本章欲解决的问题
移动平均：也即是不定具体数值内值的平均，类似于看同花顺的时候，可以看五分线，十分线，时K，周K等。所给的例子为(k,t,v):其它k为键(股票名字)，t是时间(时分秒)，v是相关联的值(价格)等。
本章的输入和输出
股票价格(k,t,v)，在其特定的时间内输出(k,t,周期内平均值)
本章共有四种实现方式
- 基于内存实现的创建mapreduce
- 分区分组的shuffle方式的mapreduce
- 基于传统的内存式Scala实现
- 基于分区分组的Scala实现
首先介绍java实现移动平局的两种方式
//基于队列实现
private double sum = 0;
private int period;
private Queue<Double> window = new LinkedList<>();
public SimpleMovingAverage(int preriod) {
this.period = preriod;
}
public void addNumber(double number) {
sum += number;
window.add(number);
if(window.size()>period) {
sum -= window.remove();
}
}
public double getMovingAverage() {
return sum/window.size();
}
//基于数组实现
public class SimpleMovingAverageUsingArray {
private double sum=0;
private int preiod;
private double[] window = null;
private int pointer = 0;
private int size = 0;
public SimpleMovingAverageUsingArray(int preiod) {
this.preiod = preiod;
window =new double[preiod];
}
public void addNewNuber(double number) {
sum += number;
if(size<preiod) {
window[pointer++] = number;
size++;
}else {
pointer = pointer%preiod;
sum -= window[pointer];
window[pointer++]=number;
}
}
public double getMovingAverage() {
return sum/size;
}
public static void main(String[] args) {
double[] testData= {10,18,20,30,24,33,27};
int[] allWindowSize= {3,4};
for(int windowSize:allWindowSize) {
SimpleMovingAverageUsingArray sau = new SimpleMovingAverageUsingArray(windowSize);
for(double x:testData) {
sau.addNewNuber(x);
System.out.println("Next Number= "+x+" sau: "+sau.getMovingAverage());
}
System.out.println("");
}
}
}
基于内存实现的创建mapreduce
输入格式为：<name-as-string><,><timestamp><,><value-as-double>,设置key为：<name-as-string>，value为：(<timestamp><,><value-as-double>)
| 类名 | 描述 |
|---|---|
| SortInMemory_MovingAverageDriver | 提交Hadoop作业的驱动器程序 |
| SortInMemory_MovingAverageMapper | 定义map()函数 |
| SortInMemory_MovingAverageReducer | 定义reduce()函数 |
//map端函数
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String record = value.toString();
if ((record == null) || (record.length() == 0)) {
return;
}
String[] tokens = StringUtils.split(record.trim(), ",");
if (tokens.length == 3) {
// tokens[0] = name of timeseries as string
// tokens[1] = timestamp
// tokens[2] = value of timeseries as double
Date date = DateUtil.getDate(tokens[1]);
if (date == null) {
return;
}
//发出key为name of timeseries as string，value为(timestamp ,value of timeseries as double)
//TimeSeriesData javaBean 定义了它们
reducerKey.set(tokens[0]); // set the name as key
reducerValue.set(date.getTime(), Double.parseDouble(tokens[2]));
context.write(reducerKey, reducerValue);
}
else {
// log as error, not enough tokens
}
}
//reduce函数
public void setup(Context context)
throws IOException, InterruptedException {
this.windowSize = context.getConfiguration().getInt("moving.average.window.size", 5);
System.out.println("setup(): key="+windowSize);
}
public void reduce(Text key, Iterable<TimeSeriesData> values, Context context)
throws IOException, InterruptedException {
System.out.println("reduce(): key="+key.toString());
// build the unsorted list of timeseries
List<TimeSeriesData> timeseries = new ArrayList<TimeSeriesData>();
for (TimeSeriesData tsData : values) {
TimeSeriesData copy = TimeSeriesData.copy(tsData);
timeseries.add(copy);
}
//在TimeSeriesData有重写了sort的compareT方法了
Collections.sort(timeseries);
System.out.println("reduce(): timeseries="+timeseries.toString());
// 先把小于预设值的先计算
double sum = 0.0;
for (int i=0; i < windowSize-1; i++) {
sum += timeseries.get(i).getValue();
}
// 大于预设值的，则到达一个发送一个
Text outputValue = new Text();
//遍历序列timeseries
for (int i = windowSize-1; i < timeseries.size(); i++) {
System.out.println("reduce(): key="+key.toString() + " i="+i);
sum += timeseries.get(i).getValue();
double movingAverage = sum / windowSize;
