Examples with DataflowTemplateJob org.apache.beam.runners.dataflow.util.DataflowTemplateJob used on opensource projects

Example 1 with DataflowTemplateJob

use of org.apache.beam.runners.dataflow.util.DataflowTemplateJob in project beam by apache.

the class DataflowRunner method run.

@Override
public DataflowPipelineJob run(Pipeline pipeline) {
    if (useUnifiedWorker(options)) {
        // non-null if useUnifiedWorker is true
        List<String> experiments = options.getExperiments();
        if (!experiments.contains("use_runner_v2")) {
            experiments.add("use_runner_v2");
        }
        if (!experiments.contains("use_unified_worker")) {
            experiments.add("use_unified_worker");
        }
        if (!experiments.contains("beam_fn_api")) {
            experiments.add("beam_fn_api");
        }
        if (!experiments.contains("use_portable_job_submission")) {
            experiments.add("use_portable_job_submission");
        }
        options.setExperiments(ImmutableList.copyOf(experiments));
    }
    logWarningIfPCollectionViewHasNonDeterministicKeyCoder(pipeline);
    if (containsUnboundedPCollection(pipeline)) {
        options.setStreaming(true);
    }
    LOG.info("Executing pipeline on the Dataflow Service, which will have billing implications " + "related to Google Compute Engine usage and other Google Cloud Services.");
    DataflowPipelineOptions dataflowOptions = options.as(DataflowPipelineOptions.class);
    String workerHarnessContainerImageURL = DataflowRunner.getContainerImageForJob(dataflowOptions);
    // This incorrectly puns the worker harness container image (which implements v1beta3 API)
    // with the SDK harness image (which implements Fn API).
    // 
    // The same Environment is used in different and contradictory ways, depending on whether
    // it is a v1 or v2 job submission.
    RunnerApi.Environment defaultEnvironmentForDataflow = Environments.createDockerEnvironment(workerHarnessContainerImageURL);
    // The SdkComponents for portable an non-portable job submission must be kept distinct. Both
    // need the default environment.
    SdkComponents portableComponents = SdkComponents.create();
    portableComponents.registerEnvironment(defaultEnvironmentForDataflow.toBuilder().addAllDependencies(getDefaultArtifacts()).addAllCapabilities(Environments.getJavaCapabilities()).build());
    RunnerApi.Pipeline portablePipelineProto = PipelineTranslation.toProto(pipeline, portableComponents, false);
    // Note that `stageArtifacts` has to be called before `resolveArtifact` because
    // `resolveArtifact` updates local paths to staged paths in pipeline proto.
    List<DataflowPackage> packages = stageArtifacts(portablePipelineProto);
    portablePipelineProto = resolveArtifacts(portablePipelineProto);
    portablePipelineProto = applySdkEnvironmentOverrides(portablePipelineProto, options);
    if (LOG.isDebugEnabled()) {
        LOG.debug("Portable pipeline proto:\n{}", TextFormat.printer().printToString(portablePipelineProto));
    }
    // Stage the portable pipeline proto, retrieving the staged pipeline path, then update
    // the options on the new job
    // TODO: add an explicit `pipeline` parameter to the submission instead of pipeline options
    LOG.info("Staging portable pipeline proto to {}", options.getStagingLocation());
    byte[] serializedProtoPipeline = portablePipelineProto.toByteArray();
    DataflowPackage stagedPipeline = options.getStager().stageToFile(serializedProtoPipeline, PIPELINE_FILE_NAME);
    dataflowOptions.setPipelineUrl(stagedPipeline.getLocation());
    // Now rewrite things to be as needed for v1 (mutates the pipeline)
    // This way the job submitted is valid for v1 and v2, simultaneously
    replaceV1Transforms(pipeline);
    // Capture the SdkComponents for look up during step translations
    SdkComponents dataflowV1Components = SdkComponents.create();
    dataflowV1Components.registerEnvironment(defaultEnvironmentForDataflow.toBuilder().addAllDependencies(getDefaultArtifacts()).addAllCapabilities(Environments.getJavaCapabilities()).build());
    RunnerApi.Pipeline dataflowV1PipelineProto = PipelineTranslation.toProto(pipeline, dataflowV1Components, true);
    if (LOG.isDebugEnabled()) {
        LOG.debug("Dataflow v1 pipeline proto:\n{}", TextFormat.printer().printToString(dataflowV1PipelineProto));
    }
    // Set a unique client_request_id in the CreateJob request.
