Jan 01, 1970
Apache SeaTunnel Zeta इन्जिनको स्रोत कोड विश्लेषण (भाग 3): सर्भर-साइड टास्क सबमिशन द्वारा@williamguo
480 पढाइहरू
Apache SeaTunnel Zeta इन्जिनको स्रोत कोड विश्लेषण (भाग 3): सर्भर-साइड टास्क सबमिशन
धेरै लामो; पढ्नकाे लागि
यस पटक, यो सर्भर-साइड टास्क सबमिशन प्रक्रियाको बारेमा हो।यो Apache SeaTunnel Zeta इन्जिन स्रोत कोडको विश्लेषण गर्न श्रृंखला लेखहरूको अन्तिम टुक्रा हो; पुरा तस्वीर प्राप्त गर्न अघिल्लो श्रृंखला समीक्षा गर्नुहोस्:
- Apache SeaTunnel Zeta इन्जिनको स्रोत कोड विश्लेषण (भाग 1): सर्भर प्रारम्भिकरण
- Apache SeaTunnel Zeta इन्जिनको स्रोत कोड विश्लेषण (भाग २): ग्राहक पक्षमा कार्य पेस गर्ने प्रक्रिया
सर्भर सुरु भएपछि कार्यान्वयन गर्ने घटकहरूको समीक्षा गरौं:
- CoordinatorService : मास्टर/स्ट्यान्डबाइ नोडहरूमा मात्र सक्षम, क्लस्टर स्थिति सुन्छ, र मास्टर-स्ट्यान्डबाइ फेलओभरहरू ह्यान्डल गर्दछ।
- SlotService : कार्यकर्ता नोडहरूमा सक्षम गरिएको, आवधिक रूपमा मास्टरलाई यसको स्थिति रिपोर्ट गर्दछ।
- TaskExecutionService : कार्यकर्ता नोडहरूमा सक्षम पारिएको, आवधिक रूपमा IMAP मा कार्य मेट्रिक्स अद्यावधिक गर्दछ।
जब क्लस्टरद्वारा कुनै कार्यहरू प्राप्त हुँदैनन्, यी कम्पोनेन्टहरू चल्छन्। यद्यपि, जब ग्राहकले सर्भरमा SeaTunnelSubmitJobCodec सन्देश पठाउँछ, सर्भरले यसलाई कसरी ह्यान्डल गर्छ?
सन्देश रिसेप्शन
क्लाइन्ट र सर्भर फरक-फरक मेसिनहरूमा भएकाले, विधि कलहरू प्रयोग गर्न सकिँदैन; बरु, सन्देश पठाउने काम गरिन्छ। सन्देश प्राप्त गरेपछि, सर्भरले यसलाई कसरी प्रशोधन गर्छ?
सर्वप्रथम, ग्राहकले SeaTunnelSubmitJobCodec प्रकारको सन्देश पठाउँछ:
// Client code ClientMessage request = SeaTunnelSubmitJobCodec.encodeRequest( jobImmutableInformation.getJobId(), seaTunnelHazelcastClient .getSerializationService() .toData(jobImmutableInformation), jobImmutableInformation.isStartWithSavePoint()); PassiveCompletableFuture<Void> submitJobFuture = seaTunnelHazelcastClient.requestOnMasterAndGetCompletableFuture(request); SeaTunnelSubmitJobCodec वर्गमा, यो SeaTunnelMessageTaskFactoryProvider वर्गसँग सम्बन्धित छ, जसले सन्देश प्रकारहरूलाई MessageTask कक्षाहरूमा नक्सा गर्छ। SeaTunnelSubmitJobCodec को लागि, यसले SubmitJobTask वर्गमा नक्सा गर्छ:
private final Int2ObjectHashMap<MessageTaskFactory> factories = new Int2ObjectHashMap<>(60); private void initFactories() { factories.put( SeaTunnelPrintMessageCodec.REQUEST_MESSAGE_TYPE, (clientMessage, connection) -> new PrintMessageTask(clientMessage, node, connection)); factories.put( SeaTunnelSubmitJobCodec.REQUEST_MESSAGE_TYPE, (clientMessage, connection) -> new SubmitJobTask(clientMessage, node, connection)); ..... }
SubmitJobTask कक्षाको जाँच गर्दा, यसले SubmitJobOperation वर्गलाई आह्वान गर्छ:
@Override protected Operation prepareOperation() { return new SubmitJobOperation( parameters.jobId, parameters.jobImmutableInformation, parameters.isStartWithSavePoint); }
SubmitJobOperation कक्षामा, कार्य जानकारी यसको submitJob विधि मार्फत CoordinatorService कम्पोनेन्टलाई हस्तान्तरण गरिन्छ:
@Override protected PassiveCompletableFuture<?> doRun() throws Exception { SeaTunnelServer seaTunnelServer = getService(); return seaTunnelServer .getCoordinatorService() .submitJob(jobId, jobImmutableInformation, isStartWithSavePoint); }यस बिन्दुमा, ग्राहक सन्देश प्रभावकारी रूपमा विधि आह्वानका लागि सर्भरमा हस्तान्तरण गरिएको छ। अन्य प्रकारका अपरेशनहरू पनि यस्तै तरिकाले पत्ता लगाउन सकिन्छ।
संयोजक सेवा
अर्को, CoordinatorService जागिर सबमिशनहरू कसरी ह्यान्डल गर्छ भनेर अन्वेषण गरौं:
public PassiveCompletableFuture<Void> submitJob( long jobId, Data jobImmutableInformation, boolean isStartWithSavePoint) { CompletableFuture<Void> jobSubmitFuture = new CompletableFuture<>(); // First, check if a job with the same ID already exists if (getJobMaster(jobId) != null) { logger.warning( String.format( "The job %s is currently running; no need to submit again.", jobId)); jobSubmitFuture.complete(null); return new PassiveCompletableFuture<>(jobSubmitFuture); } // Initialize JobMaster object JobMaster jobMaster = new JobMaster( jobImmutableInformation, this.nodeEngine, executorService, getResourceManager(), getJobHistoryService(), runningJobStateIMap, runningJobStateTimestampsIMap, ownedSlotProfilesIMap, runningJobInfoIMap, metricsImap, engineConfig, seaTunnelServer); executorService.submit( () -> { try { // Ensure no duplicate tasks with the same ID if (!isStartWithSavePoint && getJobHistoryService().getJobMetrics(jobId) != null) { throw new JobException( String.format( "The job id %s has already been submitted and is not starting with a savepoint.", jobId)); } // Add task info to IMAP runningJobInfoIMap.put( jobId, new JobInfo(System.currentTimeMillis(), jobImmutableInformation)); runningJobMasterMap.put(jobId, jobMaster); // Initialize JobMaster jobMaster.init( runningJobInfoIMap.get(jobId).getInitializationTimestamp(), false); // Task creation successful jobSubmitFuture.complete(null); } catch (Throwable e) { String errorMsg = ExceptionUtils.getMessage(e); logger.severe(String.format("submit job %s error %s ", jobId, errorMsg)); jobSubmitFuture.completeExceptionally(new JobException(errorMsg)); } if (!jobSubmitFuture.isCompletedExceptionally()) { // Start job execution try { jobMaster.run(); } finally { // Remove jobMaster from map if not cancelled if (!jobMaster.getJobMasterCompleteFuture().isCancelled()) { runningJobMasterMap.remove(jobId); } } } else { runningJobInfoIMap.remove(jobId); runningJobMasterMap.remove(jobId); } }); return new PassiveCompletableFuture<>(jobSubmitFuture); } सर्भरमा, व्यक्तिगत कार्य व्यवस्थापन गर्न JobMaster वस्तु सिर्जना गरिएको छ। JobMaster सिर्जना गर्दा, यसले getResourceManager() मार्फत स्रोत प्रबन्धक र getJobHistoryService() मार्फत रोजगार इतिहास जानकारी प्राप्त गर्दछ। jobHistoryService लाई स्टार्टअपमा प्रारम्भ गरिएको छ, जबकि ResourceManager पहिलो कार्य पेस गर्दा अल्छी रूपमा लोड हुन्छ:
/** Lazy load for resource manager */ public ResourceManager getResourceManager() { if (resourceManager == null) { synchronized (this) { if (resourceManager == null) { ResourceManager manager = new ResourceManagerFactory(nodeEngine, engineConfig) .getResourceManager(); manager.init(); resourceManager = manager; } } } return resourceManager; }संसाधन प्रबन्धक
हाल, SeaTunnel ले स्ट्यान्डअलोन डिप्लोइमेन्ट मात्र समर्थन गर्दछ। ResourceManager सुरु गर्दा, यसले सबै क्लस्टर नोडहरू जम्मा गर्छ र नोड जानकारी प्राप्त गर्नको लागि SyncWorkerProfileOperation पठाउँछ, आन्तरिक registerWorker स्थिति अद्यावधिक गर्दै:
@Override public void init() { log.info("Init ResourceManager"); initWorker(); } private void initWorker() { log.info("initWorker... "); List<Address> aliveNode = nodeEngine.getClusterService().getMembers().stream() .map(Member::getAddress) .collect(Collectors.toList()); log.info("init live nodes: {}", aliveNode); List<CompletableFuture<Void>> futures = aliveNode.stream() .map( node -> sendToMember(new SyncWorkerProfileOperation(), node) .thenAccept( p -> { if (p != null) { registerWorker.put( node, (WorkerProfile) p); log.info( "received new worker register: " + ((WorkerProfile) p) .getAddress()); } })) .collect(Collectors.toList()); futures.forEach(CompletableFuture::join); log.info("registerWorker: {}", registerWorker); } पहिले, हामीले हेरेका थियौं कि SlotService ले प्रत्येक नोडबाट मास्टरलाई आवधिक रूपमा हृदयघात सन्देशहरू पठाउँछ। यी हृदयघातहरू प्राप्त गरेपछि, ResourceManager यसको आन्तरिक स्थितिमा नोड स्थितिहरू अद्यावधिक गर्दछ।
@Override public void heartbeat(WorkerProfile workerProfile) { if (!registerWorker.containsKey(workerProfile.getAddress())) { log.info("received new worker register: " + workerProfile.getAddress()); sendToMember(new ResetResourceOperation(), workerProfile.getAddress()).join(); } else { log.debug("received worker heartbeat from: " + workerProfile.getAddress()); } registerWorker.put(workerProfile.getAddress(), workerProfile); }JobMaster
CoordinatorService मा, JobMaster उदाहरण सिर्जना गरिन्छ र यसको init विधि भनिन्छ। जब init विधि पूरा हुन्छ, यो कार्य सिर्जना सफल भएको मानिन्छ। run विधिलाई औपचारिक रूपमा कार्य कार्यान्वयन गर्न भनिन्छ।
शुरुवात र init विधि हेरौं।
