Java tutorial
/* * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.flink.graph.drivers; import org.apache.commons.lang3.StringEscapeUtils; import org.apache.commons.lang3.text.StrBuilder; import org.apache.commons.lang3.text.WordUtils; import org.apache.commons.math3.random.JDKRandomGenerator; import org.apache.flink.api.common.JobExecutionResult; import org.apache.flink.api.java.DataSet; import org.apache.flink.api.java.ExecutionEnvironment; import org.apache.flink.api.java.io.CsvOutputFormat; import org.apache.flink.api.java.utils.DataSetUtils; import org.apache.flink.api.java.utils.ParameterTool; import org.apache.flink.client.program.ProgramParametrizationException; import org.apache.flink.graph.Graph; import org.apache.flink.graph.GraphCsvReader; import org.apache.flink.graph.asm.simple.undirected.Simplify; import org.apache.flink.graph.asm.translate.TranslateGraphIds; import org.apache.flink.graph.asm.translate.translators.LongValueToUnsignedIntValue; import org.apache.flink.graph.generator.RMatGraph; import org.apache.flink.graph.generator.random.JDKRandomGeneratorFactory; import org.apache.flink.graph.generator.random.RandomGenerableFactory; import org.apache.flink.graph.library.similarity.JaccardIndex.Result; import org.apache.flink.types.IntValue; import org.apache.flink.types.LongValue; import org.apache.flink.types.NullValue; import org.apache.flink.types.StringValue; import java.text.NumberFormat; import static org.apache.flink.api.common.ExecutionConfig.PARALLELISM_DEFAULT; /** * Driver for the library implementation of Jaccard Index. * * This example reads a simple, undirected graph from a CSV file or generates * an undirected RMat graph with the given scale and edge factor then calculates * all non-zero Jaccard Index similarity scores between vertices. * * @see org.apache.flink.graph.library.similarity.JaccardIndex */ public class JaccardIndex { private static final int DEFAULT_SCALE = 10; private static final int DEFAULT_EDGE_FACTOR = 16; private static final boolean DEFAULT_CLIP_AND_FLIP = true; private static String getUsage(String message) { return new StrBuilder().appendNewLine() .appendln(WordUtils.wrap("The Jaccard Index measures the similarity between vertex" + " neighborhoods and is computed as the number of shared neighbors divided by the number of" + " distinct neighbors. Scores range from 0.0 (no shared neighbors) to 1.0 (all neighbors are" + " shared).", 80)) .appendNewLine() .appendln(WordUtils.wrap("This algorithm returns 4-tuples containing two vertex IDs, the" + " number of shared neighbors, and the number of distinct neighbors.", 80)) .appendNewLine().appendln("usage: JaccardIndex --input <csv | rmat> --output <print | hash | csv>") .appendNewLine().appendln("options:") .appendln( " --input csv --type <integer | string> [--simplify <true | false>] --input_filename FILENAME [--input_line_delimiter LINE_DELIMITER] [--input_field_delimiter FIELD_DELIMITER]") .appendln(" --input rmat [--scale SCALE] [--edge_factor EDGE_FACTOR]").appendNewLine() .appendln(" --output print").appendln(" --output hash") .appendln( " --output csv --output_filename FILENAME [--output_line_delimiter LINE_DELIMITER] [--output_field_delimiter FIELD_DELIMITER]") .appendNewLine().appendln("Usage error: " + message).toString(); } 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); int little_parallelism = parameters.getInt("little_parallelism", PARALLELISM_DEFAULT); DataSet ji; 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.getRequired("input_filename"), env) .ignoreCommentsEdges("#").lineDelimiterEdges(lineDelimiter).fieldDelimiterEdges(fieldDelimiter); switch (parameters.get("type", "")) { case "integer": { Graph<LongValue, NullValue, NullValue> graph = reader.keyType(LongValue.class); if (parameters.getBoolean("simplify", false)) { graph = graph.run( new org.apache.flink.graph.asm.simple.undirected.Simplify<LongValue, NullValue, NullValue>( false).setParallelism(little_parallelism)); } ji = graph.run( new org.apache.flink.graph.library.similarity.JaccardIndex<LongValue, NullValue, NullValue>() .setLittleParallelism(little_parallelism)); } break; case "string": { Graph<StringValue, NullValue, NullValue> graph = reader.keyType(StringValue.class); if (parameters.getBoolean("simplify", false)) { graph = graph.run( new org.apache.flink.graph.asm.simple.undirected.Simplify<StringValue, NullValue, NullValue>( false).setParallelism(little_parallelism)); } ji = graph.run( new org.apache.flink.graph.library.similarity.JaccardIndex<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(); boolean clipAndFlip = parameters.getBoolean("clip_and_flip", DEFAULT_CLIP_AND_FLIP); if (scale > 32) { ji = graph .run(new Simplify<LongValue, NullValue, NullValue>(clipAndFlip) .setParallelism(little_parallelism)) .run(new org.apache.flink.graph.library.similarity.JaccardIndex<LongValue, NullValue, NullValue>() .setLittleParallelism(little_parallelism)); } else { ji = graph .run(new TranslateGraphIds<LongValue, IntValue, NullValue, NullValue>( new LongValueToUnsignedIntValue()).setParallelism(little_parallelism)) .run(new Simplify<IntValue, NullValue, NullValue>(clipAndFlip) .setParallelism(little_parallelism)) .run(new org.apache.flink.graph.library.similarity.JaccardIndex<IntValue, NullValue, NullValue>() .setLittleParallelism(little_parallelism)); } } break; default: throw new ProgramParametrizationException(getUsage("invalid input type")); } switch (parameters.get("output", "")) { case "print": System.out.println(); for (Object e : ji.collect()) { Result result = (Result) e; System.out.println(result.toPrintableString()); } break; case "hash": System.out.println(); System.out.println(DataSetUtils.checksumHashCode(ji)); break; case "csv": String filename = parameters.getRequired("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)); ji.writeAsCsv(filename, lineDelimiter, fieldDelimiter); env.execute("Jaccard Index"); break; default: throw new ProgramParametrizationException(getUsage("invalid output type")); } JobExecutionResult result = env.getLastJobExecutionResult(); NumberFormat nf = NumberFormat.getInstance(); System.out.println(); System.out.println("Execution runtime: " + nf.format(result.getNetRuntime()) + " ms"); } }