nimbus启动场景分析
本文详细介绍了twitter storm中的nimbus节点的启动场景，分析nimbus是如何一步步实现定义于storm.thrift中的service，以及如何利用curator来和zookeeper server建立通讯。
对于storm client来说，nimbus是storm cluster与外部的唯一接口，是总的接口人，在这个接口上使用thrift定义的各种service。但是nimbus光接单并不干活，具体的脏活累活，这哥们都是分配到各个slots上的，让nimbus来具体管理各个slots也就是worker，似乎还是太累了，中层干部supervisor同学适时参与了。
nimbus并不知道到底有哪些supervisor会加入到自己的团队中，它啥时规定了每个supervisor最多能带几个worker。对于supervisor的加入与退出，是通过zookeeper server来告知的。好了，在下面的分析中，每个接口上的初始化工作具体有哪些将一一呈现。
worker进程内消息接收与处理全景图
先上幅图简要勾勒出worker进程接收到tuple消息之后的处理全过程
IConnection的建立与使用
话说在mk-threads :bolt函数的实现中有这么一段代码,其主要功能是实现tuple的emit功能
bolt-emit (fn [stream anchors values task] (let [out-tasks (if task (tasks-fn task stream values) (tasks-fn stream values))] (fast-list-iter [t out-tasks] (let [anchors-to-ids (HashMap.)] (fast-list-iter [^TupleImpl a anchors] (let [root-ids (-> a .getMessageId .getAnchorsToIds .keySet)] (when (pos? (count root-ids)) (let [edge-id (MessageId/generateId rand)] (.updateAckVal a edge-id) (fast-list-iter [root-id root-ids] (put-xor! anchors-to-ids root-id edge-id)) )))) (transfer-fn t (TupleImpl. worker-context values task-id stream (MessageId/makeId anchors-to-ids))))) (or out-tasks [])))
加亮为蓝色的部分实现的功能是另外发送tuple,那么transfer-fn函数的定义在哪呢？见mk-threads的let部分，能见到下述一行代码
:transfer-fn (mk-executor-transfer-fn batch-transfer->worker)
在继续往下看每个函数实现之前，先确定一下这节代码阅读的目的。storm在线程之间使用disruptor进行通讯，在进程之间进行消息通讯使用的是zeromq或netty, 所以需要从transfer-fn追踪到使用zeromq或netty api的位置。
再看mk-executor-transfer-fn函数实现
(defn mk-executor-transfer-fn [batch-transfer->worker] (fn this ([task tuple block? ^List overflow-buffer] (if (and overflow-buffer (not (.isEmpty overflow-buffer))) (.add overflow-buffer [task tuple]) (try-cause (disruptor/publish batch-transfer->worker [task tuple] block?) (catch InsufficientCapacityException e (if overflow-buffer (.add overflow-buffer [task tuple]) (throw e)) )))) ([task tuple overflow-buffer] (this task tuple (nil? overflow-buffer) overflow-buffer)) ([task tuple] (this task tuple nil) )))
disruptor/publish表示将消息从本线程发送出去，至于谁是该消息的接收者，请继续往下看。
worker进程中，有一个receiver-thread是用来专门接收来自外部进程的消息，那么与之相对的是有一个transfer-thread用来将本进程的消息发送给外部进程。所以刚才的disruptor/publish发送出来的消息应该被transfer-thread接收到。
在transfer-thread中，能找到这行下述一行代码
transfer-thread (disruptor/consume-loop* (:transfer-queue worker) transfer-tuples)
对于接收到来自本进程中其它线程发送过来的消息利用transfer-tuples进行处理，transfer-tuples使用mk-transfer-tuples-handler来创建，所以需要看看mk-transfer-tuples-handler能否与zeromq或netty联系上呢？
(defn mk-transfer-tuples-handler [worker] (let [^DisruptorQueue transfer-queue (:transfer-queue worker) drainer (ArrayList.) node+port->socket (:cached-node+port->socket worker) task->node+port (:cached-task->node+port worker) endpoint-socket-lock (:endpoint-socket-lock worker) ] (disruptor/clojure-handler (fn [packets _ batch-end?] (.addAll drainer packets) (when batch-end? (read-locked endpoint-socket-lock (let [node+port->socket @node+port->socket task->node+port @task->node+port] ;; consider doing some automatic batching here (would need to not be serialized at this point to remo ;; try using multipart messages ... first sort the tuples by the target node (without changing the lo 17 (fast-list-iter [[task ser-tuple] drainer] ;; TODO: consider write a batch of tuples here to every target worker ;; group by node+port, do multipart send (let [node-port (get task->node+port task)] (when node-port (.send ^IConnection (get node+port->socket node-port) task ser-tuple)) )))) (.clear drainer))))))
上述代码中出现了与zeromq可能有联系的部分了即加亮为红色的一行。
那凭什么说加亮的IConnection一行与zeromq有关系的，这话得慢慢说起，需要从配置文件开始。