public JobMaster( @NonNull Data jobImmutableInformationData, @NonNull NodeEngine nodeEngine, @NonNull ExecutorService executorService, @NonNull ResourceManager resourceManager, @NonNull JobHistoryService jobHistoryService, @NonNull IMap runningJobStateIMap, @NonNull IMap runningJobStateTimestampsIMap, @NonNull IMap ownedSlotProfilesIMap, @NonNull IMap<Long, JobInfo> runningJobInfoIMap, @NonNull IMap<Long, HashMap<TaskLocation, SeaTunnelMetricsContext>> metricsImap, EngineConfig engineConfig, SeaTunnelServer seaTunnelServer) { this.jobImmutableInformationData = jobImmutableInformationData; this.nodeEngine = nodeEngine; this.executorService = executorService; flakeIdGenerator = this.nodeEngine .getHazelcastInstance() .getFlakeIdGenerator(Constant.SEATUNNEL_ID_GENERATOR_NAME); this.ownedSlotProfilesIMap = ownedSlotProfilesIMap; this.resourceManager = resourceManager; this.jobHistoryService = jobHistoryService; this.runningJobStateIMap = runningJobStateIMap; this.runningJobStateTimestampsIMap = runningJobStateTimestampsIMap; this.runningJobInfoIMap = runningJobInfoIMap; this.engineConfig = engineConfig; this.metricsImap = metricsImap; this.seaTunnelServer = seaTunnelServer; this.releasedSlotWhenTaskGroupFinished = new ConcurrentHashMap<>(); } प्रारम्भिक समयमा, केवल साधारण चर असाइनमेन्टहरू कुनै महत्त्वपूर्ण अपरेशनहरू बिना प्रदर्शन गरिन्छ। हामीले init विधिमा ध्यान केन्द्रित गर्न आवश्यक छ।
public synchronized void init(long initializationTimestamp, boolean restart) throws Exception { // The server receives a binary object from the client, // which is first converted to a JobImmutableInformation object, the same object sent by the client jobImmutableInformation = nodeEngine.getSerializationService().toObject(jobImmutableInformationData); // Get the checkpoint configuration, such as the interval, timeout, etc. jobCheckpointConfig = createJobCheckpointConfig( engineConfig.getCheckpointConfig(), jobImmutableInformation.getJobConfig()); LOGGER.info( String.format( "Init JobMaster for Job %s (%s) ", jobImmutableInformation.getJobConfig().getName(), jobImmutableInformation.getJobId())); LOGGER.info( String.format( "Job %s (%s) needed jar urls %s", jobImmutableInformation.getJobConfig().getName(), jobImmutableInformation.getJobId(), jobImmutableInformation.getPluginJarsUrls())); ClassLoader appClassLoader = Thread.currentThread().getContextClassLoader(); // Get the ClassLoader ClassLoader classLoader = seaTunnelServer .getClassLoaderService() .getClassLoader( jobImmutableInformation.getJobId(), jobImmutableInformation.getPluginJarsUrls()); // Deserialize the logical DAG from the client-provided information logicalDag = CustomClassLoadedObject.deserializeWithCustomClassLoader( nodeEngine.getSerializationService(), classLoader, jobImmutableInformation.getLogicalDag()); try { Thread.currentThread().setContextClassLoader(classLoader); // Execute save mode functionality, such as table creation and deletion if (!restart && !logicalDag.isStartWithSavePoint() && ReadonlyConfig.fromMap(logicalDag.getJobConfig().getEnvOptions()) .get(EnvCommonOptions.SAVEMODE_EXECUTE_LOCATION) .equals(SaveModeExecuteLocation.CLUSTER)) { logicalDag.getLogicalVertexMap().values().stream() .map(LogicalVertex::getAction) .filter(action -> action instanceof SinkAction) .map(sink -> ((SinkAction<?, ?, ?, ?>) sink).getSink()) .forEach(JobMaster::handleSaveMode); } // Parse the logical plan into a physical plan final Tuple2<PhysicalPlan, Map<Integer, CheckpointPlan>> planTuple = PlanUtils.fromLogicalDAG( logicalDag, nodeEngine, jobImmutableInformation, initializationTimestamp, executorService, flakeIdGenerator, runningJobStateIMap, runningJobStateTimestampsIMap, engineConfig.getQueueType(), engineConfig); this.physicalPlan = planTuple.f0(); this.physicalPlan.setJobMaster(this); this.checkpointPlanMap = planTuple.f1(); } finally { // Reset the current thread's ClassLoader and release the created classLoader Thread.currentThread().setContextClassLoader(appClassLoader); seaTunnelServer .getClassLoaderService() .releaseClassLoader( jobImmutableInformation.getJobId(), jobImmutableInformation.getPluginJarsUrls()); } Exception initException = null; try { // Initialize the checkpoint manager this.initCheckPointManager(restart); } catch (Exception e) { initException = e; } // Add some callback functions for job state listening this.initStateFuture(); if (initException != null) { if (restart) { cancelJob(); } throw initException; } }
अन्तमा, run विधि हेरौं:
public void run() { try { physicalPlan.startJob(); } catch (Throwable e) { LOGGER.severe( String.format( "Job %s (%s) run error with: %s", physicalPlan.getJobImmutableInformation().getJobConfig().getName(), physicalPlan.getJobImmutableInformation().getJobId(), ExceptionUtils.getMessage(e))); } finally { jobMasterCompleteFuture.join(); if (engineConfig.getConnectorJarStorageConfig().getEnable()) { List<ConnectorJarIdentifier> pluginJarIdentifiers = jobImmutableInformation.getPluginJarIdentifiers(); seaTunnelServer .getConnectorPackageService() .cleanUpWhenJobFinished( jobImmutableInformation.getJobId(), pluginJarIdentifiers); } } } यो विधि तुलनात्मक रूपमा सीधा छ, उत्पन्न भौतिक योजना कार्यान्वयन गर्नका लागि physicalPlan.startJob() लाई कल गर्दै।
माथिको कोडबाट, यो स्पष्ट छ कि सर्भरले क्लाइन्ट टास्क सबमिशन अनुरोध प्राप्त गरेपछि, यसले JobMaster क्लास सुरु गर्छ, जसले तार्किक योजनाबाट भौतिक योजना उत्पन्न गर्छ, र त्यसपछि भौतिक योजना कार्यान्वयन गर्दछ।
अर्को, हामीले तार्किक योजनालाई भौतिक योजनामा कसरी रूपान्तरण गरिन्छ भन्ने बारे अध्ययन गर्न आवश्यक छ।
तार्किक योजनाबाट भौतिक योजनामा रूपान्तरण
JobMaster मा निम्न विधि कल गरेर भौतिक योजनाको उत्पादन गरिन्छ:
final Tuple2<PhysicalPlan, Map<Integer, CheckpointPlan>> planTuple = PlanUtils.fromLogicalDAG( logicalDag, nodeEngine, jobImmutableInformation, initializationTimestamp, executorService, flakeIdGenerator, runningJobStateIMap, runningJobStateTimestampsIMap, engineConfig.getQueueType(), engineConfig);भौतिक योजना उत्पन्न गर्ने विधिमा, तार्किक योजनालाई पहिले कार्यान्वयन योजनामा रूपान्तरण गरिन्छ, र त्यसपछि कार्यान्वयन योजनालाई भौतिक योजनामा रूपान्तरण गरिन्छ।
public static Tuple2<PhysicalPlan, Map<Integer, CheckpointPlan>> fromLogicalDAG( @NonNull LogicalDag logicalDag, @NonNull NodeEngine nodeEngine, @NonNull JobImmutableInformation jobImmutableInformation, long initializationTimestamp, @NonNull ExecutorService executorService, @NonNull FlakeIdGenerator flakeIdGenerator, @NonNull IMap runningJobStateIMap, @NonNull IMap runningJobStateTimestampsIMap, @NonNull QueueType queueType, @NonNull EngineConfig engineConfig) { return new PhysicalPlanGenerator( new ExecutionPlanGenerator( logicalDag, jobImmutableInformation, engineConfig) .generate(), nodeEngine, jobImmutableInformation, initializationTimestamp, executorService, flakeIdGenerator, runningJobStateIMap, runningJobStateTimestampsIMap, queueType) .generate(); }कार्यान्वयन योजनाको उत्पादन
public ExecutionPlanGenerator( @NonNull LogicalDag logicalPlan, @NonNull JobImmutableInformation jobImmutableInformation, @NonNull EngineConfig engineConfig) { checkArgument( logicalPlan.getEdges().size() > 0, "ExecutionPlan Builder must have LogicalPlan."); this.logicalPlan = logicalPlan; this.jobImmutableInformation = jobImmutableInformation; this.engineConfig = engineConfig; } public ExecutionPlan generate() { log.debug("Generate execution plan using logical plan:"); Set<ExecutionEdge> executionEdges = generateExecutionEdges(logicalPlan.getEdges()); log.debug("Phase 1: generate execution edge list {}", executionEdges); executionEdges = generateShuffleEdges(executionEdges); log.debug("Phase 2: generate shuffle edge list {}", executionEdges); executionEdges = generateTransformChainEdges(executionEdges); log.debug("Phase 3: generate transform chain edge list {}", executionEdges); List<Pipeline> pipelines = generatePipelines(executionEdges); log.debug("Phase 4: generate pipeline list {}", pipelines); ExecutionPlan executionPlan = new ExecutionPlan(pipelines, jobImmutableInformation); log.debug("Phase 5 : generate execution plan {}", executionPlan); return executionPlan; } ExecutionPlanGenerator वर्गले तार्किक योजना लिन्छ र कार्यान्वयन किनाराहरू, शफल किनाराहरू, रूपान्तरण चेन किनारहरू, र अन्तमा, पाइपलाइनहरू उत्पन्न गर्ने सहित चरणहरूको श्रृंखला मार्फत कार्यान्वयन योजना उत्पादन गर्दछ।
भौतिक योजनाको उत्पादन
PhysicalPlanGenerator वर्गले कार्यान्वयन योजनालाई भौतिक योजनामा रूपान्तरण गर्छ:
public PhysicalPlanGenerator( @NonNull ExecutionPlan executionPlan, @NonNull NodeEngine nodeEngine, @NonNull JobImmutableInformation jobImmutableInformation, long initializationTimestamp, @NonNull ExecutorService executorService, @NonNull FlakeIdGenerator flakeIdGenerator, @NonNull IMap runningJobStateIMap, @NonNull IMap runningJobStateTimestampsIMap, @NonNull QueueType queueType) { this.executionPlan = executionPlan; this.nodeEngine = nodeEngine; this.jobImmutableInformation = jobImmutableInformation; this.initializationTimestamp = initializationTimestamp; this.executorService = executorService; this.flakeIdGenerator = flakeIdGenerator; this.runningJobStateIMap = runningJobStateIMap; this.runningJobStateTimestampsIMap = runningJobStateTimestampsIMap; this.queueType = queueType; } public PhysicalPlan generate() { List<PhysicalVertex> physicalVertices = generatePhysicalVertices(executionPlan); List<PhysicalEdge> physicalEdges = generatePhysicalEdges(executionPlan); PhysicalPlan physicalPlan = new PhysicalPlan(physicalVertices, physicalEdges); log.debug("Generate physical plan: {}", physicalPlan); return physicalPlan; }
