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Storm-源码分析-Stats (backtype.storm.stats)

2013-07-29 15:58 253 查看
会发现, 现在storm里面有两套metrics系统, metrics framework和stats framework

并且在所有地方都是同时注册两套, 貌似准备用metrics来替代stats, 但当前版本UI仍然使用stats

这个模块统计的数据怎么被使用,

1. 在worker中, 会定期调用do-executor-heartbeats去往zk同步hb
可以看到, stats也会作为hb的一部分被同步到zk上

(defnk do-executor-heartbeats [worker :executors nil]
;; stats is how we know what executors are assigned to this worker
(let [stats (if-not executors
(into {} (map (fn [e] {e nil}) (:executors worker)))
(->> executors
(map (fn [e] {(executor/get-executor-id e) (executor/render-stats e)}))
(apply merge)))
zk-hb {:storm-id (:storm-id worker)
:

executor-stats stats
:uptime ((:uptime worker))
:time-secs (current-time-secs)
}]
;; do the zookeeper heartbeat
(.worker-heartbeat! (:storm-cluster-state worker) (:storm-id worker) (:assignment-id worker) (:port worker) zk-hb)
))


2. 现在任何人都可以通过nimbus的thrift接口来得到相关信息

(^TopologyInfo getTopologyInfo [this ^String storm-id]
beats (.executor-beats storm-cluster-state storm-id (:executor->node+port assignment))
stats (:stats heartbeat))


3. 最直接的用户就是storm UI, 在准备topology page的时候, 就会调用getTopologyInfo来获取数据

(defn topology-page [id window include-sys?]
(with-nimbus nimbus
(let [summ (.getTopologyInfo ^Nimbus$Client nimbus id)]
)


Stats

这个模块用于spout和bolt来抽样统计数据, 需要统计的具体metics如下

(def COMMON-FIELDS [:emitted :transferred])
(defrecord CommonStats [emitted transferred rate])

(def BOLT-FIELDS [:acked :failed :process-latencies :executed :execute-latencies])
;;acked and failed count individual tuples
(defrecord BoltExecutorStats [common acked failed process-latencies executed execute-latencies])

(def SPOUT-FIELDS [:acked :failed :complete-latencies])
;;acked and failed count tuple completion
(defrecord SpoutExecutorStats [common acked failed complete-latencies])


抽样的比例在storm-conf, TOPOLOGY_STATS_SAMPLE_RATE, 配置


为什么统计时每次加rate, 而不是加1?

因为这里的统计是抽样的, 所以如果抽样比例是10%, 那么发现一个, 应该加1/(10%), 10个

(defn sampling-rate [conf]
(->> (conf TOPOLOGY-STATS-SAMPLE-RATE)
(/ 1)
int))




然后统计是基于时间窗口的, 底下是对应默认的bucket和时间窗口的定义

(def NUM-STAT-BUCKETS 20) ;;bucket数
;; 10 minutes, 3 hours, 1 day ;;定义3种时间窗口
(def STAT-BUCKETS [30 540 4320]) ;;bucket大小分别是30，540，4320秒


核心数据结构是RollingWindowSet, 包含:

统计数据需要的函数, updater extractor, 之所以治理也需要是因为需要统计all-time
一组rolling windows, 默认是3个时间窗, 10 minutes, 3 hours, 1 day

all-time, 在完整的时间区间上的统计结果

(defrecord RollingWindowSet [updater extractor windows all-time])
(defn rolling-window-set [updater merger extractor num-buckets & bucket-sizes]
(RollingWindowSet. updater extractor (dofor [s bucket-sizes] (rolling-window updater merger extractor s num-buckets)) nil)
)


继续看看rolling window的定义,

核心数据, buckets, hashmap, {streamid, data}, 初始化为{}

统计data需要的函数, updater merger extractor

时间窗口, buckets大小和buckets个数

(defrecord RollingWindow [updater merger extractor bucket-size-secs num-buckets buckets])
(defn rolling-window [updater merger extractor bucket-size-secs num-buckets]
(RollingWindow. updater merger extractor bucket-size-secs num-buckets {}))


