Examples with PluginFunctionContext co.cask.cdap.etl.spark.function.PluginFunctionContext used on opensource projects

Example 6 with PluginFunctionContext

use of co.cask.cdap.etl.spark.function.PluginFunctionContext in project cdap by caskdata.

the class SparkPipelineRunner method runPipeline.

public void runPipeline(PipelinePhase pipelinePhase, String sourcePluginType, JavaSparkExecutionContext sec, Map<String, Integer> stagePartitions, PluginContext pluginContext, Map<String, StageStatisticsCollector> collectors) throws Exception {
    MacroEvaluator macroEvaluator = new DefaultMacroEvaluator(new BasicArguments(sec), sec.getLogicalStartTime(), sec, sec.getNamespace());
    Map<String, EmittedRecords> emittedRecords = new HashMap<>();
    // should never happen, but removes warning
    if (pipelinePhase.getDag() == null) {
        throw new IllegalStateException("Pipeline phase has no connections.");
    }
    for (String stageName : pipelinePhase.getDag().getTopologicalOrder()) {
        StageSpec stageSpec = pipelinePhase.getStage(stageName);
        // noinspection ConstantConditions
        String pluginType = stageSpec.getPluginType();
        EmittedRecords.Builder emittedBuilder = EmittedRecords.builder();
        // don't want to do an additional filter for stages that can emit errors,
        // but aren't connected to an ErrorTransform
        // similarly, don't want to do an additional filter for alerts when the stage isn't connected to
        // an AlertPublisher
        boolean hasErrorOutput = false;
        boolean hasAlertOutput = false;
        Set<String> outputs = pipelinePhase.getStageOutputs(stageSpec.getName());
        for (String output : outputs) {
            String outputPluginType = pipelinePhase.getStage(output).getPluginType();
            // noinspection ConstantConditions
            if (ErrorTransform.PLUGIN_TYPE.equals(outputPluginType)) {
                hasErrorOutput = true;
            } else if (AlertPublisher.PLUGIN_TYPE.equals(outputPluginType)) {
                hasAlertOutput = true;
            }
        }
        SparkCollection<Object> stageData = null;
        Map<String, SparkCollection<Object>> inputDataCollections = new HashMap<>();
        Set<String> stageInputs = pipelinePhase.getStageInputs(stageName);
        for (String inputStageName : stageInputs) {
            StageSpec inputStageSpec = pipelinePhase.getStage(inputStageName);
            if (inputStageSpec == null) {
                // means the input to this stage is in a separate phase. For example, it is an action.
                continue;
            }
            String port = null;
            // not errors or alerts or output port records
            if (!Constants.Connector.PLUGIN_TYPE.equals(inputStageSpec.getPluginType()) && !Constants.Connector.PLUGIN_TYPE.equals(pluginType)) {
                port = inputStageSpec.getOutputPorts().get(stageName).getPort();
            }
            SparkCollection<Object> inputRecords = port == null ? emittedRecords.get(inputStageName).outputRecords : emittedRecords.get(inputStageName).outputPortRecords.get(port);
            inputDataCollections.put(inputStageName, inputRecords);
        }
        // initialize the stageRDD as the union of all input RDDs.
        if (!inputDataCollections.isEmpty()) {
            Iterator<SparkCollection<Object>> inputCollectionIter = inputDataCollections.values().iterator();
            stageData = inputCollectionIter.next();
            // don't union inputs records if we're joining or if we're processing errors
            while (!BatchJoiner.PLUGIN_TYPE.equals(pluginType) && !ErrorTransform.PLUGIN_TYPE.equals(pluginType) && inputCollectionIter.hasNext()) {
                stageData = stageData.union(inputCollectionIter.next());
            }
        }
        boolean isConnectorSource = Constants.Connector.PLUGIN_TYPE.equals(pluginType) && pipelinePhase.getSources().contains(stageName);
        boolean isConnectorSink = Constants.Connector.PLUGIN_TYPE.equals(pluginType) && pipelinePhase.getSinks().contains(stageName);
        StageStatisticsCollector collector = collectors.get(stageName) == null ? new NoopStageStatisticsCollector() : collectors.get(stageName);
        PluginFunctionContext pluginFunctionContext = new PluginFunctionContext(stageSpec, sec, collector);
        if (stageData == null) {
            // null in the other else-if conditions
            if (sourcePluginType.equals(pluginType) || isConnectorSource) {
                SparkCollection<RecordInfo<Object>> combinedData = getSource(stageSpec, collector);
                emittedBuilder = addEmitted(emittedBuilder, pipelinePhase, stageSpec, combinedData, hasErrorOutput, hasAlertOutput);
            } else {
                throw new IllegalStateException(String.format("Stage '%s' has no input and is not a source.", stageName));
            }
        } else if (BatchSink.PLUGIN_TYPE.equals(pluginType) || isConnectorSink) {
            stageData.store(stageSpec, Compat.convert(new BatchSinkFunction(pluginFunctionContext)));
        } else if (Transform.PLUGIN_TYPE.equals(pluginType)) {
            SparkCollection<RecordInfo<Object>> combinedData = stageData.transform(stageSpec, collector);
            emittedBuilder = addEmitted(emittedBuilder, pipelinePhase, stageSpec, combinedData, hasErrorOutput, hasAlertOutput);
        } else if (SplitterTransform.PLUGIN_TYPE.equals(pluginType)) {
            SparkCollection<RecordInfo<Object>> combinedData = stageData.multiOutputTransform(stageSpec, collector);
            emittedBuilder = addEmitted(emittedBuilder, pipelinePhase, stageSpec, combinedData, hasErrorOutput, hasAlertOutput);
        } else if (ErrorTransform.PLUGIN_TYPE.equals(pluginType)) {
            // union all the errors coming into this stage
            SparkCollection<ErrorRecord<Object>> inputErrors = null;
            for (String inputStage : stageInputs) {
                SparkCollection<ErrorRecord<Object>> inputErrorsFromStage = emittedRecords.get(inputStage).errorRecords;
                if (inputErrorsFromStage == null) {
                    continue;
                }
                if (inputErrors == null) {