यी दुई कक्षाका सामग्रीहरू जाँच गरौं।
public class ExecutionPlan { private final List<Pipeline> pipelines; private final JobImmutableInformation jobImmutableInformation; } public class Pipeline { /** The ID of the pipeline. */ private final Integer id; private final List<ExecutionEdge> edges; private final Map<Long, ExecutionVertex> vertexes; } public class ExecutionEdge { private ExecutionVertex leftVertex; private ExecutionVertex rightVertex; } public class ExecutionVertex { private Long vertexId; private Action action; private int parallelism; }
यसलाई तार्किक योजनासँग तुलना गरौं:
public class LogicalDag implements IdentifiedDataSerializable { @Getter private JobConfig jobConfig; private final Set<LogicalEdge> edges = new LinkedHashSet<>(); private final Map<Long, LogicalVertex> logicalVertexMap = new LinkedHashMap<>(); private IdGenerator idGenerator; private boolean isStartWithSavePoint = false; } public class LogicalEdge implements IdentifiedDataSerializable { private LogicalVertex inputVertex; private LogicalVertex targetVertex; private Long inputVertexId; private Long targetVertexId; } public class LogicalVertex implements IdentifiedDataSerializable { private Long vertexId; private Action action; private int parallelism; }यस्तो देखिन्छ कि प्रत्येक पाइपलाइन एक तार्किक योजना जस्तै छ। हामीलाई यो रूपान्तरण चरण किन चाहिन्छ? तार्किक योजना उत्पन्न गर्ने प्रक्रियालाई नजिकबाट हेरौं।
माथि देखाइए अनुसार, कार्यान्वयन योजना सिर्जना गर्न पाँच चरणहरू समावेश छन्, जसलाई हामी एक-एक गरेर समीक्षा गर्नेछौं।
- चरण १: तार्किक योजनालाई कार्यान्वयन योजनामा रूपान्तरण गर्दै
// Input is a set of logical plan edges, where each edge stores upstream and downstream nodes private Set<ExecutionEdge> generateExecutionEdges(Set<LogicalEdge> logicalEdges) { Set<ExecutionEdge> executionEdges = new LinkedHashSet<>(); Map<Long, ExecutionVertex> logicalVertexIdToExecutionVertexMap = new HashMap(); // Sort in order: first by input node, then by output node List<LogicalEdge> sortedLogicalEdges = new ArrayList<>(logicalEdges); Collections.sort( sortedLogicalEdges, (o1, o2) -> { if (o1.getInputVertexId() != o2.getInputVertexId()) { return o1.getInputVertexId() > o2.getInputVertexId() ? 1 : -1; } if (o1.getTargetVertexId() != o2.getTargetVertexId()) { return o1.getTargetVertexId() > o2.getTargetVertexId() ? 1 : -1; } return 0; }); // Loop to convert each logical plan edge to an execution plan edge for (LogicalEdge logicalEdge : sortedLogicalEdges) { LogicalVertex logicalInputVertex = logicalEdge.getInputVertex(); ExecutionVertex executionInputVertex = logicalVertexIdToExecutionVertexMap.computeIfAbsent( logicalInputVertex.getVertexId(), vertexId -> { long newId = idGenerator.getNextId(); // Recreate Action for each logical plan node Action newLogicalInputAction = recreateAction( logicalInputVertex.getAction(), newId, logicalInputVertex.getParallelism()); // Convert to execution plan node return new ExecutionVertex( newId, newLogicalInputAction, logicalInputVertex.getParallelism()); }); // Similarly, recreate execution plan nodes for target nodes LogicalVertex logicalTargetVertex = logicalEdge.getTargetVertex(); ExecutionVertex executionTargetVertex = logicalVertexIdToExecutionVertexMap.computeIfAbsent( logicalTargetVertex.getVertexId(), vertexId -> { long newId = idGenerator.getNextId(); Action newLogicalTargetAction = recreateAction( logicalTargetVertex.getAction(), newId, logicalTargetVertex.getParallelism()); return new ExecutionVertex( newId, newLogicalTargetAction, logicalTargetVertex.getParallelism()); }); // Generate execution plan edge ExecutionEdge executionEdge = new ExecutionEdge(executionInputVertex, executionTargetVertex); executionEdges.add(executionEdge); } return executionEdges; }- चरण २
private Set<ExecutionEdge> generateShuffleEdges(Set<ExecutionEdge> executionEdges) { // Map of upstream node ID to list of downstream nodes Map<Long, List<ExecutionVertex>> targetVerticesMap = new LinkedHashMap<>(); // Store only nodes of type Source Set<ExecutionVertex> sourceExecutionVertices = new HashSet<>(); executionEdges.forEach( edge -> { ExecutionVertex leftVertex = edge.getLeftVertex(); ExecutionVertex rightVertex = edge.getRightVertex(); if (leftVertex.getAction() instanceof SourceAction) { sourceExecutionVertices.add(leftVertex); } targetVerticesMap .computeIfAbsent(leftVertex.getVertexId(), id -> new ArrayList<>()) .add(rightVertex); }); if (sourceExecutionVertices.size() != 1) { return executionEdges; } ExecutionVertex sourceExecutionVertex = sourceExecutionVertices.stream().findFirst().get(); Action sourceAction = sourceExecutionVertex.getAction(); List<CatalogTable> producedCatalogTables = new ArrayList<>(); if (sourceAction instanceof SourceAction) { try { producedCatalogTables = ((SourceAction<?, ?, ?>) sourceAction) .getSource() .getProducedCatalogTables(); } catch (UnsupportedOperationException e) { } } else if (sourceAction instanceof TransformChainAction) { return executionEdges; } else { throw new SeaTunnelException( "source action must be SourceAction or TransformChainAction"); } // If the source produces a single table or // the source has only one downstream output, return directly if (producedCatalogTables.size() <= 1 || targetVerticesMap.get(sourceExecutionVertex.getVertexId()).size() <= 1) { return executionEdges; } List<ExecutionVertex> sinkVertices = targetVerticesMap.get(sourceExecutionVertex.getVertexId()); // Check if there are other types of actions, currently downstream nodes should ideally have two types: Transform and Sink; here we check if only Sink type is present Optional<ExecutionVertex> hasOtherAction = sinkVertices.stream() .filter(vertex -> !(vertex.getAction() instanceof SinkAction)) .findFirst(); checkArgument(!hasOtherAction.isPresent()); // After executing the above code, the current scenario is: // There is only one data source, this source produces multiple tables, and multiple sink nodes depend on these tables // This means the task has only two types of nodes: a source node that produces multiple tables and a group of sink nodes depending on this source // A new shuffle node will be created and added between the source and sinks // Modify the dependency relationship to source -> shuffle -> multiple sinks Set<ExecutionEdge> newExecutionEdges = new LinkedHashSet<>(); // Shuffle strategy will not be explored in detail here ShuffleStrategy shuffleStrategy = ShuffleMultipleRowStrategy.builder() .jobId(jobImmutableInformation.getJobId()) .inputPartitions(sourceAction.getParallelism()) .catalogTables(producedCatalogTables) .queueEmptyQueueTtl( (int) (engineConfig.getCheckpointConfig().getCheckpointInterval() * 3)) .build(); ShuffleConfig shuffleConfig = ShuffleConfig.builder().shuffleStrategy(shuffleStrategy).build(); long shuffleVertexId = idGenerator.getNextId(); String shuffleActionName = String.format("Shuffle [%s]", sourceAction.getName()); ShuffleAction shuffleAction = new ShuffleAction(shuffleVertexId, shuffleActionName, shuffleConfig); shuffleAction.setParallelism(sourceAction.getParallelism()); ExecutionVertex shuffleVertex = new ExecutionVertex(shuffleVertexId, shuffleAction, shuffleAction.getParallelism()); ExecutionEdge sourceToShuffleEdge = new ExecutionEdge(sourceExecutionVertex, shuffleVertex); newExecutionEdges.add(sourceToShuffleEdge); // Set the parallelism of multiple sink nodes to 1 for (ExecutionVertex sinkVertex : sinkVertices) { sinkVertex.setParallelism(1); sinkVertex.getAction().setParallelism(1); ExecutionEdge shuffleToSinkEdge = new ExecutionEdge(shuffleVertex, sinkVertex); newExecutionEdges.add(shuffleToSinkEdge); } return newExecutionEdges; }शफल चरणले विशिष्ट परिदृश्यहरूलाई सम्बोधन गर्दछ जहाँ स्रोतले धेरै तालिकाहरू पढ्न समर्थन गर्दछ, र त्यहाँ यस स्रोतमा आधारित धेरै सिङ्क नोडहरू छन्। यस्तो अवस्थामा, एक फेरबदल नोड बीचमा थपिएको छ।
चरण 3
