public static void main(String[] args) throws Exception { final long numSamples = args.length > 0 ? Long.parseLong(args[0]) : 1000000; final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); // count how many of the samples would randomly fall into // the unit circle DataSet<Long> count = env.generateSequence(1, numSamples) .map(new Sampler()) .reduce(new SumReducer()); long theCount = count.collect().get(0); System.out.println("We estimate Pi to be: " + (theCount * 4.0 / numSamples)); }
/** * Count the number of elements in a DataSet. * * @param input DataSet of elements to be counted * @param <T> element type * @return count */ public static <T> DataSet<LongValue> count(DataSet<T> input) { return input .map(new MapTo<>(new LongValue(1))) .returns(LONG_VALUE_TYPE_INFO) .name("Emit 1") .reduce(new AddLongValue()) .name("Sum"); }
@Override protected void testProgram() throws Exception { ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); env.setParallelism(4); DataSet<Long> input = env.generateSequence(1, 10); DataSet<Long> bc1 = env.generateSequence(1, 5); DataSet<Long> bc2 = env.generateSequence(6, 10); List<Long> result = input .map(new Mapper()) .withBroadcastSet(bc1.union(bc2), BC_NAME) .reduce(new Reducer()) .collect(); Assert.assertEquals(Long.valueOf(3025), result.get(0)); }
.reduce(new UpdateAccumulator())
.rebalance() .map(new FailingMapper2<Long>()) .reduce(new ReduceFunction<Long>() { @Override public Long reduce(Long value1, Long value2) {
.rebalance() .map(new FailingMapper3<Long>()) .reduce(new ReduceFunction<Long>() { @Override public Long reduce(Long value1, Long value2) {
.reduce(new ReduceFunction<Long>() { @Override public Long reduce(Long value1, Long value2) {
.reduce(new SelectOneReducer<Long>()); DataSet<Long> bcInput2 = env.generateSequence(1, 10);
.reduce(new ReduceFunction<Long>() { @Override public Long reduce(Long value1, Long value2) {
.reduce(new SelectOneReducer<Long>());
public static void main(String[] args) throws Exception { final long numSamples = args.length > 0 ? Long.parseLong(args[0]) : 1000000; final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); // count how many of the samples would randomly fall into // the unit circle DataSet<Long> count = env.generateSequence(1, numSamples) .map(new Sampler()) .reduce(new SumReducer()); long theCount = count.collect().get(0); System.out.println("We estimate Pi to be: " + (theCount * 4.0 / numSamples)); }
public static void main(String[] args) throws Exception { final long numSamples = args.length > 0 ? Long.parseLong(args[0]) : 1000000; final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); // count how many of the samples would randomly fall into // the unit circle DataSet<Long> count = env.generateSequence(1, numSamples) .map(new Sampler()) .reduce(new SumReducer()); long theCount = count.collect().get(0); System.out.println("We estimate Pi to be: " + (theCount * 4.0 / numSamples)); }
public static void main(String[] args) throws Exception { final long numSamples = args.length > 0 ? Long.parseLong(args[0]) : 1000000; final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); // count how many of the samples would randomly fall into // the unit circle DataSet<Long> count = env.generateSequence(1, numSamples) .map(new Sampler()) .reduce(new SumReducer()); long theCount = count.collect().get(0); System.out.println("We estimate Pi to be: " + (theCount * 4.0 / numSamples)); }
public static void main(String[] args) throws Exception { final long numSamples = args.length > 0 ? Long.parseLong(args[0]) : 1000000; final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); // count how many of the samples would randomly fall into // the unit circle DataSet<Long> count = env.generateSequence(1, numSamples) .map(new Sampler()) .reduce(new SumReducer()); long theCount = count.collect().get(0); System.out.println("We estimate Pi to be: " + (theCount * 4.0 / numSamples)); }
/** * Count the number of elements in a DataSet. * * @param input DataSet of elements to be counted * @param <T> element type * @return count */ public static <T> DataSet<LongValue> count(DataSet<T> input) { return input .map(new MapTo<>(new LongValue(1))) .returns(LONG_VALUE_TYPE_INFO) .name("Emit 1") .reduce(new AddLongValue()) .name("Sum"); }
/** * Count the number of elements in a DataSet. * * @param input DataSet of elements to be counted * @param <T> element type * @return count */ public static <T> DataSet<LongValue> count(DataSet<T> input) { return input .map(new MapTo<T, LongValue>(new LongValue(1))) .returns(LONG_VALUE_TYPE_INFO) .name("Emit 1") .reduce(new AddLongValue()) .name("Sum"); }
/** * Count the number of elements in a DataSet. * * @param input DataSet of elements to be counted * @param <T> element type * @return count */ public static <T> DataSet<LongValue> count(DataSet<T> input) { return input .map(new MapTo<>(new LongValue(1))) .returns(LONG_VALUE_TYPE_INFO) .name("Emit 1") .reduce(new AddLongValue()) .name("Sum"); }
/** * {@inheritDoc} */ @Override public double updateModel(DataFlink<DataInstance> dataUpdate) { try { Configuration config = new Configuration(); config.setString(BN_NAME, this.dag.getName()); config.setBytes(EFBN_NAME, Serialization.serializeObject(efBayesianNetwork)); DataSet<DataInstance> dataset = dataUpdate.getDataSet(); this.sumSS = dataset.map(new SufficientSatisticsMAP()) .withParameters(config) .reduce(new SufficientSatisticsReduce()) .collect().get(0); //Add the prior sumSS.sum(efBayesianNetwork.createInitSufficientStatistics()); JobExecutionResult result = dataset.getExecutionEnvironment().getLastJobExecutionResult(); numInstances = result.getAccumulatorResult(ParallelMaximumLikelihood.COUNTER_NAME+"_"+this.dag.getName()); numInstances++;//Initial counts }catch(Exception ex){ throw new UndeclaredThrowableException(ex); } return this.getLogMarginalProbability(); }