    // This is used to ensure idempotence of job creation across retried
    // attempts to create a job. Specifically, if the service returns a job with
    // a different client_request_id, it means the returned one is a different
    // job previously created with the same job name, and that the job creation
    // has been effectively rejected. The SDK should return
    // Error::Already_Exists to user in that case.
    int randomNum = new Random().nextInt(9000) + 1000;
    String requestId = DateTimeFormat.forPattern("YYYYMMddHHmmssmmm").withZone(DateTimeZone.UTC).print(DateTimeUtils.currentTimeMillis()) + "_" + randomNum;
    // Try to create a debuggee ID. This must happen before the job is translated since it may
    // update the options.
    maybeRegisterDebuggee(dataflowOptions, requestId);
    JobSpecification jobSpecification = translator.translate(pipeline, dataflowV1PipelineProto, dataflowV1Components, this, packages);
    if (!isNullOrEmpty(dataflowOptions.getDataflowWorkerJar()) && !useUnifiedWorker(options)) {
        List<String> experiments = firstNonNull(dataflowOptions.getExperiments(), Collections.emptyList());
        if (!experiments.contains("use_staged_dataflow_worker_jar")) {
            dataflowOptions.setExperiments(ImmutableList.<String>builder().addAll(experiments).add("use_staged_dataflow_worker_jar").build());
        }
    }
    Job newJob = jobSpecification.getJob();
    try {
        newJob.getEnvironment().setSdkPipelineOptions(MAPPER.readValue(MAPPER_WITH_MODULES.writeValueAsBytes(options), Map.class));
    } catch (IOException e) {
        throw new IllegalArgumentException("PipelineOptions specified failed to serialize to JSON.", e);
    }
    newJob.setClientRequestId(requestId);
    DataflowRunnerInfo dataflowRunnerInfo = DataflowRunnerInfo.getDataflowRunnerInfo();
    String version = dataflowRunnerInfo.getVersion();
    checkState(!"${pom.version}".equals(version), "Unable to submit a job to the Dataflow service with unset version ${pom.version}");
    LOG.info("Dataflow SDK version: {}", version);
    newJob.getEnvironment().setUserAgent((Map) dataflowRunnerInfo.getProperties());
    // must be verified.
    if (!isNullOrEmpty(options.getGcpTempLocation())) {
        newJob.getEnvironment().setTempStoragePrefix(dataflowOptions.getPathValidator().verifyPath(options.getGcpTempLocation()));
    }
    newJob.getEnvironment().setDataset(options.getTempDatasetId());
    if (options.getWorkerRegion() != null) {
        newJob.getEnvironment().setWorkerRegion(options.getWorkerRegion());
    }
    if (options.getWorkerZone() != null) {
        newJob.getEnvironment().setWorkerZone(options.getWorkerZone());
    }
    if (options.getFlexRSGoal() == DataflowPipelineOptions.FlexResourceSchedulingGoal.COST_OPTIMIZED) {
        newJob.getEnvironment().setFlexResourceSchedulingGoal("FLEXRS_COST_OPTIMIZED");
    } else if (options.getFlexRSGoal() == DataflowPipelineOptions.FlexResourceSchedulingGoal.SPEED_OPTIMIZED) {
        newJob.getEnvironment().setFlexResourceSchedulingGoal("FLEXRS_SPEED_OPTIMIZED");
    }
    // Represent the minCpuPlatform pipeline option as an experiment, if not already present.
    if (!isNullOrEmpty(dataflowOptions.getMinCpuPlatform())) {
        List<String> experiments = firstNonNull(dataflowOptions.getExperiments(), Collections.emptyList());
        List<String> minCpuFlags = experiments.stream().filter(p -> p.startsWith("min_cpu_platform")).collect(Collectors.toList());
        if (minCpuFlags.isEmpty()) {
            dataflowOptions.setExperiments(ImmutableList.<String>builder().addAll(experiments).add("min_cpu_platform=" + dataflowOptions.getMinCpuPlatform()).build());
        } else {
            LOG.warn("Flag min_cpu_platform is defined in both top level PipelineOption, " + "as well as under experiments. Proceed using {}.", minCpuFlags.get(0));
        }
    }
    newJob.getEnvironment().setExperiments(ImmutableList.copyOf(firstNonNull(dataflowOptions.getExperiments(), Collections.emptyList())));
    // Set the Docker container image that executes Dataflow worker harness, residing in Google
    // Container Registry. Translator is guaranteed to create a worker pool prior to this point.
    // For runner_v1, only worker_harness_container is set.
    // For runner_v2, both worker_harness_container and sdk_harness_container are set to the same
    // value.