long timestamp = timeseries.get(i).getTimestamp();
outputValue.set(DateUtil.getDateAsString(timestamp) + "," + movingAverage);
// send output to HDFS
context.write(key, outputValue);
//减出第一个，为下一次迭代做准备
sum -= timeseries.get(i-windowSize+1).getValue();
}
} // reduce
分区分组的shuffle方式的mapreduce
基于非内存的排序，也即是二次排序，需要经过洗牌阶段
| 类名 | 描述 |
|---|---|
| CompositeKey | 定义一个定制组合键{String,timestamp} |
| CompositeKeyComparator | 映射阶段的数据输出发送到洗牌阶段之前先分区 |
| MovingAverage | 一个简单移动平均算法 |
| NaturalKeyGroupingComparator | 洗牌阶段对组合键分组 |
| NaturalKeyPartitioner | 洗牌阶段之前先分区 |
| SortByMRF_MovingAverageDriver | Hadoop作业的驱动器 |
| SortByMRF_MovingAverageMapper | 定义map()函数 |
| SortByMRF_MovingAverageReducer | 定义reduce()函数 |
//整个作业的驱动器
//自然键分区，根据哈希算法
jobconf.setPartitionerClass(NaturalKeyPartitioner.class);
//分组之后内排序
jobconf.setOutputKeyComparatorClass(CompositeKeyComparator.class);
//reduce阶段对自然键排序
jobconf.setOutputValueGroupingComparator(NaturalKeyGroupingComparator.class);
//MovingAverage基于数组的方式来计算移动平均值
public void addNewNumber(double number) {
sum += number;
if (size < period) {
window[pointer++] = number;
size++;
}
else {
// size = period (size cannot be > period)
pointer = pointer % period;
sum -= window[pointer];
window[pointer++] = number;
}
}
public double getMovingAverage() {
if (size == 0) {
throw new IllegalArgumentException("average is undefined");
}
//
return sum / size;
}
//map()函数
@Override
public void map(LongWritable inkey, Text value,
OutputCollector<CompositeKey, TimeSeriesData> output,
Reporter reporter) throws IOException {
String record = value.toString();
if ((record == null) || (record.length() == 0)) {
return;
}
String[] tokens = StringUtils.split(record, ",");
if (tokens.length == 3) {
// tokens[0] = name of timeseries as string
// tokens[1] = timestamp
// tokens[2] = value of timeseries as double
Date date = DateUtil.getDate(tokens[1]);
if (date == null) {
return;
}
long timestamp = date.getTime();
//reducerKey为:name,timestamp
//reducerKey为：timestamp，value
reducerKey.set(tokens[0], timestamp);
reducerValue.set(timestamp, Double.parseDouble(tokens[2]));
// emit key-value pair
output.collect(reducerKey, reducerValue);
}
else {
// log as error, not enough tokens
}
}
//reduce函数
@Override
public void reduce(CompositeKey key,
Iterator<TimeSeriesData> values,
OutputCollector<Text, Text> output,
Reporter reporter)
throws IOException {
// note that values are sorted.
// apply moving average algorithm to sorted timeseries
Text outputKey = new Text();
Text outputValue = new Text();
//设置窗口的大小
MovingAverage ma = new MovingAverage(this.windowSize);
while (values.hasNext()) {
TimeSeriesData data = values.next();
ma.addNewNumber(data.getValue());
//调用getMovingAverage函数获取平均值
double movingAverage = ma.getMovingAverage();
long timestamp = data.getTimestamp();
String dateAsString = DateUtil.getDateAsString(timestamp);
outputValue.set(dateAsString + "," + movingAverage);
outputKey.set(key.getName());
output.collect(outputKey, outputValue);
}
}
基于传统的内存式Scala实现
//基于内存排序的传统Scala形式
def main(args: Array[String]): Unit = {
if (args.size < 3) {
println("Usage: MovingAverageInMemory <window> <input-dir> <output-dir>")
sys.exit(1)
}
//定义一个sparkConf对象
val sparkConf = new SparkConf().setAppName("MovingAverageInMemory")
val sc = new SparkContext(sparkConf)
val window = args(0).toInt
val input = args(1)
val output = args(2)
//把窗口的数量设置为一个广播的变量
val brodcastWindow = sc.broadcast(window)
val rawData = sc.textFile(input)
val keyValue = rawData.map(line => {
val tokens = line.split(",")
(tokens(0), (tokens(1), tokens(2).toDouble))
})
// 对Key进行排序，key的格式如：IBM, GOOG, AAPL, etc
val groupByStockSymbol = keyValue.groupByKey()
val result = groupByStockSymbol.mapValues(values => {
val dateFormat = new java.text.SimpleDateFormat("yyyy-MM-dd")
// 在内存中排序，mapValues只对value操作，其中s._1为时间，s._2为价格