private Set<ExecutionEdge> generateTransformChainEdges(Set<ExecutionEdge> executionEdges) { // Uses three structures: stores all Source nodes and the input/output nodes for each // inputVerticesMap stores all upstream input nodes by downstream node id as the key // targetVerticesMap stores all downstream output nodes by upstream node id as the key Map<Long, List<ExecutionVertex>> inputVerticesMap = new HashMap<>(); Map<Long, List<ExecutionVertex>> targetVerticesMap = new HashMap<>(); Set<ExecutionVertex> sourceExecutionVertices = new HashSet<>(); executionEdges.forEach( edge -> { ExecutionVertex leftVertex = edge.getLeftVertex(); ExecutionVertex rightVertex = edge.getRightVertex(); if (leftVertex.getAction() instanceof SourceAction) { sourceExecutionVertices.add(leftVertex); } inputVerticesMap .computeIfAbsent(rightVertex.getVertexId(), id -> new ArrayList<>()) .add(leftVertex); targetVerticesMap .computeIfAbsent(leftVertex.getVertexId(), id -> new ArrayList<>()) .add(rightVertex); }); Map<Long, ExecutionVertex> transformChainVertexMap = new HashMap<>(); Map<Long, Long> chainedTransformVerticesMapping = new HashMap<>(); // Loop over each source, starting with all head nodes in the DAG for (ExecutionVertex sourceVertex : sourceExecutionVertices) { List<ExecutionVertex> vertices = new ArrayList<>(); vertices.add(sourceVertex); for (int index = 0; index < vertices.size(); index++) { ExecutionVertex vertex = vertices.get(index); fillChainedTransformExecutionVertex( vertex, chainedTransformVerticesMapping, transformChainVertexMap, executionEdges, Collections.unmodifiableMap(inputVerticesMap), Collections.unmodifiableMap(targetVerticesMap)); // If the current node has downstream nodes, add all downstream nodes to the list // The second loop will recalculate the newly added downstream nodes, which could be Transform nodes or Sink nodes if (targetVerticesMap.containsKey(vertex.getVertexId())) { vertices.addAll(targetVerticesMap.get(vertex.getVertexId())); } } } // After looping, chained Transform nodes will be chained, and the chainable edges will be removed from the execution plan // Therefore, the logical plan at this point cannot form the graph relationship and needs to be rebuilt Set<ExecutionEdge> transformChainEdges = new LinkedHashSet<>(); // Loop over existing relationships for (ExecutionEdge executionEdge : executionEdges) { ExecutionVertex leftVertex = executionEdge.getLeftVertex(); ExecutionVertex rightVertex = executionEdge.getRightVertex(); boolean needRebuild = false; // Check if the input or output nodes of the current edge are in the chain mapping // If so, the node has been chained, and we need to find the chained node in the mapping // and rebuild the DAG if (chainedTransformVerticesMapping.containsKey(leftVertex.getVertexId())) { needRebuild = true; leftVertex = transformChainVertexMap.get( chainedTransformVerticesMapping.get(leftVertex.getVertexId())); } if (chainedTransformVerticesMapping.containsKey(rightVertex.getVertexId())) { needRebuild = true; rightVertex = transformChainVertexMap.get( chainedTransformVerticesMapping.get(rightVertex.getVertexId())); } if (needRebuild) { executionEdge = new ExecutionEdge(leftVertex, rightVertex); } transformChainEdges.add(executionEdge); } return transformChainEdges; } private void fillChainedTransformExecutionVertex( ExecutionVertex currentVertex, Map<Long, Long> chainedTransformVerticesMapping, Map<Long, ExecutionVertex> transformChainVertexMap, Set<ExecutionEdge> executionEdges, Map<Long, List<ExecutionVertex>> inputVerticesMap, Map<Long, List<ExecutionVertex>> targetVerticesMap) { // Exit if the map already contains the current node if (chainedTransformVerticesMapping.containsKey(currentVertex.getVertexId())) { return; } List<ExecutionVertex> transformChainedVertices = new ArrayList<>(); collectChainedVertices( currentVertex, transformChainedVertices, executionEdges, inputVerticesMap, targetVerticesMap); // If the list is not empty, it means the Transform nodes in the list can be merged into one if (transformChainedVertices.size() > 0) { long newVertexId = idGenerator.getNextId(); List<SeaTunnelTransform> transforms = new ArrayList<>(transformChainedVertices.size()); List<String> names = new ArrayList<>(transformChainedVertices.size()); Set<URL> jars = new HashSet<>(); Set<ConnectorJarIdentifier> identifiers = new HashSet<>(); transformChainedVertices.stream() .peek( // Add all historical node IDs and new node IDs to the mapping vertex -> chainedTransformVerticesMapping.put( vertex.getVertexId(), newVertexId)) .map(ExecutionVertex::getAction) .map(action -> (TransformAction) action) .forEach( action -> { transforms.add(action.getTransform()); jars.addAll(action.getJarUrls()); identifiers.addAll(action.getConnectorJarIdentifiers()); names.add(action.getName()); }); String transformChainActionName = String.format("TransformChain[%s]", String.join("->", names)); // Merge multiple TransformActions into one TransformChainAction TransformChainAction transformChainAction = new TransformChainAction( newVertexId, transformChainActionName, jars, identifiers, transforms); transformChainAction.setParallelism(currentVertex.getAction().getParallelism()); ExecutionVertex executionVertex = new ExecutionVertex( newVertexId, transformChainAction, currentVertex.getParallelism()); // Store the modified node information in the state transformChainVertexMap.put(newVertexId, executionVertex); chainedTransformVerticesMapping.put( currentVertex.getVertexId(), executionVertex.getVertexId()); } } private void collectChainedVertices( ExecutionVertex currentVertex, List<ExecutionVertex> chainedVertices, Set<ExecutionEdge> executionEdges, Map<Long, List<ExecutionVertex>> inputVerticesMap, Map<Long, List<ExecutionVertex>> targetVerticesMap) { Action action = currentVertex.getAction(); // Only merge TransformAction if (action instanceof TransformAction) { if (chainedVertices.size() == 0) { // If the list of vertices to be merged is empty, add itself to the list // The condition for entering this branch is that the current node is a TransformAction and the list to be merged is empty // There may be several scenarios: the first Transform node enters, and this Transform node has no constraints chainedVertices.add(currentVertex); } else if (inputVerticesMap.get(currentVertex.getVertexId()).size() == 1) { // When this condition is entered, it means: // The list of vertices to be merged already has at least one TransformAction // The scenario at this point is that the upstream Transform node has only one downstream node, ie, the current node. This constraint is ensured by the following judgment // Chain the current TransformAction node with the previous TransformAction node // Delete this relationship from the execution plan executionEdges.remove( new ExecutionEdge( chainedVertices.get(chainedVertices.size() - 1), currentVertex)); // Add itself to the list of nodes to be merged chainedVertices.add(currentVertex); } else { return; } } else { return; } // It cannot chain to any target vertex if it has multiple target vertices. if (targetVerticesMap.get(currentVertex.getVertexId()).size() == 1) { // If the current node has only one downstream node, try chaining again // If the current node has multiple downstream nodes, it will not chain the downstream nodes, so it can be ensured that the above chaining is a one-to-one relationship // This call occurs when the Transform node has only one downstream node collectChainedVertices( targetVerticesMap.get(currentVertex.getVertexId()).get(0), chainedVertices, executionEdges, inputVerticesMap, targetVerticesMap); } }चरण 4
