Examples with MacroEvaluator co.cask.cdap.api.macro.MacroEvaluator used on opensource projects

Example 11 with MacroEvaluator

use of co.cask.cdap.api.macro.MacroEvaluator in project cdap by caskdata.

the class StreamingAlertPublishFunction method call.

@Override
public Void call(JavaRDD<Alert> data, Time batchTime) throws Exception {
    MacroEvaluator evaluator = new DefaultMacroEvaluator(new BasicArguments(sec), batchTime.milliseconds(), sec.getSecureStore(), sec.getNamespace());
    PluginContext pluginContext = new SparkPipelinePluginContext(sec.getPluginContext(), sec.getMetrics(), stageSpec.isStageLoggingEnabled(), stageSpec.isProcessTimingEnabled());
    String stageName = stageSpec.getName();
    AlertPublisher alertPublisher = pluginContext.newPluginInstance(stageName, evaluator);
    PipelineRuntime pipelineRuntime = new SparkPipelineRuntime(sec, batchTime.milliseconds());
    AlertPublisherContext alertPublisherContext = new DefaultAlertPublisherContext(pipelineRuntime, stageSpec, sec.getMessagingContext(), sec.getAdmin());
    alertPublisher.initialize(alertPublisherContext);
    StageMetrics stageMetrics = new DefaultStageMetrics(sec.getMetrics(), stageName);
    TrackedIterator<Alert> trackedAlerts = new TrackedIterator<>(data.collect().iterator(), stageMetrics, Constants.Metrics.RECORDS_IN);
    alertPublisher.publish(trackedAlerts);
    alertPublisher.destroy();
    return null;
}

Example 12 with MacroEvaluator

use of co.cask.cdap.api.macro.MacroEvaluator in project cdap by caskdata.

the class ETLSpark method initialize.

@Override
@TransactionPolicy(TransactionControl.EXPLICIT)
public void initialize() throws Exception {
    final SparkClientContext context = getContext();
    cleanupFiles = new ArrayList<>();
    List<Finisher> finishers = new ArrayList<>();
    SparkConf sparkConf = new SparkConf();
    sparkConf.set("spark.driver.extraJavaOptions", "-XX:MaxPermSize=256m");
    sparkConf.set("spark.executor.extraJavaOptions", "-XX:MaxPermSize=256m");
    sparkConf.set("spark.speculation", "false");
    context.setSparkConf(sparkConf);
    Map<String, String> properties = context.getSpecification().getProperties();
    BatchPhaseSpec phaseSpec = GSON.fromJson(properties.get(Constants.PIPELINEID), BatchPhaseSpec.class);
    for (Map.Entry<String, String> pipelineProperty : phaseSpec.getPipelineProperties().entrySet()) {
        sparkConf.set(pipelineProperty.getKey(), pipelineProperty.getValue());
    }
    MacroEvaluator evaluator = new DefaultMacroEvaluator(new BasicArguments(context), context.getLogicalStartTime(), context, context.getNamespace());
    final SparkBatchSourceFactory sourceFactory = new SparkBatchSourceFactory();
    final SparkBatchSinkFactory sinkFactory = new SparkBatchSinkFactory();
    final Map<String, Integer> stagePartitions = new HashMap<>();
    PluginContext pluginContext = new SparkPipelinePluginContext(context, context.getMetrics(), phaseSpec.isStageLoggingEnabled(), phaseSpec.isProcessTimingEnabled());
    PipelinePluginInstantiator pluginInstantiator = new PipelinePluginInstantiator(pluginContext, context.getMetrics(), phaseSpec, new SingleConnectorFactory());
    final PipelineRuntime pipelineRuntime = new PipelineRuntime(context);
    final Admin admin = context.getAdmin();
    PipelinePhase phase = phaseSpec.getPhase();
    // go through in topological order so that arguments set by one stage are seen by stages after it
    for (final String stageName : phase.getDag().getTopologicalOrder()) {
        final StageSpec stageSpec = phase.getStage(stageName);
        String pluginType = stageSpec.getPluginType();
        boolean isConnectorSource = Constants.Connector.PLUGIN_TYPE.equals(pluginType) && phase.getSources().contains(stageName);
        boolean isConnectorSink = Constants.Connector.PLUGIN_TYPE.equals(pluginType) && phase.getSinks().contains(stageName);
        SubmitterPlugin submitterPlugin = null;
        if (BatchSource.PLUGIN_TYPE.equals(pluginType) || isConnectorSource) {
            BatchConfigurable<BatchSourceContext> batchSource = pluginInstantiator.newPluginInstance(stageName, evaluator);
            ContextProvider<BatchSourceContext> contextProvider = new ContextProvider<BatchSourceContext>() {

