java.util.stream.Collectors.groupingByConcurrent()方法的使用及代码示例

x33g5p2x  于2022-01-17 转载在 其他  
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本文整理了Java中java.util.stream.Collectors.groupingByConcurrent()方法的一些代码示例，展示了Collectors.groupingByConcurrent()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Collectors.groupingByConcurrent()方法的具体详情如下：
包路径：java.util.stream.Collectors
类名称：Collectors
方法名：groupingByConcurrent

Collectors.groupingByConcurrent介绍

暂无

代码示例

代码示例来源：origin: oracle/opengrok

  1. Collectors.groupingByConcurrent((x) -> {
  2.   try {
  3.     doLink(x);

代码示例来源：origin: oracle/opengrok

  1. Collectors.groupingByConcurrent((x) -> {
  2.   try {
  3.     doDelete(x);

代码示例来源：origin: oracle/opengrok

  1. Collectors.groupingByConcurrent((x) -> {
  2.   try {
  3.     doRename(x);

代码示例来源：origin: oracle/opengrok

  1. bySuccess = parallelizer.getForkJoinPool().submit(() ->
  2.   args.works.parallelStream().collect(
  3.   Collectors.groupingByConcurrent((x) -> {
  4.     int tries = 0;
  5.     Ctags pctags = null;

代码示例来源：origin: stackoverflow.com

  1. Collectors.groupingByConcurrent(
  2.       Function.identity(), Collectors.<String> counting()));
  3. bh.consume(counts);

代码示例来源：origin: ripreal/V8LogScanner

  1. private TreeMap<SortingKey, List<String>> mapLogs(ArrayList<String> sourceCol) {
  2.   TreeMap<SortingKey, List<String>> mapped = sourceCol
  3.           .stream()
  4.           .parallel()
  5.           .filter(n -> RgxOpManager.anyMatch(n, eventPatterns, integerFilters, integerCompTypes))
  6.           .collect(Collectors.collectingAndThen(
  7.               Collectors.groupingByConcurrent(this::createSortingKey),
  8.               map -> {
  9.                 TreeMap<SortingKey, List<String>> tm = new TreeMap<>();
  10.                 map.entrySet().forEach(n -> tm.put(n.getKey(), n.getValue()));
  11.                 return tm;
  12.               }
  13.           ));
  14.   return mapped;
  15. }

代码示例来源：origin: ripreal/V8LogScanner

  1. private TreeMap<SortingKey, List<String>> finalReduction(Collection<String> sourceCol) {
  2.   TreeMap<SortingKey, List<String>> mapped = sourceCol
  3.           .parallelStream()
  4.           .collect(Collectors.collectingAndThen(
  5.               Collectors.groupingByConcurrent(this::createSortingKey),
  6.               map -> {
  7.                 TreeMap<SortingKey, List<String>> tm = new TreeMap<>();
  8.                 map.entrySet().forEach(n -> tm.put(n.getKey(), n.getValue()));
  9.                 return tm;
  10.               })
  11.           );
  12.   TreeMap<SortingKey, List<String>> rgxResult = mapped.entrySet()
  13.       .stream()
  14.       .sequential()
  15.       .flatMap(n -> n.getValue().stream())
  16.       .limit(limit)
  17.       .collect(Collectors.collectingAndThen(
  18.           Collectors.groupingByConcurrent(this::createSortingKey),
  19.           map -> {
  20.             TreeMap<SortingKey, List<String>> tm = new TreeMap<>();
  21.             map.entrySet().forEach(n -> tm.put(n.getKey(), n.getValue()));
  22.             return tm;
  23.           })
  24.       );
  25.   return rgxResult;
  26. }

代码示例来源：origin: gauravrmazra/gauravbytes

  1. .collect(Collectors.groupingByConcurrent(Person::getAge));
  2. System.out.println(personByAgeConcurrent);

