use of org.apache.beam.examples.complete.game.utils.WriteWindowedToBigQuery in project beam by apache.
the class GameStats method main.
public static void main(String[] args) throws Exception {
Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
// Enforce that this pipeline is always run in streaming mode.
options.setStreaming(true);
ExampleUtils exampleUtils = new ExampleUtils(options);
Pipeline pipeline = Pipeline.create(options);
// Read Events from Pub/Sub using custom timestamps
PCollection<GameActionInfo> rawEvents = pipeline.apply(PubsubIO.readStrings().withTimestampAttribute(GameConstants.TIMESTAMP_ATTRIBUTE).fromTopic(options.getTopic())).apply("ParseGameEvent", ParDo.of(new ParseEventFn()));
// Extract username/score pairs from the event stream
PCollection<KV<String, Integer>> userEvents = rawEvents.apply("ExtractUserScore", MapElements.into(TypeDescriptors.kvs(TypeDescriptors.strings(), TypeDescriptors.integers())).via((GameActionInfo gInfo) -> KV.of(gInfo.getUser(), gInfo.getScore())));
// Calculate the total score per user over fixed windows, and
// cumulative updates for late data.
final PCollectionView<Map<String, Integer>> spammersView = userEvents.apply("FixedWindowsUser", Window.into(FixedWindows.of(Duration.standardMinutes(options.getFixedWindowDuration())))).apply("CalculateSpammyUsers", new CalculateSpammyUsers()).apply("CreateSpammersView", View.asMap());
// [START DocInclude_FilterAndCalc]
// Calculate the total score per team over fixed windows,
// and emit cumulative updates for late data. Uses the side input derived above-- the set of
// suspected robots-- to filter out scores from those users from the sum.
// Write the results to BigQuery.
rawEvents.apply("WindowIntoFixedWindows", Window.into(FixedWindows.of(Duration.standardMinutes(options.getFixedWindowDuration())))).apply("FilterOutSpammers", ParDo.of(new DoFn<GameActionInfo, GameActionInfo>() {
@ProcessElement
public void processElement(ProcessContext c) {
// If the user is not in the spammers Map, output the data element.
if (c.sideInput(spammersView).get(c.element().getUser().trim()) == null) {
c.output(c.element());
}
}
}).withSideInputs(spammersView)).apply("ExtractTeamScore", new ExtractAndSumScore("team")).apply("WriteTeamSums", new WriteWindowedToBigQuery<>(options.as(GcpOptions.class).getProject(), options.getDataset(), options.getGameStatsTablePrefix() + "_team", configureWindowedWrite()));
// [START DocInclude_SessionCalc]
// Detect user sessions-- that is, a burst of activity separated by a gap from further
// activity. Find and record the mean session lengths.
// This information could help the game designers track the changing user engagement
// as their set of games changes.
userEvents.apply("WindowIntoSessions", Window.<KV<String, Integer>>into(Sessions.withGapDuration(Duration.standardMinutes(options.getSessionGap()))).withTimestampCombiner(TimestampCombiner.END_OF_WINDOW)).apply(Combine.perKey(x -> 0)).apply("UserSessionActivity", ParDo.of(new UserSessionInfoFn())).apply("WindowToExtractSessionMean", Window.into(FixedWindows.of(Duration.standardMinutes(options.getUserActivityWindowDuration())))).apply(Mean.<Integer>globally().withoutDefaults()).apply("WriteAvgSessionLength", new WriteWindowedToBigQuery<>(options.as(GcpOptions.class).getProject(), options.getDataset(), options.getGameStatsTablePrefix() + "_sessions", configureSessionWindowWrite()));
// [END DocInclude_Rewindow]
// Run the pipeline and wait for the pipeline to finish; capture cancellation requests from the
// command line.
PipelineResult result = pipeline.run();
exampleUtils.waitToFinish(result);
}
Aggregations