1. GeneralTopologyContext
记录了Topology的基本信息, 包含StormTopology, StormConf
已经从他们推导出的, task和component, component的streams, input/output信息
public class GeneralTopologyContext implements JSONAware {
private StormTopology _topology;
private Map<Integer, String> _taskToComponent;
private Map<String, List<Integer>> _componentToTasks;
private Map<String, Map<String, Fields>> _componentToStreamToFields; //ComponentCommon.streams, map<string, StreamInfo>
private String _stormId; ;;topology id
protected Map _stormConf;
}
StormTopology, worker从磁盘stormcode.ser中读出
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;
}
StormConf, worker从磁盘stormconf.ser中读出
taskToComponent, componentToTasks, task和component的对应关系
componentToStreamToFields, component包含哪些streams, 每个stream包含哪些fields
除了显而易见的操作以外, 还有如下操作以获得component的输入和输出
/**
* Gets the declared inputs to the specified component.
*
* @return A map from subscribed component/stream to the grouping subscribed with.
*/
public Map<GlobalStreamId, Grouping> getSources(String componentId) {
return getComponentCommon(componentId).get_inputs(); //ComponentCommon.inputs,map<GlobalStreamId, Grouping>
}
/**
* Gets information about who is consuming the outputs of the specified component,
* and how.
*
* @return Map from stream id to component id to the Grouping used.
*/
public Map<String, Map<String, Grouping>> getTargets(String componentId) {
Map<String, Map<String, Grouping>> ret = new HashMap<String, Map<String, Grouping>>();
for(String otherComponentId: getComponentIds()) { //对所有components的id
Map<GlobalStreamId, Grouping> inputs = getComponentCommon(otherComponentId).get_inputs(); //取出component的inputs
for(GlobalStreamId id: inputs.keySet()) { //对inputs里面的每个stream-id
if(id.get_componentId().equals(componentId)) { //判断stream的源component是否是该component
Map<String, Grouping> curr = ret.get(id.get_streamId());
if(curr==null) curr = new HashMap<String, Grouping>();
curr.put(otherComponentId, inputs.get(id));
ret.put(id.get_streamId(), curr);
}
}
}
return ret; // [steamid, [target-componentid, grouping]]
}
这里面的getComponentCommon和getComponentIds, 来自ThriftTopologyUtils类
不要误解, 不是通过thriftAPI去nimbus获取信息, 只是从StormTopology里面读信息, 而StormTopology类本身是generated by thrift
thrift产生的class, 是有metaDataMap的, 所以实现如下
public static Set<String> getComponentIds(StormTopology topology) {
Set<String> ret = new HashSet<String>();
for(StormTopology._Fields f: StormTopology.metaDataMap.keySet()) {
Map<String, Object> componentMap = (Map<String, Object>) topology.getFieldValue(f);
ret.addAll(componentMap.keySet());
}
return ret;
}
通过metaDataMap读出StormTopology里面有哪些field, spouts,bolts,state_spouts, 然后遍历getFieldValue, 将value中的keyset返回
这样做的好处是, 动态, 当StormTopology发生变化时, 代码不用改, 对于普通java class应该无法实现这样的功能, 但是对于python这样的动态语言, 就简单了
当然这里其实也可以不用ThriftTopologyUtils, 直接写死从StormTopology.spouts…中去读
从storm.thrift里面看看ComponentCommon的定义, 上面两个函数就很好理解了
getTargets的实现, 需要看看, 因为是从inputs去推出outputs
因为在ComponentCommon只记录了output的streamid以及fields, 但无法知道这个stream发往哪个component
但对于input, streamid是GlobalStreamId类型, GlobalStreamId里面不但包含streamid,还有源component的componentid
所以从这个可以反推, 只要源component是当前component, 那么说明该component是源component的target component
struct ComponentCommon {
1: required map<GlobalStreamId, Grouping> inputs;
2: required map<string, StreamInfo> streams; //key is stream id, outputs
3: optional i32 parallelism_hint; //how many threads across the cluster should be dedicated to this component
4: optional string json_conf;
}
struct SpoutSpec {
1: required ComponentObject spout_object;