private List<Pipeline> generatePipelines(Set<ExecutionEdge> executionEdges) { // Stores each execution plan node Set<ExecutionVertex> executionVertices = new LinkedHashSet<>(); for (ExecutionEdge edge : executionEdges) { executionVertices.add(edge.getLeftVertex()); executionVertices.add(edge.getRightVertex()); } // Calls the Pipeline generator to convert the execution plan into Pipelines PipelineGenerator pipelineGenerator = new PipelineGenerator(executionVertices, new ArrayList<>(executionEdges)); List<Pipeline> pipelines = pipelineGenerator.generatePipelines(); Set<String> duplicatedActionNames = new HashSet<>(); Set<String> actionNames = new HashSet<>(); for (Pipeline pipeline : pipelines) { Integer pipelineId = pipeline.getId(); for (ExecutionVertex vertex : pipeline.getVertexes().values()) { // Get each execution node of the current Pipeline, reset the Action name, and add the pipeline name Action action = vertex.getAction(); String actionName = String.format("pipeline-%s [%s]", pipelineId, action.getName()); action.setName(actionName); if (actionNames.contains(actionName)) { duplicatedActionNames.add(actionName); } actionNames.add(action Name); } } if (duplicatedActionNames.size() > 0) { throw new RuntimeException( String.format( "Duplicated Action names found: %s", duplicatedActionNames)); } return pipelines; } public PipelineGenerator(Collection<ExecutionVertex> vertices, List<ExecutionEdge> edges) { this.vertices = vertices; this.edges = edges; } public List<Pipeline> generatePipelines() { List<ExecutionEdge> executionEdges = expandEdgeByParallelism(edges); // Split the execution plan into unrelated execution plans based on their relationships // Divide into several unrelated execution plans List<List<ExecutionEdge>> edgesList = splitUnrelatedEdges(executionEdges); edgesList = edgesList.stream() .flatMap(e -> this.splitUnionEdge(e).stream()) .collect(Collectors.toList()); // Just convert execution plan to pipeline at now. We should split it to multi pipeline with // cache in the future IdGenerator idGenerator = new IdGenerator(); // Convert execution plan graph to Pipeline return edgesList.stream() .map( e -> { Map<Long, ExecutionVertex> vertexes = new HashMap<>(); List<ExecutionEdge> pipelineEdges = e.stream() .map( edge -> { if (!vertexes.containsKey( edge.getLeftVertexId())) { vertexes.put( edge.getLeftVertexId(), edge.getLeftVertex()); } ExecutionVertex source = vertexes.get( edge.getLeftVertexId()); if (!vertexes.containsKey( edge.getRightVertexId())) { vertexes.put( edge.getRightVertexId(), edge.getRightVertex()); } ExecutionVertex destination = vertexes.get( edge.getRightVertexId()); return new ExecutionEdge( source, destination); }) .collect(Collectors.toList()); return new Pipeline( (int) idGenerator.getNextId(), pipelineEdges, vertexes); }) .collect(Collectors.toList()); }- चरण 5
चरण पाँचमा कार्यान्वयन योजना उदाहरणहरू उत्पन्न गर्ने, चौथो चरणमा उत्पन्न पाइपलाइन प्यारामिटरहरू पार गर्ने समावेश छ।
सारांश:
कार्यान्वयन योजनाले तार्किक योजनामा निम्न कार्यहरू गर्दछ:
- जब एक स्रोतले धेरै तालिकाहरू उत्पन्न गर्दछ र धेरै सिंक नोडहरू यस स्रोतमा निर्भर हुन्छन्, बीचमा शफल नोड थपिन्छ।
- चेन मर्ज ट्रान्सफॉर्म नोडहरू, एक नोडमा बहु रूपान्तरण नोडहरू संयोजन गर्ने प्रयास।
- कार्यहरू विभाजन गर्नुहोस्,
configuration file/LogicalDagList<Pipeline>को रूपमा प्रतिनिधित्व गरिएका धेरै असंबद्ध कार्यहरूमा विभाजन गर्नुहोस्।
भौतिक योजना जेनेरेसन
भौतिक योजना उत्पादनको बारेमा जानकारी लिनु अघि, उत्पन्न भौतिक योजनामा कुन जानकारी समावेश गरिएको छ भनेर पहिले समीक्षा गरौं र यसको आन्तरिक घटकहरूको जाँच गरौं।
public class PhysicalPlan { private final List<SubPlan> pipelineList; private final AtomicInteger finishedPipelineNum = new AtomicInteger(0); private final AtomicInteger canceledPipelineNum = new AtomicInteger(0); private final AtomicInteger failedPipelineNum = new AtomicInteger(0); private final JobImmutableInformation jobImmutableInformation; private final IMap<Object, Object> runningJobStateIMap; private final IMap<Object, Long[]> runningJobStateTimestampsIMap; private CompletableFuture<JobResult> jobEndFuture; private final AtomicReference<String> errorBySubPlan = new AtomicReference<>(); private final String jobFullName; private final long jobId; private JobMaster jobMaster; private boolean makeJobEndWhenPipelineEnded = true; private volatile boolean isRunning = false; }
यस वर्गमा, एउटा प्रमुख क्षेत्र pipelineList हो, जुन SubPlan उदाहरणहरूको सूची हो:
public class SubPlan { private final int pipelineMaxRestoreNum; private final int pipelineRestoreIntervalSeconds; private final List<PhysicalVertex> physicalVertexList; private final List<PhysicalVertex> coordinatorVertexList; private final int pipelineId; private final AtomicInteger finishedTaskNum = new AtomicInteger(0); private final AtomicInteger canceledTaskNum = new AtomicInteger(0); private final AtomicInteger failedTaskNum = new AtomicInteger(0); private final String pipelineFullName; private final IMap<Object, Object> runningJobStateIMap; private final Map<String, String> tags; private final IMap<Object, Long[]> runningJobStateTimestampsIMap; private CompletableFuture<PipelineExecutionState> pipelineFuture; private final PipelineLocation pipelineLocation; private AtomicReference<String> errorByPhysicalVertex = new AtomicReference<>(); private final ExecutorService executorService; private JobMaster jobMaster; private PassiveCompletableFuture<Void> reSchedulerPipelineFuture; private Integer pipelineRestoreNum; private final Object restoreLock = new Object(); private volatile PipelineStatus currPipelineStatus; public volatile boolean isRunning = false; private Map<TaskGroupLocation, SlotProfile> slotProfiles; }
SubPlan वर्गले PhysicalVertex उदाहरणहरूको सूची राख्छ, भौतिक योजना नोडहरू र समन्वयक नोडहरूमा विभाजित:
public class PhysicalVertex { private final TaskGroupLocation taskGroupLocation; private final String taskFullName; private final TaskGroupDefaultImpl taskGroup; private final ExecutorService executorService; private final FlakeIdGenerator flakeIdGenerator; private final Set<URL> pluginJarsUrls; private final Set<ConnectorJarIdentifier> connectorJarIdentifiers; private final IMap<Object, Object> runningJobStateIMap; private CompletableFuture<TaskExecutionState> taskFuture; private final IMap<Object, Long[]> runningJobStateTimestampsIMap; private final NodeEngine nodeEngine; private JobMaster jobMaster; private volatile ExecutionState currExecutionState = ExecutionState.CREATED; public volatile boolean isRunning = false; private AtomicReference<String> errorByPhysicalVertex = new AtomicReference<>(); } public class TaskGroupDefaultImpl implements TaskGroup { private final TaskGroupLocation taskGroupLocation; private final String taskGroupName; // Stores the tasks that the physical node needs to execute // Each task could be for reading data, writing data, data splitting, checkpoint tasks, etc. private final Map<Long, Task> tasks; }
PhysicalPlanGenerator कार्यान्वयन योजनालाई SeaTunnelTask मा रूपान्तरण गर्न र कार्यान्वयनको क्रममा डाटा विभाजन, डाटा कमिटिङ, र चेकपोइन्ट कार्यहरू जस्ता विभिन्न समन्वय कार्यहरू थप्न जिम्मेवार छ।
public PhysicalPlanGenerator( @NonNull ExecutionPlan executionPlan, @NonNull NodeEngine nodeEngine, @NonNull JobImmutableInformation jobImmutableInformation, long initializationTimestamp, @NonNull ExecutorService executorService, @NonNull FlakeIdGenerator flakeIdGenerator, @NonNull IMap runningJobStateIMap, @NonNull IMap runningJobStateTimestampsIMap, @NonNull QueueType queueType) { this.pipelines = executionPlan.getPipelines(); this.nodeEngine = nodeEngine; this.jobImmutableInformation = jobImmutableInformation; this.initializationTimestamp = initializationTimestamp; this.executorService = executorService; this.flakeIdGenerator = flakeIdGenerator; // the checkpoint of a pipeline this.pipelineTasks = new HashSet<>(); this.startingTasks = new HashSet<>(); this.subtaskActions = new HashMap<>(); this.runningJobStateIMap = runningJobStateIMap; this.runningJobStateTimestampsIMap = runningJobStateTimestampsIMap; this.queueType = queueType; } public Tuple2<PhysicalPlan, Map<Integer, CheckpointPlan>> generate() { // Get the node filter conditions from user configuration to select the nodes where tasks will run Map<String, String> tagFilter = (Map<String, String>) jobImmutableInformation .getJobConfig() .getEnvOptions() .get(EnvCommonOptions.NODE_TAG_FILTER.key()); // TODO Determine which tasks do not need to be restored according to state CopyOnWriteArrayList<PassiveCompletableFuture<PipelineStatus>> waitForCompleteBySubPlanList = new CopyOnWriteArrayList<>(); Map<Integer, CheckpointPlan> checkpointPlans = new HashMap<>(); final int totalPipelineNum = pipelines.size(); Stream<SubPlan> subPlanStream = pipelines.stream() .map( pipeline -> { // Clear the state each time this.pipelineTasks.clear(); this.startingTasks.clear(); this.subtaskActions.clear(); final int pipelineId = pipeline.getId(); // Get current task information final List<ExecutionEdge> edges = pipeline.getEdges(); // Get all SourceActions List<SourceAction<?, ?, ?