                    inputErrors = inputErrorsFromStage;
                } else {
                    inputErrors = inputErrors.union(inputErrorsFromStage);
                }
            }
            if (inputErrors != null) {
                SparkCollection<RecordInfo<Object>> combinedData = inputErrors.flatMap(stageSpec, Compat.convert(new ErrorTransformFunction<>(pluginFunctionContext)));
                emittedBuilder = addEmitted(emittedBuilder, pipelinePhase, stageSpec, combinedData, hasErrorOutput, hasAlertOutput);
            }
        } else if (SparkCompute.PLUGIN_TYPE.equals(pluginType)) {
            SparkCompute<Object, Object> sparkCompute = pluginContext.newPluginInstance(stageName, macroEvaluator);
            emittedBuilder = emittedBuilder.setOutput(stageData.compute(stageSpec, sparkCompute));
        } else if (SparkSink.PLUGIN_TYPE.equals(pluginType)) {
            SparkSink<Object> sparkSink = pluginContext.newPluginInstance(stageName, macroEvaluator);
            stageData.store(stageSpec, sparkSink);
        } else if (BatchAggregator.PLUGIN_TYPE.equals(pluginType)) {
            Integer partitions = stagePartitions.get(stageName);
            SparkCollection<RecordInfo<Object>> combinedData = stageData.aggregate(stageSpec, partitions, collector);
            emittedBuilder = addEmitted(emittedBuilder, pipelinePhase, stageSpec, combinedData, hasErrorOutput, hasAlertOutput);
        } else if (BatchJoiner.PLUGIN_TYPE.equals(pluginType)) {
            BatchJoiner<Object, Object, Object> joiner = pluginContext.newPluginInstance(stageName, macroEvaluator);
            BatchJoinerRuntimeContext joinerRuntimeContext = pluginFunctionContext.createBatchRuntimeContext();
            joiner.initialize(joinerRuntimeContext);
            Map<String, SparkPairCollection<Object, Object>> preJoinStreams = new HashMap<>();
            for (Map.Entry<String, SparkCollection<Object>> inputStreamEntry : inputDataCollections.entrySet()) {
                String inputStage = inputStreamEntry.getKey();
                SparkCollection<Object> inputStream = inputStreamEntry.getValue();
                preJoinStreams.put(inputStage, addJoinKey(stageSpec, inputStage, inputStream, collector));
            }
            Set<String> remainingInputs = new HashSet<>();
            remainingInputs.addAll(inputDataCollections.keySet());
            Integer numPartitions = stagePartitions.get(stageName);
            SparkPairCollection<Object, List<JoinElement<Object>>> joinedInputs = null;
            // inner join on required inputs
            for (final String inputStageName : joiner.getJoinConfig().getRequiredInputs()) {
                SparkPairCollection<Object, Object> preJoinCollection = preJoinStreams.get(inputStageName);
                if (joinedInputs == null) {
                    joinedInputs = preJoinCollection.mapValues(new InitialJoinFunction<>(inputStageName));
                } else {
                    JoinFlattenFunction<Object> joinFlattenFunction = new JoinFlattenFunction<>(inputStageName);
                    joinedInputs = numPartitions == null ? joinedInputs.join(preJoinCollection).mapValues(joinFlattenFunction) : joinedInputs.join(preJoinCollection, numPartitions).mapValues(joinFlattenFunction);
                }
                remainingInputs.remove(inputStageName);
            }
            // outer join on non-required inputs
            boolean isFullOuter = joinedInputs == null;
            for (final String inputStageName : remainingInputs) {
                SparkPairCollection<Object, Object> preJoinStream = preJoinStreams.get(inputStageName);
                if (joinedInputs == null) {
                    joinedInputs = preJoinStream.mapValues(new InitialJoinFunction<>(inputStageName));
                } else {
                    if (isFullOuter) {
                        OuterJoinFlattenFunction<Object> flattenFunction = new OuterJoinFlattenFunction<>(inputStageName);
                        joinedInputs = numPartitions == null ? joinedInputs.fullOuterJoin(preJoinStream).mapValues(flattenFunction) : joinedInputs.fullOuterJoin(preJoinStream, numPartitions).mapValues(flattenFunction);
                    } else {
                        LeftJoinFlattenFunction<Object> flattenFunction = new LeftJoinFlattenFunction<>(inputStageName);
                        joinedInputs = numPartitions == null ? joinedInputs.leftOuterJoin(preJoinStream).mapValues(flattenFunction) : joinedInputs.leftOuterJoin(preJoinStream, numPartitions).mapValues(flattenFunction);
                    }
                }
            }
            // should never happen, but removes warnings
            if (joinedInputs == null) {
                throw new IllegalStateException("There are no inputs into join stage " + stageName);
            }
            emittedBuilder = emittedBuilder.setOutput(mergeJoinResults(stageSpec, joinedInputs, collector).cache());
        } else if (Windower.PLUGIN_TYPE.equals(pluginType)) {
            Windower windower = pluginContext.newPluginInstance(stageName, macroEvaluator);
            emittedBuilder = emittedBuilder.setOutput(stageData.window(stageSpec, windower));
        } else if (AlertPublisher.PLUGIN_TYPE.equals(pluginType)) {
            // union all the alerts coming into this stage
            SparkCollection<Alert> inputAlerts = null;
            for (String inputStage : stageInputs) {
                SparkCollection<Alert> inputErrorsFromStage = emittedRecords.get(inputStage).alertRecords;
                if (inputErrorsFromStage == null) {
                    continue;
                }
                if (inputAlerts == null) {
                    inputAlerts = inputErrorsFromStage;
                } else {
                    inputAlerts = inputAlerts.union(inputErrorsFromStage);
                }
            }
            if (inputAlerts != null) {
                inputAlerts.publishAlerts(stageSpec, collector);
            }
        } else {
            throw new IllegalStateException(String.format("Stage %s is of unsupported plugin type %s.", stageName, pluginType));
        }
        emittedRecords.put(stageName, emittedBuilder.build());
    }
}

