更多代码请见：https://github.com/xubo245/SparkLearning
spark源码解读系列环境：spark-2.0.1 （20161103github下载版）
1.理解
输出读取中常用到topK算法，RDD也提供了top方法。特别是RDD过大时，要慎用RDD的collect方法，建议使用take和top方法。如果要有序，可以使用top方法。
1.1 定义
def top(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope {
takeOrdered(num)(ord.reverse)
}
num为要取的额个数，ord为隐式转换，可以取最高的topK，也可以放入逆序取最低的topK，top方法调用的是takeOrdered方法。
1.2 源码理解
1.2.1 takeOrdered
top调用的是takeOrdered，top调用的是takeOrdered的源码为：
/**
* Returns the first k (smallest) elements from this RDD as defined by the specified
* implicit Ordering[T] and maintains the ordering. This does the opposite of [[top]].
* For example:
* {{{
* sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1)
* // returns Array(2)
*
* sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2)
* // returns Array(2, 3)
* }}}
*
* @note this method should only be used if the resulting array is expected to be small, as
* all the data is loaded into the driver's memory.
*
* @param num k, the number of elements to return
* @param ord the implicit ordering for T
* @return an array of top elements
*/
def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope {
if (num == 0) {
Array.empty
} else {
val mapRDDs = mapPartitions { items =>
// Priority keeps the largest elements, so let's reverse the ordering.
val queue = new BoundedPriorityQueue[T](num)(ord.reverse)
queue ++= util.collection.Utils.takeOrdered(items, num)(ord)
Iterator.single(queue)
}
if (mapRDDs.partitions.length == 0) {
Array.empty
} else {
mapRDDs.reduce { (queue1, queue2) =>
queue1 ++= queue2
queue1
}.toArray.sorted(ord)
}
}
}
理解：
1.2.1.1 takeOrdered会使用有界的优先队列BoundedPriorityQueue，存储返回的k个元素。
1.2.1.2 mapPartitions是对每一个partition进行操作，对每个partition元素集合items,调用org.apache.spark.util.collection.takeOrdered取num个数，然后生成由若干个partition组成的mapRDDs，每个partition为大小为k的有界优先队列queue
1.2.1.3 然后进行reduce操作，reduce是将两个queue进行++操作，即将两个长度为k的queue1和queue2合并成一个长为1的queue。然后进行toArray和sort（ord）。++方法为BoundedPriorityQueue类中的方法，++会调用+=方法进行操作：
override def ++=(xs: TraversableOnce[A]): this.type = {
xs.foreach { this += _ }
this
}
override def +=(elem: A): this.type = {
if (size < maxSize) {
underlying.offer(elem)
} else {
maybeReplaceLowest(elem)
}
this
}
具体可以查看BoundedPriorityQueue的方法
sorted(ord)方法调用java.util.Arrays.sort，后面1.2.3.1 会讲到
1.2.2 org.apache.spark.util.collection.takeOrdered
org.apache.spark.util.collection.takeOrdered的takeOrdered是调用的com.google.common.collect.{Ordering => GuavaOrdering}
的方法，并且重写了compare方法，主要是Ordering默认的是从小到大，而top默认是取最大的num个元素
val ordering = new GuavaOrdering[T] {
override def compare(l: T, r: T): Int = ord.compare(l, r)
}
然后再调用 ordering的leastOf方法，中间有java和scala的iterator容器的相互转换：
ordering.leastOf(input.asJava, num).iterator.asScala
1.2.3 com.google.common.collect.Ordering的leastOf方法
包的引入方式：在maven的pom文件中加入
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>14.0.1</version>
<scope>provided</scope>
</dependency>
源码：
/**
* Returns the {@code k} least elements from the given iterator according to
* this ordering, in order from least to greatest. If there are fewer than
* {@code k} elements present, all will be included.
*
* <p>The implementation does not necessarily use a <i>stable</i> sorting
* algorithm; when multiple elements are equivalent, it is undefined which
* will come first.