                @Override
                public BatchSourceContext getContext(DatasetContext datasetContext) {
                    return new SparkBatchSourceContext(sourceFactory, context, pipelineRuntime, datasetContext, stageSpec);
                }
            };
            submitterPlugin = new SubmitterPlugin(stageName, context, batchSource, contextProvider);
        } else if (Transform.PLUGIN_TYPE.equals(pluginType)) {
            Transform transform = pluginInstantiator.newPluginInstance(stageName, evaluator);
            ContextProvider<StageSubmitterContext> contextProvider = new ContextProvider<StageSubmitterContext>() {

                @Override
                public StageSubmitterContext getContext(DatasetContext datasetContext) {
                    return new SparkBatchSourceContext(sourceFactory, context, pipelineRuntime, datasetContext, stageSpec);
                }
            };
            submitterPlugin = new SubmitterPlugin(stageName, context, transform, contextProvider);
        } else if (BatchSink.PLUGIN_TYPE.equals(pluginType) || isConnectorSink) {
            BatchConfigurable<BatchSinkContext> batchSink = pluginInstantiator.newPluginInstance(stageName, evaluator);
            ContextProvider<BatchSinkContext> contextProvider = new ContextProvider<BatchSinkContext>() {

                @Override
                public BatchSinkContext getContext(DatasetContext datasetContext) {
                    return new SparkBatchSinkContext(sinkFactory, context, pipelineRuntime, datasetContext, stageSpec);
                }
            };
            submitterPlugin = new SubmitterPlugin(stageName, context, batchSink, contextProvider);
        } else if (SparkSink.PLUGIN_TYPE.equals(pluginType)) {
            BatchConfigurable<SparkPluginContext> sparkSink = pluginInstantiator.newPluginInstance(stageName, evaluator);
            ContextProvider<SparkPluginContext> contextProvider = new ContextProvider<SparkPluginContext>() {

                @Override
                public SparkPluginContext getContext(DatasetContext datasetContext) {
                    return new BasicSparkPluginContext(context, pipelineRuntime, stageSpec, datasetContext, admin);
                }
            };
            submitterPlugin = new SubmitterPlugin(stageName, context, sparkSink, contextProvider);
        } else if (BatchAggregator.PLUGIN_TYPE.equals(pluginType)) {
            BatchAggregator aggregator = pluginInstantiator.newPluginInstance(stageName, evaluator);
            ContextProvider<DefaultAggregatorContext> contextProvider = new AggregatorContextProvider(pipelineRuntime, stageSpec, admin);
            submitterPlugin = new SubmitterPlugin(stageName, context, aggregator, contextProvider);
        } else if (BatchJoiner.PLUGIN_TYPE.equals(pluginType)) {
            BatchJoiner joiner = pluginInstantiator.newPluginInstance(stageName, evaluator);
            ContextProvider<DefaultJoinerContext> contextProvider = new JoinerContextProvider(pipelineRuntime, stageSpec, admin);
            submitterPlugin = new SubmitterPlugin<>(stageName, context, joiner, contextProvider, new SubmitterPlugin.PrepareAction<DefaultJoinerContext>() {

                @Override
                public void act(DefaultJoinerContext sparkJoinerContext) {
                    stagePartitions.put(stageName, sparkJoinerContext.getNumPartitions());
                }
            });
        }
        if (submitterPlugin != null) {
            submitterPlugin.prepareRun();
            finishers.add(submitterPlugin);
        }
    }
    File configFile = File.createTempFile("HydratorSpark", ".config");
    cleanupFiles.add(configFile);
    try (Writer writer = Files.newBufferedWriter(configFile.toPath(), StandardCharsets.UTF_8)) {
        SparkBatchSourceSinkFactoryInfo sourceSinkInfo = new SparkBatchSourceSinkFactoryInfo(sourceFactory, sinkFactory, stagePartitions);
        writer.write(GSON.toJson(sourceSinkInfo));
    }
    finisher = new CompositeFinisher(finishers);
    context.localize("HydratorSpark.config", configFile.toURI());
    WorkflowToken token = context.getWorkflowToken();
    if (token != null) {
        for (Map.Entry<String, String> entry : pipelineRuntime.getArguments().getAddedArguments().entrySet()) {
            token.put(entry.getKey(), entry.getValue());
        }
    }
}

