| commit | ee28c6e98e1b111bfe90e8936bce0a6e71500797 | [log] [tgz] |
|---|---|---|
| author | Maximilian Michels <mxm@apache.org> | Fri Nov 08 15:41:43 2019 +0100 |
| committer | Maximilian Michels <mxm@apache.org> | Tue Nov 12 20:20:23 2019 +0100 |
| tree | 30857dc5b4bbf04de0bbdce14900e2428c277084 | |
| parent | 19303ab9992a02b761c85c3b1cf24c644a3b0d04 [diff] |
[BEAM-8157] Add LengthPrefixCoder around unknown key coder for stateful ProcessBundleDescriptor
Flink and other Runners use the serialized key in stateful ExecutableStages to
key partition the data. When state requests are issued from the SDK Harness the
key is also sent over serialized. However, the binary representation of that key
currently does not always match the Runner's representation. This causes
troubles with persisting keyed state for checkpoints.
When the key coder is known by the Runner, then it uses the same encoding scheme
as the SDK Harness. However, when the coder is unknown, it will be replaced by a
"LengthPrefixCoder<ByteArrayCoder>" which is problematic because the SDK Harness
does not add this coder and thus may produce a different encoding as the Runner.
The solution is to add a LengthPrefixCoder around unknown key coders in the
ProcessBundleDescriptor, such that the SDK Harness will length prefix the key
correctly.
Otherwise we run into this error:
```
Caused by: java.util.concurrent.ExecutionException: java.lang.RuntimeException: Error received from SDK harness for instruction 4: Traceback (most recent call last):
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker.py", line 168, in _execute
response = task()
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker.py", line 201, in <lambda>
self._execute(lambda: worker.do_instruction(work), work)
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker.py", line 356, in do_instruction
request.instruction_id)
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker.py", line 382, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/bundle_processor.py", line 667, in process_bundle
data.ptransform_id].process_encoded(data.data)
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/bundle_processor.py", line 143, in process_encoded
self.output(decoded_value)
File "apache_beam/runners/worker/operations.py", line 255, in apache_beam.runners.worker.operations.Operation.output
def output(self, windowed_value, output_index=0):
File "apache_beam/runners/worker/operations.py", line 256, in apache_beam.runners.worker.operations.Operation.output
cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value)
File "apache_beam/runners/worker/operations.py", line 143, in apache_beam.runners.worker.operations.SingletonConsumerSet.receive
self.consumer.process(windowed_value)
File "apache_beam/runners/worker/operations.py", line 593, in apache_beam.runners.worker.operations.DoOperation.process
with self.scoped_process_state:
File "apache_beam/runners/worker/operations.py", line 594, in apache_beam.runners.worker.operations.DoOperation.process
delayed_application = self.dofn_receiver.receive(o)
File "apache_beam/runners/common.py", line 776, in apache_beam.runners.common.DoFnRunner.receive
self.process(windowed_value)
File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.DoFnRunner.process
self._reraise_augmented(exn)
File "apache_beam/runners/common.py", line 849, in apache_beam.runners.common.DoFnRunner._reraise_augmented
raise_with_traceback(new_exn)
File "apache_beam/runners/common.py", line 780, in apache_beam.runners.common.DoFnRunner.process
return self.do_fn_invoker.invoke_process(windowed_value)
File "apache_beam/runners/common.py", line 587, in apache_beam.runners.common.PerWindowInvoker.invoke_process
self._invoke_process_per_window(
File "apache_beam/runners/common.py", line 659, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
output_processor.process_outputs(
File "apache_beam/runners/common.py", line 880, in apache_beam.runners.common._OutputProcessor.process_outputs
def process_outputs(self, windowed_input_element, results):
File "apache_beam/runners/common.py", line 895, in apache_beam.runners.common._OutputProcessor.process_outputs
for result in results:
File "pricingrealtime/event_processing/stateful_event_processing.py", line 55, in process
recent_events_map = StatefulEventDoFn._load_recent_events_map(recent_events_state)
File "pricingrealtime/event_processing/stateful_event_processing.py", line 127, in _load_recent_events_map
items_in_recent_events_bag = [e for e in recent_events_state.read()]
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/bundle_processor.py", line 335, in __iter__
for elem in self.first:
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker.py", line 723, in _materialize_iter
self._underlying.get_raw(state_key, continuation_token)
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker.py", line 603, in get_raw
continuation_token=continuation_token)))
File "/srv/venvs/service/trusty/service_venv/local/lib/python2.7/site-packages/apache_beam/runners/worker/sdk_worker.py", line 637, in _blocking_request
raise RuntimeError(response.error)
RuntimeError: java.lang.IllegalStateException: The current key '[1, -104, -97, -93, -34, -73, -128, -42, 36]' with key group index '274' does not belong to the key group range 'KeyGroupRange{startKeyGroup=153, endKeyGroup=154}'. Runner KeyCoder: LengthPrefixCoder(ByteArrayCoder). Ptransformid: ref_AppliedPTransform_process_events_with_stateful_dofn_23 Userstateid: recent_events
