Troubleshooting and debugging

This page provides troubleshooting tips and debugging strategies that you might find helpful if you're having trouble building or running your Cloud Dataflow pipeline. This information can help you detect a pipeline failure, determine the reason behind a failed pipeline run, and suggest some courses of action to correct the problem.

Cloud Dataflow provides real-time feedback on your job, and there is a basic set of steps you can use to check the error messages, logs, and for conditions such as your job's progress having stalled.

For guidance on common errors you might encounter when running your Cloud Dataflow job, see the Common error guidance page.

Note: If a job fails, Cloud Dataflow might not automatically clean up your temporary and staged files. In this case, manually remove these files from any Cloud Storage buckets that are used as temporary buckets.

Checking your pipeline's status

You can detect any errors in your pipeline runs by using the Cloud Dataflow monitoring interface.

1. Go to the Google Cloud Platform Console.
2. Select your Google Cloud Platform (GCP) project from the project list.
3. Click the menu in the upper left corner.
4. Navigate to the Big Data section and click Dataflow. A list of running jobs appears in the right-hand pane.
5. Select the pipeline job you want to view. You can see the jobs' status at a glance in the Status field: "Running," "Succeeded," or "Failed."

Figure 1: A list of Cloud Dataflow jobs in the Developers Console with jobs in the running, succeeded, and failed states.

Basic troubleshooting workflow

If one of your pipeline jobs has failed, you can select the job to view more detailed information on errors and run results. When you select a job, you can view the execution graph as well as some information about the job on the Summary page to the right of the graph. During execution, the top of the page contains a button to view logs, as well as indicators if the job generated errors or warnings.

Figure 2: A Cloud Dataflow Job Summary with errors indicated.

Checking job error messages

You can click the Logs button to view log messages generated by your pipeline code and the Cloud Dataflow service. Filter the messages that appear in the logs panel by using the Minimum Severity drop-down menu. Select the Error filter to displays error messages only.

Click the triangle icon next to each error message to expand it.

Figure 3: A list of Cloud Dataflow Job Error messages, with one message expanded.

Viewing step logs for your job

When you select a step in your pipeline graph, the logs panel toggles from displaying Job Logs generated by the Cloud Dataflow service, to showing logs from the Compute Engine instances running your pipeline step.

Figure 4: The Cloud Logging button in the Cloud Dataflow Job Summary.

Stackdriver Logging combines all of the collected logs from your project's Compute Engine instances in one location. See Logging pipeline messages for more information on using Cloud Dataflow's various logging capabilities.

Handling automated pipeline rejection

In some cases, the Cloud Dataflow service identifies that your pipeline might trigger known SDK issues. To prevent pipelines that will likely encounter issues, from being submitted, Cloud Dataflow will automatically reject your pipeline and display the following message:

The workflow was automatically rejected by the service because it may trigger an
identified bug in the SDK (details below). If you think this identification is
in error, and would like to override this automated rejection, please re-submit
this workflow with the following override flag: [OVERRIDE FLAG].
Bug details: [BUG DETAILS].
Contact Google Cloud Support for further help.
Please use this identifier in your communication: [BUG ID].

After reading the caveats in the linked bug details, if you want to try to run your pipeline anyway, you can override the automated rejection. Add the flag --experiments=<override-flag> and resubmit your pipeline.

Determining the cause of a pipeline failure

Typically, a failed Apache Beam pipeline run can be attributed to one of the following causes:

• Graph or pipeline construction errors. These errors occur when Cloud Dataflow runs into a problem building the graph of steps that compose your pipeline, as described by your Apache Beam pipeline.
• Errors in job validation. The Cloud Dataflow service validates any pipeline job you launch. Errors in the validation process can prevent your job from being successfully created or executed. Validation errors can include problems with your GCP project's Cloud Storage bucket, or with your project's permissions.
• Exceptions in worker code. These errors occur when there are errors or bugs in the user-provided code that Cloud Dataflow distributes to parallel workers, such as the DoFn instances of a ParDo transform.
• Slow-running pipelines or lack of output. If your pipeline runs slowly or runs for a long period of time without reporting results, you might check your quotas for streaming data sources and sinks such as Cloud Pub/Sub. There are also certain transforms that are better-suited to high-volume streaming pipelines than others.
• Errors caused by transient failures in other GCP services. Your pipeline may fail because of a temporary outage or other problem in the GCP services upon which Cloud Dataflow depends, such as Compute Engine or Cloud Storage.

Detecting graph or pipeline construction errors

A graph construction error can occur when Cloud Dataflow is building the execution graph for your pipeline from the code in your Cloud Dataflow program. During graph construction time, Cloud Dataflow checks for illegal operations.

