Java tutorial
/* * Grakn - A Distributed Semantic Database * Copyright (C) 2016-2018 Grakn Labs Limited * * Grakn is free software: you can redistribute it and/or modify * it under the terms of the GNU Affero General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * Grakn is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with Grakn. If not, see <http://www.gnu.org/licenses/gpl.txt>. */ package ai.grakn.kb.internal.computer; import org.apache.commons.configuration.ConfigurationUtils; import org.apache.commons.configuration.FileConfiguration; import org.apache.commons.configuration.PropertiesConfiguration; import org.apache.commons.lang3.concurrent.BasicThreadFactory; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.mapreduce.InputFormat; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.spark.HashPartitioner; import org.apache.spark.Partitioner; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.launcher.SparkLauncher; import org.apache.spark.storage.StorageLevel; import org.apache.tinkerpop.gremlin.hadoop.Constants; import org.apache.tinkerpop.gremlin.hadoop.process.computer.AbstractHadoopGraphComputer; import org.apache.tinkerpop.gremlin.hadoop.process.computer.util.ComputerSubmissionHelper; import org.apache.tinkerpop.gremlin.hadoop.structure.HadoopConfiguration; import org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph; import org.apache.tinkerpop.gremlin.hadoop.structure.io.FileSystemStorage; import org.apache.tinkerpop.gremlin.hadoop.structure.io.GraphFilterAware; import org.apache.tinkerpop.gremlin.hadoop.structure.io.VertexWritable; import org.apache.tinkerpop.gremlin.hadoop.structure.util.ConfUtil; import org.apache.tinkerpop.gremlin.process.computer.ComputerResult; import org.apache.tinkerpop.gremlin.process.computer.GraphComputer; import org.apache.tinkerpop.gremlin.process.computer.MapReduce; import org.apache.tinkerpop.gremlin.process.computer.Memory; import org.apache.tinkerpop.gremlin.process.computer.VertexProgram; import org.apache.tinkerpop.gremlin.process.computer.util.DefaultComputerResult; import org.apache.tinkerpop.gremlin.process.computer.util.MapMemory; import org.apache.tinkerpop.gremlin.process.traversal.TraversalStrategies; import org.apache.tinkerpop.gremlin.process.traversal.util.TraversalInterruptedException; import org.apache.tinkerpop.gremlin.spark.process.computer.payload.ViewIncomingPayload; import org.apache.tinkerpop.gremlin.spark.process.computer.traversal.strategy.optimization.SparkInterceptorStrategy; import org.apache.tinkerpop.gremlin.spark.process.computer.traversal.strategy.optimization.SparkSingleIterationStrategy; import org.apache.tinkerpop.gremlin.spark.structure.Spark; import org.apache.tinkerpop.gremlin.spark.structure.io.InputFormatRDD; import org.apache.tinkerpop.gremlin.spark.structure.io.InputOutputHelper; import org.apache.tinkerpop.gremlin.spark.structure.io.InputRDD; import org.apache.tinkerpop.gremlin.spark.structure.io.OutputFormatRDD; import org.apache.tinkerpop.gremlin.spark.structure.io.OutputRDD; import org.apache.tinkerpop.gremlin.spark.structure.io.PersistedInputRDD; import org.apache.tinkerpop.gremlin.spark.structure.io.PersistedOutputRDD; import org.apache.tinkerpop.gremlin.spark.structure.io.SparkContextStorage; import org.apache.tinkerpop.gremlin.spark.structure.io.gryo.GryoSerializer; import org.apache.tinkerpop.gremlin.structure.Direction; import org.apache.tinkerpop.gremlin.structure.io.Storage; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import java.io.File; import java.io.IOException; import java.util.HashSet; import java.util.Set; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Future; import java.util.concurrent.ThreadFactory; import java.util.concurrent.ThreadLocalRandom; /** * <p> * This