    String containerImage = getContainerImageForJob(options);
    for (WorkerPool workerPool : newJob.getEnvironment().getWorkerPools()) {
        workerPool.setWorkerHarnessContainerImage(containerImage);
    }
    configureSdkHarnessContainerImages(options, portablePipelineProto, newJob);
    newJob.getEnvironment().setVersion(getEnvironmentVersion(options));
    if (hooks != null) {
        hooks.modifyEnvironmentBeforeSubmission(newJob.getEnvironment());
    }
    // will be downloaded from GCS by the service.
    if (hasExperiment(options, "upload_graph")) {
        DataflowPackage stagedGraph = options.getStager().stageToFile(DataflowPipelineTranslator.jobToString(newJob).getBytes(UTF_8), DATAFLOW_GRAPH_FILE_NAME);
        newJob.getSteps().clear();
        newJob.setStepsLocation(stagedGraph.getLocation());
    }
    if (!isNullOrEmpty(options.getDataflowJobFile()) || !isNullOrEmpty(options.getTemplateLocation())) {
        boolean isTemplate = !isNullOrEmpty(options.getTemplateLocation());
        if (isTemplate) {
            checkArgument(isNullOrEmpty(options.getDataflowJobFile()), "--dataflowJobFile and --templateLocation are mutually exclusive.");
        }
        String fileLocation = firstNonNull(options.getTemplateLocation(), options.getDataflowJobFile());
        checkArgument(fileLocation.startsWith("/") || fileLocation.startsWith("gs://"), "Location must be local or on Cloud Storage, got %s.", fileLocation);
        ResourceId fileResource = FileSystems.matchNewResource(fileLocation, false);
        String workSpecJson = DataflowPipelineTranslator.jobToString(newJob);
        try (PrintWriter printWriter = new PrintWriter(new BufferedWriter(new OutputStreamWriter(Channels.newOutputStream(FileSystems.create(fileResource, MimeTypes.TEXT)), UTF_8)))) {
            printWriter.print(workSpecJson);
            LOG.info("Printed job specification to {}", fileLocation);
        } catch (IOException ex) {
            String error = String.format("Cannot create output file at %s", fileLocation);
            if (isTemplate) {
                throw new RuntimeException(error, ex);
            } else {
                LOG.warn(error, ex);
            }
        }
        if (isTemplate) {
            LOG.info("Template successfully created.");
            return new DataflowTemplateJob();
        }
    }
    String jobIdToUpdate = null;
    if (options.isUpdate()) {
        jobIdToUpdate = getJobIdFromName(options.getJobName());
        newJob.setTransformNameMapping(options.getTransformNameMapping());
        newJob.setReplaceJobId(jobIdToUpdate);
    }
    if (options.getCreateFromSnapshot() != null && !options.getCreateFromSnapshot().isEmpty()) {
        newJob.setCreatedFromSnapshotId(options.getCreateFromSnapshot());
    }
    Job jobResult;
    try {
        jobResult = dataflowClient.createJob(newJob);
    } catch (GoogleJsonResponseException e) {
        String errorMessages = "Unexpected errors";
        if (e.getDetails() != null) {
            if (Utf8.encodedLength(newJob.toString()) >= CREATE_JOB_REQUEST_LIMIT_BYTES) {
                errorMessages = "The size of the serialized JSON representation of the pipeline " + "exceeds the allowable limit. " + "For more information, please see the documentation on job submission:\n" + "https://cloud.google.com/dataflow/docs/guides/deploying-a-pipeline#jobs";
            } else {
                errorMessages = e.getDetails().getMessage();
            }
        }
        throw new RuntimeException("Failed to create a workflow job: " + errorMessages, e);
    } catch (IOException e) {
        throw new RuntimeException("Failed to create a workflow job", e);
    }
    // Use a raw client for post-launch monitoring, as status calls may fail
    // regularly and need not be retried automatically.
    DataflowPipelineJob dataflowPipelineJob = new DataflowPipelineJob(DataflowClient.create(options), jobResult.getId(), options, jobSpecification != null ? jobSpecification.getStepNames() : Collections.emptyMap(), portablePipelineProto);
    // depending on whether this is a reload or not.
    if (jobResult.getClientRequestId() != null && !jobResult.getClientRequestId().isEmpty() && !jobResult.getClientRequestId().equals(requestId)) {
        // If updating a job.
        if (options.isUpdate()) {
            throw new DataflowJobAlreadyUpdatedException(dataflowPipelineJob, String.format("The job named %s with id: %s has already been updated into job id: %s " + "and cannot be updated again.", newJob.getName(), jobIdToUpdate, jobResult.getId()));
        } else {
            throw new DataflowJobAlreadyExistsException(dataflowPipelineJob, String.format("There is already an active job named %s with id: %s. If you want to submit a" + " second job, try again by setting a different name using --jobName.", newJob.getName(), jobResult.getId()));
        }
    }
    LOG.info("To access the Dataflow monitoring console, please navigate to {}", MonitoringUtil.getJobMonitoringPageURL(options.getProject(), options.getRegion(), jobResult.getId()));
    LOG.info("Submitted job: {}", jobResult.getId());
    LOG.info("To cancel the job using the 'gcloud' tool, run:\n> {}", MonitoringUtil.getGcloudCancelCommand(options, jobResult.getId()));
    return dataflowPipelineJob;
}