Component包含Executor(threads)的个数
在StormBase中的num-executors, 这对应于你写topology代码时, 为每个component指定的并发数(通过setBolt和setSpout)
Component和Task的对应关系, (storm-task-info)
默认你可以不指定task数, 那么task和executor为1:1关系
当然也可以通过ComponentConfigurationDeclarer#setNumTasks()去设置TOPOLOGY_TASKS
这个函数, 首先读出所有components
对每个component, 读出ComponentComm中的json_conf, 然后从里面读出上面设置的TOPOLOGY_TASKS
最后用递增序列产生taskid, 并最终生成component和task的对应关系
如果不设置, task数等于executor数, 后面分配就很容易, 否则就涉及task分配问题
(defn storm-task-info
"Returns map from task -> component id"
[^StormTopology user-topology storm-conf]
(->> (system-topology! storm-conf user-topology)
all-components
(map-val (comp #(get % TOPOLOGY-TASKS) component-conf))
(sort-by first)
(mapcat (fn [[c num-tasks]] (repeat num-tasks c)))
(map (fn [id comp] [id comp]) (iterate (comp int inc) (int 1)))
(into {})
))
首先产生system-topology!, 因为system-topology!会增加系统components, acker, systemBolt, metricsBlot, 这些也都是topology中不可缺少的部分, 所以单纯使用用户定义的topology是不够的
然后取出topology里面所有component
(defn all-components [^StormTopology topology]
(apply merge {}
(for [f thrift/STORM-TOPOLOGY-FIELDS]
(.getFieldValue topology f)
)))
使用thrift/STORM-TOPOLOGY-FIELDS从StormTopology的metadata里面读出每个fieldid, 并取出value进行merge
所以结果就是下面3个map, merge在一起的集合
struct StormTopology {
//ids must be unique across maps
// #workers to use is in conf
1: required map<string, SpoutSpec> spouts;
2: required map<string, Bolt> bolts;
3: required map<string, StateSpoutSpec> state_spouts;
}使用map-value对map中的component进行如下操作
取出component里面的ComponentComm对象(.getcommon), 并读出json_conf, 最终读出conf中TOPOLOGY-TASKS
(defn component-conf [component]
(->> component
.get_common
.get_json_conf
from-json))
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
// component specific configuration
4: optional string json_conf;
}输出{component-string:tasknum}, 按component-string排序, 再进行mapcat
{c1 3, c2 2, c3 1} –> (c1,c1,c1,c2,c2,c3)
再加上递增编号, into到map, {1 c1, 2 c1, 3 c1, 4 c2, 5 c2, 6 c3}
Topology中, Task和Executor的分配关系, (compute-executors)
上面已经产生, component->executors 和 component->task, 现在根据component对应的task和executor个数进行task分配(到executor)
默认是1:1分配, 但如果设置了task数,
比如对于c1, 2个executor, 3个tasks [1 2 3], 分配结果就是['(1 2) ‘(3)]
最终to-executor-id, 列出每个executor中task id的范围([(first task-ids) (last task-ids)])
(defn- compute-executors [nimbus storm-id]
(let [conf (:conf nimbus)
storm-base (.storm-base (:storm-cluster-state nimbus) storm-id nil)
component->executors (:component->executors storm-base) ;从storm-base中获取每个component配置的(executor)线程数
storm-conf (read-storm-conf conf storm-id)
topology (read-storm-topology conf storm-id)
task->component (storm-task-info topology storm-conf)]
(->> (storm-task-info topology storm-conf)
reverse-map ;{“c1” [1,2,3], “c2” [4,5], “c3” 6}
(map-val sort)
(join-maps component->executors) ; {"c1" ‘(2 [1 2 3]), "c2" ‘(2 [4 5]), "c3" ‘(1 6)}
(map-val (partial apply partition-fixed)) ; {"c1" ['(1 2) '(3)], "c2" ['(4) '(5)], "c3" ['(6)]}
(mapcat second) ;((1 2) (3) (4) (5) (6))
(map to-executor-id) ;([1 2] [3 3] [4 4] [5 5] [6 6])
)))
Topology中, Executor和component的关系, (compute-executor->component ), 根据(executor:task)关系和(task:component)关系join
(defn- compute-executor->component [nimbus storm-id]
(let [conf (:conf nimbus)
executors (compute-executors nimbus storm-id)
topology (read-storm-topology conf storm-id)
storm-conf (read-storm-conf conf storm-id)
task->component (storm-task-info topology storm-conf)
executor->component (into {} (for [executor executors
:let [start-task (first executor)
component (task->component start-task)]]
{executor component}))]
executor->component)) ;{[1 2] “c1”, [3 3] “c1”, [4 4] “c2”, [5 5] “c2”, [6 6] “c3”}
最终目的就是获得executor->component关系, 用于后面的assignment, 其中每个executor包含task范围[starttask, endtask]