代码示例来源：origin: spotify/styx

  1. public static ConcurrentMap<String, Long> getResourceUsage(boolean globalConcurrencyEnabled,
  2.                               List<InstanceState> activeStates,
  3.                               Set<WorkflowInstance> timedOutInstances,
  4.                               WorkflowResourceDecorator resourceDecorator,
  5.                               Map<WorkflowId, Workflow> workflows) {
  6.  return activeStates.parallelStream()
  7.    .filter(entry -> !timedOutInstances.contains(entry.workflowInstance()))
  8.    .filter(entry -> isConsumingResources(entry.runState().state()))
  9.    .flatMap(instanceState -> pairWithResources(globalConcurrencyEnabled, instanceState,
  10.      workflows, resourceDecorator))
  11.    .collect(groupingByConcurrent(
  12.      ResourceWithInstance::resource,
  13.      ConcurrentHashMap::new,
  14.      counting()));
  15. }

代码示例来源：origin: exomiser/Exomiser

  1. private Stream<Optional<GeneModelPhenotypeMatch>> getBestModelsByGene(List<GeneModelPhenotypeMatch> organismModels) {
  2.   if (options.isBenchmarkingEnabled()) {
  3.     return organismModels.parallelStream()
  4.         .filter(model -> model.getScore() > 0)
  5.         // catch hit to known disease-gene association for purposes of benchmarking i.e to simulate novel gene discovery performance
  6.         .filter(model -> !options.isBenchmarkHit(model))
  7.         .collect(groupingByConcurrent(GeneModelPhenotypeMatch::getEntrezGeneId, maxBy(comparingDouble(GeneModelPhenotypeMatch::getScore))))
  8.         .values()
  9.         .stream();
  10.   }
  11.   return organismModels.parallelStream()
  12.       .filter(model -> model.getScore() > 0)
  13.       .collect(groupingByConcurrent(GeneModelPhenotypeMatch::getEntrezGeneId, maxBy(comparingDouble(GeneModelPhenotypeMatch::getScore))))
  14.       .values()
  15.       .stream();
  16. }

代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core

  1. /**
  2.  * Saves the final clustering for each data point.
  3.  */
  4. protected void saveFinalClustering()
  5. {
  6.   if (clusters.size() > 0)
  7.   {
  8.     List<? extends DataType> data = getData();
  9.     assignments = assignDataToClusters(data);
  10.     clusters.forEach(cluster -> cluster.getMembers().clear());
  11.     IntStream.range(0, assignments.length).parallel()
  12.       .mapToObj(Integer::valueOf)
  13.       .collect(
  14.         groupingByConcurrent(idx -> assignments[idx]))
  15.       .forEach((clusterIdx, clusterPoints)
  16.         -> clusters.get(clusterIdx).getMembers()
  17.         .addAll(
  18.           clusterPoints.stream()
  19.           .map(idx -> data.get(idx))
  20.           .collect(toList())));
  21.   }
  22. }

代码示例来源：origin: algorithmfoundry/Foundry

  1. /**
  2.  * Saves the final clustering for each data point.
  3.  */
  4. protected void saveFinalClustering()
  5. {
  6.   if (clusters.size() > 0)
  7.   {
  8.     List<? extends DataType> data = getData();
  9.     assignments = assignDataToClusters(data);
  10.     clusters.forEach(cluster -> cluster.getMembers().clear());
  11.     IntStream.range(0, assignments.length).parallel()
  12.       .mapToObj(Integer::valueOf)
  13.       .collect(
  14.         groupingByConcurrent(idx -> assignments[idx]))
  15.       .forEach((clusterIdx, clusterPoints)
  16.         -> clusters.get(clusterIdx).getMembers()
  17.         .addAll(
  18.           clusterPoints.stream()
  19.           .map(idx -> data.get(idx))
  20.           .collect(toList())));
  21.   }
  22. }

代码示例来源：origin: horrorho/LiquidDonkey

  1. /**
  2.  * Returns a new instance.
  3.  *
  4.  * @param snapshot not null
  5.  * @param fileConfig not null
  6.  * @return a new instance, not null
  7.  */
  8. public static SignatureManager from(Snapshot snapshot, FileConfig fileConfig) {
  9.   logger.trace("<< from() < dsPrsId: {} udid: {} snapshot: {} fileConfig: {}",
  10.       snapshot.dsPrsID(), snapshot.backupUDID(), snapshot.snapshotID(), fileConfig);
  11.   CloudFileWriter cloudWriter = CloudFileWriter.from(snapshot, fileConfig);
  12.   Map<ByteString, Set<ICloud.MBSFile>> signatures = snapshot.files().stream()
  13.       .collect(Collectors.groupingByConcurrent(ICloud.MBSFile::getSignature, Collectors.toSet()));
  14.   long totalBytes = signatures.values().stream()
  15.       .flatMap(Set::stream)
  16.       .mapToLong(ICloud.MBSFile::getSize)
  17.       .sum();
  18.   Lock lock = new ReentrantLock();
  19.   SignatureManager instance
  20.       = new SignatureManager(signatures, cloudWriter, lock, totalBytes, new AtomicLong(0), new AtomicLong(0));
  21.   logger.trace(">> from() > {}", instance);
  22.   return instance;
  23. }

