* 基于IDEA集成开发环境,编程实现从TCP Socket实时读取流式数据,对每批次中数据进行词频统计。 */object StreamingTransformRDD { defmain(args: Array[String]): Unit = {// 1. 构建StreamingContext流式上下文实例对象val ssc: StreamingContext = {// a. 创建SparkConf对象,设置应用配置信息valsparkConf=newSpa...
Here is my draw function. I tried printing the size of items in my mouseClicked...How to return an object that was deleted? I have a method that is supposed to delete an InventoryItem (i) in an array list (iList) when part of the description of that InventoryItem is entered. The m...
(allRDDs.toSeq.asInstanceOf[Seq[RDD[(K, _)]]], partitioner) // val mergeValuesFunc = mergeValues(oldRDDs.size, newRDDs.size) _ val numOldValues = oldRDDs.size val numNewValues = newRDDs.size val mergeValues = (arrayOfValues: Array[Iterable[V]]) => { if (arrayOfValues....
object Main{ def main(args:Array[String]): Unit ={ val conf = new SparkConf(); val sc = new SparkContext(conf); //模拟5个分区的数据 val data1 = sc.parallelize(1 to 10,5); //注意,RDD一定要是key-value,才能使用用户自定义的分区类,通过key来确定分区 val data2 = data1.map((_,1...
问对DStream进行排序并取topNENPart I：词频统计并返回topN 统计的文本数据： what do you do how do...
def main(args: Array[String]): Unit = { // 1.创建SparkConf对象 val sparkConf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkStreaming02_updateStateByKey") // 2.创建StreamingContext对象,指定时间区间 val ssc = new StreamingContext(sparkConf,Seconds(5)) ...
Here is the class. Here is my draw function. I tried printing the size of items in my mouseClicked...How to return an object that was deleted? I have a method that is supposed to delete an InventoryItem (i) in an array list (iList) when part of the description of that Inventory...
new UnionDStream[T](Array(this, that)) }/** * Return all the RDDs defined by the Interval object (both end times included) */ def slice(interval: Interval): Seq[RDD[T]] = ssc.withScope { slice(interval.beginTime, interval.endTime) ...
valleft = num - buf.sizevalp = partsScanned until math.min(partsScanned + numPartsToTry, totalParts)valres = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal =true) res.foreach(buf ++= _.take(num - buf.size)) ...
(args: Array[String]): Unit ={9val conf =newSparkConf()10.setAppName("StreamingWindowOfKafka")11.setMaster("local[*]")12val sc =SparkContext.getOrCreate(conf)13val ssc =newStreamingContext(sc, Seconds(5))14//当调用updateStateByKey函数API的时候,必须给定checkpoint dir15//路径对应的文件...