com.google.cloud.dataflow.sdk.transforms.windowing.FixedWindows Java Examples

public static void main(String[] args) {
  CustomPipelineOptions options =
      PipelineOptionsFactory.fromArgs(args).withValidation().as(CustomPipelineOptions.class);
  Pipeline p = Pipeline.create(options);

  p.apply(PubsubIO.Read.named("read from PubSub")
      .topic(String.format("projects/%s/topics/%s", options.getSourceProject(), options.getSourceTopic()))
      .timestampLabel("ts")
      .withCoder(TableRowJsonCoder.of()))

   .apply("window 1s", Window.into(FixedWindows.of(Duration.standardSeconds(1))))
   .apply("mark rides", MapElements.via(new MarkRides()))
   .apply("count similar", Count.perKey())
   .apply("format rides", MapElements.via(new TransformRides()))

   .apply(PubsubIO.Write.named("WriteToPubsub")
      .topic(String.format("projects/%s/topics/%s", options.getSinkProject(), options.getSinkTopic()))
      .withCoder(TableRowJsonCoder.of()));

  p.run();
}
 

public static void main(String[] args) {
  CustomPipelineOptions options =
      PipelineOptionsFactory.fromArgs(args).withValidation().as(CustomPipelineOptions.class);
  Pipeline p = Pipeline.create(options);

  p.apply(PubsubIO.Read.named("read from PubSub")
      .topic(String.format("projects/%s/topics/%s", options.getSourceProject(), options.getSourceTopic()))
      .timestampLabel("ts")
      .withCoder(TableRowJsonCoder.of()))

   .apply("window 1s", Window.into(FixedWindows.of(Duration.standardSeconds(1))))

   .apply("parse timestamps",
      MapElements.via(
        (TableRow e) ->
          Instant.from(DateTimeFormatter.ISO_DATE_TIME.parse(e.get("timestamp").toString())).toEpochMilli())
      .withOutputType(TypeDescriptor.of(Long.class)))

   .apply("max timestamp in window", Max.longsGlobally().withoutDefaults())

   .apply("transform",
      MapElements.via(
        (Long t) -> {
          TableRow ride = new TableRow();
          ride.set("timestamp", Instant.ofEpochMilli(t).toString());
          return ride;
        })
      .withOutputType(TypeDescriptor.of(TableRow.class)))

   .apply(PubsubIO.Write.named("write to PubSub")
      .topic(String.format("projects/%s/topics/%s", options.getSinkProject(), options.getSinkTopic()))
      .withCoder(TableRowJsonCoder.of()));
  p.run();
}
 

public PCollection<KV<String, TSProto>> generateCompleteWindowData(Pipeline pipeline,
    List<KV<String, TSProto>> data, WorkPacketConfig packetConfig) {

  LOG.info("Check to see that time streams with missing 'ticks' have been corrected");

  PCollection<KV<String, TSProto>> tsData = setupDataInput(pipeline, data);


  PCollection<KV<String, TSProto>> windowedData =
      tsData.apply("CandleResolutionWindow", Window.<KV<String, TSProto>>into(FixedWindows
          .of(Duration.standardSeconds(((FXTimeSeriesPipelineOptions) pipeline.getOptions())
              .getCandleResolution()))));

  // Determine streams that are missing in this Window and generate values for them

  PCollection<KV<String, TSProto>> generatedValues =
      windowedData
          .apply(
              "DetectMissingTimeSeriesValues",
              Combine.globally(new DetectMissingTimeSeriesValuesCombiner(packetConfig))
                  .withoutDefaults()).apply(ParDo.of(new CreateMissingTimeSeriesValuesDoFn()))
          .setName("CreateMissingTimeSeriesValues");

  // Flatten the live streams and the generated streams together

  PCollection<KV<String, TSProto>> completeWindowData =
      PCollectionList.of(windowedData).and(generatedValues)
          .apply("MergeGeneratedLiveValues", Flatten.<KV<String, TSProto>>pCollections());


  return completeWindowData;
}
 

@Override
public PCollection<KV<String, TSAggValueProto>> apply(PCollection<KV<String, TSProto>> input) {



