Storm自定义调度器实现--DirectScheduler

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前言

最近在研究Storm的任务调度相关的知识，于是就想要试着去改造一下Storm的任务调度，来满足一下现实状况中的一些场景。

Storm调度的相关术语

在看Storm的Scheduler代码么之前，得要弄明白几个概念，这样可以帮助大家更好的理解后面的调度过程。
1、slot。这代表一个Supervisor节点上的一个单位资源。每个slot对应一个port，一个slot只能被一个Worker占用。
2、Worker，Executor.Task,1个Worker包含1个或多个Executor执行器，每个执行器包含多个Task。
3、Executor的表现形式为[1-1],[2-2],中括号内的数字代表该Executor中的起始Task id到末尾Task id,1个Worker就相当于在外面加个大括号{[1-1],[2-2]}
4.Component。Storm中的每个组件就是指一类Spout或1个类型的Bolt，这里指的是名称类型，不包含个数。
下面是调度器的核心实现。

代码实现

import backtype.storm.scheduler.*;import clojure.lang.PersistentArrayMap;import java.util.*;/** * 直接分配调度器,可以分配组件到指定节点中 * Created by zhexuan on 15/7/6. */public class DirectScheduler implements IScheduler{@Overridepublic void prepare(Map conf) {}@Overridepublic void schedule(Topologies topologies, Cluster cluster) {    System.out.println("DirectScheduler: begin scheduling");    // Gets the topology which we want to schedule    Collection<TopologyDetails> topologyDetailes;    TopologyDetails topology;    //作业是否要指定分配的标识    String assignedFlag;    Map map;    Iterator<String> iterator = null;    topologyDetailes = topologies.getTopologies();    for(TopologyDetails td: topologyDetailes){        map = td.getConf();        assignedFlag = (String)map.get("assigned_flag");        //如何找到的拓扑逻辑的分配标为1则代表是要分配的,否则走系统的调度        if(assignedFlag != null && assignedFlag.equals("1")){            System.out.println("finding topology named " + td.getName());            topologyAssign(cluster, td, map);        }else {            System.out.println("topology assigned is null");        }    }    //其余的任务由系统自带的调度器执行    new EvenScheduler().schedule(topologies, cluster);}/** * 拓扑逻辑的调度 * @param cluster * 集群 * @param topology * 具体要调度的拓扑逻辑 * @param map * map配置项 */private void topologyAssign(Cluster cluster, TopologyDetails topology, Map map){    Set<String> keys;    PersistentArrayMap designMap;    Iterator<String> iterator;    iterator = null;    // make sure the special topology is submitted,    if (topology != null) {        designMap = (PersistentArrayMap)map.get("design_map");        if(designMap != null){            System.out.println("design map size is " + designMap.size());            keys = designMap.keySet();            iterator = keys.iterator();            System.out.println("keys size is " + keys.size());        }        if(designMap == null || designMap.size() == 0){            System.out.println("design map is null");        }        boolean needsScheduling = cluster.needsScheduling(topology);        if (!needsScheduling) {            System.out.println("Our special topology does not need scheduling.");        } else {            System.out.println("Our special topology needs scheduling.");            // find out all the needs-scheduling components of this topology            Map<String, List<ExecutorDetails>> componentToExecutors = cluster.getNeedsSchedulingComponentToExecutors(topology);            System.out.println("needs scheduling(component->executor): " + componentToExecutors);            System.out.println("needs scheduling(executor->components): " + cluster.getNeedsSchedulingExecutorToComponents(topology));            SchedulerAssignment currentAssignment = cluster.getAssignmentById(topology.getId());            if (currentAssignment != null) {                System.out.println("current assignments: " + currentAssignment.getExecutorToSlot());            } else {                System.out.println("current assignments: {}");            }            String componentName;            String nodeName;            if(designMap != null && iterator != null){                while (iterator.hasNext()){                    componentName = iterator.next();                    nodeName = (String)designMap.get(componentName);                    System.out.println("现在进行调度 组件名称->节点名称:" + componentName + "->" + nodeName);                    componentAssign(cluster, topology, componentToExecutors, componentName, nodeName);                }            }        }    }}/** * 组件调度 * @param cluster * 集群的信息 * @param topology * 待调度的拓扑细节信息 * @param totalExecutors * 组件的执行器 * @param componentName * 组件的名称 * @param supervisorName * 节点的名称 */private void componentAssign(Cluster cluster, TopologyDetails topology, Map<String, List<ExecutorDetails>> totalExecutors, String componentName, String supervisorName){    if (!totalExecutors.containsKey(componentName)) {        System.out.println("Our special-spout does not need scheduling.");    } else {        System.out.println("Our special-spout needs scheduling.");        List<ExecutorDetails> executors = totalExecutors.get(componentName);        // find out the our "special-supervisor" from the supervisor metadata        Collection<SupervisorDetails> supervisors = cluster.getSupervisors().values();        SupervisorDetails specialSupervisor = null;        for (SupervisorDetails supervisor : supervisors) {            Map meta = (Map) supervisor.getSchedulerMeta();            if(meta != null && meta.get("name") != null){                System.out.println("supervisor name:" + meta.get("name"));                if (meta.get("name").equals(supervisorName)) {                    System.out.println("Supervisor finding");                    specialSupervisor = supervisor;                    break;                }            }else {                System.out.println("Supervisor meta null");            }        }        // found the special supervisor        if (specialSupervisor != null) {            System.out.println("Found the special-supervisor");            List<WorkerSlot> availableSlots = cluster.getAvailableSlots(specialSupervisor);            // 如果目标节点上已经没有空闲的slot,则进行强制释放            if (availableSlots.isEmpty() && !executors.isEmpty()) {                for (Integer port : cluster.getUsedPorts(specialSupervisor)) {                    cluster.freeSlot(new WorkerSlot(specialSupervisor.getId(), port));                }            }            // 重新获取可用的slot            availableSlots = cluster.getAvailableSlots(specialSupervisor);            // 选取节点上第一个slot,进行分配            cluster.assign(availableSlots.get(0), topology.getId(), executors);            System.out.println("We assigned executors:" + executors + " to slot: [" + availableSlots.get(0).getNodeId() + ", " + availableSlots.get(0).getPort() + "]");        } else {            System.out.println("There is no supervisor find!!!");        }    }}
}

