I realized that BucketingSink must not play any role in this problem. This
is because only when the 24-hour window triggers, BucketinSink gets a burst
of input. Around the state restoring point (middle of the day) it doesn't
get any input, so it can't lose anything either (right?).
I will next try removing the allowedLateness entirely from the equation.
In the meanwhile, please let me know if you have any suggestions for
debugging the lost data, for example what logs to enable.
We use FlinkKafkaConsumer010 btw. Are there any known issues with that,
that could contribute to lost data when restoring a savepoint?
On Fri, Aug 17, 2018 at 4:23 PM Juho Autio <juho.au...@rovio.com> wrote:
> Some data is silently lost on my Flink stream job when state is restored
> from a savepoint.
>
> Do you have any debugging hints to find out where exactly the data gets
> dropped?
>
> My job gathers distinct values using a 24-hour window. It doesn't have any
> custom state management.
>
> When I cancel the job with savepoint and restore from that savepoint, some
> data is missed. It seems to be losing just a small amount of data. The
> event time of lost data is probably around the time of savepoint. In other
> words the rest of the time window is not entirely missed – collection works
> correctly also for (most of the) events that come in after restoring.
>
> When the job processes a full 24-hour window without interruptions it
> doesn't miss anything.
>
> Usually the problem doesn't happen in test environments that have smaller
> parallelism and smaller data volumes. But in production volumes the job
> seems to be consistently missing at least something on every restore.
>
> This issue has consistently happened since the job was initially created.
> It was at first run on an older version of Flink 1.5-SNAPSHOT and it still
> happens on both Flink 1.5.2 & 1.6.0.
>
> I'm wondering if this could be for example some synchronization issue
> between the kafka consumer offsets vs. what's been written by BucketingSink?
>
> 1. Job content, simplified
>
> kafkaStream
> .flatMap(new ExtractFieldsFunction())
> .keyBy(new MapKeySelector(1, 2, 3, 4))
> .timeWindow(Time.days(1))
> .allowedLateness(allowedLateness)
> .sideOutputLateData(lateDataTag)
> .reduce(new DistinctFunction())
> .addSink(sink)
> // use a fixed number of output partitions
> .setParallelism(8))
>
> /**
> * Usage: .keyBy("the", "distinct", "fields").reduce(new
> DistinctFunction())
> */
> public class DistinctFunction implements
> ReduceFunction<java.util.Map<String, String>> {
> @Override
> public Map<String, String> reduce(Map<String, String> value1,
> Map<String, String> value2) {
> return value1;
> }
> }
>
> 2. State configuration
>
> boolean enableIncrementalCheckpointing = true;
> String statePath = "s3n://bucket/savepoints";
> new RocksDBStateBackend(statePath, enableIncrementalCheckpointing);
>
> Checkpointing Mode Exactly Once
> Interval 1m 0s
> Timeout 10m 0s
> Minimum Pause Between Checkpoints 1m 0s
> Maximum Concurrent Checkpoints 1
> Persist Checkpoints Externally Enabled (retain on cancellation)
>
> 3. BucketingSink configuration
>
> We use BucketingSink, I don't think there's anything special here, if not
> the fact that we're writing to S3.
>
> String outputPath = "s3://bucket/output";
> BucketingSink<Map<String, String>> sink = new
> BucketingSink<Map<String, String>>(outputPath)
> .setBucketer(new ProcessdateBucketer())
> .setBatchSize(batchSize)
> .setInactiveBucketThreshold(inactiveBucketThreshold)
>
> .setInactiveBucketCheckInterval(inactiveBucketCheckInterval);
> sink.setWriter(new IdJsonWriter());
>
> 4. Kafka & event time
>
> My flink job reads the data from Kafka, using a
> BoundedOutOfOrdernessTimestampExtractor on the kafka consumer to
> synchronize watermarks accross all kafka partitions. We also write late
> data to side output, but nothing is written there – if it would, it could
> explain missed data in the main output (I'm also sure that our late data
> writing works, because we previously had some actual late data which ended
> up there).
>
> 5. allowedLateness
>
> It may be or may not be relevant that I have also enabled allowedLateness
> with 1 minute lateness on the 24-hour window:
>
> If that makes sense, I could try removing allowedLateness entirely? That
> would be just to rule out that Flink doesn't have a bug that's related to
> restoring state in combination with the allowedLateness feature. After all,
> all of our data should be in a good enough order to not be late, given the
> max out of orderness used on kafka consumer timestamp extractor.
>
> Thank you in advance!
>