*
* @return an immutable {@code RandomAccess} list of the {@code k} least
* elements in ascending order
* @throws IllegalArgumentException if {@code k} is negative
* @since 14.0
*/
public <E extends T> List<E> leastOf(Iterator<E> elements, int k) {
checkNotNull(elements);
checkArgument(k >= 0, "k (%s) must be nonnegative", k);
if (k == 0 || !elements.hasNext()) {
return ImmutableList.of();
} else if (k >= Integer.MAX_VALUE / 2) {
// k is really large; just do a straightforward sorted-copy-and-sublist
ArrayList<E> list = Lists.newArrayList(elements);
Collections.sort(list, this);
if (list.size() > k) {
list.subList(k, list.size()).clear();
}
list.trimToSize();
return Collections.unmodifiableList(list);
}
/*
* Our goal is an O(n) algorithm using only one pass and O(k) additional
* memory.
*
* We use the following algorithm: maintain a buffer of size 2*k. Every time
* the buffer gets full, find the median and partition around it, keeping
* only the lowest k elements. This requires n/k find-median-and-partition
* steps, each of which take O(k) time with a traditional quickselect.
*
* After sorting the output, the whole algorithm is O(n + k log k). It
* degrades gracefully for worst-case input (descending order), performs
* competitively or wins outright for randomly ordered input, and doesn't
* require the whole collection to fit into memory.
*/
int bufferCap = k * 2;
@SuppressWarnings("unchecked") // we'll only put E's in
E[] buffer = (E[]) new Object[bufferCap];
E threshold = elements.next();
buffer[0] = threshold;
int bufferSize = 1;
// threshold is the kth smallest element seen so far. Once bufferSize >= k,
// anything larger than threshold can be ignored immediately.
while (bufferSize < k && elements.hasNext()) {
E e = elements.next();
buffer[bufferSize++] = e;
threshold = max(threshold, e);
}
while (elements.hasNext()) {
E e = elements.next();
if (compare(e, threshold) >= 0) {
continue;
}
buffer[bufferSize++] = e;
if (bufferSize == bufferCap) {
// We apply the quickselect algorithm to partition about the median,
// and then ignore the last k elements.
int left = 0;
int right = bufferCap - 1;
int minThresholdPosition = 0;
// The leftmost position at which the greatest of the k lower elements
// -- the new value of threshold -- might be found.
while (left < right) {
int pivotIndex = (left + right + 1) >>> 1;
int pivotNewIndex = partition(buffer, left, right, pivotIndex);
if (pivotNewIndex > k) {
right = pivotNewIndex - 1;
} else if (pivotNewIndex < k) {
left = Math.max(pivotNewIndex, left + 1);
minThresholdPosition = pivotNewIndex;
} else {
break;
}
}
bufferSize = k;
threshold = buffer[minThresholdPosition];
for (int i = minThresholdPosition + 1; i < bufferSize; i++) {
threshold = max(threshold, buffer[i]);
}
}
}
Arrays.sort(buffer, 0, bufferSize, this);
bufferSize = Math.min(bufferSize, k);
return Collections.unmodifiableList(
Arrays.asList(ObjectArrays.arraysCopyOf(buffer, bufferSize)));
// We can't use ImmutableList; we have to be null-friendly!
}
private <E extends T> int partition(
E[] values, int left, int right, int pivotIndex) {
E pivotValue = values[pivotIndex];
values[pivotIndex] = values[right];
values[right] = pivotValue;
int storeIndex = left;
for (int i = left; i < right; i++) {
if (compare(values[i], pivotValue) < 0) {
ObjectArrays.swap(values, storeIndex, i);
storeIndex++;
}
}
ObjectArrays.swap(values, right, storeIndex);
return storeIndex;
}
源码分析
1.2.3.1 当k满足(k >= Integer.MAX_VALUE / 2)时，采用“straightforward sorted-copy-and-sublist”，直接排序-复制和取子串的方式操作
其中排序算法直接调用 Collections.sort(list, this)，而其又调用 Arrays.sort(a, (Comparator)c);
Arrays.sort源码：
public static <T> void sort(T[] a, Comparator<? super T> c) {
if (LegacyMergeSort.userRequested)
legacyMergeSort(a, c);
else
TimSort.sort(a, c);
}
legacyMergeSort方法为传统的归并排序，当分到小于INSERTIONSORT_THRESHOLD（代码中设为7）时，采用插入排序，当大于INSERTIONSORT_THRESHOLD时采用归并排序，代码可见：java.util.Arrays#mergeSort(java.lang.Object[], java.lang.Object[], int, int, int)，不详细讲
Array的sort方法中还提供年了TimSort：
TimSort.sort(a, c);
具体采用的是Tim Peters’s list sort for Python
(
TimSort).