at org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Preconditions.checkState(Preconditions.java:531)
at org.apache.beam.runners.flink.translation.wrappers.streaming.ExecutableStageDoFnOperator$BagUserStateFactory$1.prepareStateBackend(ExecutableStageDoFnOperator.java:387)
at org.apache.beam.runners.flink.translation.wrappers.streaming.ExecutableStageDoFnOperator$BagUserStateFactory$1.get(ExecutableStageDoFnOperator.java:309)
at org.apache.beam.runners.flink.translation.wrappers.streaming.ExecutableStageDoFnOperator$BagUserStateFactory$1.get(ExecutableStageDoFnOperator.java:303)
at org.apache.beam.runners.fnexecution.state.StateRequestHandlers$ByteStringStateRequestHandlerToBagUserStateHandlerFactoryAdapter.handleGetRequest(StateRequestHandlers.java:468)
at org.apache.beam.runners.fnexecution.state.StateRequestHandlers$ByteStringStateRequestHandlerToBagUserStateHandlerFactoryAdapter.handle(StateRequestHandlers.java:415)
at org.apache.beam.runners.fnexecution.state.StateRequestHandlers$StateKeyTypeDelegatingStateRequestHandler.handle(StateRequestHandlers.java:206)
at org.apache.beam.runners.fnexecution.state.GrpcStateService$Inbound.onNext(GrpcStateService.java:130)
at org.apache.beam.runners.fnexecution.state.GrpcStateService$Inbound.onNext(GrpcStateService.java:118)
at org.apache.beam.vendor.grpc.v1p21p0.io.grpc.stub.ServerCalls$StreamingServerCallHandler$StreamingServerCallListener.onMessage(ServerCalls.java:249)
at org.apache.beam.vendor.grpc.v1p21p0.io.grpc.ForwardingServerCallListener.onMessage(ForwardingServerCallListener.java:33)
at org.apache.beam.vendor.grpc.v1p21p0.io.grpc.Contexts$ContextualizedServerCallListener.onMessage(Contexts.java:76)
at org.apache.beam.vendor.grpc.v1p21p0.io.grpc.internal.ServerCallImpl$ServerStreamListenerImpl.messagesAvailable(ServerCallImpl.java:297)
at org.apache.beam.vendor.grpc.v1p21p0.io.grpc.internal.ServerImpl$JumpToApplicationThreadServerStreamListener$1MessagesAvailable.runInContext(ServerImpl.java:738)
at org.apache.beam.vendor.grpc.v1p21p0.io.grpc.internal.ContextRunnable.run(ContextRunnable.java:37)
at org.apache.beam.vendor.grpc.v1p21p0.io.grpc.internal.SerializingExecutor.run(SerializingExecutor.java:123)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
[while running 'process_events_with_stateful_dofn']
```
Apache Beam is a unified model for defining both batch and streaming data-parallel processing pipelines, as well as a set of language-specific SDKs for constructing pipelines and Runners for executing them on distributed processing backends, including Apache Apex, Apache Flink, Apache Spark, Google Cloud Dataflow and Hazelcast Jet.
| Lang | SDK | Apex | Dataflow | Flink | Gearpump | Samza | Spark |
|---|---|---|---|---|---|---|---|
| Go | --- | --- | --- | --- | |||
| Java | |||||||
| Python | --- | --- | --- | ||||
| XLang | --- | --- | --- | --- | --- | --- |
Beam provides a general approach to expressing embarrassingly parallel data processing pipelines and supports three categories of users, each of which have relatively disparate backgrounds and needs.
The model behind Beam evolved from a number of internal Google data processing projects, including MapReduce, FlumeJava, and Millwheel. This model was originally known as the “Dataflow Model”.
To learn more about the Beam Model (though still under the original name of Dataflow), see the World Beyond Batch: Streaming 101 and Streaming 102 posts on O’Reilly’s Radar site, and the VLDB 2015 paper.
The key concepts in the Beam programming model are:
PCollection: represents a collection of data, which could be bounded or unbounded in size.PTransform: represents a computation that transforms input PCollections into output PCollections.Pipeline: manages a directed acyclic graph of PTransforms and PCollections that is ready for execution.PipelineRunner: specifies where and how the pipeline should execute.Beam supports multiple language specific SDKs for writing pipelines against the Beam Model.
Currently, this repository contains SDKs for Java, Python and Go.
Have ideas for new SDKs or DSLs? See the JIRA.
Beam supports executing programs on multiple distributed processing backends through PipelineRunners. Currently, the following PipelineRunners are available:
DirectRunner runs the pipeline on your local machine.ApexRunner runs the pipeline on an Apache Hadoop YARN cluster (or in embedded mode).DataflowRunner submits the pipeline to the Google Cloud Dataflow.FlinkRunner runs the pipeline on an Apache Flink cluster. The code has been donated from dataArtisans/flink-dataflow and is now part of Beam.SparkRunner runs the pipeline on an Apache Spark cluster. The code has been donated from cloudera/spark-dataflow and is now part of Beam.JetRunner runs the pipeline on a Hazelcast Jet cluster. The code has been donated from hazelcast/hazelcast-jet and is now part of Beam.Have ideas for new Runners? See the JIRA.
Please refer to the Quickstart[Java, Python, Go] available on our website.
If you'd like to build and install the whole project from the source distribution, you may need some additional tools installed in your system. In a Debian-based distribution:
sudo apt-get install \
openjdk-8-jdk \
python-setuptools \
python-pip \
virtualenv
Then please use the standard ./gradlew build command.
To get involved in Apache Beam:
We also have a contributor's guide.