代码示例来源：origin: com.opsbears.webcomponents/graph

  1. private ImmutableMap<Node<VALUETYPE>, ImmutableList<Edge<VALUETYPE>>> createEdgeCache(
  2.   ImmutableCollection<Node<VALUETYPE>> nodes,
  3.   ImmutableCollection<Edge<VALUETYPE>> edges,
  4.   Function<? super Edge<VALUETYPE>, ? extends Node<VALUETYPE>> mappingFunction
  5. ) {
  6.   final Map<Node<VALUETYPE>, List<Edge<VALUETYPE>>> optimizedEdges = edges
  7.     .stream()
  8.     .collect(
  9.       Collectors.groupingByConcurrent(
  10.         mappingFunction,
  11.         Collectors.toList()
  12.       )
  13.     );
  14.   nodes.forEach(node -> optimizedEdges.putIfAbsent(node, new ArrayList<>()));
  15.   return new ImmutableHashMap<>(
  16.     optimizedEdges.keySet().stream().collect(
  17.       Collectors.toMap(
  18.         node -> node,
  19.         node -> new ImmutableArrayList<>(optimizedEdges.get(node))
  20.       )
  21.     )
  22.   );
  23. }

代码示例来源：origin: algorithmfoundry/Foundry

  1. /**
  2.  * Saves the final clustering for each data point.
  3.  */
  4. protected void saveFinalClustering()
  5. {
  6.   if (clusters.size() > 0)
  7.   {
  8.     List<? extends DataType> data = getData();
  9.     assignments = assignDataToClusters(data);
  10.     clusters.forEach(cluster -> cluster.getMembers().clear());
  11.     IntStream.range(0, assignments.length).parallel()
  12.       .mapToObj(Integer::valueOf)
  13.       .collect(
  14.         groupingByConcurrent(idx -> assignments[idx]))
  15.       .forEach((clusterIdx, clusterPoints)
  16.         -> clusters.get(clusterIdx).getMembers()
  17.         .addAll(
  18.           clusterPoints.stream()
  19.           .map(idx -> data.get(idx))
  20.           .collect(toList())));
  21.   }
  22. }

代码示例来源：origin: exomiser/Exomiser

  1. @Override
  2.   public Stream<PhivePriorityResult> prioritise(List<String> hpoIds, List<Gene> genes) {
  3.     logger.info("Starting {}", PRIORITY_TYPE);
  5.     List<PhenotypeTerm> hpoPhenotypeTerms = priorityService.makePhenotypeTermsFromHpoIds(hpoIds);
  6.     PhenotypeMatcher humanMousePhenotypeMatcher = priorityService.getPhenotypeMatcherForOrganism(hpoPhenotypeTerms, Organism.MOUSE);
  8.     Set<Integer> wantedGeneIds = genes.stream().map(Gene::getEntrezGeneID).collect(ImmutableSet.toImmutableSet());
  10.     Set<GeneModel> modelsToScore = priorityService.getModelsForOrganism(Organism.MOUSE).stream()
  11.         .filter(model -> wantedGeneIds.contains(model.getEntrezGeneId()))
  12.         .collect(ImmutableSet.toImmutableSet());
  14.     List<GeneModelPhenotypeMatch> scoredModels = scoreModels(humanMousePhenotypeMatcher, modelsToScore);
  16.     //n.b. this will contain models but with a phenotype score of zero
  17.     Map<Integer, Optional<GeneModelPhenotypeMatch>> geneModelPhenotypeMatches = scoredModels.parallelStream()
  18. //                .filter(model -> model.getScore() > 0)
  19.         .collect(groupingByConcurrent(GeneModelPhenotypeMatch::getEntrezGeneId, maxBy(comparingDouble(GeneModelPhenotypeMatch::getScore))));
  21.     return genes.stream().map(getPhivePriorityResult(geneModelPhenotypeMatches));
  22.   }