Example 13 with MacroEvaluator

use of co.cask.cdap.api.macro.MacroEvaluator in project cdap by caskdata.

the class ExternalSparkProgram method initialize.

@Override
protected void initialize() throws Exception {
    SparkClientContext context = getContext();
    SparkConf sparkConf = new SparkConf();
    sparkConf.set("spark.driver.extraJavaOptions", "-XX:MaxPermSize=256m " + sparkConf.get("spark.driver.extraJavaOptions", ""));
    sparkConf.set("spark.executor.extraJavaOptions", "-XX:MaxPermSize=256m " + sparkConf.get("spark.executor.extraJavaOptions", ""));
    context.setSparkConf(sparkConf);
    String stageName = context.getSpecification().getProperty(STAGE_NAME);
    Class<?> externalProgramClass = context.loadPluginClass(stageName);
    // If the external program implements Spark, instantiate it and call initialize() to provide full lifecycle support
    if (Spark.class.isAssignableFrom(externalProgramClass)) {
        MacroEvaluator macroEvaluator = new DefaultMacroEvaluator(new BasicArguments(context), context.getLogicalStartTime(), context, context.getNamespace());
        delegateSpark = context.newPluginInstance(stageName, macroEvaluator);
        if (delegateSpark instanceof AbstractSpark) {
            // noinspection unchecked
            ((AbstractSpark) delegateSpark).initialize(context);
        }
    }
}

Example 14 with MacroEvaluator

use of co.cask.cdap.api.macro.MacroEvaluator in project cdap by caskdata.

the class SmartWorkflow method initialize.

@Override
public void initialize(WorkflowContext context) throws Exception {
    super.initialize(context);
    TriggeringScheduleInfo scheduleInfo = context.getTriggeringScheduleInfo();
    if (scheduleInfo != null) {
        String propertiesMappingString = scheduleInfo.getProperties().get(TRIGGERING_PROPERTIES_MAPPING);
        if (propertiesMappingString != null) {
            TriggeringPropertyMapping propertiesMapping = GSON.fromJson(propertiesMappingString, TriggeringPropertyMapping.class);
            updateTokenWithTriggeringProperties(scheduleInfo, propertiesMapping, context.getToken());
        }
    }
    PipelineRuntime pipelineRuntime = new PipelineRuntime(context, workflowMetrics);
    WRAPPERLOGGER.info("Pipeline '{}' is started by user '{}' with arguments {}", context.getApplicationSpecification().getName(), UserGroupInformation.getCurrentUser().getShortUserName(), pipelineRuntime.getArguments().asMap());
    alertPublishers = new HashMap<>();
    postActions = new LinkedHashMap<>();
    spec = GSON.fromJson(context.getWorkflowSpecification().getProperty(Constants.PIPELINE_SPEC_KEY), BatchPipelineSpec.class);
    stageSpecs = new HashMap<>();
    MacroEvaluator macroEvaluator = new DefaultMacroEvaluator(pipelineRuntime.getArguments(), context.getLogicalStartTime(), context, context.getNamespace());
    PluginContext pluginContext = new PipelinePluginContext(context, workflowMetrics, spec.isStageLoggingEnabled(), spec.isProcessTimingEnabled());
    for (ActionSpec actionSpec : spec.getEndingActions()) {
        String stageName = actionSpec.getName();
        postActions.put(stageName, (PostAction) pluginContext.newPluginInstance(stageName, macroEvaluator));
        stageSpecs.put(stageName, StageSpec.builder(stageName, actionSpec.getPluginSpec()).setStageLoggingEnabled(spec.isStageLoggingEnabled()).setProcessTimingEnabled(spec.isProcessTimingEnabled()).build());
    }
    for (StageSpec stageSpec : spec.getStages()) {
        String stageName = stageSpec.getName();
        stageSpecs.put(stageName, stageSpec);
        if (AlertPublisher.PLUGIN_TYPE.equals(stageSpec.getPluginType())) {
            AlertPublisher alertPublisher = context.newPluginInstance(stageName, macroEvaluator);
            alertPublishers.put(stageName, alertPublisher);
        }
    }
    WRAPPERLOGGER.info("Pipeline '{}' running", context.getApplicationSpecification().getName());
}