If Cloud Dataflow detects an error in graph construction, keep in mind that no job will be created on the Cloud Dataflow service. Thus, you won't see any feedback in the Cloud Dataflow monitoring interface. Instead, you'll see an error message similar to the following in the console or terminal window where you ran your Apache Beam pipeline:

Java: SDK 2.x

For example, if your pipeline attempts to perform an aggregation like GroupByKey on a globally windowed, non-triggered, unbounded PCollection, you'll see an error message similar to the following:

...
... Exception in thread "main" java.lang.IllegalStateException:
... GroupByKey cannot be applied to non-bounded PCollection in the GlobalWindow without a trigger.
... Use a Window.into or Window.triggering transform prior to GroupByKey
...

Python

For example, if your pipeline uses type hints and the argument type in one of the transforms is not as expected, you'll see an error message similar to the following:

... in <module> run()
... in run | beam.Map('count', lambda (word, ones): (word, sum(ones))))
... in __or__ return self.pipeline.apply(ptransform, self)
... in apply transform.type_check_inputs(pvalueish)
... in type_check_inputs self.type_check_inputs_or_outputs(pvalueish, 'input')
... in type_check_inputs_or_outputs pvalue_.element_type))
google.cloud.dataflow.typehints.decorators.TypeCheckError: Input type hint violation at group: expected Tuple[TypeVariable[K], TypeVariable[V]], got <type 'str'>

Java: SDK 1.x

For example, if your pipeline attempts to perform an aggregation like GroupByKey on a globally windowed, non-triggered, unbounded PCollection, you'll see an error message similar to the following:

...
... Exception in thread "main" java.lang.IllegalStateException:
... GroupByKey cannot be applied to non-bounded PCollection in the GlobalWindow without a trigger.
... Use a Window.into or Window.triggering transform prior to GroupByKey
...

Should you encounter such an error, check your pipeline code to ensure that your pipeline's operations are legal.

Detecting errors in Cloud Dataflow job validation

Once the Cloud Dataflow service has received your pipeline's graph, the service will attempt to validate your job. This validation includes the following:

• Making sure the service can access your job's associated Cloud Storage buckets for file staging and temporary output.
• Checking for the required permissions in your GCP project.
• Making sure the service can access input and output sources, such as files.

If your job fails the validation process, you'll see an error message in the Cloud Dataflow monitoring interface, as well as in your console or terminal window if you are using blocking execution. The error message will look similar to the following:

Java: SDK 2.x

INFO: To access the Cloud Dataflow monitoring console, please navigate to
  https://console.developers.google.com/project/google.com%3Aclouddfe/dataflow/job/2016-03-08_18_59_25-16868399470801620798
Submitted job: 2016-03-08_18_59_25-16868399470801620798
...
... Starting 3 workers...
... Executing operation BigQuery-Read+AnonymousParDo+BigQuery-Write
... Executing BigQuery import job "dataflow_job_16868399470801619475".
... Stopping worker pool...
... Workflow failed. Causes: ...BigQuery-Read+AnonymousParDo+BigQuery-Write failed.
Causes: ... BigQuery getting table "non_existent_table" from dataset "cws_demo" in project "my_project" failed.
Message: Not found: Table x:cws_demo.non_existent_table HTTP Code: 404
... Worker pool stopped.
... com.google.cloud.dataflow.sdk.runners.BlockingDataflowPipelineRunner run
INFO: Job finished with status FAILED
Exception in thread "main" com.google.cloud.dataflow.sdk.runners.DataflowJobExecutionException:
  Job 2016-03-08_18_59_25-16868399470801620798 failed with status FAILED
    at com.google.cloud.dataflow.sdk.runners.DataflowRunner.run(DataflowRunner.java:155)
    at com.google.cloud.dataflow.sdk.runners.DataflowRunner.run(DataflowRunner.java:56)
    at com.google.cloud.dataflow.sdk.Pipeline.run(Pipeline.java:180)
    at com.google.cloud.dataflow.integration.BigQueryCopyTableExample.main(BigQueryCopyTableExample.java:74)

Python

INFO:root:Created job with id: [2016-03-08_14_12_01-2117248033993412477]
... Checking required Cloud APIs are enabled.
... Job 2016-03-08_14_12_01-2117248033993412477 is in state JOB_STATE_RUNNING.
... Combiner lifting skipped for step group: GroupByKey not followed by a combiner.
... Expanding GroupByKey operations into optimizable parts.
... Lifting ValueCombiningMappingFns into MergeBucketsMappingFns
... Annotating graph with Autotuner information.
... Fusing adjacent ParDo, Read, Write, and Flatten operations
... Fusing consumer split into read
...
... Starting 1 workers...
...
... Executing operation read+split+pair_with_one+group/Reify+group/Write
... Executing failure step failure14
... Workflow failed.
Causes: ... read+split+pair_with_one+group/Reify+group/Write failed.
Causes: ... Unable to view metadata for files: gs://dataflow-samples/shakespeare/missing.txt.
... Cleaning up.
... Tearing down pending resources...
INFO:root:Job 2016-03-08_14_12_01-2117248033993412477 is in state JOB_STATE_FAILED.