代码示例来源：origin: ripreal/V8LogScanner

  1. private ConcurrentMap<String, List<String>> mapLogs(ArrayList<String> sourceCol, String filename) {
  2.   ConcurrentMap<String, List<String>> mapResults = null;
  3.   if (groupType == GroupTypes.BY_PROPS) {
  4.     mapResults = sourceCol
  5.         .parallelStream()
  6.         .unordered()
  7.         .filter(n -> RgxOpManager.anyMatch(n, eventPatterns, integerFilters, integerCompTypes))
  8.         .collect(Collectors.groupingByConcurrent(input -> {
  9.               return RgxOpManager.getEventProperty(input, eventPatterns, cleanPropsRgx, groupPropsRgx);
  10.             }
  11.         ));
  12.   } else if (groupType == GroupTypes.BY_FILE_NAMES) {
  13.     mapResults = sourceCol
  14.         .parallelStream()
  15.         .unordered()
  16.         .filter(n -> RgxOpManager.anyMatch(n, eventPatterns, integerFilters, integerCompTypes))
  17.         .collect(Collectors.groupingByConcurrent(n -> filename));
  18.   }
  19.   return mapResults;
  20. }

代码示例来源：origin: one.util/streamex

  1. if (isParallel() && downstream.characteristics().contains(Characteristics.UNORDERED)
  2.   && mapSupplier.get() instanceof ConcurrentMap) {
  3.   return (M) collect(Collectors.groupingByConcurrent(keyMapper,
  4.     (Supplier<? extends ConcurrentMap<K, D>>) mapSupplier, mapping));

代码示例来源：origin: org.infinispan/infinispan-core

  1. public void testObjCollectorGroupBy() {
  2.  Cache<Integer, String> cache = getCache(0);
  3.  int range = 10;
  4.  // First populate the cache with a bunch of values
  5.  IntStream.range(0, range).boxed().forEach(i -> cache.put(i, i + "-value"));
  6.  assertEquals(range, cache.size());
  7.  CacheSet<Map.Entry<Integer, String>> entrySet = cache.entrySet();
  8.  ConcurrentMap<Boolean, List<Map.Entry<Integer, String>>> grouped = createStream(entrySet).collect(
  9.        () -> Collectors.groupingByConcurrent(k -> k.getKey() % 2 == 0));
  10.  grouped.get(true).parallelStream().forEach(e -> assertTrue(e.getKey() % 2 == 0));
  11.  grouped.get(false).parallelStream().forEach(e -> assertTrue(e.getKey() % 2 == 1));
  12. }

代码示例来源：origin: amidst/toolbox

  1. public static double[][] learnKMeans(int k, DataStream<DataInstance> data){
  2.   setK(k);
  3.   Attributes atts = data.getAttributes();
  4.   double[][] centroids = new double[getK()][atts.getNumberOfAttributes()];
  5.   AtomicInteger index = new AtomicInteger();
  6.   data.stream().limit(getK()).forEach(dataInstance -> centroids[index.getAndIncrement()]=dataInstance.toArray());
  7.   data.restart();
  8.   boolean change = true;
  9.   while(change){
  10.     Map<Integer, Averager> newCentroidsAv =
  11.         data.parallelStream(batchSize)
  12.             .map(instance -> Pair.newPair(centroids, instance))
  13.             .collect(Collectors.groupingByConcurrent(Pair::getClusterID,
  14.                 Collectors.reducing(new Averager(atts.getNumberOfAttributes()), p -> new Averager(p.getDataInstance()), Averager::combine)));
  15.     double error = IntStream.rangeClosed(0, centroids.length - 1).mapToDouble( i -> {
  16.       double distance = Pair.getED(centroids[i], newCentroidsAv.get(i).average());
  17.       centroids[i]=newCentroidsAv.get(i).average();
  18.       return distance;
  19.     }).average().getAsDouble();
  20.     if (error<epsilon)
  21.       change = false;
  22.     data.restart();
  23.   }
  24.   return centroids;
  25. }

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