Examples with LongValue org.apache.flink.types.LongValue used on opensource projects

Example 6 with LongValue

use of org.apache.flink.types.LongValue in project flink by apache.

the class CompleteGraph method generate.

@Override
public Graph<LongValue, NullValue, NullValue> generate() {
    // Vertices
    DataSet<Vertex<LongValue, NullValue>> vertices = GraphGeneratorUtils.vertexSequence(env, parallelism, vertexCount);
    // Edges
    LongValueSequenceIterator iterator = new LongValueSequenceIterator(0, this.vertexCount - 1);
    DataSet<Edge<LongValue, NullValue>> edges = env.fromParallelCollection(iterator, LongValue.class).setParallelism(parallelism).name("Edge iterators").flatMap(new LinkVertexToAll(vertexCount)).setParallelism(parallelism).name("Complete graph edges");
    // Graph
    return Graph.fromDataSet(vertices, edges, env);
}

Example 7 with LongValue

use of org.apache.flink.types.LongValue in project flink by apache.

the class GraphGeneratorUtils method vertexSequence.

/**
	 * Generates {@link Vertex Vertices} with sequential, numerical labels.
	 *
	 * @param env the Flink execution environment.
	 * @param parallelism operator parallelism
	 * @param vertexCount number of sequential vertex labels
	 * @return {@link DataSet} of sequentially labeled {@link Vertex Vertices}
	 */
public static DataSet<Vertex<LongValue, NullValue>> vertexSequence(ExecutionEnvironment env, int parallelism, long vertexCount) {
    LongValueSequenceIterator iterator = new LongValueSequenceIterator(0, vertexCount - 1);
    DataSource<LongValue> vertexLabels = env.fromParallelCollection(iterator, LongValue.class).setParallelism(parallelism).name("Vertex iterators");
    return vertexLabels.map(new CreateVertex()).setParallelism(parallelism).name("Vertex sequence");
}

Example 8 with LongValue

use of org.apache.flink.types.LongValue in project flink by apache.

the class GridGraph method generate.

@Override
public Graph<LongValue, NullValue, NullValue> generate() {
    if (dimensions.isEmpty()) {
        throw new RuntimeException("No dimensions added to GridGraph");
    }
    // Vertices
    DataSet<Vertex<LongValue, NullValue>> vertices = GraphGeneratorUtils.vertexSequence(env, parallelism, vertexCount);
    // Edges
    LongValueSequenceIterator iterator = new LongValueSequenceIterator(0, this.vertexCount - 1);
    DataSet<Edge<LongValue, NullValue>> edges = env.fromParallelCollection(iterator, LongValue.class).setParallelism(parallelism).name("Edge iterators").flatMap(new LinkVertexToNeighbors(vertexCount, dimensions)).setParallelism(parallelism).name("Grid graph edges");
    // Graph
    return Graph.fromDataSet(vertices, edges, env);
}

Example 9 with LongValue

use of org.apache.flink.types.LongValue in project flink by apache.

the class StreamRecordWriterTest method testPropagateAsyncFlushError.

/**
	 * Verifies that exceptions during flush from the output flush thread are
	 * recognized in the writer.
	 */
@Test
public void testPropagateAsyncFlushError() {
    FailingWriter<LongValue> testWriter = null;
    try {
        ResultPartitionWriter mockResultPartitionWriter = getMockWriter(5);
        // test writer that flushes every 5ms and fails after 3 flushes
        testWriter = new FailingWriter<LongValue>(mockResultPartitionWriter, new RoundRobinChannelSelector<LongValue>(), 5, 3);
        try {
            // in max 20 seconds (conservative)
            long deadline = System.currentTimeMillis() + 20000;
            long l = 0L;
            while (System.currentTimeMillis() < deadline) {
                testWriter.emit(new LongValue(l++));
            }
            fail("This should have failed with an exception");
        } catch (IOException e) {
            assertNotNull(e.getCause());
            assertTrue(e.getCause().getMessage().contains("Test Exception"));
        }
    } catch (Exception e) {
        e.printStackTrace();
        fail(e.getMessage());
    } finally {
        if (testWriter != null) {
            testWriter.close();
        }
    }
}

Example 10 with LongValue

use of org.apache.flink.types.LongValue in project flink by apache.

the class ClusteringCoefficient method main.