  PCollection<KV<String, TSProto>> windowedData =
      input.apply("CandleResolutionWindow", Window.<KV<String, TSProto>>into(
          FixedWindows.of(Duration.standardSeconds(options.getCandleResolution()))));

  // Determine streams that are missing in this Window and generate values for them

  PCollection<KV<String, TSProto>> generatedValues = windowedData
      .apply("DetectMissingTimeSeriesValues",
          Combine.globally(new DetectMissingTimeSeriesValuesCombiner(packetConfig))
              .withoutDefaults())
      .apply(ParDo.of(new CreateMissingTimeSeriesValuesDoFn()))
      .setName("CreateMissingTimeSeriesValues");

  // Flatten the live streams and the generated streams together

  PCollection<KV<String, TSProto>> completeWindowData =
      PCollectionList.of(windowedData).and(generatedValues).apply("MergeGeneratedLiveValues",
          Flatten.<KV<String, TSProto>>pCollections());

  // Create partial aggregates, at this stage we will not bring forward the previous windows close
  // value
  PCollection<KV<String, TSAggValueProto>> parital = completeWindowData
      .apply("CreatePartialAggregates", Combine.perKey(new PartialTimeSeriesAggCombiner()));

  // When these aggregates go through the Global Window they will lose their time value
  // We will embed the window close into the data so we can access it later on

  PCollection<KV<String, TSAggValueProto>> paritalWithWindowBoundary =
      parital.apply(ParDo.of(new EmbedWindowTimeIntoAggregateDoFn()));

  // Create a Global window which can retain the last value held in memory We must use
  // outputAtEarliestInputTimestamp as later on we re-attach the timestamp from within the data
  // point, for us not to hit 'skew' issues we need to ensure the output timestamp value is always
  // the smallest value
  PCollection<KV<String, TSAggValueProto>> completeAggregationStage1 =
      paritalWithWindowBoundary.apply("completeAggregationStage1",
          Window.<KV<String, TSAggValueProto>>into(new GlobalWindows())
              .triggering(Repeatedly.forever(AfterPane.elementCountAtLeast(1)))
              .withOutputTimeFn(OutputTimeFns.outputAtEarliestInputTimestamp())
              .accumulatingFiredPanes());

  PCollection<KV<String, TSAggValueProto>> completeAggregationStage2 = completeAggregationStage1
      .apply("CreateCompleteCandles", Combine.perKey(new CompleteTimeSeriesAggCombiner()))
      .apply("FlattenIterables", ParDo.of(new FlattenKVIterableDoFn()));



  // Reset timestamps after global window
  PCollection<KV<String, TSAggValueProto>> completeAggregationStage3 =
      completeAggregationStage2.apply("ResetTimestampsAfterGlobalWindow",
          ParDo.of(new DoFn<KV<String, TSAggValueProto>, KV<String, TSAggValueProto>>() {

            @Override
            public void processElement(
                DoFn<KV<String, TSAggValueProto>, KV<String, TSAggValueProto>>.ProcessContext c)
                throws Exception {
              //
              // TODO When the local Dataflow runners shuts down there will be some values
              // produced for the end of the the GlobalWindow. We can remove these values by
              // filtering out anything from year 3000+ for now. Better solution will be to check
              // the WINDOW PANE
              //
          	  Instant time = c.timestamp();
          	  
              if (time.isBefore(new Instant(32530703764000L))) {

                // The timestamp produced from the Combiner after the GlobalWindow loses fidelity,
                // we can add this back by looking at the value in the data

                if (time
                    .isAfter(new Instant(c.element().getValue().getCloseState().getTime()))) {

                  LOG.error(
                      "There was a timestamp before earlier than the window and skew must be 0 :: "
                          + TextFormat.shortDebugString(c.element().getValue()));

                } else {
                  c.outputWithTimestamp(c.element(),
                      new Instant(c.element().getValue().getCloseTime()));

                }
              }

            }

          }));

  return completeAggregationStage3;
}