在storm.yaml中有这么一行配置项，即
storm.messaging.transport: "backtype.storm.messaging.zmq"
这个配置项与worker中的mqcontext相对应,所以在worker中以mqcontext为线索,就能够一步步找到IConnection的实现。connections在函数mk-refresh-connections中建立
refresh-connections (mk-refresh-connections worker)
mk-refresh-connection函数中与mq-context相关联的一部分代码如下所示
(swap! (:cached-node+port->socket worker) #(HashMap. (merge (into {} %1) %2)) (into {} (dofor [endpoint-str new-connections :let [[node port] (string->endpoint endpoint-str)]] [endpoint-str (.connect ^IContext (:mq-context worker) storm-id ((:node->host assignment) node) port) ] )))
注意加亮部分，利用mq-conext中connect函数来创建IConnection. 当打开zmq.clj时候，就能验证我们的猜测。
(^IConnection connect [this ^String storm-id ^String host ^int port] (require 'backtype.storm.messaging.zmq) (-> context (mq/socket mq/push) (mq/set-hwm hwm) (mq/set-linger linger-ms) (mq/connect (get-connect-zmq-url local? host port)) mk-connection))
代码走到这里，IConnection什么时候建立起来的谜底就揭开了，消息是如何从bolt或spout线程传递到transfer-thread，再由zeromq将tuple发送给下跳的路径打通了。
tuple的分发策略 grouping
从一个bolt中产生的tuple可以有多个bolt接收,到底发送给哪一个bolt呢?这牵扯到分发策略问题,其实在twitter storm中有两个层面的分发策略问题,一个是对于task level的,在讲topology submit的时候已经涉及到。另一个就是现在要讨论的针对tuple level的分发。
再次将视线拉回到bolt-emit中，这次将目光集中在变量t的前前后后。
(let [out-tasks (if task
(tasks-fn task stream values)
(tasks-fn stream values))]
(fast-list-iter [t out-tasks]
(let [anchors-to-ids (HashMap.)]
(fast-list-iter [^TupleImpl a anchors]
(let [root-ids (-> a .getMessageId .getAnchorsToIds .keySet)]
(when (pos? (count root-ids))
(let [edge-id (MessageId/generateId rand)]
(.updateAckVal a edge-id)
(fast-list-iter [root-id root-ids]
(put-xor! anchors-to-ids root-id edge-id))
))))
(transfer-fn t
(TupleImpl. worker-context
values
task-id
stream
(MessageId/makeId anchors-to-ids)))))
上述代码显示t从out-tasks来，而out-tasks是tasks-fn的返回值
tasks-fn (:tasks-fn task-data)
一谈tasks-fn，原来从未涉及的文件task.clj这次被挂上了，task-data与由task/mk-task创建。将中间环节跳过，调用关系如下所列。
- mk-task
- mk-task-data
- mk-tasks-fn
tasks-fn中会使用到grouping,处理代码如下
fn ([^Integer out-task-id ^String stream ^List values] (when debug? (log-message "Emitting direct: " out-task-id "; " component-id " " stream " " values)) (let [target-component (.getComponentId worker-context out-task-id) component->grouping (get stream->component->grouper stream) grouping (get component->grouping target-component) out-task-id (if grouping out-task-id)] (when (and (not-nil? grouping) (not= :direct grouping)) (throw (IllegalArgumentException. "Cannot emitDirect to a task expecting a regular grouping"))) (apply-hooks user-context .emit (EmitInfo. values stream task-id [out-task-id])) (when (emit-sampler) (builtin-metrics/emitted-tuple! (:builtin-metrics task-data) executor-stats stream) (stats/emitted-tuple! executor-stats stream) (if out-task-id (stats/transferred-tuples! executor-stats stream 1) (builtin-metrics/transferred-tuple! (:builtin-metrics task-data) executor-stats stream 1))) (if out-task-id [out-task-id]) ))
而每个topology中的grouping策略又是如何被executor知道的呢，这从另一端executor-data说起。
在mk-executor-data中有下面一行代码
:stream->component->grouper (outbound-components worker-context component-id)
outbound-components的定义如下
(defn outbound-components "Returns map of stream id to component id to grouper" [^WorkerTopologyContext worker-context component-id] (->> (.getTargets worker-context component-id) clojurify-structure (map (fn [[stream-id component->grouping]] [stream-id (outbound-groupings worker-context component-id stream-id (.getComponentOutputFields worker-context component-id stream-id) component->grouping)])) (into {}) (HashMap.)))
