org.apache.spark.sql.KeyValueGroupedDataset Java Examples

The following examples show how to use org.apache.spark.sql.KeyValueGroupedDataset. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: GroupByKeyTranslatorBatch.java    From beam with Apache License 2.0 5 votes vote down vote up 
@Override
public void translateTransform(
    PTransform<PCollection<KV<K, V>>, PCollection<KV<K, Iterable<V>>>> transform,
    TranslationContext context) {

  @SuppressWarnings("unchecked")
  final PCollection<KV<K, V>> inputPCollection = (PCollection<KV<K, V>>) context.getInput();
  Dataset<WindowedValue<KV<K, V>>> input = context.getDataset(inputPCollection);
  WindowingStrategy<?, ?> windowingStrategy = inputPCollection.getWindowingStrategy();
  KvCoder<K, V> kvCoder = (KvCoder<K, V>) inputPCollection.getCoder();
  Coder<V> valueCoder = kvCoder.getValueCoder();

  // group by key only
  Coder<K> keyCoder = kvCoder.getKeyCoder();
  KeyValueGroupedDataset<K, WindowedValue<KV<K, V>>> groupByKeyOnly =
      input.groupByKey(KVHelpers.extractKey(), EncoderHelpers.fromBeamCoder(keyCoder));

  // group also by windows
  WindowedValue.FullWindowedValueCoder<KV<K, Iterable<V>>> outputCoder =
      WindowedValue.FullWindowedValueCoder.of(
          KvCoder.of(keyCoder, IterableCoder.of(valueCoder)),
          windowingStrategy.getWindowFn().windowCoder());
  Dataset<WindowedValue<KV<K, Iterable<V>>>> output =
      groupByKeyOnly.flatMapGroups(
          new GroupAlsoByWindowViaOutputBufferFn<>(
              windowingStrategy,
              new InMemoryStateInternalsFactory<>(),
              SystemReduceFn.buffering(valueCoder),
              context.getSerializableOptions()),
          EncoderHelpers.fromBeamCoder(outputCoder));

  context.putDataset(context.getOutput(), output);
}
Example #2
Source File: VideoStreamProcessor.java    From video-stream-classification with Apache License 2.0 4 votes vote down vote up 
public static void main(String[] args) throws Exception {
//Read properties
Properties prop = PropertyFileReader.readPropertyFile();

//SparkSesion
SparkSession spark = SparkSession
	      .builder()
	      .appName("VideoStreamProcessor")
	      .master(prop.getProperty("spark.master.url"))
	      .getOrCreate();	

//directory to save image files with motion detected
final String processedImageDir = prop.getProperty("processed.output.dir");
logger.warn("Output directory for saving processed images is set to "+processedImageDir+". This is configured in processed.output.dir key of property file.");

//create schema for json message
StructType schema =  DataTypes.createStructType(new StructField[] { 
		DataTypes.createStructField("cameraId", DataTypes.StringType, true),
		DataTypes.createStructField("timestamp", DataTypes.TimestampType, true),
		DataTypes.createStructField("rows", DataTypes.IntegerType, true),
		DataTypes.createStructField("cols", DataTypes.IntegerType, true),
		DataTypes.createStructField("type", DataTypes.IntegerType, true),
		DataTypes.createStructField("data", DataTypes.StringType, true)
		});


//Create DataSet from stream messages from kafka
   Dataset<VideoEventData> ds = spark
     .readStream()
     .format("kafka")
     .option("kafka.bootstrap.servers", prop.getProperty("kafka.bootstrap.servers"))
     .option("subscribe", prop.getProperty("kafka.topic"))
     .option("kafka.max.partition.fetch.bytes", prop.getProperty("kafka.max.partition.fetch.bytes"))
     .option("kafka.max.poll.records", prop.getProperty("kafka.max.poll.records"))
     .load()
     .selectExpr("CAST(value AS STRING) as message")
     .select(functions.from_json(functions.col("message"),schema).as("json"))
     .select("json.*")
     .as(Encoders.bean(VideoEventData.class)); 
   
