Nimbus Server
Nimbus server, 首先从启动命令开始, 同样是使用storm命令"storm nimbus”来启动
看下源码, 此处和上面client不同, jvmtype="-server", 最终调用"backtype.storm.daemon.nimbus"的main
nimbus是用clojure实现的, 但是clojure是基于JVM的, 所以在最终发布的时候会产生nimbus.class, 所以在用户使用的时候完全可以不知道clojure, 看上去所有都是Java
clojure只是用于提高开发效率而已.
def nimbus():
"""Syntax: [storm nimbus]
Launches the nimbus daemon. This command should be run under
supervision with a tool like daemontools or monit.
See Setting up a Storm cluster for more information.
(https://github.com/nathanmarz/storm/wiki/Setting-up-a-Storm-cluster)
"""
cppaths = [STORM_DIR + "/log4j", STORM_DIR + "/conf"]
childopts = confvalue("nimbus.childopts", cppaths) + " -Dlogfile.name=nimbus.log -Dlog4j.configuration=storm.log.properties"
exec_storm_class(
"backtype.storm.daemon.nimbus",
jvmtype="-server",
extrajars=cppaths,
childopts=childopts)
launch-server!
来看看nimbus的main, 最终会调到launch-server!, conf参数是调用read-storm-config读出的配置参数, 而nimbus是INimbus接口(backtype.storm.scheduler.INimbus)的实现, 可以参考standalone-nimbus
(defn -main [] (-launch (standalone-nimbus)))
(defn -launch [nimbus]
(launch-server! (read-storm-config) nimbus))
(defn launch-server! [conf nimbus]
(validate-distributed-mode! conf)
(let [service-handler (service-handler conf nimbus)
options (-> (TNonblockingServerSocket. (int (conf NIMBUS-THRIFT-PORT)))
(THsHaServer$Args.)
(.workerThreads 64)
(.protocolFactory (TBinaryProtocol$Factory.))
(.processor (Nimbus$Processor. service-handler))
)
server (THsHaServer. options)]
(.addShutdownHook (Runtime/getRuntime) (Thread. (fn [] (.shutdown service-handler) (.stop server))))
(log-message "Starting Nimbus server...")
(.serve server)))
1. service-handler
(defserverfn service-handler [conf inimbus]
(reify Nimbus$Iface
...)
)
(defmacro defserverfn [name & body]
`(let [exec-fn# (fn ~@body)]
(defn ~name [& args#]0
(try-cause
(apply exec-fn# args#)
(catch InterruptedException e#
(throw e#))
(catch Throwable t#
(log-error t# "Error on initialization of server " ~(str name))
(halt-process! 13 "Error on initialization")
)))))
这个macro两个参数, 结合例子, name = service-handler, body就是后面所有的,包括参数和函数体
定义匿名函数 fn[conf inimbus] (……)
定义函数defn service-handler [& args], 里面只是简单的调用fn…使用这个macro和直接定义defn service-handler [conf inimbus]几乎没有啥区别
我有个疑问, 为什么要定义这个无聊的macro, 难道就是为了便于后面的exception处理
在service-handler函数里面最主要就是实现NimbusIface接口(backtype.storm.generated.NimbusIface接口(backtype.storm.generated.NimbusIface, $在class文件里面就是这样写的, 应该是java的命名规则)
2. server
生成server option参数, 使用TNonblockingServerSocket, 定义的work thread数目, 使用的protocol和使用的processor
其中processor, 是server上主要的处理进程, 使用传入的service-handler进行数据处理
最终启动nimbus server.
Nimbus$Iface
Nimbus server已经启动, 剩下就是处理从client传来的RPC调用, 关键就是Nimbus$Iface的实现
在下面的实现中总是用到nimbus这个变量, nimbus-data, 用于存放nimbus相关配置和全局的参数
let [nimbus (nimbus-data conf inimbus)]
(defn nimbus-data [conf inimbus]
(let [forced-scheduler (.getForcedScheduler inimbus)]
{:conf conf
:inimbus inimbus
:submitted-count (atom 0) ;记录多少topology被submit
:storm-cluster-state (cluster/mk-storm-cluster-state conf) ;抽象Zookeeper接口(Zookeeper用于存放cluster state)
:submit-lock (Object.) ;创建锁对象,用于各个topology之间的互斥操作, 比如建目录
:heartbeats-cache (atom {}) ;记录各个Topology的heartbeats的cache
:downloaders (file-cache-map conf)
:uploaders (file-cache-map conf)
:uptime (uptime-computer)
:validator (new-instance (conf NIMBUS-TOPOLOGY-VALIDATOR))
:timer (mk-timer :kill-fn (fn [t]
(log-error t "Error when processing event")
(halt-process! 20 "Error when processing an event")
))
:scheduler (mk-scheduler conf inimbus)
}))
接着重点看下submitTopology,
4个参数,
^String storm-name, storm名字
^String uploadedJarLocation, 上传Jar的目录
^String serializedConf, 序列化过的Conf信息
^StormTopology topology, topology对象(thrift对象), 由topologyBuilder产生
(^void submitTopology
[this ^String storm-name ^String uploadedJarLocation ^String serializedConf ^StormTopology topology]
(try
(validate-topology-name! storm-name) ;;名字起的是否符合规范
(check-storm-active! nimbus storm-name false) ;;check是否active
(.validate ^backtype.storm.nimbus.ITopologyValidator (:validator nimbus) ;;调用用户定义的validator.validate
storm-name
(from-json serializedConf)
topology)
(swap! (:submitted-count nimbus) inc) ;;submitted-count加1, 表示nimbus上submit的topology的数量
(let [storm-id (str storm-name "-" @(:submitted-count nimbus) "-" (current-time-secs)) ;;生成storm-id
storm-conf (normalize-conf ;;转化成json,增加kv,最终生成storm-conf
conf
(-> serializedConf
from-json
(assoc STORM-ID storm-id)
(assoc TOPOLOGY-NAME storm-name))
topology)
total-storm-conf (merge conf storm-conf)
topology (normalize-topology total-storm-conf topology) ;;规范化的topology对象
topology (if (total-storm-conf TOPOLOGY-OPTIMIZE)
(optimize-topology topology)
topology)
storm-cluster-state (:storm-cluster-state nimbus)] ;;操作zk的interface
(system-topology! total-storm-conf topology) ;; this validates the structure of the topology, 1. System-topology!