val sortedValues = values.map(s => (dateFormat.parse(s._1).getTime.toLong, s._2)).toSeq.sortBy(_._1)
val queue = new scala.collection.mutable.Queue[Double]()
//把value添加到队列中，之后判断队列的数量
for (tup <- sortedValues) yield {
queue.enqueue(tup._2)
if (queue.size > brodcastWindow.value)
queue.dequeue
(dateFormat.format(new java.util.Date(tup._1)), (queue.sum / queue.size))
}
})
// 输出整个结果值
// <stock_symbol><,><date><,><moving_average>
val formattedResult = result.flatMap(kv => {
kv._2.map(v => (kv._1 + "," + v._1 + "," + v._2.toString()))
})
formattedResult.saveAsTextFile(output)
//done
sc.stop()
}
基于分区分组的Scala实现
//基于二次排序的Scala实现
def main(args: Array[String]): Unit = {
if (args.size < 4) {
println("Usage: MemoryMovingAverage <window> <number-of-partitions> <input-dir> <output-dir>")
sys.exit(1)
}
val sparkConf = new SparkConf().setAppName("MovingAverage")
val sc = new SparkContext(sparkConf)
val window = args(0).toInt
val numPartitions = args(1).toInt // number of partitions in secondary sorting, choose a high value
val input = args(2)
val output = args(3)
val brodcastWindow = sc.broadcast(window)
val rawData = sc.textFile(input)
// Key contains part of value (closing date in this case)
val valueTokey = rawData.map(line => {
val tokens = line.split(",")
val dateFormat = new java.text.SimpleDateFormat("yyyy-MM-dd")
val timestamp = dateFormat.parse(tokens(1)).getTime
(CompositeKey(tokens(0), timestamp), TimeSeriesData(timestamp, tokens(2).toDouble))
})
// 进行分区再分组的二次排序，其中CompositeKeyPartitioner进行分区，CompositeKey进行内排序
val sortedData = valueTokey.repartitionAndSortWithinPartitions(new CompositeKeyPartitioner(numPartitions))
//获得（CompositeKey(stockSymbol, timestamp)，TimeSeriesData(timestamp, price）
val keyValue = sortedData.map(k => (k._1.stockSymbol, (k._2)))
val groupByStockSymbol = keyValue.groupByKey()
//对value进行操作，与内存的不一样的是，这里没用到sort
val movingAverage = groupByStockSymbol.mapValues(values => {
val dateFormat = new java.text.SimpleDateFormat("yyyy-MM-dd")
val queue = new scala.collection.mutable.Queue[Double]()
for (TimeSeriesData <- values) yield {
queue.enqueue(TimeSeriesData.closingStockPrice)
if (queue.size > brodcastWindow.value)
queue.dequeue
(dateFormat.format(new java.util.Date(TimeSeriesData.timeStamp)), (queue.sum / queue.size))
}
})
// output will be in CSV format
// <stock_symbol><,><date><,><moving_average>
val formattedResult = movingAverage.flatMap(kv => {
kv._2.map(v => (kv._1 + "," + v._1 + "," + v._2.toString()))
})
formattedResult.saveAsTextFile(output)
// done
sc.stop()
}
}
// 定义两个Case class的javabean
case class CompositeKey(stockSymbol: String, timeStamp: Long)
case class TimeSeriesData(timeStamp: Long, closingStockPrice: Double)
// 定义CompositeKey的排序函数，也即可是组内排序
object CompositeKey {
implicit def ordering[A <: CompositeKey]: Ordering[A] = {
Ordering.by(fk => (fk.stockSymbol, fk.timeStamp))
}
}
//---------------------------------------------------------
// the following class defines a custom partitioner by
// extending abstract class org.apache.spark.Partitioner
//---------------------------------------------------------
import org.apache.spark.Partitioner
class CompositeKeyPartitioner(partitions: Int) extends Partitioner {
require(partitions >= 0, s"Number of partitions ($partitions) cannot be negative.")
def numPartitions: Int = partitions
//getPartition用于划定分区
def getPartition(key: Any): Int = key match {
case k: CompositeKey => math.abs(k.stockSymbol.hashCode % numPartitions)
case null => 0
case _ => math.abs(key.hashCode % numPartitions)
}
override def equals(other: Any): Boolean = other match {
case h: CompositeKeyPartitioner => h.numPartitions == numPartitions
case _ => false
}
override def hashCode: Int = numPartitions
}
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