public static void main(String[] args) throws Exception {
    // Set up the execution environment
    final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
    env.getConfig().enableObjectReuse();
    ParameterTool parameters = ParameterTool.fromArgs(args);
    env.getConfig().setGlobalJobParameters(parameters);
    if (!parameters.has("directed")) {
        throw new ProgramParametrizationException(getUsage("must declare execution mode as '--directed true' or '--directed false'"));
    }
    boolean directedAlgorithm = parameters.getBoolean("directed");
    int little_parallelism = parameters.getInt("little_parallelism", PARALLELISM_DEFAULT);
    // global and local clustering coefficient results
    GraphAnalytic gcc;
    GraphAnalytic acc;
    DataSet lcc;
    switch(parameters.get("input", "")) {
        case "csv":
            {
                String lineDelimiter = StringEscapeUtils.unescapeJava(parameters.get("input_line_delimiter", CsvOutputFormat.DEFAULT_LINE_DELIMITER));
                String fieldDelimiter = StringEscapeUtils.unescapeJava(parameters.get("input_field_delimiter", CsvOutputFormat.DEFAULT_FIELD_DELIMITER));
                GraphCsvReader reader = Graph.fromCsvReader(parameters.get("input_filename"), env).ignoreCommentsEdges("#").lineDelimiterEdges(lineDelimiter).fieldDelimiterEdges(fieldDelimiter);
                switch(parameters.get("type", "")) {
                    case "integer":
                        {
                            Graph<LongValue, NullValue, NullValue> graph = reader.keyType(LongValue.class);
                            if (directedAlgorithm) {
                                if (parameters.getBoolean("simplify", false)) {
                                    graph = graph.run(new org.apache.flink.graph.asm.simple.directed.Simplify<LongValue, NullValue, NullValue>().setParallelism(little_parallelism));
                                }
                                gcc = graph.run(new org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                acc = graph.run(new org.apache.flink.graph.library.clustering.directed.AverageClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                lcc = graph.run(new org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                            } else {
                                if (parameters.getBoolean("simplify", false)) {
                                    graph = graph.run(new org.apache.flink.graph.asm.simple.undirected.Simplify<LongValue, NullValue, NullValue>(false).setParallelism(little_parallelism));
                                }
                                gcc = graph.run(new org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                acc = graph.run(new org.apache.flink.graph.library.clustering.undirected.AverageClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                lcc = graph.run(new org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                            }
                        }
                        break;
                    case "string":
                        {
                            Graph<StringValue, NullValue, NullValue> graph = reader.keyType(StringValue.class);
                            if (directedAlgorithm) {
                                if (parameters.getBoolean("simplify", false)) {
                                    graph = graph.run(new org.apache.flink.graph.asm.simple.directed.Simplify<StringValue, NullValue, NullValue>().setParallelism(little_parallelism));
                                }
                                gcc = graph.run(new org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient<StringValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                acc = graph.run(new org.apache.flink.graph.library.clustering.directed.AverageClusteringCoefficient<StringValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                lcc = graph.run(new org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient<StringValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                            } else {
                                if (parameters.getBoolean("simplify", false)) {
                                    graph = graph.run(new org.apache.flink.graph.asm.simple.undirected.Simplify<StringValue, NullValue, NullValue>(false).setParallelism(little_parallelism));
                                }
                                gcc = graph.run(new org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient<StringValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                acc = graph.run(new org.apache.flink.graph.library.clustering.undirected.AverageClusteringCoefficient<StringValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                                lcc = graph.run(new org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient<StringValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                            }
                        }
                        break;
                    default:
                        throw new ProgramParametrizationException(getUsage("invalid CSV type"));
                }
            }
            break;
        case "rmat":
            {
                int scale = parameters.getInt("scale", DEFAULT_SCALE);
                int edgeFactor = parameters.getInt("edge_factor", DEFAULT_EDGE_FACTOR);
                RandomGenerableFactory<JDKRandomGenerator> rnd = new JDKRandomGeneratorFactory();
                long vertexCount = 1L << scale;
                long edgeCount = vertexCount * edgeFactor;