1.2.3.2 当k < Integer.MAX_VALUE / 2时，新建一个buffer，大小为2*k，当buffer元素小于k且有元素时，直接插入：
while (bufferSize < k && elements.hasNext()) {
E e = elements.next();
buffer[bufferSize++] = e;
threshold = max(threshold, e);
}
threshold取max，max不一定是最大值，里面调用了compare，compare方法重写了，所以需要根据实际情况分析，top方法默认的max是取最小值
当buffer中元素多于k时，则与threshold比较，如果campare结果符合才插入，当buffer元素达到2*k时，会调用 quickselect algorithm 即快速选择算法，取buffer符合要求的前k个，实际没有删除，而是移动元素，将符合的放在buffer中的前k个，后k个后面可能会被覆盖。
1.2.3.2.1 quickselect algorithm
quickselect algorithm 大致思路是去中间值作为划分界限，然后遍历buffer中元素，compare符合的放在k前面，不符合的放在k后面，里面会调用partition去操作，并且返回中间值移动后的位置storeIndex，然后将该位置storeIndex再与比较k比较，如果大于k，则在left到storeIndex间继续partition操作，如果storeIndex小于k，则在storeIndex到right间partition操作，否则正好符合要求
1.2.3.3 返回
最后在Arrays.sort方法，对buffer中的元素进行排序，最后取k个，copy返回
Arrays.sort(buffer, 0, bufferSize, this);
bufferSize = Math.min(bufferSize, k);
return Collections.unmodifiableList(
Arrays.asList(ObjectArrays.arraysCopyOf(buffer, bufferSize)));
2.代码：
（1）使用
取最大的topK：
val nums = Array(4,5,3,2,1,6,7,9,8,10)
val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2)
val topK = ints.top(5)
topK.foreach(println)
assert(topK.size === 5)
输出：
10
9
8
7
6
取最小的topK：
val nums = Array(4,5,3,2,1,6,7,9,8,10)
implicit val ord = implicitly[Ordering[Int]].reverse
val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2)
val topK = ints.top(5)
topK.foreach(println)
输出：
1
2
3
4
5
每个细节可以debug具体去看
3.结果：
样例运行成功，top方法基本理解，有几个疑问：
3.1 为什么reduce结果toArray后要sorted？reduce返回的是有界的BoundedPriorityQueue对象，而且有序，为什么不用reverse操作，复杂度更低？
可能情况：保证结果稳定？
代码：org.apache.spark.rdd.RDD#takeOrdered
mapRDDs.reduce { (queue1, queue2) =>
queue1 ++= queue2
queue1
}.toArray.sorted(ord)
3.2 快排中为什么用2*k的buffer？为什么不直接用有界的优先队列？这样操作也简单，时间也更低？
可能情况：避免极端情况？值相同的有多个；k比较大时维护有界的成本较大？
代码：com.google.common.collect.Ordering#leastOf(java.util.Iterator, int)
int bufferCap = k * 2;
@SuppressWarnings("unchecked") // we'll only put E's in
E[] buffer = (E[]) new Object[bufferCap];
E threshold = elements.next();
buffer[0] = threshold;
int bufferSize = 1;
参考
【1】http://spark.apache.org/
【2】http://spark.apache.org/docs/1.5.2/programming-guide.html
【3】https://github.com/xubo245/SparkLearning
【4】book:《深入理解spark核心思想与源码分析》
【5】book:《spark核心源码分析和开发实战》