Java: SDK 1.x

INFO: To access the Cloud Dataflow monitoring console, please navigate to
  https://console.developers.google.com/project/google.com%3Aclouddfe/dataflow/job/2016-03-08_18_59_25-16868399470801620798
Submitted job: 2016-03-08_18_59_25-16868399470801620798
...
... Starting 3 workers...
... Executing operation BigQuery-Read+AnonymousParDo+BigQuery-Write
... Executing BigQuery import job "dataflow_job_16868399470801619475".
... Stopping worker pool...
... Workflow failed. Causes: ...BigQuery-Read+AnonymousParDo+BigQuery-Write failed.
Causes: ... BigQuery getting table "non_existent_table" from dataset "cws_demo" in project "x" failed.
Message: Not found: Table x:cws_demo.non_existent_table HTTP Code: 404
... Worker pool stopped.
... com.google.cloud.dataflow.sdk.runners.BlockingDataflowPipelineRunner run
INFO: Job finished with status FAILED
Exception in thread "main" com.google.cloud.dataflow.sdk.runners.DataflowJobExecutionException:
  Job 2016-03-08_18_59_25-16868399470801620798 failed with status FAILED
    at com.google.cloud.dataflow.sdk.runners.BlockingDataflowPipelineRunner.run(BlockingDataflowPipelineRunner.java:155)
    at com.google.cloud.dataflow.sdk.runners.BlockingDataflowPipelineRunner.run(BlockingDataflowPipelineRunner.java:56)
    at com.google.cloud.dataflow.sdk.Pipeline.run(Pipeline.java:180)
    at com.google.cloud.dataflow.integration.BigQueryCopyTableExample.main(BigQueryCopyTableExample.java:74)

Detecting an exception in worker code

While your job is running, you may encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Cloud Dataflow job.

Exceptions in user code (for example, your DoFn instances) are reported in the Cloud Dataflow monitoring interface. If you run your pipeline with blocking execution, you'll also see error messages printed in your console or terminal window, such as the following:

Java: SDK 2.x

INFO: To access the Cloud Dataflow monitoring console, please navigate to https://console.developers.google.com/project/example_project/dataflow/job/2017-05-23_14_02_46-1117850763061203461
Submitted job: 2017-05-23_14_02_46-1117850763061203461
...
... To cancel the job using the 'gcloud' tool, run: gcloud beta dataflow jobs --project=example_project cancel 2017-05-23_14_02_46-1117850763061203461
... Autoscaling is enabled for job 2017-05-23_14_02_46-1117850763061203461.
... The number of workers will be between 1 and 15.
... Autoscaling was automatically enabled for job 2017-05-23_14_02_46-1117850763061203461.
...
... Executing operation BigQueryIO.Write/BatchLoads/Create/Read(CreateSource)+BigQueryIO.Write/BatchLoads/GetTempFilePrefix+BigQueryIO.Write/BatchLoads/TempFilePrefixView/BatchViewOverrides.GroupByWindowHashAsKeyAndWindowAsSortKey/ParDo(UseWindowHashAsKeyAndWindowAsSortKey)+BigQueryIO.Write/BatchLoads/TempFilePrefixView/Combine.GloballyAsSingletonView/Combine.globally(Singleton)/WithKeys/AddKeys/Map/ParMultiDo(Anonymous)+BigQueryIO.Write/BatchLoads/TempFilePrefixView/Combine.GloballyAsSingletonView/Combine.globally(Singleton)/Combine.perKey(Singleton)/GroupByKey/Reify+BigQueryIO.Write/BatchLoads/TempFilePrefixView/Combine.GloballyAsSingletonView/Combine.globally(Singleton)/Combine.perKey(Singleton)/GroupByKey/Write+BigQueryIO.Write/BatchLoads/TempFilePrefixView/BatchViewOverrides.GroupByWindowHashAsKeyAndWindowAsSortKey/BatchViewOverrides.GroupByKeyAndSortValuesOnly/Write
... Workers have started successfully.
...
... org.apache.beam.runners.dataflow.util.MonitoringUtil$LoggingHandler process SEVERE: 2017-05-23T21:06:33.711Z: (c14bab21d699a182): java.lang.RuntimeException: org.apache.beam.sdk.util.UserCodeException: java.lang.ArithmeticException: / by zero
        at com.google.cloud.dataflow.worker.runners.worker.GroupAlsoByWindowsParDoFn$1.output(GroupAlsoByWindowsParDoFn.java:146)
        at com.google.cloud.dataflow.worker.runners.worker.GroupAlsoByWindowFnRunner$1.outputWindowedValue(GroupAlsoByWindowFnRunner.java:104)
        at com.google.cloud.dataflow.worker.util.BatchGroupAlsoByWindowAndCombineFn.closeWindow(BatchGroupAlsoByWindowAndCombineFn.java:191)
...
... Cleaning up.
... Stopping worker pool...
... Worker pool stopped.