is a modified version of Spark Computer. * We change its behaviour so it can won't destroy the rdd after every job. * </p> * * @author Jason Liu * @author Marko A. Rodriguez */ public final class GraknSparkComputer extends AbstractHadoopGraphComputer { private static final Logger LOGGER = LoggerFactory.getLogger(GraknSparkComputer.class); private final org.apache.commons.configuration.Configuration sparkConfiguration; private boolean workersSet = false; private final ThreadFactory threadFactoryBoss = new BasicThreadFactory.Builder() .namingPattern(GraknSparkComputer.class.getSimpleName() + "-boss").build(); private final ExecutorService computerService = Executors.newSingleThreadExecutor(threadFactoryBoss); static { TraversalStrategies.GlobalCache.registerStrategies(GraknSparkComputer.class, TraversalStrategies.GlobalCache.getStrategies(GraphComputer.class).clone().addStrategies( SparkSingleIterationStrategy.instance(), SparkInterceptorStrategy.instance())); } private String jobGroupId = null; public GraknSparkComputer(final HadoopGraph hadoopGraph) { super(hadoopGraph); this.sparkConfiguration = new HadoopConfiguration(); ConfigurationUtils.copy(this.hadoopGraph.configuration(), this.sparkConfiguration); } @Override public GraphComputer workers(final int workers) { super.workers(workers); if (this.sparkConfiguration.containsKey(SparkLauncher.SPARK_MASTER) && this.sparkConfiguration.getString(SparkLauncher.SPARK_MASTER).startsWith("local")) { this.sparkConfiguration.setProperty(SparkLauncher.SPARK_MASTER, "local[" + this.workers + "]"); } this.workersSet = true; return this; } @Override public GraphComputer configure(final String key, final Object value) { this.sparkConfiguration.setProperty(key, value); return this; } @Override public Future<ComputerResult> submit() { this.validateStatePriorToExecution(); return ComputerSubmissionHelper.runWithBackgroundThread(exec -> submitWithExecutor(), "SparkSubmitter"); } public void cancelJobs() { if (jobGroupId != null) { Spark.getContext().cancelJobGroup(jobGroupId); } } @SuppressWarnings("PMD.UnusedFormalParameter") private Future<ComputerResult> submitWithExecutor() { jobGroupId = Integer.toString(ThreadLocalRandom.current().nextInt(Integer.MAX_VALUE)); String jobDescription = this.vertexProgram == null ? this.mapReducers.toString() : this.vertexProgram + "+" + this.mapReducers; // Use different output locations this.sparkConfiguration.setProperty(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION, this.sparkConfiguration.getString(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION) + "/" + jobGroupId); updateConfigKeys(sparkConfiguration); final Future<ComputerResult> result = computerService.submit(() -> { final long startTime = System.currentTimeMillis(); // apache and hadoop configurations that are used throughout the graph computer computation final org.apache.commons.configuration.Configuration graphComputerConfiguration = new HadoopConfiguration( this.sparkConfiguration); if (!graphComputerConfiguration.containsKey(Constants.SPARK_SERIALIZER)) { graphComputerConfiguration.setProperty(Constants.SPARK_SERIALIZER, GryoSerializer.class.getCanonicalName()); } graphComputerConfiguration.setProperty(Constants.GREMLIN_HADOOP_GRAPH_WRITER_HAS_EDGES, this.persist.equals(GraphComputer.Persist.EDGES)); final Configuration hadoopConfiguration = ConfUtil.makeHadoopConfiguration(graphComputerConfiguration); final Storage fileSystemStorage = FileSystemStorage.open(hadoopConfiguration); final boolean inputFromHDFS = FileInputFormat.class.isAssignableFrom( hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_READER, Object.class)); final boolean inputFromSpark = PersistedInputRDD.class.isAssignableFrom( hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_READER, Object.class)); final