Example 15 with MacroEvaluator

use of co.cask.cdap.api.macro.MacroEvaluator in project cdap by caskdata.

the class SmartWorkflow method destroy.

@Override
public void destroy() {
    WorkflowContext workflowContext = getContext();
    PipelineRuntime pipelineRuntime = new PipelineRuntime(workflowContext, workflowMetrics);
    // Execute the post actions only if pipeline is not running in preview mode.
    if (!workflowContext.getDataTracer(PostAction.PLUGIN_TYPE).isEnabled()) {
        for (Map.Entry<String, PostAction> endingActionEntry : postActions.entrySet()) {
            String name = endingActionEntry.getKey();
            PostAction action = endingActionEntry.getValue();
            StageSpec stageSpec = stageSpecs.get(name);
            BatchActionContext context = new WorkflowBackedActionContext(workflowContext, pipelineRuntime, stageSpec);
            try {
                action.run(context);
            } catch (Throwable t) {
                LOG.error("Error while running post action {}.", name, t);
            }
        }
    }
    // publish all alerts
    for (Map.Entry<String, AlertPublisher> alertPublisherEntry : alertPublishers.entrySet()) {
        String name = alertPublisherEntry.getKey();
        AlertPublisher alertPublisher = alertPublisherEntry.getValue();
        PartitionedFileSet alertConnector = workflowContext.getDataset(name);
        try (CloseableIterator<Alert> alerts = new AlertReader(alertConnector.getPartitions(PartitionFilter.ALWAYS_MATCH))) {
            if (!alerts.hasNext()) {
                continue;
            }
            StageMetrics stageMetrics = new DefaultStageMetrics(workflowMetrics, name);
            StageSpec stageSpec = stageSpecs.get(name);
            AlertPublisherContext alertContext = new DefaultAlertPublisherContext(pipelineRuntime, stageSpec, workflowContext, workflowContext.getAdmin());
            alertPublisher.initialize(alertContext);
            TrackedIterator<Alert> trackedIterator = new TrackedIterator<>(alerts, stageMetrics, Constants.Metrics.RECORDS_IN);
            alertPublisher.publish(trackedIterator);
        } catch (Exception e) {
            LOG.warn("Stage {} had errors publishing alerts. Alerts may not have been published.", name, e);
        } finally {
            try {
                alertPublisher.destroy();
            } catch (Exception e) {
                LOG.warn("Error destroying alert publisher for stage {}", name, e);
            }
        }
    }
    ProgramStatus status = getContext().getState().getStatus();
    if (status == ProgramStatus.FAILED) {
        WRAPPERLOGGER.error("Pipeline '{}' failed.", getContext().getApplicationSpecification().getName());
    } else {
        WRAPPERLOGGER.info("Pipeline '{}' {}.", getContext().getApplicationSpecification().getName(), status == ProgramStatus.COMPLETED ? "succeeded" : status.name().toLowerCase());
    }
    MacroEvaluator macroEvaluator = new DefaultMacroEvaluator(pipelineRuntime.getArguments(), workflowContext.getLogicalStartTime(), workflowContext, workflowContext.getNamespace());
    // Get resolved plugin properties
    Map<String, Map<String, String>> resolvedProperties = new HashMap<>();
    for (StageSpec spec : stageSpecs.values()) {
        String stageName = spec.getName();
        resolvedProperties.put(stageName, workflowContext.getPluginProperties(stageName, macroEvaluator).getProperties());
    }
    // Add resolved plugin properties to workflow token as a JSON String
    workflowContext.getToken().put(RESOLVED_PLUGIN_PROPERTIES_MAP, GSON.toJson(resolvedProperties));
}