApplicationThreadServerStreamListener$1MessagesAvailable.runInContext(ServerImpl.java:683)
org.apache.beam.vendor.grpc.v1p13p1.io.grpc.internal.ContextRunnable.run(ContextRunnable.java:37)
org.apache.beam.vendor.grpc.v1p13p1.io.grpc.internal.SerializingExecutor.run(SerializingExecutor.java:123)
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
java.lang.Thread.run(Thread.java:745)
每次尝试写入 GCS 时，上面的异常都会以非阻塞方式显示。这让我想到了一种情况，当它尝试输出时，肯定会生成一个新的文本文件，但文本内容始终与第一个窗口输出相同。这显然是不受欢迎的。
异常在堆栈跟踪中的嵌套如此之深，以至于很难猜测根本原因是什么，而且我不知道为什么它在 DirectRunner 上运行良好，但在 DataflowRunner 上却完全不运行。它似乎在管道的某个地方说，全局窗口值被转换为非全局窗口值，尽管我在管道的第二阶段使用了非全局窗口转换。添加自定义触发器没有帮助。
最佳答案
我遇到了同样的错误，并找到了解决方法，但没有找到解决方法:
TypeError: Cannot convert GlobalWindow to apache_beam.utils.windowed_value._IntervalWindowBase [while running 'test-file-out/Write/WriteImpl/WriteBundles']
使用 DirectRunner 在本地运行，使用 DataflowRunner 在数据流上运行。
恢复到 apache-beam[gcp]==2.9.0 允许我的管道按预期运行。
关于python - 如何在 Python 中创建从 Pub/Sub 到 GCS 的数据流管道，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/54745869/
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