(log-message "Received topology submission for " storm-name " with conf " storm-conf)
;; lock protects against multiple topologies being submitted at once and
;; cleanup thread killing topology in b/w assignment and starting the topology
(locking (:submit-lock nimbus)
(setup-storm-code conf storm-id uploadedJarLocation storm-conf topology) ;;2. 建立topology的本地目录
(.setup-heartbeats! storm-cluster-state storm-id) ;;3. 建立Zookeeper heartbeats
(start-storm nimbus storm-name storm-id) ;;4. start-storm
(mk-assignments nimbus))) ;;5. mk-assignments
(catch Throwable e
(log-warn-error e "Topology submission exception. (topology name='" storm-name "')")
(throw e))))
1. System-topology!
Validate Topology, 比如使用的comonentid, steamid是否合法
添加系统所需要的component, 比如acker等, 不过没有用到, 不知道为什么要调用System-topology!
(system-topology! total-storm-conf topology) ;; this validates the structure of the topology
(defn system-topology! [storm-conf ^StormTopology topology]
(validate-basic! topology)
(let [ret (.deepCopy topology)]
(add-acker! storm-conf ret)
(add-metric-components! storm-conf ret)
(add-system-components! storm-conf ret)
(add-metric-streams! ret)
(add-system-streams! ret)
(validate-structure! ret)
ret
))
2. 建立topology的本地目录 (这步开始需要lock互斥)
Jars and configs are kept on local filesystem because they're too big for Zookeeper. The jar and configs are copied into the path {nimbus local dir}/stormdist/{topology id}
(setup-storm-code conf storm-id uploadedJarLocation storm-conf topology) 借用这张图, 比较清晰, 先创建目录, 并将Jar move到当前目录 再将topology对象和conf对象都序列化保存到目录中
3. 建立Zookeeper heartbeats
就是按照下面图示在Zookeeper建立topology的心跳目录
(.setup-heartbeats! storm-cluster-state storm-id)
(setup-heartbeats! [this storm-id]
(mkdirs cluster-state (workerbeat-storm-root storm-id)))
(defn mkdirs [^CuratorFramework zk ^String path]
(let [path (normalize-path path)]
(when-not (or (= path "/") (exists-node? zk path false))
(mkdirs zk (parent-path path))
(try-cause
(create-node zk path (barr 7) :persistent)
(catch KeeperException$NodeExistsException e
;; this can happen when multiple clients doing mkdir at same time
))
)))
4. start-storm, 产生StormBase
虽然叫做start-storm, 其实做的事情只是把StormBase结构序列化并放到zookeeper上
这个StormBase和topology对象有什么区别,
topology对象, topology的静态信息, 包含components的详细信息和之间的拓扑关系, 内容比较多所以存储在磁盘上stormcode.ser
而StormBase, topology的动态信息, 只记录了launch时间, status, worker数, component的executor数运行态数据, 比较mini, 所以放在zk上
(defn- start-storm [nimbus storm-name storm-id]
(let [storm-cluster-state (:storm-cluster-state nimbus)
conf (:conf nimbus)
storm-conf (read-storm-conf conf storm-id)
topology (system-topology! storm-conf (read-storm-topology conf storm-id))
num-executors (->> (all-components topology) (map-val num-start-executors))]
(log-message "Activating " storm-name ": " storm-id)
(.activate-storm! storm-cluster-state
storm-id
(StormBase. storm-name
(current-time-secs)
{:type :active}
(storm-conf TOPOLOGY-WORKERS)
num-executors))))
;; component->executors is a map from spout/bolt id to number of executors for that component (defrecord StormBase [storm-name launch-time-secs status num-workers component->executors])
struct ComponentCommon {
1: required map<GlobalStreamId, Grouping> inputs;
2: required map<string, StreamInfo> streams; //key is stream id
3: optional i32 parallelism_hint; //how many threads across the cluster should be dedicated to this component
4: optional string json_conf;
}
重上面可以看出StormBase是定义的一个record, 包含storm-name, 当前时间戳, topology的初始状态(active或inactive), worker数目, 和executor的数目
其中计算num-executors, 使用->>, 其实等于(map-val num-start-executors (all-components topology)), map-value就是对(k,v)中的value执行num-start-executors, 而这个函数其实就是去读ComponentCommon里面的parallelism_hint, 所以num-executors, 描述每个component需要几个executors(线程)
(activate-storm! [this storm-id storm-base] (set-data cluster-state (storm-path storm-id) (Utils/serialize storm-base)) )
(defn storm-path [id] (str STORMS-SUBTREE "/" id)) ;/storms/id
(defn set-data [^CuratorFramework zk ^String path ^bytes data] (.. zk (setData) (forPath (normalize-path path) data)))
最终调用activate-storm!将storm-base序列化后的数据存到Zookeeper的"/storms/id”目录下 
5. mk-assignments
Storm-源码分析-Topology Submit-Nimbus-mk-assignments
本文章摘自博客园，原文发布日期：2013-06-19