Apache Beam job fails when doing session windowing on a large dataset

Question

I'm working on a Python Apache Beam job with session windowing on a bounded dataset. It works for small datasets, but the job dies when I increase the size of input data.

The job ID is 2019-06-10_07_28_32-2942508228086251217.

elements = (p | 'IngestData' >> beam.io.Read(big_query_source))

        elements | 'AddEventTimestamp' >> beam.ParDo(AddTimestampDoFn()) \
                        | 'SessionWindow' >> beam.WindowInto(window.Sessions(10 * 60)) \
                        | 'CreateTuple' >> beam.Map(lambda row: (row['id'], {'attribute1': row['attribute1'], 'date': row['date']})) \
                        | 'GroupById1' >> beam.GroupByKey() \
                        | 'AggregateSessions' >> beam.ParDo(AggregateTransactions()) \
                        | 'MergeWindows' >> beam.WindowInto(window.GlobalWindows()) \
                        | 'GroupById2' >> beam.GroupByKey() \
                        | 'MapSessionsToLists' >> beam.Map(lambda x: (x[0], [y for y in x[1]])) \
                        | 'BiggestSession' >> beam.ParDo(MaximumSession()) \
                        | "PrepForWrite" >> beam.Map(lambda x: x[1].update({"id": x[0]}) or x[1]) \
                        | 'WriteResult' >> WriteToText(known_args.output)

With the DoFn classes being

class AddTimestampDoFn(beam.DoFn):
    def process(self, element):
        date = datetime.datetime.strptime(element['date'][:-4], '%Y-%m-%d %H:%M:%S.%f')
        unix_timestamp = float(date.strftime('%s'))
        yield beam.window.TimestampedValue(element, unix_timestamp)


class AggregateTransactions(beam.DoFn):
    def process(self, element, window=beam.DoFn.WindowParam):
        session_count = len(element[1])
        attributes = list(map(lambda row: row['attribute1'], element[1]))
        std = np.std(amounts)

        return [(element[0], {'session_count': session_count, 'session_std': std, 'window_start': window.start
                                                                                    .to_utc_datetime()
                                                                                    .strftime('%d-%b-%Y %H:%M:%S')})]


class MaximumSession(beam.DoFn):
    def process(self, element):
        sorted_counts = sorted(element[1], key = lambda x: x['session_count'], reverse=True)

        return [(element[0], {'session_count': sorted_counts[0]['session_count'], 
                                        'session_std': sorted_counts[0]['session_std'], 
                                        'window_start_time': sorted_counts[0]['window_start']})]

The job fails and gives me this error: The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors. The work item was attempted on these workers:

The particular worker logs on stackdriver aren't suggestive of anything. I'm just getting a combination of these entries:

processing lull for over 431.44 seconds in state process-msecs in step s5

Refusing to split at ' \xaaG \x00\x01': proposed split position is out of range

Retry with exponential backoff: waiting for 4.69305060273 seconds before retrying lease_work because we caught exception: SSLError: ('The read operation timed out',)

The rest of the entries are informational.

The latest memory usage was 43413 MB for that particular worker. Since I'm using n1-highmem-32 machines, I don't think that memory could be an issue here.

On client side, Cloud Shell, where I'm triggering this job, I just got a lot of

INFO:oauth2client.transport:Refreshing due to a 401 (attempt 1/2)
INFO:oauth2client.transport:Refreshing due to a 401 (attempt 2/2)
INFO:oauth2client.transport:Refreshing due to a 401 (attempt 1/2)
INFO:oauth2client.transport:Refreshing due to a 401 (attempt 1/2)
INFO:oauth2client.transport:Refreshing due to a 401 (attempt 2/2)
INFO:oauth2client.transport:Refreshing due to a 401 (attempt 2/2)
INFO:oauth2client.transport:Refreshing due to a 401 (attempt 1/2)
INFO:oauth2client.transport:Refreshing due to a 401 (attempt 2/2)

before the job crashed.

Any ideas?

Thanks

Apache Beam job fails when doing session windowing on a large dataset

Answers (1)

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