   //key-value pair of cameraId-VideoEventData
KeyValueGroupedDataset<String, VideoEventData> kvDataset = ds.groupByKey(new MapFunction<VideoEventData, String>() {
	@Override
	public String call(VideoEventData value) throws Exception {
		return value.getCameraId();
	}
}, Encoders.STRING());
	
//process
Dataset<VideoEventData> processedDataset = kvDataset.mapGroupsWithState(new MapGroupsWithStateFunction<String, VideoEventData, VideoEventData,VideoEventData>(){
	@Override
	public VideoEventData call(String key, Iterator<VideoEventData> values, GroupState<VideoEventData> state) throws Exception {
		logger.warn("CameraId="+key+" PartitionId="+TaskContext.getPartitionId());
		VideoEventData existing = null;
		//check previous state
		if (state.exists()) {
			existing = state.get();
		}
		//classify image
		VideoEventData processed = ImageProcessor.process(key,values,processedImageDir,existing);
		
		//update last processed
		if(processed != null){
			state.update(processed);
		}
		return processed;
	}}, Encoders.bean(VideoEventData.class), Encoders.bean(VideoEventData.class));

//start
 StreamingQuery query = processedDataset.writeStream()
	      .outputMode("update")
	      .format("console")
	      .start();
 
 //await
    query.awaitTermination();
}
Example #3
Source File: VideoStreamProcessor.java    From video-stream-analytics with Apache License 2.0 4 votes vote down vote up 
public static void main(String[] args) throws Exception {
//Read properties
Properties prop = PropertyFileReader.readPropertyFile();

//SparkSesion
SparkSession spark = SparkSession
	      .builder()
	      .appName("VideoStreamProcessor")
	      .master(prop.getProperty("spark.master.url"))
	      .getOrCreate();	

//directory to save image files with motion detected
final String processedImageDir = prop.getProperty("processed.output.dir");
logger.warn("Output directory for saving processed images is set to "+processedImageDir+". This is configured in processed.output.dir key of property file.");

//create schema for json message
StructType schema =  DataTypes.createStructType(new StructField[] { 
		DataTypes.createStructField("cameraId", DataTypes.StringType, true),
		DataTypes.createStructField("timestamp", DataTypes.TimestampType, true),
		DataTypes.createStructField("rows", DataTypes.IntegerType, true),
		DataTypes.createStructField("cols", DataTypes.IntegerType, true),
		DataTypes.createStructField("type", DataTypes.IntegerType, true),
		DataTypes.createStructField("data", DataTypes.StringType, true)
		});


//Create DataSet from stream messages from kafka
   Dataset<VideoEventData> ds = spark
     .readStream()
     .format("kafka")
     .option("kafka.bootstrap.servers", prop.getProperty("kafka.bootstrap.servers"))
     .option("subscribe", prop.getProperty("kafka.topic"))
     .option("kafka.max.partition.fetch.bytes", prop.getProperty("kafka.max.partition.fetch.bytes"))
     .option("kafka.max.poll.records", prop.getProperty("kafka.max.poll.records"))
     .load()
     .selectExpr("CAST(value AS STRING) as message")
     .select(functions.from_json(functions.col("message"),schema).as("json"))
     .select("json.*")
     .as(Encoders.bean(VideoEventData.class)); 
   
   //key-value pair of cameraId-VideoEventData
KeyValueGroupedDataset<String, VideoEventData> kvDataset = ds.groupByKey(new MapFunction<VideoEventData, String>() {
	@Override
	public String call(VideoEventData value) throws Exception {
		return value.getCameraId();
	}
}, Encoders.STRING());
	
//process
Dataset<VideoEventData> processedDataset = kvDataset.mapGroupsWithState(new MapGroupsWithStateFunction<String, VideoEventData, VideoEventData,VideoEventData>(){
	@Override
	public VideoEventData call(String key, Iterator<VideoEventData> values, GroupState<VideoEventData> state) throws Exception {
		logger.warn("CameraId="+key+" PartitionId="+TaskContext.getPartitionId());
		VideoEventData existing = null;
		//check previous state
		if (state.exists()) {
			existing = state.get();
		}
		//detect motion
		VideoEventData processed = VideoMotionDetector.detectMotion(key,values,processedImageDir,existing);
		