说明部分

Storm自定义实现直接分配调度器,代码修改自Twitter Storm核心贡献者徐明明,此处为链接.

开发背景

在准备开发Storm自定义之前,事先已经了解了下现有Storm使用的调度器,默认是DefaultScheduler,调度原理大体如下:
* 在新的调度开始之前,先扫描一遍集群,如果有未释放掉的slot,则先进行释放
* 然后优先选择supervisor节点中有空闲的slot,进行分配,以达到最终平均分配资源的目标

现有scheduler的不足之处

上述的调度器基本可以满足一般要求,但是针对下面个例还是无法满足:
* 让spout分配到固定的机器上去,因为所需的数据就在那上面
* 不想让2个Topology运行在同一机器上,因为这2个Topology都很耗CPU

DirectScheduler的作用

DirectScheduler把划分单位缩小到组件级别,1个Spout和1个Bolt可以指定到某个节点上运行,如果没有指定,还是按照系统自带的调度器进行调度.这个配置在Topology提交的Conf配置中可配.

使用方法

集群配置

打包此项目,将jar包拷贝到STORM_HOME/lib目录下,在nimbus节点上的Storm包
在nimbus节点的storm.yaml配置中,进行如下的配置:
```
storm.scheduler: "storm.DirectScheduler"
```
然后是在supervisor的节点中进行名称的配置,配置项如下:
supervisor.scheduler.meta: name: "your-supervisor-name"

在集群这部分的配置就结束了,然后重启nimbus,supervisor节点即可,集群配置只要1次配置即可.

拓扑逻辑配置

见下面的代码设置,主要是把组件名和节点名称作为映射值传入

int numOfParallel;TopologyBuilder builder;StormTopology stormTopology;Config config;//待分配的组件名称与节点名称的映射关系HashMap<String, String> component2Node;//任务并行化数设为10个numOfParallel = 2;builder = new TopologyBuilder();String desSpout = "my_spout";String desBolt = "my_bolt";//设置spout数据源builder.setSpout(desSpout, new TestSpout(), numOfParallel);builder.setBolt(desBolt, new TestBolt(), numOfParallel)            .shuffleGrouping(desSpout);config = new Config();config.setNumWorkers(numOfParallel);config.setMaxSpoutPending(65536);config.put(Config.STORM_ZOOKEEPER_CONNECTION_TIMEOUT, 40000);config.put(Config.STORM_ZOOKEEPER_SESSION_TIMEOUT, 40000);component2Node = new HashMap<>();component2Node.put(desSpout, "special-supervisor1");component2Node.put(desBolt, "special-supervisor2");//此标识代表topology需要被调度config.put("assigned_flag", "1");//具体的组件节点对信息config.put("design_map", component2Node);StormSubmitter.submitTopology("test", config, builder.createTopology());

拓扑逻辑作业具体要被调度时,传入配置参数即可.

调度器后期优化

DirectScheduler只是针对原有的调度实现做了1层包装,后期可以进行更深层次的改造,涉及到节点在分配的时候slot的排序等等.

完整代码地址

https://github.com/linyiqun/storm-scheduler

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