1. mk-stats

在mk-executedata的时候需要创建stats

mk-executor-stats <> (sampling-rate storm-conf)


;; TODO: refactor this to be part of an executor-specific map
(defmethod mk-executor-stats :spout [_ rate]
(stats/mk-spout-stats rate))
(defmethod mk-executor-stats :bolt [_ rate]
(stats/mk-bolt-stats rate))


第一个参数忽略, 其实就是分别调用stats/mk-spout-stats或stats/mk-bolt-stats, 可见就是对于每个需要统计的数据, 创建一个rolling-windows-set

(defn- mk-common-stats [rate]
(CommonStats. (atom (apply keyed-counter-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
(atom (apply keyed-counter-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
rate
))

(defn mk-bolt-stats [rate]
(BoltExecutorStats. (mk-common-stats rate)
(atom (apply keyed-counter-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
(atom (apply keyed-counter-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
(atom (apply keyed-avg-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
(atom (apply keyed-counter-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
(atom (apply keyed-avg-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
))

(defn mk-spout-stats [rate]
(SpoutExecutorStats. (mk-common-stats rate)
(atom (apply keyed-counter-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
(atom (apply keyed-counter-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
(atom (apply keyed-avg-rolling-window-set NUM-STAT-BUCKETS STAT-BUCKETS))
))


2. 数据更新

(defn spout-acked-tuple! [^SpoutExecutorStats stats stream latency-ms]
(update-executor-stat! stats :acked stream (stats-rate stats))
(update-executor-stat! stats :complete-latencies stream latency-ms)
)


(defmacro update-executor-stat! [stats path & args]
(let [path (collectify path)]
`(swap! (-> ~stats ~@path) update-rolling-window-set ~@args)
))


就以update-executor-stat! stats :acked stream (stats-rate stats)为例子看看怎么做的?

SpoutExecutorStats取出用于记录spout acked情况的rolling-windows-set
然后使用update-rolling-window-set来swap这个atom

来看看记录acked的rolling-windows-set是如何定义的?

keyed-counter-rolling-window-set, 预定义了updater merger extractor

updater, incr-val [amap key amt], 把给定的值amt加到amap的对应的key的value上

merger, (partial merge-with +), 用+作为map merge的逻辑, 即出现相同key则相加

extractor, counter-extract, (if v v {}), 有则返回, 无则返回{}

windows, rolling-window的list

all-time, 初始化为nil

(defn keyed-counter-rolling-window-set [num-buckets & bucket-sizes]
(apply rolling-window-set incr-val (partial merge-with +) counter-extract num-buckets bucket-sizes))


好, 下面就看看, 当spout-acked-tuple!时更新:acked时, 如何update的?

首先更新每个rolling-window, 并把更新过的rolling-window-set更新到:windows
并且更新:all-time, (apply (:updater rws) (:all-time rws) args)

updated, incr-val [amap key amt]

args, steamid, rate

all-time, 是用来记录整个时间区间上的, 某个stream的统计情况

(defn update-rolling-window-set
([^RollingWindowSet rws & args]
(let [now (current-time-secs)
new-windows (dofor [w (:windows rws)]
(apply update-rolling-window w now args))]
(assoc rws :windows new-windows :all-time (apply (:updater rws) (:all-time rws) args))
)))


看下如何更新某个rolling-windw
根据now算出当前属于哪个bucket, time-bucket

取出buckets, 并使用:updater更新相应的bucket, 这里的操作仍然是把rate叠加到streamid的value上

(defn update-rolling-window
([^RollingWindow rw time-secs & args]
;; this is 2.5x faster than using update-in...
(let [time-bucket (curr-time-bucket time-secs (:bucket-size-secs rw))
buckets (:buckets rw)
curr (get buckets time-bucket)
curr (apply (:updater rw) curr args)
]
(assoc rw :buckets (assoc buckets time-bucket curr))
)))
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