1. /*
2. *schedule()解决了spark资源调度的问题
3. */
4. rivate def schedule() {
5. //首先判断，master状态不是ALIVE的话，直接返回
6. //也就是说，stanby master是不会进行application等资源调度的
7. if (state != RecoveryState.ALIVE) { return }
8.
9. // First schedule drivers, they take strict precedence over applications
10. // Randomization helps balance drivers
11.
12. //Random.shuffle的原理，大家要清楚，就是对传入的集合的元素进行随机的打乱
13. //取出了workers中的所有之前注册上来的worker,进行过滤，必须是状态为ALIVE的worker
14. //对状态为ALIVE的worker，调用Random的shuffle方法进行随机的打乱
15. val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE))
16. val numWorkersAlive = shuffledAliveWorkers.size
17. 0
18.
19. //首先，调度driver
20. //为什么要调度driver,大家想一下，什么情况下，会注册driver,并且会导致driver被调度
21. //其实 ，只有用yarn-cluster模式提交的时候，才会注册driver;因为standalone和yarn-client模式，都会在本地直接
22. //启动driver，而不会来注册driver，就更不可能让master调度driver了
23.
24. //driver调度机制
25. //遍历waittingDrivers ArrayBuffer
26. for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers
27. // We assign workers to each waiting driver in a round-robin fashion. For each driver, we
28. // start from the last worker that was assigned a driver, and continue onwards until we have
29. // explored all alive workers.
30. false
31. 0
32.
33. //while的条件，numWorkersVisited小于numWorkersAlive
34. //什么意思？就是说，只要还有活着的worker没有遍历到，那么就继续进行遍历
35. //而且，当前这个driver还没有被启动，也就是launched为false
36. while (numWorkersVisited < numWorkersAlive && !launched) {
37. val worker = shuffledAliveWorkers(curPos)
38. 1
39.
40. //如果当前这个worker的空闲内存量大于等于，driver需要的内存
41. //并且worker的空闲cpu数量，大于等于driver需要的cpu数量
42. if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) {
43. //启动driver
44. launchDriver(worker, driver)
45. //并且将driver从waitingDrivers队列中移除
46. waitingDrivers -= driver
47. true
48. }
49.
50. //将指针指向下一个worker
51. 1) % numWorkersAlive
52. }
53. }
54.
55. // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app
56. // in the queue, then the second app, etc.
57. // Application的调度机制（核心之核心，重中之重）
58. // 首先， application的调度算法有两种，一种是spreadOutApps，另一种是非spreadOutApps
59. if (spreadOutApps) {
60. // Try to spread out each app among all the nodes, until it has all its cores
61.
62. //首先，遍历waitingApps中的ApplicationInfo,并且过滤出application还需要高度的cores的application
63. for (app <- waitingApps if app.coresLeft > 0) {
64. //从workers中，过滤状态为ALIVE的，再次过滤可以被Application使用的Worker，然后按照剩余cpu数量倒序排序
65. val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE)
66. .filter(canUse(app, _)).sortBy(_.coresFree).reverse
67. val numUsable = usableWorkers.length
68. //创建一个空数组，存储了要分配给每个worker的cpu数量
69. new Array[Int](numUsable) // Number of cores to give on each node
70. //获取到底要分配多少cpu，取app剩余要分配的cpu的数量和worker总共可用cpu数量的最小值
71. var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum)
72.
73. //通过这种算法，其实会将每个application，要启动的executor,都平均分布到各个worker上去
74. //比如有20个cpu core要分配，那么实际会循环两遍worker，每次循环，给每个worker分配1个core
75. //最后每个worker分配了2个core
76.
77. //while条件，只要要分配的cpu，还没有分配完，就继续循环
78. 0
79. while (toAssign > 0) {
80. //每一个worker，如果空闲的cpu数量大于，已经分配出去的cpu数量
81. //也就是说，worker还有可分配的cpu
82. if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
83. //将总共要分配的cpu数量-1，因为这里已经决定在这个worker上分配一个cpu了
84. 1
85. //给这个worker分配的cpu数量，加1
86. 1
87. }
88. //指针移动到下一下worker
89. 1) % numUsable
90. }
91.
92. // Now that we've decided how many cores to give on each node, let's actually give them
93. // 给每个worker分配完application要求的cpu core之后
94. // 遍历worker
95. for (pos <- 0 until numUsable) {
96. //只要判断之前给这个worker分配到了core
97. if (assigned(pos) > 0) {
98. //首先，在application内部缓存结构中，添加executor
99. //并且创建ExecutorDesc对象，其中封装了，给这个executor分配多少个cpu core
100. //在spark-submit脚本中，可以指定要多少executor，每个execuor多少个cpu,多少内存
101. //那么基于源码机制，实际上，executor的实际数量，以及每个executor的cpu，可能与配置是不一样的
102. //因为，我人帝里基于总的cpu来分配的，就是比如，要求3个executor,每个要3个cpu,那么比如，有9个workers,每个有1个cpu
103. //那么其实总其知道，要分配9个core，其实根据这种算法，会给每个worker分配一个core,然后给每个worker启动一个executor
104. //最后会启动，9个executor,每个executor有1个cpu core
105. val exec = app.addExecutor(usableWorkers(pos), assigned(pos))
106. //那么就在worker上启动executor
107. launchExecutor(usableWorkers(pos), exec)
108. //将application状态设置为running
109. app.state = ApplicationState.RUNNING
110. }
111. }
112. }
113. else {
114. // Pack each app into as few nodes as possible until we've assigned all its cores
115.
116. //非spreadOutApps调度算法
117.
118. //这种算法与spreadOutApps算法正好相反,1、spreadOutApp尽量平均分配到每个executor上；2、非spreadOutApp尽量在使用单个executor的资源。
119. //每个application，都尽可能分配到尽量少的worker上去，比如总其有10个worker,每个有10个core
120. //app总共要分配 20个core,那么其实，只会分配到两个worker上，每个worker都占满10个core
121. //那么，其余的app,就只能 分配到下一个worker了
122. //比如，spark-submit里，配置的是要10个executor,每个要2个core,那么总共是20个croe
123. //只会启动2个executor,每个有10个cores
124.
125. //将每个Application，尽可能少的分配到worker上去
126. //首先，遍历worker，并且是状态为ALIVE，还有空闲cpu的worker
127. for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) {
128. //遍历application,并且是还有城朵分配的core的application
129. for (app <- waitingApps if app.coresLeft > 0) {
130. //判断，如果当前这个worker可以被 application使用
131. if (canUse(app, worker)) {
132. //取worker剩余cpu数量，与app要分配的cpu数量的最小值
133. val coresToUse = math.min(worker.coresFree, app.coresLeft)
134. //如果Worker剩余cpu为0了，就不分配了
135. if (coresToUse > 0) {
136. // 给app添加一个executor
137. val exec = app.addExecutor(worker, coresToUse)
138. //在worker上启动executor
139. launchExecutor(worker, exec)
140. //将application状态设置为running
141. app.state = ApplicationState.RUNNING
142. }
143. }
144. }
145. }
146. }