boolean outputToHDFS = FileOutputFormat.class.isAssignableFrom( hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_WRITER, Object.class)); final boolean outputToSpark = PersistedOutputRDD.class.isAssignableFrom( hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_WRITER, Object.class)); final boolean skipPartitioner = graphComputerConfiguration .getBoolean(Constants.GREMLIN_SPARK_SKIP_PARTITIONER, false); final boolean skipPersist = graphComputerConfiguration .getBoolean(Constants.GREMLIN_SPARK_SKIP_GRAPH_CACHE, false); if (inputFromHDFS) { String inputLocation = Constants .getSearchGraphLocation(hadoopConfiguration.get(Constants.GREMLIN_HADOOP_INPUT_LOCATION), fileSystemStorage) .orElse(null); if (null != inputLocation) { try { graphComputerConfiguration.setProperty(Constants.MAPREDUCE_INPUT_FILEINPUTFORMAT_INPUTDIR, FileSystem.get(hadoopConfiguration).getFileStatus(new Path(inputLocation)).getPath() .toString()); hadoopConfiguration.set(Constants.MAPREDUCE_INPUT_FILEINPUTFORMAT_INPUTDIR, FileSystem.get(hadoopConfiguration).getFileStatus(new Path(inputLocation)).getPath() .toString()); } catch (final IOException e) { throw new IllegalStateException(e.getMessage(), e); } } } final InputRDD inputRDD; final OutputRDD outputRDD; final boolean filtered; try { inputRDD = InputRDD.class.isAssignableFrom( hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_READER, Object.class)) ? hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_READER, InputRDD.class, InputRDD.class).newInstance() : InputFormatRDD.class.newInstance(); outputRDD = OutputRDD.class.isAssignableFrom( hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_WRITER, Object.class)) ? hadoopConfiguration.getClass(Constants.GREMLIN_HADOOP_GRAPH_WRITER, OutputRDD.class, OutputRDD.class).newInstance() : OutputFormatRDD.class.newInstance(); // if the input class can filter on load, then set the filters if (inputRDD instanceof InputFormatRDD && GraphFilterAware.class.isAssignableFrom(hadoopConfiguration.getClass( Constants.GREMLIN_HADOOP_GRAPH_READER, InputFormat.class, InputFormat.class))) { GraphFilterAware.storeGraphFilter(graphComputerConfiguration, hadoopConfiguration, this.graphFilter); filtered = false; } else if (inputRDD instanceof GraphFilterAware) { ((GraphFilterAware) inputRDD).setGraphFilter(this.graphFilter); filtered = false; } else filtered = this.graphFilter.hasFilter(); } catch (final InstantiationException | IllegalAccessException e) { throw new IllegalStateException(e.getMessage(), e); } // create the spark context from the graph computer configuration final JavaSparkContext sparkContext = new JavaSparkContext(Spark.create(hadoopConfiguration)); final Storage sparkContextStorage = SparkContextStorage.open(); sparkContext.setJobGroup(jobGroupId, jobDescription); GraknSparkMemory memory = null; // delete output location final String outputLocation = hadoopConfiguration.get(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION, null); if (null != outputLocation) { if (outputToHDFS && fileSystemStorage.exists(outputLocation)) { fileSystemStorage.rm(outputLocation); } if (outputToSpark && sparkContextStorage.exists(outputLocation)) { sparkContextStorage.rm(outputLocation); } } // the Spark application name will always be set by SparkContextStorage, // thus, INFO the name to make it easier to debug logger.debug(Constants.GREMLIN_HADOOP_SPARK_JOB_PREFIX + (null == this.vertexProgram ? "No VertexProgram" : this.vertexProgram) + "[" + this.mapReducers + "]"); // add the project jars to the cluster this.loadJars(hadoopConfiguration, sparkContext); updateLocalConfiguration(sparkContext, hadoopConfiguration); // create a message-passing friendly rdd from the input rdd boolean partitioned = false; JavaPairRDD<Object, VertexWritable> loadedGraphRDD = inputRDD.readGraphRDD(graphComputerConfiguration, sparkContext); // if there are vertex or edge filters, filter the loaded graph rdd prior to partitioning and persisting if (filtered) { this.logger.debug("Filtering the loaded graphRDD: " + this.graphFilter); loadedGraphRDD = GraknSparkExecutor.applyGraphFilter(loadedGraphRDD, this.graphFilter); } // if the loaded graph RDD is already partitioned use that partitioner, // else partition it with HashPartitioner if (loadedGraphRDD.partitioner().isPresent()) { this.logger.debug("Using the existing partitioner associated with the loaded graphRDD: " + loadedGraphRDD.partitioner().get()); } else { if (!skipPartitioner) { final Partitioner partitioner = new HashPartitioner( this.workersSet ? this.workers : loadedGraphRDD.partitions().size()); this.logger.debug("Partitioning the loaded graphRDD: " + partitioner); loadedGraphRDD = loadedGraphRDD.partitionBy(partitioner); partitioned = true; assert loadedGraphRDD.partitioner().isPresent(); } else { // no easy way to test this with a test case assert skipPartitioner == !loadedGraphRDD.partitioner().isPresent(); this.logger.debug("Partitioning has been skipped for the loaded graphRDD via " + Constants.GREMLIN_SPARK_SKIP_PARTITIONER); } } // if the loaded graphRDD was already partitioned previous, // then this coalesce/repartition will not take place if (this.workersSet) { // ensures that the loaded graphRDD does not have more partitions than workers if (loadedGraphRDD.partitions().size() > this.workers) { loadedGraphRDD = loadedGraphRDD.coalesce(this.workers); } else { // ensures that the loaded graphRDD does not have less partitions than workers if (loadedGraphRDD.partitions().size() < this.workers) { loadedGraphRDD = loadedGraphRDD.repartition(this.workers); } } } // persist the vertex program loaded graph as specified by configuration // or else use default cache() which is MEMORY_ONLY if (!skipPersist && (!inputFromSpark || partitioned || filtered)) { loadedGraphRDD = loadedGraphRDD.persist(StorageLevel.fromString( hadoopConfiguration.get(Constants.GREMLIN_SPARK_GRAPH_STORAGE_LEVEL, "MEMORY_ONLY"))); } // final graph with view // (for persisting and/or mapReducing -- may be null and thus, possible to save space/time) JavaPairRDD<Object, VertexWritable> computedGraphRDD = null; try { //////////////////////////////// // process the vertex program // //////////////////////////////// if (null != this.vertexProgram) { memory = new GraknSparkMemory(this.vertexProgram, this.mapReducers, sparkContext); ///////////////// // if there is a registered VertexProgramInterceptor, use it to bypass the GraphComputer semantics if (graphComputerConfiguration .containsKey(Constants.GREMLIN_HADOOP_VERTEX_PROGRAM_INTERCEPTOR)) { try { final GraknSparkVertexProgramInterceptor<VertexProgram> interceptor = (GraknSparkVertexProgramInterceptor) Class .forName(graphComputerConfiguration .getString(Constants.GREMLIN_HADOOP_VERTEX_PROGRAM_INTERCEPTOR)) .newInstance(); computedGraphRDD = interceptor.apply(this.vertexProgram, loadedGraphRDD, memory); } catch (final ClassNotFoundException | IllegalAccessException | InstantiationException e) { throw new IllegalStateException(e.getMessage()); } } else { // standard GraphComputer semantics // get a configuration that will be propagated to all workers final HadoopConfiguration vertexProgramConfiguration = new HadoopConfiguration(); this.vertexProgram.storeState(vertexProgramConfiguration); // set up the vertex program and wire up configurations this.vertexProgram.setup(memory); JavaPairRDD<Object, ViewIncomingPayload<Object>> viewIncomingRDD = null; memory.broadcastMemory(sparkContext); // execute the vertex program while (true) { if (Thread.interrupted()) { sparkContext.cancelAllJobs(); throw new TraversalInterruptedException(); } memory.setInExecute(true); viewIncomingRDD = GraknSparkExecutor.executeVertexProgramIteration(loadedGraphRDD, viewIncomingRDD, memory, graphComputerConfiguration, vertexProgramConfiguration); memory.setInExecute(false); if (this.vertexProgram.terminate(memory)) { break; } else { memory.incrIteration(); memory.broadcastMemory(sparkContext); } } // if the graph will be continued to be used (persisted or mapreduced), // then generate a view+graph if ((null != outputRDD && !this.persist.equals(Persist.NOTHING)) || !this.mapReducers.isEmpty()) { computedGraphRDD = GraknSparkExecutor.prepareFinalGraphRDD(loadedGraphRDD, viewIncomingRDD, this.vertexProgram.getVertexComputeKeys()); assert null != computedGraphRDD && computedGraphRDD != loadedGraphRDD; } else { // ensure that the computedGraphRDD was not created assert null == computedGraphRDD; } } ///////////////// memory.complete(); // drop all transient memory keys // write the computed graph to the respective output (rdd or output format) if (null != outputRDD && !this.persist.equals(Persist.NOTHING)) { // the logic holds that a computeGraphRDD must be created at this point assert null != computedGraphRDD; outputRDD.writeGraphRDD(graphComputerConfiguration, computedGraphRDD); } } final boolean computedGraphCreated = computedGraphRDD != null && computedGraphRDD != loadedGraphRDD; if (!computedGraphCreated) { computedGraphRDD = loadedGraphRDD; } final Memory.Admin finalMemory = null == memory ? new MapMemory() : new MapMemory(memory); ////////////////////////////// // process the map reducers // ////////////////////////////// if (!this.mapReducers.isEmpty()) { // create a mapReduceRDD for executing the map reduce jobs on JavaPairRDD<Object, VertexWritable> mapReduceRDD = computedGraphRDD; if (computedGraphCreated && !outputToSpark) { // drop all the edges of the graph as they are not used in mapReduce processing mapReduceRDD = computedGraphRDD.mapValues(vertexWritable -> { vertexWritable.get().dropEdges(Direction.BOTH); return vertexWritable; }); // if there is only one MapReduce to execute, don't bother wasting the clock cycles. if (this.mapReducers.size() > 1) { mapReduceRDD = mapReduceRDD.persist(StorageLevel.fromString(hadoopConfiguration .get(Constants.GREMLIN_SPARK_GRAPH_STORAGE_LEVEL, "MEMORY_ONLY"))); } } for (final MapReduce mapReduce : this.mapReducers) { // execute the map reduce job final HadoopConfiguration newApacheConfiguration = new HadoopConfiguration( graphComputerConfiguration); mapReduce.storeState(newApacheConfiguration); // map final JavaPairRDD mapRDD = GraknSparkExecutor.executeMap(mapReduceRDD, mapReduce, newApacheConfiguration); // combine final JavaPairRDD combineRDD = mapReduce.doStage(MapReduce.Stage.COMBINE) ? GraknSparkExecutor.executeCombine(mapRDD, newApacheConfiguration) : mapRDD; // reduce final JavaPairRDD reduceRDD = mapReduce.doStage(MapReduce.Stage.REDUCE) ? GraknSparkExecutor.executeReduce(combineRDD, mapReduce, newApacheConfiguration) : combineRDD; // write the map reduce output back to disk and computer result memory if (null != outputRDD) { mapReduce.addResultToMemory(finalMemory, outputRDD.writeMemoryRDD( graphComputerConfiguration, mapReduce.getMemoryKey(), reduceRDD)); } } // if the mapReduceRDD is not simply the computed graph, unpersist the mapReduceRDD if (computedGraphCreated && !outputToSpark) { assert loadedGraphRDD != computedGraphRDD; assert mapReduceRDD != computedGraphRDD; mapReduceRDD.unpersist(); } else { assert mapReduceRDD == computedGraphRDD; } } // unpersist the loaded graph if it will not be used again (no PersistedInputRDD) // if the graphRDD was loaded from Spark, but then partitioned