Example 7 with PluginFunctionContext

use of co.cask.cdap.etl.spark.function.PluginFunctionContext in project cdap by caskdata.

the class SparkStreamingPipelineRunner method getSource.

@Override
protected SparkCollection<Tuple2<Boolean, Object>> getSource(StageInfo stageInfo) throws Exception {
    StreamingSource<Object> source;
    if (checkpointsDisabled) {
        PluginFunctionContext pluginFunctionContext = new PluginFunctionContext(stageInfo, sec);
        source = pluginFunctionContext.createPlugin();
    } else {
        // check for macros in any StreamingSource. If checkpoints are enabled,
        // SparkStreaming will serialize all InputDStreams created in the checkpoint, which means
        // the InputDStream is deserialized directly from the checkpoint instead of instantiated through CDAP.
        // This means there isn't any way for us to perform macro evaluation on sources when they are loaded from
        // checkpoints. We can work around this in all other pipeline stages by dynamically instantiating the
        // plugin in all DStream functions, but can't for InputDStreams because the InputDStream constructor
        // adds itself to the context dag. Yay for constructors with global side effects.
        // TODO: (HYDRATOR-1030) figure out how to do this at configure time instead of run time
        MacroEvaluator macroEvaluator = new ErrorMacroEvaluator("Due to spark limitations, macro evaluation is not allowed in streaming sources when checkpointing " + "is enabled.");
        PluginContext pluginContext = new SparkPipelinePluginContext(sec.getPluginContext(), sec.getMetrics(), spec.isStageLoggingEnabled(), spec.isProcessTimingEnabled());
        source = pluginContext.newPluginInstance(stageInfo.getName(), macroEvaluator);
    }
    DataTracer dataTracer = sec.getDataTracer(stageInfo.getName());
    StreamingContext sourceContext = new DefaultStreamingContext(stageInfo, sec, streamingContext);
    JavaDStream<Object> javaDStream = source.getStream(sourceContext);
    if (dataTracer.isEnabled()) {
        // it will create a new function for each RDD, which would limit each RDD but not the entire DStream.
        javaDStream = javaDStream.transform(new LimitingFunction<>(spec.getNumOfRecordsPreview()));
    }
    JavaDStream<Tuple2<Boolean, Object>> outputDStream = javaDStream.transform(new CountingTransformFunction<>(stageInfo.getName(), sec.getMetrics(), "records.out", dataTracer)).map(new WrapOutputTransformFunction<>());
    return new DStreamCollection<>(sec, outputDStream);
}