2: required ComponentCommon common;
// can force a spout to be non-distributed by overriding the component configuration
// and setting TOPOLOGY_MAX_TASK_PARALLELISM to 1
}
struct Bolt {
1: required ComponentObject bolt_object;
2: required ComponentCommon common;
}
2. WorkerTopologyContext
WorkerTopologyContext封装了些worker相关信息
public class WorkerTopologyContext extends GeneralTopologyContext {
public static final String SHARED_EXECUTOR = "executor";
private Integer _workerPort; ;;worker进程的port
private List<Integer> _workerTasks; ;;worker包含的taskids
private String _codeDir; ;;supervisor上的代码目录, stormdist/stormid
private String _pidDir; ;;记录worker运行进程(可能多个)的pids的目录,workid/pids
Map<String, Object> _userResources;
Map<String, Object> _defaultResources;
}
3. TopologyContext
看注释, TopologyContext会作为bolt和spout的prepare(or open)函数的参数
所以用openOrPrepareWasCalled, 表示该TopologyContext是否被prepare调用过
registerMetric, 可以用于往_registeredMetrics中注册metics
注册的结构, [timeBucketSizeInSecs, [taskId, [name, metric]]]
_hooks, 用于注册task hook
/**
* A TopologyContext is given to bolts and spouts in their "prepare" and "open"
* methods, respectively. This object provides information about the component's
* place within the topology, such as task ids, inputs and outputs, etc.
*
* <p>The TopologyContext is also used to declare ISubscribedState objects to
* synchronize state with StateSpouts this object is subscribed to.</p>
*/
public class TopologyContext extends WorkerTopologyContext implements IMetricsContext {
private Integer _taskId;
private Map<String, Object> _taskData = new HashMap<String, Object>();
private List<ITaskHook> _hooks = new ArrayList<ITaskHook>();
private Map<String, Object> _executorData;
private Map<Integer,Map<Integer, Map<String, IMetric>>> _registeredMetrics;
private clojure.lang.Atom _openOrPrepareWasCalled;
public TopologyContext(StormTopology topology, Map stormConf,
Map<Integer, String> taskToComponent, Map<String, List<Integer>> componentToSortedTasks,
Map<String, Map<String, Fields>> componentToStreamToFields,
String stormId, String codeDir, String pidDir, Integer taskId,
Integer workerPort, List<Integer> workerTasks, Map<String, Object> defaultResources,
Map<String, Object> userResources, Map<String, Object> executorData, Map registeredMetrics,
clojure.lang.Atom openOrPrepareWasCalled) {
super(topology, stormConf, taskToComponent, componentToSortedTasks,
componentToStreamToFields, stormId, codeDir, pidDir,
workerPort, workerTasks, defaultResources, userResources);
_taskId = taskId;
_executorData = executorData;
_registeredMetrics = registeredMetrics;
_openOrPrepareWasCalled = openOrPrepareWasCalled;
}
4. 使用
mk-task-data, 创建每个task的topology context
user-context (user-topology-context (:worker executor-data) executor-data task-id)
(defn user-topology-context [worker executor-data tid]
((mk-topology-context-builder
worker
executor-data
(:topology worker))
tid)) (defn mk-topology-context-builder [worker executor-data topology]
(let [conf (:conf worker)]
#(TopologyContext.
topology
(:storm-conf worker)
(:task->component worker)
(:component->sorted-tasks worker)
(:component->stream->fields worker)
(:storm-id worker)
(supervisor-storm-resources-path
(supervisor-stormdist-root conf (:storm-id worker)))
(worker-pids-root conf (:worker-id worker))
(int %)
(:port worker)
(:task-ids worker)
(:default-shared-resources worker)
(:user-shared-resources worker)
(:shared-executor-data executor-data)
(:interval->task->metric-registry executor-data)
(:open-or-prepare-was-called? executor-data))))