		//update last processed
		if(processed != null){
			state.update(processed);
		}
		return processed;
	}}, Encoders.bean(VideoEventData.class), Encoders.bean(VideoEventData.class));

//start
 StreamingQuery query = processedDataset.writeStream()
	      .outputMode("update")
	      .format("console")
	      .start();
 
 //await
    query.awaitTermination();
}
Example #4
Source File: CombinePerKeyTranslatorBatch.java    From beam with Apache License 2.0 4 votes vote down vote up 
@Override
public void translateTransform(
    PTransform<PCollection<KV<K, InputT>>, PCollection<KV<K, OutputT>>> transform,
    TranslationContext context) {

  Combine.PerKey combineTransform = (Combine.PerKey) transform;
  @SuppressWarnings("unchecked")
  final PCollection<KV<K, InputT>> input = (PCollection<KV<K, InputT>>) context.getInput();
  @SuppressWarnings("unchecked")
  final PCollection<KV<K, OutputT>> output = (PCollection<KV<K, OutputT>>) context.getOutput();
  @SuppressWarnings("unchecked")
  final Combine.CombineFn<InputT, AccumT, OutputT> combineFn =
      (Combine.CombineFn<InputT, AccumT, OutputT>) combineTransform.getFn();
  WindowingStrategy<?, ?> windowingStrategy = input.getWindowingStrategy();

  Dataset<WindowedValue<KV<K, InputT>>> inputDataset = context.getDataset(input);

  KvCoder<K, InputT> inputCoder = (KvCoder<K, InputT>) input.getCoder();
  Coder<K> keyCoder = inputCoder.getKeyCoder();
  KvCoder<K, OutputT> outputKVCoder = (KvCoder<K, OutputT>) output.getCoder();
  Coder<OutputT> outputCoder = outputKVCoder.getValueCoder();

  KeyValueGroupedDataset<K, WindowedValue<KV<K, InputT>>> groupedDataset =
      inputDataset.groupByKey(KVHelpers.extractKey(), EncoderHelpers.fromBeamCoder(keyCoder));

  Coder<AccumT> accumulatorCoder = null;
  try {
    accumulatorCoder =
        combineFn.getAccumulatorCoder(
            input.getPipeline().getCoderRegistry(), inputCoder.getValueCoder());
  } catch (CannotProvideCoderException e) {
    throw new RuntimeException(e);
  }

  Dataset<Tuple2<K, Iterable<WindowedValue<OutputT>>>> combinedDataset =
      groupedDataset.agg(
          new AggregatorCombiner<K, InputT, AccumT, OutputT, BoundedWindow>(
                  combineFn, windowingStrategy, accumulatorCoder, outputCoder)
              .toColumn());

  // expand the list into separate elements and put the key back into the elements
  WindowedValue.WindowedValueCoder<KV<K, OutputT>> wvCoder =
      WindowedValue.FullWindowedValueCoder.of(
          outputKVCoder, input.getWindowingStrategy().getWindowFn().windowCoder());
  Dataset<WindowedValue<KV<K, OutputT>>> outputDataset =
      combinedDataset.flatMap(
          (FlatMapFunction<
                  Tuple2<K, Iterable<WindowedValue<OutputT>>>, WindowedValue<KV<K, OutputT>>>)
              tuple2 -> {
                K key = tuple2._1();
                Iterable<WindowedValue<OutputT>> windowedValues = tuple2._2();
                List<WindowedValue<KV<K, OutputT>>> result = new ArrayList<>();
                for (WindowedValue<OutputT> windowedValue : windowedValues) {
                  KV<K, OutputT> kv = KV.of(key, windowedValue.getValue());
                  result.add(
                      WindowedValue.of(
                          kv,
                          windowedValue.getTimestamp(),
                          windowedValue.getWindows(),
                          windowedValue.getPane()));
                }
                return result.iterator();
              },
          EncoderHelpers.fromBeamCoder(wvCoder));
  context.putDataset(output, outputDataset);
}