Example 8 with PluginFunctionContext

use of co.cask.cdap.etl.spark.function.PluginFunctionContext in project cdap by caskdata.

the class RDDCollection method aggregate.

@Override
public SparkCollection<Tuple2<Boolean, Object>> aggregate(StageInfo stageInfo, @Nullable Integer partitions) {
    PluginFunctionContext pluginFunctionContext = new PluginFunctionContext(stageInfo, sec);
    PairFlatMapFunc<T, Object, T> groupByFunction = new AggregatorGroupByFunction<>(pluginFunctionContext);
    PairFlatMapFunction<T, Object, T> sparkGroupByFunction = Compat.convert(groupByFunction);
    JavaPairRDD<Object, T> keyedCollection = rdd.flatMapToPair(sparkGroupByFunction);
    JavaPairRDD<Object, Iterable<T>> groupedCollection = partitions == null ? keyedCollection.groupByKey() : keyedCollection.groupByKey(partitions);
    FlatMapFunc<Tuple2<Object, Iterable<T>>, Tuple2<Boolean, Object>> aggregateFunction = new AggregatorAggregateFunction<>(pluginFunctionContext);
    FlatMapFunction<Tuple2<Object, Iterable<T>>, Tuple2<Boolean, Object>> sparkAggregateFunction = Compat.convert(aggregateFunction);
    return wrap(groupedCollection.flatMap(sparkAggregateFunction));
}

Example 9 with PluginFunctionContext

use of co.cask.cdap.etl.spark.function.PluginFunctionContext in project cdap by caskdata.

the class RDDCollection method publishAlerts.

@Override
public void publishAlerts(StageSpec stageSpec, StageStatisticsCollector collector) throws Exception {
    PluginFunctionContext pluginFunctionContext = new PluginFunctionContext(stageSpec, sec, collector);
    AlertPublisher alertPublisher = pluginFunctionContext.createPlugin();
    PipelineRuntime pipelineRuntime = new SparkPipelineRuntime(sec);
    AlertPublisherContext alertPublisherContext = new DefaultAlertPublisherContext(pipelineRuntime, stageSpec, sec.getMessagingContext(), sec.getAdmin());
    alertPublisher.initialize(alertPublisherContext);
    StageMetrics stageMetrics = new DefaultStageMetrics(sec.getMetrics(), stageSpec.getName());
    TrackedIterator<Alert> trackedAlerts = new TrackedIterator<>(((JavaRDD<Alert>) rdd).collect().iterator(), stageMetrics, Constants.Metrics.RECORDS_IN);
    alertPublisher.publish(trackedAlerts);
    alertPublisher.destroy();
}

Example 10 with PluginFunctionContext

use of co.cask.cdap.etl.spark.function.PluginFunctionContext in project cdap by caskdata.

the class DynamicSparkCompute method lazyInit.

// when checkpointing is enabled, and Spark is loading DStream operations from an existing checkpoint,
// delegate will be null and the initialize() method won't have been called. So we need to instantiate
// the delegate and initialize it.
private void lazyInit(final JavaSparkContext jsc) throws Exception {
    if (delegate == null) {
        PluginFunctionContext pluginFunctionContext = dynamicDriverContext.getPluginFunctionContext();
        delegate = pluginFunctionContext.createPlugin();
        final StageSpec stageSpec = pluginFunctionContext.getStageSpec();
        final JavaSparkExecutionContext sec = dynamicDriverContext.getSparkExecutionContext();
        Transactionals.execute(sec, new TxRunnable() {

            @Override
            public void run(DatasetContext datasetContext) throws Exception {
                PipelineRuntime pipelineRuntime = new SparkPipelineRuntime(sec);
                SparkExecutionPluginContext sparkPluginContext = new BasicSparkExecutionPluginContext(sec, jsc, datasetContext, pipelineRuntime, stageSpec);
                delegate.initialize(sparkPluginContext);
            }
        }, Exception.class);
    }
}