or filtered, its a different RDD if (!inputFromSpark || partitioned || filtered) { loadedGraphRDD.unpersist(); } // unpersist the computed graph if it will not be used again (no PersistedOutputRDD) // if the computed graph is the loadedGraphRDD because it was not mutated and not-unpersisted, // then don't unpersist the computedGraphRDD/loadedGraphRDD if ((!outputToSpark || this.persist.equals(GraphComputer.Persist.NOTHING)) && computedGraphCreated) { computedGraphRDD.unpersist(); } // delete any file system or rdd data if persist nothing if (null != outputLocation && this.persist.equals(GraphComputer.Persist.NOTHING)) { if (outputToHDFS) { fileSystemStorage.rm(outputLocation); } if (outputToSpark) { sparkContextStorage.rm(outputLocation); } } // update runtime and return the newly computed graph finalMemory.setRuntime(System.currentTimeMillis() - startTime); // clear properties that should not be propagated in an OLAP chain graphComputerConfiguration.clearProperty(Constants.GREMLIN_HADOOP_GRAPH_FILTER); graphComputerConfiguration.clearProperty(Constants.GREMLIN_HADOOP_VERTEX_PROGRAM_INTERCEPTOR); graphComputerConfiguration.clearProperty(Constants.GREMLIN_SPARK_SKIP_GRAPH_CACHE); graphComputerConfiguration.clearProperty(Constants.GREMLIN_SPARK_SKIP_PARTITIONER); return new DefaultComputerResult(InputOutputHelper.getOutputGraph(graphComputerConfiguration, this.resultGraph, this.persist), finalMemory.asImmutable()); } catch (Exception e) { // So it throws the same exception as tinker does throw new RuntimeException(e); } }); computerService.shutdown(); return result; } private static void updateConfigKeys(org.apache.commons.configuration.Configuration sparkConfiguration) { Set<String> wrongKeys = new HashSet<>(); sparkConfiguration.getKeys().forEachRemaining(wrongKeys::add); wrongKeys.forEach(key -> { if (key.startsWith("janusmr")) { String newKey = "janusgraphmr" + key.substring(7); sparkConfiguration.setProperty(newKey, sparkConfiguration.getString(key)); } }); } ///////////////// @Override protected void loadJar(final Configuration hadoopConfiguration, final File file, final Object... params) { final JavaSparkContext sparkContext = (JavaSparkContext) params[0]; sparkContext.addJar(file.getAbsolutePath()); } /** * When using a persistent context the running Context's configuration will override a passed * in configuration. Spark allows us to override these inherited properties via * SparkContext.setLocalProperty */ private static void updateLocalConfiguration(final JavaSparkContext sparkContext, final Configuration configuration) { /* * While we could enumerate over the entire SparkConfiguration and copy into the Thread * Local properties of the Spark Context this could cause adverse effects with future * versions of Spark. Since the api for setting multiple local properties at once is * restricted as private, we will only set those properties we know can effect SparkGraphComputer * Execution rather than applying the entire configuration. */ final String[] validPropertyNames = { "spark.job.description", "spark.jobGroup.id", "spark.job.interruptOnCancel", "spark.scheduler.pool" }; for (String propertyName : validPropertyNames) { String propertyValue = configuration.get(propertyName); if (propertyValue != null) { LOGGER.info("Setting Thread Local SparkContext Property - " + propertyName + " : " + propertyValue); sparkContext.setLocalProperty(propertyName, configuration.get(propertyName)); } } } public static void main(final String[] args) throws Exception { final FileConfiguration configuration = new PropertiesConfiguration(args[0]); new GraknSparkComputer(HadoopGraph.open(configuration)) .program(VertexProgram.createVertexProgram(HadoopGraph.open(configuration), configuration)).submit() .get(); } }