                Graph<LongValue, NullValue, NullValue> graph = new RMatGraph<>(env, rnd, vertexCount, edgeCount).setParallelism(little_parallelism).generate();
                if (directedAlgorithm) {
                    if (scale > 32) {
                        Graph<LongValue, NullValue, NullValue> newGraph = graph.run(new org.apache.flink.graph.asm.simple.directed.Simplify<LongValue, NullValue, NullValue>().setParallelism(little_parallelism));
                        gcc = newGraph.run(new org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        acc = newGraph.run(new org.apache.flink.graph.library.clustering.directed.AverageClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        lcc = newGraph.run(new org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient<LongValue, NullValue, NullValue>().setIncludeZeroDegreeVertices(false).setLittleParallelism(little_parallelism));
                    } else {
                        Graph<IntValue, NullValue, NullValue> newGraph = graph.run(new TranslateGraphIds<LongValue, IntValue, NullValue, NullValue>(new LongValueToUnsignedIntValue()).setParallelism(little_parallelism)).run(new org.apache.flink.graph.asm.simple.directed.Simplify<IntValue, NullValue, NullValue>().setParallelism(little_parallelism));
                        gcc = newGraph.run(new org.apache.flink.graph.library.clustering.directed.GlobalClusteringCoefficient<IntValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        acc = newGraph.run(new org.apache.flink.graph.library.clustering.directed.AverageClusteringCoefficient<IntValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        lcc = newGraph.run(new org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient<IntValue, NullValue, NullValue>().setIncludeZeroDegreeVertices(false).setLittleParallelism(little_parallelism));
                    }
                } else {
                    boolean clipAndFlip = parameters.getBoolean("clip_and_flip", DEFAULT_CLIP_AND_FLIP);
                    if (scale > 32) {
                        Graph<LongValue, NullValue, NullValue> newGraph = graph.run(new org.apache.flink.graph.asm.simple.undirected.Simplify<LongValue, NullValue, NullValue>(clipAndFlip).setParallelism(little_parallelism));
                        gcc = newGraph.run(new org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        acc = newGraph.run(new org.apache.flink.graph.library.clustering.undirected.AverageClusteringCoefficient<LongValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        lcc = newGraph.run(new org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient<LongValue, NullValue, NullValue>().setIncludeZeroDegreeVertices(false).setLittleParallelism(little_parallelism));
                    } else {
                        Graph<IntValue, NullValue, NullValue> newGraph = graph.run(new TranslateGraphIds<LongValue, IntValue, NullValue, NullValue>(new LongValueToUnsignedIntValue()).setParallelism(little_parallelism)).run(new org.apache.flink.graph.asm.simple.undirected.Simplify<IntValue, NullValue, NullValue>(clipAndFlip).setParallelism(little_parallelism));
                        gcc = newGraph.run(new org.apache.flink.graph.library.clustering.undirected.GlobalClusteringCoefficient<IntValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        acc = newGraph.run(new org.apache.flink.graph.library.clustering.undirected.AverageClusteringCoefficient<IntValue, NullValue, NullValue>().setLittleParallelism(little_parallelism));
                        lcc = newGraph.run(new org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient<IntValue, NullValue, NullValue>().setIncludeZeroDegreeVertices(false).setLittleParallelism(little_parallelism));
                    }
                }
            }
            break;
        default:
            throw new ProgramParametrizationException(getUsage("invalid input type"));
    }
    switch(parameters.get("output", "")) {
        case "print":
            if (directedAlgorithm) {
                for (Object e : lcc.collect()) {
                    org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient.Result result = (org.apache.flink.graph.library.clustering.directed.LocalClusteringCoefficient.Result) e;
                    System.out.println(result.toPrintableString());
                }
            } else {
                for (Object e : lcc.collect()) {
                    org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient.Result result = (org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient.Result) e;
                    System.out.println(result.toPrintableString());
                }
            }
            break;
        case "hash":
            System.out.println(DataSetUtils.checksumHashCode(lcc));
            break;
        case "csv":
            String filename = parameters.get("output_filename");
            String lineDelimiter = StringEscapeUtils.unescapeJava(parameters.get("output_line_delimiter", CsvOutputFormat.DEFAULT_LINE_DELIMITER));
            String fieldDelimiter = StringEscapeUtils.unescapeJava(parameters.get("output_field_delimiter", CsvOutputFormat.DEFAULT_FIELD_DELIMITER));
            lcc.writeAsCsv(filename, lineDelimiter, fieldDelimiter);
            env.execute("Clustering Coefficient");
            break;
        default:
            throw new ProgramParametrizationException(getUsage("invalid output type"));
    }
    System.out.println(gcc.getResult());
    System.out.println(acc.getResult());
    JobExecutionResult result = env.getLastJobExecutionResult();
    NumberFormat nf = NumberFormat.getInstance();
    System.out.println("Execution runtime: " + nf.format(result.getNetRuntime()) + " ms");
}