>> sources = findSourceAction(edges); // Generate Source data slice tasks, ie, SourceSplitEnumeratorTask // This task calls the SourceSplitEnumerator class in the connector if supported List<PhysicalVertex> coordinatorVertexList = getEnumeratorTask( sources, pipelineId, totalPipelineNum); // Generate Sink commit tasks, ie, SinkAggregatedCommitterTask // This task calls the SinkAggregatedCommitter class in the connector if supported // These two tasks are executed as coordination tasks coordinatorVertexList.addAll( getCommitterTask(edges, pipelineId, totalPipelineNum)); List<PhysicalVertex> physicalVertexList = getSourceTask( edges, sources, pipelineId, totalPipelineNum); // physicalVertexList.addAll( getShuffleTask(edges, pipelineId, totalPipelineNum)); CompletableFuture<PipelineStatus> pipelineFuture = new CompletableFuture<>(); waitForCompleteBySubPlanList.add( new PassiveCompletableFuture<>(pipelineFuture)); // Add checkpoint tasks checkpointPlans.put( pipelineId, CheckpointPlan.builder() .pipelineId(pipelineId) .pipelineSubtasks(pipelineTasks) .startingSubtasks(startingTasks) .pipelineActions(pipeline.getActions()) .subtaskActions(subtaskActions) .build()); return new SubPlan( pipelineId, totalPipelineNum, initializationTimestamp, physicalVertexList, coordinatorVertexList, jobImmutableInformation, executorService, runningJobStateIMap, runningJobStateTimestampsIMap, tagFilter); }); PhysicalPlan physicalPlan = new PhysicalPlan( subPlanStream.collect(Collectors.toList()), executorService, jobImmutableInformation, initializationTimestamp, runningJobStateIMap, runningJobStateTimestampsIMap); return Tuple2.tuple2(physicalPlan, checkpointPlans); } भौतिक योजना उत्पन्न गर्ने प्रक्रियामा कार्यान्वयन योजनालाई SeaTunnelTask मा रूपान्तरण गर्ने र विभिन्न समन्वय कार्यहरू थप्ने समावेश छ, जस्तै डाटा विभाजन कार्यहरू, डाटा कमिट गर्ने कार्यहरू, र चेकपोइन्ट कार्यहरू।
SeaTunnelTask मा, कार्यहरूलाई SourceFlowLifeCycle , SinkFlowLifeCycle , TransformFlowLifeCycle , ShuffleSinkFlowLifeCycle , ShuffleSourceFlowLifeCycle मा रूपान्तरण गरिन्छ।
उदाहरणका लागि, SourceFlowLifeCycle र SinkFlowLifeCycle वर्गहरू निम्नानुसार छन्:
- SourceFlowLifeCycle
@Override public void init() throws Exception { this.splitSerializer = sourceAction.getSource().getSplitSerializer(); this.reader = sourceAction .getSource() .createReader( new SourceReaderContext( indexID, sourceAction.getSource().getBoundedness(), this, metricsContext, eventListener)); this.enumeratorTaskAddress = getEnumeratorTaskAddress(); } @Override public void open() throws Exception { reader.open(); register(); } public void collect() throws Exception { if (!prepareClose) { if (schemaChanging()) { log.debug("schema is changing, stop reader collect records"); Thread.sleep(200); return; } reader.pollNext(collector); if (collector.isEmptyThisPollNext()) { Thread.sleep(100); } else { collector.resetEmptyThisPollNext(); /** * The current thread obtain a checkpoint lock in the method {@link * SourceReader#pollNext( Collector)}. When trigger the checkpoint or savepoint, * other threads try to obtain the lock in the method {@link * SourceFlowLifeCycle#triggerBarrier(Barrier)}. When high CPU load, checkpoint * process may be blocked as long time. So we need sleep to free the CPU. */ Thread.sleep(0L); } if (collector.captureSchemaChangeBeforeCheckpointSignal()) { if (schemaChangePhase.get() != null) { throw new IllegalStateException( "previous schema changes in progress, schemaChangePhase: " + schemaChangePhase.get()); } schemaChangePhase.set(SchemaChangePhase.createBeforePhase()); runningTask.triggerSchemaChangeBeforeCheckpoint().get(); log.info("triggered schema-change-before checkpoint, stopping collect data"); } else if (collector.captureSchemaChangeAfterCheckpointSignal()) { if (schemaChangePhase.get() != null) { throw new IllegalStateException( "previous schema changes in progress, schemaChangePhase: " + schemaChangePhase.get()); } schemaChangePhase.set(SchemaChangePhase.createAfterPhase()); runningTask.triggerSchemaChangeAfterCheckpoint().get(); log.info("triggered schema-change-after checkpoint, stopping collect data"); } } else { Thread.sleep(100); } } SourceFlowLifeCycle मा, डेटा रिडिङ collect विधिमा गरिन्छ। डेटा पढिसकेपछि, यसलाई SeaTunnelSourceCollector मा राखिन्छ। जब डाटा प्राप्त हुन्छ, कलेक्टरले मेट्रिक्स अपडेट गर्दछ र डाटालाई डाउनस्ट्रीम कम्पोनेन्टहरूमा पठाउँदछ।
@Override public void collect(T row) { try { if (row instanceof SeaTunnelRow) { String tableId = ((SeaTunnelRow) row).getTableId(); int size; if (rowType instanceof SeaTunnelRowType) { size = ((SeaTunnelRow) row).getBytesSize((SeaTunnelRowType) rowType); } else if (rowType instanceof MultipleRowType) { size = ((SeaTunnelRow) row).getBytesSize(rowTypeMap.get(tableId)); } else { throw new SeaTunnelEngineException( "Unsupported row type: " + rowType.getClass().getName()); } sourceReceivedBytes.inc(size); sourceReceivedBytesPerSeconds.markEvent(size); flowControlGate.audit((SeaTunnelRow) row); if (StringUtils.isNotEmpty(tableId)) { String tableName = getFullName(TablePath.of(tableId)); Counter sourceTableCounter = sourceReceivedCountPerTable.get(tableName); if (Objects.nonNull(sourceTableCounter)) { sourceTableCounter.inc(); } else { Counter counter = metricsContext.counter(SOURCE_RECEIVED_COUNT + "#" + tableName); counter.inc(); sourceReceivedCountPerTable.put(tableName, counter); } } } sendRecordToNext(new Record<>(row)); emptyThisPollNext = false; sourceReceivedCount.inc(); sourceReceivedQPS.markEvent(); } catch (IOException e) { throw new RuntimeException(e); } } public void sendRecordToNext(Record<?> record) throws IOException { synchronized (checkpointLock) { for (OneInputFlowLifeCycle<Record<?>> output : outputs) { output.received(record); } } }- सिंकफ्लोलाइफ साइकल
@Override public void received(Record<?> record) { try { if (record.getData() instanceof Barrier) { long startTime = System.currentTimeMillis(); Barrier barrier = (Barrier) record.getData(); if (barrier.prepareClose(this.taskLocation)) { prepareClose = true; } if (barrier.snapshot()) { try { lastCommitInfo = writer.prepareCommit(); } catch (Exception e) { writer.abortPrepare(); throw e; } List<StateT> states = writer.snapshotState(barrier.getId()); if (!writerStateSerializer.isPresent()) { runningTask.addState( barrier, ActionStateKey.of(sinkAction), Collections.emptyList()); } else { runningTask.addState( barrier, ActionStateKey.of(sinkAction), serializeStates(writerStateSerializer.get(), states)); } if (containAggCommitter) { CommitInfoT commitInfoT = null; if (lastCommitInfo.isPresent()) { commitInfoT = lastCommitInfo.get(); } runningTask .getExecutionContext() .sendToMember( new SinkPrepareCommitOperation<CommitInfoT>( barrier, committerTaskLocation, commitInfoSerializer.isPresent() ? commitInfoSerializer .get() .serialize(commitInfoT) : null), committerTaskAddress) .join(); } } else { if (containAggCommitter) { runningTask .getExecutionContext() .sendToMember( new BarrierFlowOperation(barrier, committerTaskLocation), committerTaskAddress) .join(); } } runningTask.ack(barrier); log.debug( "trigger barrier [{}] finished, cost {}ms. taskLocation [{}]", barrier.getId(), System.currentTimeMillis() - startTime, taskLocation); } else if (record.getData() instanceof SchemaChangeEvent) { if (prepareClose) { return; } SchemaChangeEvent event = (SchemaChangeEvent) record.getData(); writer.applySchemaChange(event); } else { if (prepareClose) { return; } writer.write((T) record.getData()); sinkWriteCount.inc(); sinkWriteQPS.markEvent(); if (record.getData() instanceof SeaTunnelRow) { long size = ((SeaTunnelRow) record.getData()).getBytesSize(); sinkWriteBytes.inc(size); sinkWriteBytesPerSeconds.markEvent(size); String tableId = ((SeaTunnelRow) record.getData()).getTableId(); if (StringUtils.isNotBlank(tableId)) { String tableName = getFullName(TablePath.of(tableId)); Counter sinkTableCounter = sinkWriteCountPerTable.get(tableName); if (Objects.nonNull(sinkTableCounter)) { sinkTableCounter.inc(); } else { Counter counter = metricsContext.counter(SINK_WRITE_COUNT + "#" + tableName); counter.inc(); sinkWriteCountPerTable.put(tableName, counter); } } } } } catch (Exception e) { throw new RuntimeException(e); } }कार्य कार्यान्वयन
CoordinatorService मा, init विधि मार्फत भौतिक योजना बनाइन्छ, र त्यसपछि कार्य सुरु गर्न run विधि भनिन्छ।
CoordinatorService { jobMaster.init( runningJobInfoIMap.get(jobId).getInitializationTimestamp(), false); ... jobMaster.run(); } JobMaster { public void run() { ... physicalPlan.startJob(); ... } }
JobMaster मा, कार्य सुरु गर्दा, यसले PhysicalPlan को startJob विधिलाई कल गर्छ।