Note: The Cloud Dataflow service retries failed tasks up to 4 times in batch mode, and an unlimited number of times in streaming mode. In batch mode, your job will fail; in streaming, it may stall indefinitely.

Python

INFO:root:Job 2016-03-08_14_21_32-8974754969325215880 is in state JOB_STATE_RUNNING.
...
INFO:root:... Expanding GroupByKey operations into optimizable parts.
INFO:root:... Lifting ValueCombiningMappingFns into MergeBucketsMappingFns
INFO:root:... Annotating graph with Autotuner information.
INFO:root:... Fusing adjacent ParDo, Read, Write, and Flatten operations
...
INFO:root:...: Starting 1 workers...
INFO:root:...: Executing operation group/Create
INFO:root:...: Value "group/Session" materialized.
INFO:root:...: Executing operation read+split+pair_with_one+group/Reify+group/Write
INFO:root:Job 2016-03-08_14_21_32-8974754969325215880 is in state JOB_STATE_RUNNING.
INFO:root:...: ...: Workers have started successfully.
INFO:root:Job 2016-03-08_14_21_32-8974754969325215880 is in state JOB_STATE_RUNNING.
INFO:root:...: Traceback (most recent call last):
  File ".../dataflow_worker/batchworker.py", line 384, in do_work self.current_executor.execute(work_item.map_task)
  ...
  File ".../apache_beam/examples/wordcount.runfiles/py/apache_beam/examples/wordcount.py", line 73, in <lambda>
ValueError: invalid literal for int() with base 10: 'www'

Note: The Cloud Dataflow service retries failed tasks up to 4 times in batch mode, and an unlimited number of times in streaming mode. In batch mode, your job will fail; in streaming, it may stall indefinitely.

Java: SDK 1.x

INFO: To access the Cloud Dataflow monitoring console, please navigate to https://console.developers.google.com/project/google.com%3Aclouddfe/dataflow/job/2016-03-08_19_09_07-6448127003704955959
Submitted job: 2016-03-08_19_09_07-6448127003704955959
...
... Expanding GroupByKey operations into optimizable parts.
... Lifting ValueCombiningMappingFns into MergeBucketsMappingFns
... Annotating graph with Autotuner information.
... Fusing adjacent ParDo, Read, Write, and Flatten operations
...
... Starting 1 workers...
... Executing operation TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/GroupByKey/Create
... Executing operation AnonymousParDo+TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/Window.Into()+TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/RandomKey+TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/GroupByKey/Reify+TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/GroupByKey/Write
... Workers have started successfully.
... java.lang.ArithmeticException: / by zero
    at com.google.cloud.dataflow.integration.WorkerCrashRecovery.crash(WorkerCrashRecovery.java:166)
    at com.google.cloud.dataflow.integration.WorkerCrashRecovery.access$200(WorkerCrashRecovery.java:51)
    at com.google.cloud.dataflow.integration.WorkerCrashRecovery$1.processElement(WorkerCrashRecovery.java:199)
... Executing operation TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/GroupByKey/Close
... Executing operation TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/GroupByKey/Read+TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/GroupByKey/GroupByWindow+TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.ReshardForWrite/Ungroup+TextIO.Write/DataflowPipelineRunner.BatchTextIOWrite/DataflowPipelineRunner.BatchTextIONativeWrite
... Stopping worker pool...
... Worker pool stopped.
... Cleaning up.

Note: The Cloud Dataflow service retries failed tasks up to 4 times in batch mode, and an unlimited number of times in streaming mode. In batch mode, your job will fail; in streaming, it may stall indefinitely.

