Hi Aljoscha, Thanks a lot for the explanation. Using readFile with PROCESS_CONTINUOUSLY solves it. Two more questions though:
1. Is it possible to gracefully stop the job once it has read the input once? 2. Does the watermark extraction period depend on the watch interval, or should any watch interval (except -1L) work the same way? In my case the input is indeed finite and static, but contains hundreds of GBs, which made the window state grow quickly beyond the memory capacity, and the fact that the window contents were fired periodically helped keeping it small. Best, Yassine 2016-12-14 10:38 GMT+01:00 Aljoscha Krettek <aljos...@apache.org>: > Hi Yassine, > for a bit more detailed explanation: We internally changed how the timer > system works, this timer system is also used to periodically extract > watermarks. Due to this change, in your case we don't extract watermarks > anymore. > > Internally, your call resolves to something like this: > > Env.readFile(FileInputFormat<OUT> inputFormat, String > filePath, FileProcessingMode watchType, long interval) > > with the FileProcessingMode being set to PROCESS_ONCE. > > To get back the old behaviour you can call this method directly with > PROCESS_CONTINUOUSLY. This will keep the pipeline running and will also > ensure that watermarks keep being extracted. > > In your case, it is not strictly wrong to emit only one large watermark in > the end because your processing is finite. I admit that the change from > Flink 1.1 seems a bit strange but this should only occur in toy examples > where the data is finite. > > Does that help? > > Cheers, > Aljoscha > > On Tue, 13 Dec 2016 at 18:17 Aljoscha Krettek <aljos...@apache.org> wrote: > >> Hi Yassine, >> I managed to reproduce the problem. The cause is that we recently changed >> how the timer service is being cleaned up and now the watermark timers are >> not firing anymore. >> >> I'll keep you posted and hope to find a solution fast. >> >> Cheers, >> Aljoscha >> >> On Sun, 11 Dec 2016 at 22:10 Yassine MARZOUGUI <y.marzou...@mindlytix.com> >> wrote: >> >> Hi Aljoscha, >> >> Please excuse me for the late response; I've been busy for the whole >> previous week. >> I used the custom watermark debugger (with 1.1, I changed >> super.processWatermark(mark) >> to super.output.emitWatermark(mark)), surprisingly with 1.2, only one >> watremark is printed at the end of the stream with the value WM: Watermark >> @ 9223372036854775807 (Long.MAX_VALUE), whereas with 1.1, watermarks are >> printed periodically. I am using the following revision of 1.2-SNAPSHOT : >> https://github.com/apache/flink/tree/4e336c692b74f218ba09844a46f495 >> 34e3a210e9. >> >> I uploaded the dataset I'm using as an input here : >> https://drive.google.com/file/d/0BzERCAJnxXocNGpMTGMzX09id1U/ >> view?usp=sharing ,the first column corresponds to the timestamp. >> >> You can find the code below. Thanks you for your help. >> >> import com.opencsv.CSVParser; >> import org.apache.flink.api.common.functions.RichFlatMapFunction; >> import org.apache.flink.api.java.tuple.Tuple; >> import org.apache.flink.api.java.tuple.Tuple2; >> import org.apache.flink.api.java.tuple.Tuple3; >> import org.apache.flink.configuration.Configuration; >> import org.apache.flink.streaming.api.TimeCharacteristic; >> import org.apache.flink.streaming.api.datastream.DataStream; >> import org.apache.flink.streaming.api.environment. >> StreamExecutionEnvironment; >> import org.apache.flink.streaming.api.functions.timestamps. >> AscendingTimestampExtractor; >> import org.apache.flink.streaming.api.functions.windowing.WindowFunction; >> import org.apache.flink.streaming.api.operators.AbstractStreamOperator; >> import org.apache.flink.streaming.api.operators.OneInputStreamOperator; >> import org.apache.flink.streaming.api.watermark.Watermark; >> import org.apache.flink.streaming.api.windowing.assigners. >> EventTimeSessionWindows; >> import org.apache.flink.streaming.api.windowing.time.Time; >> import org.apache.flink.streaming.api.windowing.windows.TimeWindow; >> import org.apache.flink.streaming.runtime.streamrecord.StreamRecord; >> import org.apache.flink.util.Collector; >> import java.util.*; >> >> /** >> * Created by ymarzougui on 11/1/2016. >> */ >> public class SortedSessionsAssigner { >> public static void main(String[] args) throws Exception { >> final StreamExecutionEnvironment env = StreamExecutionEnvironment. >> getExecutionEnvironment(); >> env.setParallelism(1); >> env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); >> >> DataStream<Tuple3<Long,String,String>> waterMarked = >> env.readTextFile("file:///E:\\data\\anonymized.csv") >> .flatMap(new RichFlatMapFunction<String, >> Tuple3<Long,String,String>>() { >> public CSVParser csvParser; >> >> @Override >> public void open(Configuration config) { >> csvParser = new CSVParser(',', '"'); >> } >> >> @Override >> public void flatMap(String in, >> Collector<Tuple3<Long,String,String>> clctr) throws Exception { >> String[] result = csvParser.parseLine(in); >> clctr.collect(Tuple3.of(Long.parseLong(result[0]), >> result[1], result[2])); >> } >> }) >> .assignTimestampsAndWatermarks(new >> AscendingTimestampExtractor<Tuple3<Long,String,String>>() { >> @Override >> public long >> extractAscendingTimestamp(Tuple3<Long,String,String> >> tuple3) { >> return tuple3.f0; >> } >> }); >> >> DataStream<Tuple2<TreeMap<String, Double>, Long>> sessions = >> waterMarked >> .keyBy(1) >> .window(EventTimeSessionWindows.withGap(Time.minutes(5))) >> .apply(new WindowFunction<Tuple3<Long, >> String,String>,Tuple2<TreeMap<String, Double>, Long>, Tuple, >> TimeWindow>() { >> >> @Override >> public void apply(Tuple tuple, TimeWindow timeWindow, >> Iterable<Tuple3<Long, String, String>> iterable, >> Collector<Tuple2<TreeMap<String, >> Double>, Long>> collector) throws Exception { >> TreeMap<String,Double> treeMap = new >> TreeMap<String, Double>(); >> Long session_count = 0L; >> for (Tuple3<Long, String, String> tuple3 : >> iterable){ >> treeMap.put(tuple3.f2, >> treeMap.getOrDefault(tuple3.f2, 0.0) + 1); >> session_count += 1; >> } >> collector.collect(Tuple2.of(treeMap, >> session_count)); >> >> } >> }).setParallelism(8); >> >> waterMarked.transform("WatermarkDebugger", >> waterMarked.getType(), new WatermarkDebugger<Tuple3<Long, String, >> String>>()); >> >> //sessions.writeAsCsv("file:///E:\\data\\sessions.csv", >> FileSystem.WriteMode.OVERWRITE).setParallelism(1); >> >> env.execute("Sorted Sessions Assigner"); >> >> } >> >> public static class WatermarkDebugger<T> >> extends AbstractStreamOperator<T> implements >> OneInputStreamOperator<T, T> { >> private static final long serialVersionUID = 1L; >> >> @Override >> public void processElement(StreamRecord<T> element) throws >> Exception { >> System.out.println("ELEMENT: " + element); >> output.collect(element); >> } >> >> @Override >> public void processWatermark(Watermark mark) throws Exception { >> // 1.2-snapshot >> super.processWatermark(mark); >> // 1.1-snapshot >> //super.output.emitWatermark(mark); >> System.out.println("WM: " + mark); >> } >> } >> >> } >> >> Best, >> Yassine >> >> 2016-12-06 5:57 GMT+01:00 Aljoscha Krettek <aljos...@apache.org>: >> >> Hi, >> could you please try adding this custom watermark debugger to see what's >> going on with the element timestamps and watermarks: >> >> public static class WatermarkDebugger<T> >> extends AbstractStreamOperator<T> implements >> OneInputStreamOperator<T, T> { >> private static final long serialVersionUID = 1L; >> >> @Override >> public void processElement(StreamRecord<T> element) throws Exception { >> System.out.println("ELEMENT: " + element); >> output.collect(element); >> } >> >> @Override >> public void processWatermark(Watermark mark) throws Exception { >> super.processWatermark(mark); >> System.out.println("WM: " + mark); >> } >> } >> >> you can use it like this: >> input.transform("WatermarkDebugger", input.getType(), new >> WatermarkDebugger<Tuple2<String, Integer>>()); >> >> That should give us something to work with. >> >> Cheers, >> Aljoscha >> >> On Mon, 5 Dec 2016 at 18:54 Robert Metzger <rmetz...@apache.org> wrote: >> >> I'll add Aljoscha and Kostas Kloudas to the conversation. They have the >> best overview over the changes to the window operator between 1.1. and 1.2. >> >> On Mon, Dec 5, 2016 at 11:33 AM, Yassine MARZOUGUI < >> y.marzou...@mindlytix.com> wrote: >> >> I forgot to mention : the watermark extractor is the one included in >> Flink API. >> >> 2016-12-05 11:31 GMT+01:00 Yassine MARZOUGUI <y.marzou...@mindlytix.com>: >> >> Hi robert, >> >> Yes, I am using the same code, just swithcing the version in pom.xml to >> 1.2-SNAPSHOT and the cluster binaries to the compiled lastest master (at >> the time of the question)). Here is the watermark assignment : >> >> .assignTimestampsAndWatermarks(new >> AscendingTimestampExtractor<Tuple3<Long,String,String>>() >> { >> @Override >> public long extractAscendingTimestamp(Tuple3<Long,String,String> >> tuple3) { >> return tuple3.f0; >> } >> }) >> >> Best, >> Yassine >> >> 2016-12-05 11:24 GMT+01:00 Robert Metzger <rmetz...@apache.org>: >> >> Hi Yassine, >> are you sure your watermark extractor is the same between the two >> versions. It sounds a bit like the watermarks for the 1.2 code are not >> generated correctly. >> >> Regards, >> Robert >> >> >> On Sat, Dec 3, 2016 at 9:01 AM, Yassine MARZOUGUI < >> y.marzou...@mindlytix.com> wrote: >> >> Hi all, >> >> With 1.1-SNAPSHOT, EventTimeSessionWindows fire as soon as the windows >> boundaries are detected, but with 1.2-SNAPDHOT the state keeps increasing >> in memory and the windows results are not emitted until the whole stream is >> processed. Is this a temporary behaviour due to the developments in >> 1.2-SNAPSHOT, or a bug? >> >> I am using a code similar to the follwoing: >> >> env.setParallelism(1); >> >> DataStream<T> sessions = env >> .readTextFile() >> .flatMap() >> .assignTimestampsAndWatermarks(new AscendingTimestampExtractor<>()) >> .keyBy(1) >> .window(EventTimeSessionWindows.withGap(Time.minutes(5))) >> .apply().setParallelism(32) >> >> sessions.flatMap(flatMapFunction1).setParallelism(32).writeAsCsv(); >> sessions.flatMap(flatMapFunction2).setParallelism(32).writeAsCsv(); >> >> Best, >> Yassine >> >> >> >> >> >> >>