public void startJob() { isRunning = true; log.info("{} state process is start", getJobFullName()); stateProcess(); } private synchronized void stateProcess() { if (!isRunning) { log.warn(String.format("%s state process is stopped", jobFullName)); return; } switch (getJobStatus()) { case CREATED: updateJobState(JobStatus.SCHEDULED); break; case SCHEDULED: getPipelineList() .forEach( subPlan -> { if (PipelineStatus.CREATED.equals( subPlan.getCurrPipelineStatus())) { subPlan.startSubPlanStateProcess(); } }); updateJobState(JobStatus.RUNNING); break; case RUNNING: case DOING_SAVEPOINT: break; case FAILING: case CANCELING: jobMaster.neverNeedRestore(); getPipelineList().forEach(SubPlan::cancelPipeline); break; case FAILED: case CANCELED: case SAVEPOINT_DONE: case FINISHED: stopJobStateProcess(); jobEndFuture.complete(new JobResult(getJobStatus(), errorBySubPlan.get())); return; default: throw new IllegalArgumentException("Unknown Job State: " + getJobStatus()); } } PhysicalPlan मा, कार्य सुरु गर्दा कार्यको स्थितिलाई SCHEDULED मा अद्यावधिक गर्दछ र त्यसपछि SubPlan को सुरु विधिलाई कल गर्न जारी राख्छ।
public void startSubPlanStateProcess() { isRunning = true; log.info("{} state process is start", getPipelineFullName()); stateProcess(); } private synchronized void stateProcess() { if (!isRunning) { log.warn(String.format("%s state process not start", pipelineFullName)); return; } PipelineStatus state = getCurrPipelineStatus(); switch (state) { case CREATED: updatePipelineState(PipelineStatus.SCHEDULED); break; case SCHEDULED: try { ResourceUtils.applyResourceForPipeline(jobMaster.getResourceManager(), this); log.debug( "slotProfiles: {}, PipelineLocation: {}", slotProfiles, this.getPipelineLocation()); updatePipelineState(PipelineStatus.DEPLOYING); } catch (Exception e) { makePipelineFailing(e); } break; case DEPLOYING: coordinatorVertexList.forEach( task -> { if (task.getExecutionState().equals(ExecutionState.CREATED)) { task.startPhysicalVertex(); task.makeTaskGroupDeploy(); } }); physicalVertexList.forEach( task -> { if (task.getExecutionState().equals(ExecutionState.CREATED)) { task.startPhysicalVertex(); task.makeTaskGroupDeploy(); } }); updatePipelineState(PipelineStatus.RUNNING); break; case RUNNING: break; case FAILING: case CANCELING: coordinatorVertexList.forEach( task -> { task.startPhysicalVertex(); task.cancel(); }); physicalVertexList.forEach( task -> { task.startPhysicalVertex(); task.cancel(); }); break; case FAILED: case CANCELED: if (checkNeedRestore(state) && prepareRestorePipeline()) { jobMaster.releasePipelineResource(this); restorePipeline(); return; } subPlanDone(state); stopSubPlanStateProcess(); pipelineFuture.complete( new PipelineExecutionState(pipelineId, state, errorByPhysicalVertex.get())); return; case FINISHED: subPlanDone(state); stopSubPlanStateProcess(); pipelineFuture.complete( new PipelineExecutionState( pipelineId, getPipelineState(), errorByPhysicalVertex.get())); return; default: throw new IllegalArgumentException("Unknown Pipeline State: " + getPipelineState()); } } SubPlan , सबै कार्यहरूको लागि स्रोतहरू लागू हुन्छन्। रिसोर्स एप्लिकेसन ResourceManager मार्फत गरिन्छ। संसाधन अनुप्रयोगको समयमा, नोडहरू प्रयोगकर्ता-परिभाषित ट्यागहरूको आधारमा चयन गरिन्छ कि कार्यहरू निश्चित नोडहरूमा चल्छन्, संसाधन अलगाव प्राप्त गर्न।
public static void applyResourceForPipeline( @NonNull ResourceManager resourceManager, @NonNull SubPlan subPlan) { Map<TaskGroupLocation, CompletableFuture<SlotProfile>> futures = new HashMap<>(); Map<TaskGroupLocation, SlotProfile> slotProfiles = new HashMap<>(); // TODO If there is no enough resources for tasks, we need add some wait profile subPlan.getCoordinatorVertexList() .forEach( coordinator -> futures.put( coordinator.getTaskGroupLocation(), applyResourceForTask( resourceManager, coordinator, subPlan.getTags()))); subPlan.getPhysicalVertexList() .forEach( task -> futures.put( task.getTaskGroupLocation(), applyResourceForTask( resourceManager, task, subPlan.getTags()))); futures.forEach( (key, value) -> { try { slotProfiles.put(key, value == null ? null : value.join()); } catch (CompletionException e) { // do nothing } }); // set it first, avoid can't get it when get resource not enough exception and need release // applied resource subPlan.getJobMaster().setOwnedSlotProfiles(subPlan.getPipelineLocation(), slotProfiles); if (futures.size() != slotProfiles.size()) { throw new NoEnoughResourceException(); } } public static CompletableFuture<SlotProfile> applyResourceForTask( ResourceManager resourceManager, PhysicalVertex task, Map<String, String> tags) { // TODO custom resource size return resourceManager.applyResource( task.getTaskGroupLocation().getJobId(), new ResourceProfile(), tags); } public CompletableFuture<List<SlotProfile>> applyResources( long jobId, List<ResourceProfile> resourceProfile, Map<String, String> tagFilter) throws NoEnoughResourceException { waitingWorkerRegister(); ConcurrentMap<Address, WorkerProfile> matchedWorker = filterWorkerByTag(tagFilter); if (matchedWorker.isEmpty()) { log.error("No matched worker with tag filter {}.", tagFilter); throw new NoEnoughResourceException(); } return new ResourceRequestHandler(jobId, resourceProfile, matchedWorker, this) .request(tagFilter); } जब सबै उपलब्ध नोडहरू प्राप्त हुन्छन्, नोडहरू फेरबदल गरिन्छ र आवश्यक स्रोतहरू भन्दा ठूलो स्रोतहरू भएको नोड अनियमित रूपमा चयन गरिन्छ। त्यसपछि नोडलाई सम्पर्क गरिन्छ, र यसमा RequestSlotOperation पठाइन्छ।
public Optional<WorkerProfile> preCheckWorkerResource(ResourceProfile r) { // Shuffle the order to ensure random selection of workers List<WorkerProfile> workerProfiles = Arrays.asList(registerWorker.values().toArray(new WorkerProfile[0])); Collections.shuffle(workerProfiles); // Check if there are still unassigned slots Optional<WorkerProfile> workerProfile = workerProfiles.stream() .filter( worker -> Arrays.stream(worker.getUnassignedSlots()) .anyMatch( slot -> slot.getResourceProfile() .enoughThan(r))) .findAny(); if (!workerProfile.isPresent()) { // Check if there are still unassigned resources workerProfile = workerProfiles.stream() .filter(WorkerProfile::isDynamicSlot) .filter(worker -> worker.getUnassignedResource().enoughThan(r)) .findAny(); } return workerProfile; } private CompletableFuture<SlotAndWorkerProfile> singleResourceRequestToMember( int i, ResourceProfile r, WorkerProfile workerProfile) { CompletableFuture<SlotAndWorkerProfile> future = resourceManager.sendToMember( new RequestSlotOperation(jobId, r), workerProfile.getAddress()); return future.whenComplete( withTryCatch( LOGGER, (slotAndWorkerProfile, error) -> { if (error != null) { throw new RuntimeException(error); } else { resourceManager.heartbeat(slotAndWorkerProfile.getWorkerProfile()); addSlotToCacheMap(i, slotAndWorkerProfile.getSlotProfile()); } })); } जब नोडको SlotService requestSlot अनुरोध प्राप्त गर्दछ, यसले आफ्नै जानकारी अपडेट गर्दछ र मास्टर नोडमा फिर्ता गर्दछ। यदि स्रोत अनुरोधले अपेक्षित नतिजा पूरा गर्दैन भने, कार्य असफलतालाई संकेत गर्दै NoEnoughResourceException फ्याँकिएको छ। जब संसाधन विनियोजन सफल हुन्छ, कार्य परिनियोजन task.makeTaskGroupDeploy() बाट सुरु हुन्छ, जसले कार्यलाई कार्यान्वयनको लागि worker नोडमा पठाउँछ।
TaskDeployState deployState = deploy(jobMaster.getOwnedSlotProfiles(taskGroupLocation)); public TaskDeployState deploy(@NonNull SlotProfile slotProfile) { try { if (slotProfile.getWorker().equals(nodeEngine.getThisAddress())) { return deployOnLocal(slotProfile); } else { return deployOnRemote(slotProfile); } } catch (Throwable th) { return TaskDeployState.failed(th); } } private TaskDeployState deployOnRemote(@Non Null SlotProfile slotProfile) { return deployInternal( taskGroupImmutableInformation -> { try { return (TaskDeployState) NodeEngineUtil.sendOperationToMemberNode( nodeEngine, new DeployTaskOperation( slotProfile, nodeEngine .getSerializationService() .toData( taskGroupImmutableInformation)), slotProfile.getWorker()) .get(); } catch (Exception e) { if (getExecutionState().isEndState()) { log.warn(ExceptionUtils.getMessage(e)); log.warn( String.format( "%s deploy error, but the state is already in end state %s, skip this error", getTaskFullName(), currExecutionState)); return TaskDeployState.success(); } else { return TaskDeployState.failed(e); } } }); }कार्य परिनियोजन
कार्य डिप्लोय गर्दा, कार्य जानकारी संसाधन आवंटन को समयमा प्राप्त नोड मा पठाइन्छ:
public TaskDeployState deployTask(@NonNull Data taskImmutableInformation) { TaskGroupImmutableInformation taskImmutableInfo = nodeEngine.getSerializationService().toObject(taskImmutableInformation); return deployTask(taskImmutableInfo); } public TaskDeployState deployTask(@NonNull TaskGroupImmutableInformation taskImmutableInfo) { logger.info( String.format( "received deploying task executionId [%s]", taskImmutableInfo.getExecutionId())); TaskGroup taskGroup = null; try { Set<ConnectorJarIdentifier> connectorJarIdentifiers = taskImmutableInfo.getConnectorJarIdentifiers(); Set<URL> jars = new HashSet<>(); ClassLoader classLoader; if (!CollectionUtils.isEmpty(connectorJarIdentifiers)) { // Prioritize obtaining the jar package file required for the current task execution // from the local, if it does not exist locally, it will be downloaded from the // master node. jars = serverConnectorPackageClient.getConnectorJarFromLocal( connectorJarIdentifiers); } else if (!CollectionUtils.isEmpty(taskImmutableInfo.getJars())) { jars = taskImmutableInfo.getJars(); } classLoader = classLoaderService.getClassLoader( taskImmutableInfo.getJobId(), Lists.newArrayList(jars)); if (jars.isEmpty()) { taskGroup = nodeEngine.getSerializationService().toObject(taskImmutableInfo.getGroup()); } else { taskGroup = CustomClassLoadedObject.deserializeWithCustomClassLoader( nodeEngine.getSerializationService(), classLoader, taskImmutableInfo.getGroup()); } logger.info( String.format( "deploying task %s, executionId [%s]", taskGroup.getTaskGroupLocation(), taskImmutableInfo.getExecutionId())); synchronized (this) { if (executionContexts.containsKey(taskGroup.getTaskGroupLocation())) { throw new RuntimeException( String.format( "TaskGroupLocation: %s already exists", taskGroup.getTaskGroupLocation())); } deployLocalTask(taskGroup, classLoader, jars); return TaskDeployState.success(); } } catch (Throwable t) { logger.severe( String.format( "TaskGroupID : %s deploy error with Exception: %s", taskGroup != null && taskGroup.getTaskGroupLocation() != null ? taskGroup.getTaskGroupLocation().toString() : "taskGroupLocation is null", ExceptionUtils.getMessage(t))); return TaskDeployState.failed(t); } } जब कार्यकर्ता नोडले कार्य प्राप्त गर्दछ, यसले स्टार्टअपमा सिर्जना गरिएको थ्रेड पूलमा कार्य पेश गर्न TaskExecutionService को deployTask विधिलाई कल गर्छ।
जब कार्य थ्रेड पूलमा पेश गरिन्छ:
private final class BlockingWorker implements Runnable { private final TaskTracker tracker; private final CountDownLatch startedLatch; private BlockingWorker(TaskTracker tracker, CountDownLatch startedLatch) { this.tracker = tracker; this.startedLatch = startedLatch; } @Override public void run() { TaskExecutionService.TaskGroupExecutionTracker taskGroupExecutionTracker = tracker.taskGroupExecutionTracker; ClassLoader classLoader = executionContexts .get(taskGroupExecutionTracker.taskGroup.getTaskGroupLocation()) .getClassLoader(); ClassLoader oldClassLoader = Thread.currentThread().getContextClassLoader(); Thread.currentThread().setContextClassLoader(classLoader); final Task t = tracker.task; ProgressState result = null; try { startedLatch.countDown(); t.init(); do { result = t.call(); } while (!result.isDone() && isRunning && !taskGroupExecutionTracker.executionCompletedExceptionally()); ... } } Task.call विधि आह्वान गरिएको छ, र यसरी डाटा सिङ्क्रोनाइजेसन कार्यहरू साँच्चै कार्यान्वयन गरिन्छ।
ClassLoader
SeaTunnel मा, पूर्वनिर्धारित ClassLoader लाई अन्य कम्पोनेन्ट वर्गहरूसँग द्वन्द्वबाट बच्न उपवर्गहरूलाई प्राथमिकता दिन परिमार्जन गरिएको छ:
@Override public synchronized ClassLoader getClassLoader(long jobId, Collection<URL> jars) { log.debug("Get classloader for job {} with jars {}", jobId, jars); if (cacheMode) { // with cache mode, all jobs share the same classloader if the jars are the same jobId = 1L; } if (!classLoaderCache.containsKey(jobId)) { classLoaderCache.put(jobId, new ConcurrentHashMap<>()); classLoaderReferenceCount.put(jobId, new ConcurrentHashMap<>()); } Map<String, ClassLoader> classLoaderMap = classLoaderCache.get(jobId); String key = covertJarsToKey(jars); if (classLoaderMap.containsKey(key)) { classLoaderReferenceCount.get(jobId).get(key).incrementAndGet(); return classLoaderMap.get(key); } else { ClassLoader classLoader = new SeaTunnelChildFirstClassLoader(jars); log.info("Create classloader for job {} with jars {}", jobId, jars); classLoaderMap.put(key, classLoader); classLoaderReferenceCount.get(jobId).put(key, new AtomicInteger(1)); return classLoader; } }REST API कार्य सबमिशन
SeaTunnel ले REST API मार्फत कार्य पेस गर्न पनि समर्थन गर्दछ। यो सुविधा सक्षम गर्न, hazelcast.yaml फाइलमा निम्न कन्फिगरेसन थप्नुहोस्:
network: rest-api: enabled: true endpoint-groups: CLUSTER_WRITE: enabled: true DATA: enabled: trueयो कन्फिगरेसन थपेपछि, Hazelcast नोडले HTTP अनुरोधहरू प्राप्त गर्न सक्षम हुनेछ।
कार्य सबमिशनको लागि REST API प्रयोग गरेर, क्लाइन्ट HTTP अनुरोध पठाउने नोड बन्छ, र सर्भर SeaTunnel क्लस्टर हो।
जब सर्भरले अनुरोध प्राप्त गर्दछ, यो अनुरोध URI मा आधारित उपयुक्त विधि कल गर्नेछ:
public void handle(HttpPostCommand httpPostCommand) { String uri = httpPostCommand.getURI(); try { if (uri.startsWith(SUBMIT_JOB_URL)) { handleSubmitJob(httpPostCommand, uri); } else if (uri.startsWith(STOP_JOB_URL)) { handleStopJob(httpPostCommand, uri); } else if (uri.startsWith(ENCRYPT_CONFIG)) { handleEncrypt(httpPostCommand); } else { original.handle(httpPostCommand); } } catch (IllegalArgumentException e) { prepareResponse(SC_400, httpPostCommand, exceptionResponse(e)); } catch (Throwable e) { logger.warning("An error occurred while handling request " + httpPostCommand, e); prepareResponse(SC_500, httpPostCommand, exceptionResponse(e)); } this.textCommandService.sendResponse(httpPostCommand); }काम सबमिशन अनुरोध ह्यान्डल गर्ने विधि मार्ग द्वारा निर्धारण गरिन्छ:
private void handleSubmitJob(HttpPostCommand httpPostCommand, String uri) throws IllegalArgumentException { Map<String, String> requestParams = new HashMap<>(); RestUtil.buildRequestParams(requestParams, uri); Config config = RestUtil.buildConfig(requestHandle(httpPostCommand), false); ReadonlyConfig envOptions = ReadonlyConfig.fromConfig(config.getConfig("env")); String jobName = envOptions.get(EnvCommonOptions.JOB_NAME); JobConfig jobConfig = new JobConfig(); jobConfig.setName( StringUtils.isEmpty(requestParams.get(RestConstant.JOB_NAME)) ? jobName : requestParams.get(RestConstant.JOB_NAME)); boolean startWithSavePoint = Boolean.parseBoolean(requestParams.get(RestConstant.IS_START_WITH_SAVE_POINT)); String jobIdStr = requestParams.get(RestConstant.JOB_ID); Long finalJobId = StringUtils.isNotBlank(jobIdStr) ? Long.parseLong(jobIdStr) : null; SeaTunnelServer seaTunnelServer = getSeaTunnelServer(); RestJobExecutionEnvironment restJobExecutionEnvironment = new RestJobExecutionEnvironment( seaTunnelServer, jobConfig, config, textCommandService.getNode(), startWithSavePoint, finalJobId); JobImmutableInformation jobImmutableInformation = restJobExecutionEnvironment.build(); long jobId = jobImmutableInformation.getJobId(); if (!seaTunnelServer.isMasterNode()) { NodeEngineUtil.sendOperationToMasterNode( getNode().nodeEngine, new SubmitJobOperation( jobId, getNode().nodeEngine.toData(jobImmutableInformation), jobImmutableInformation.isStartWithSavePoint())) .join(); } else { submitJob(seaTunnelServer, jobImmutableInformation, jobConfig); } this.prepareResponse( httpPostCommand, new JsonObject() .add(RestConstant.JOB_ID, String.valueOf(jobId)) .add(RestConstant.JOB_NAME, jobConfig.getName())); }यहाँ तर्क ग्राहक-पक्ष जस्तै छ। स्थानीय मोड नभएकोले स्थानीय सेवा सिर्जना गर्न आवश्यक छैन।
ग्राहक पक्षमा, ClientJobExecutionEnvironment वर्ग तार्किक योजना पार्सिङको लागि प्रयोग गरिन्छ, र त्यसै गरी, RestJobExecutionEnvironment वर्गले समान कार्यहरू गर्दछ।
कार्य पेश गर्दा, यदि हालको नोड मास्टर नोड होइन भने, यसले मास्टर नोडमा जानकारी पठाउनेछ। मास्टर नोडले कमाण्ड-लाइन क्लाइन्टबाट आदेशहरू ह्यान्डल गर्ने तरिकाले कार्य सबमिशनलाई ह्यान्डल गर्नेछ।
यदि हालको नोड मास्टर नोड हो भने, यसले सिधै submitJob विधिलाई कल गर्नेछ, जसले पछिको प्रक्रियाको लागि coordinatorService.submitJob विधिलाई आह्वान गर्दछ:
private void submitJob( SeaTunnelServer seaTunnelServer, JobImmutableInformation jobImmutableInformation, JobConfig jobConfig) { CoordinatorService coordinatorService = seaTunnelServer.getCoordinatorService(); Data data = textCommandService .getNode() .nodeEngine .getSerializationService() .toData(jobImmutableInformation); PassiveCompletableFuture<Void> voidPassiveCompletableFuture = coordinatorService.submitJob( Long.parseLong(jobConfig.getJobContext().getJobId()), data, jobImmutableInformation.isStartWithSavePoint()); voidPassiveCompletableFuture.join(); }दुबै सबमिशन विधिहरूमा सबमिशन पक्षमा तार्किक योजना पार्स गर्ने र त्यसपछि मास्टर नोडमा जानकारी पठाउने समावेश छ। मास्टर नोडले भौतिक योजना पार्सिङ, आवंटन, र अन्य कार्यहरू प्रदर्शन गर्दछ।
L O A D I N G
. . . comments & more!
. . . comments & more!