Consider guarding against errors in your code by adding exception handlers. For example, if you'd like to drop elements that fail some custom input validation done in a ParDo, handle the exception within your DoFn and drop the element. You can also track failing elements in a few different ways:

Java: SDK 2.x

• To track the properties of an executing pipeline, you can use the Metrics class, as shown in the following example:

  final Counter counter = Metrics.counter("stats", "even-items");
  PCollection<Integer> input = pipeline.apply(...);
  ...
  input.apply(ParDo.of(new DoFn<Integer, Integer>() {
    @ProcessElement
    public void processElement(ProcessContext c) {
      if (c.element() % 2 == 0) {
        counter.inc();
      }
  });
  

• You can log the failing elements and check the output using Cloud Logging.
• You can have your ParDo write the failing elements to an additional output for later inspection.

Python

• You can use the Metrics class to track the properties of an executing pipeline, as shown in the following example:

  class FilterTextFn(beam.DoFn):
        """A DoFn that filters for a specific key based on a regex."""
  
        def __init__(self, pattern):
          self.pattern = pattern
          # A custom metric can track values in your pipeline as it runs. Create
          # custom metrics to count unmatched words, and know the distribution of
          # word lengths in the input PCollection.
          self.word_len_dist = Metrics.distribution(self.__class__,
                                                    'word_len_dist')
          self.unmatched_words = Metrics.counter(self.__class__,
                                                 'unmatched_words')
  
        def process(self, element):
          word = element
          self.word_len_dist.update(len(word))
          if re.match(self.pattern, word):
            yield element
          else:
            self.unmatched_words.inc()
  
      filtered_words = (
          words | 'FilterText' >> beam.ParDo(FilterTextFn('s.*')))
  

• You can log the failing elements and check the output using Cloud Logging.
• You can have your ParDo write the failing elements to an additional output for later inspection.

Java: SDK 1.x

• You can log the failing elements and check the output using Cloud Logging.
• You can have your ParDo write the failing elements to a side output for later inspection.

Troubleshooting slow-running pipelines or lack of output

Java: SDK 2.x

If you have a high-volume streaming pipeline that is running slowly or stalled, there are a few things you can check:

Pub/Sub Quota

If your pipeline reads input from Cloud Pub/Sub, your GCP project may have insufficient Cloud Pub/Sub quota. One indication of an insufficint quota is if your job is generating a high number of 429 (Rate Limit Exceeded) errors. Try the following steps to check for such errors:

1. Go to the Google Cloud Platform Console.
2. In the left-hand navigation pane, click APIs & services.
3. In the Search Box, search for Cloud Pub/Sub.
4. Click the Usage tab.
5. Check Response Codes and look for (4xx) client error codes.

Use .withFanout In Your Combine Transforms

If your pipeline processes high-volume unbounded PCollections, we recommend:

• Use Combine.Globally.withFanout instead of Combine.Globally.
• Use Combine.PerKey.withHotKeyFanout instead of Count.PerKey.

Python

Beta

This product or feature is in a pre-release state and might change or have limited support. For more information, see Product launch stages.

If you have a high-volume streaming pipeline that is running slowly or stalled, there are a few things you can check:

Pub/Sub Quota

If your pipeline reads input from Cloud Pub/Sub, your GCP project may have insufficient Cloud Pub/Sub quota. One indication of an insufficient quota is if your job is generating a high number of 429 (Rate Limit Exceeded) errors. Try the following steps to check for such errors:

1. Go to the Google Cloud Platform Console.
2. In the left-hand navigation pane, click APIs & services.
3. In the Search Box, search for Cloud Pub/Sub.
4. Click the Usage tab.
5. Check Response Codes and look for (4xx) client error codes.

Java: SDK 1.x

If you have a high-volume streaming pipeline that is running slowly or stalled, there are a few things you can check:

Pub/Sub Quota

If your pipeline reads input from Cloud Pub/Sub, your GCP project may have insufficient Cloud Pub/Sub quota. One indication of an insufficient quota is if your job is generating a high number of 429 (Rate Limit Exceeded) errors. Try the following steps to check for such errors:

1. Go to the Google Cloud Platform Console.
2. In the left-hand navigation pane, click APIs & services.
3. In the Search Box, search for Cloud Pub/Sub.
4. Click the Usage tab.
5. Check Response Codes and look for (4xx) client error codes.

Use .withFanout In Your Combine Transforms

If your pipeline processes high-volume unbounded PCollections, we recommend:

• Use Combine.Globally.withFanout instead of Combine.Globally.
• Use Combine.PerKey.withHotKeyFanout instead of Count.PerKey.

Common errors and courses of action

For guidance on common errors you might encounter when running your Cloud Dataflow job, see the Common error guidance page.