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Apache-2.0

A Distributed Task Distribution Framework Core

一个分布式任务分发框架核心包，一个内存型的MQ。将ws-task-core引入到自己的application，你就可以将自己的普通application转变成一个分布式任务分发application。ws-task-core主要提供了Leader节点自动选举，动态增删节点，task数据分发。提供了两种类型的任务，拉数据模式，推数据模式。数据分发支持轮询，随机，HASH。可配置leader是否参与任务处理，可配置节点是否参与leader选举（见下面示例）。

主要功能

  • 提供了两种类型的任务，拉数据模式，推数据模式；
  • 数据分发支持集群模式和广播模式；集群：每一条消息只分配到一个节点；广播：每一条消息分配到所有节点；
  • 数据分发集群模式支持轮询，随机，HASH；
  • 支持配置Leader是否参与任务处理；
  • 支持配置Worker节点的数量；
  • 支持spring-boot-starter方式，使用@EnableWsTask开启功能；
  • 支持节点可视化，打开/task-ui.html即可查看任务分配节点状态；

示例

Maven: 在pom.xml中引入jar
<dependency>
  <groupId>org.ws.task</groupId>
  <artifactId>ws-task-spring-web-ui</artifactId>
  <version>0.2.0-SNAPSHOT</version>
</dependency>
<dependency>
  <groupId>org.ws.task</groupId>
  <artifactId>ws-task-spring-starter</artifactId>
  <version>0.2.0-SNAPSHOT</version>
</dependency>
JAVA:在XXXApplication.java内启动@EnableWsTask 
// 开启功能
@EnableWsTask
@SpringBootApplication
public class ExampleApplication {
    public static void main(String[] args) {
        log.info("Example Application start");
        SpringApplication.run(ExampleApplication.class, args);
    }
}
YML:在application.yml中配置参数
# 最简配置
ws-task:
  zookeeper:
    namespace: @project.artifactId@
    address: 127.0.0.1:2181
  netty:
    serverPort: 8258
    connectTimeoutMillis: 3000
任务配置
// 拉数据任务配置示例
@Configuration
public class PullTaskConfig {

  	// 一个100万的uuid任务文件路径
    @Value("${uuid-file-path}")
    private String uuidFilePath;

  	// 配置拉数据任务
    @Bean
    public PullTask<String> uuidPullTask() throws FileNotFoundException {
        return TaskBuilder.builder("uuidPullTask")
                .config()
                .chunk(10)
                .fetchTimeout(10_000)
                .sendModel(SendModel.CLUSTERING)
                .leaderProcess(false)
                .<String>pull()
                .provider(uuidItemProvider())
                .builder();
    }
		// Leader端任务数据提供者
    @Bean
    public ItemLeaderProvider<String> uuidItemProvider() throws FileNotFoundException {
        return new FileLineItemLeaderProvider(uuidFilePath);
    }
}

// 推数据任务配置
@Slf4j
@Configuration
public class PushTaskConfig {

    // 推数据任务
    @Bean
    public PushTask<String> uuidPushTask() {
        return TaskBuilder.builder("uuidPushTask")
                .config()
                .chunk(10)
                .fetchTimeout(10_000)
                .sendModel(SendModel.CLUSTERING)
                .leaderProcess(false)
                .<String>push()
                .processor(uuidItemProcessor())
                .builder();
    }
    // worker节点任务处理器Bean
    @Bean
    public UuidItemProcessor uuidItemProcessor(){
        return new UuidItemProcessor();
    }
		// worker节点任务处理器
    public static class UuidItemProcessor implements ItemWorkerProcessor<String>{
        private AtomicInteger count = new AtomicInteger(0);

        @Override
        public void process(Collection<TaskItem<String>> items) throws ItemProcessException {
            if(CollectionUtils.isNotEmpty(items)){
                int i = count.addAndGet(items.size());
                if(i/100 % 10 == 0 || i >= 500000){
                    log.info("process count:{}",i);
                }
            }
        }

        public int getCount() {
            return count.get();
        }
    }
}

// 在任意一个节点执行推送数据
public void push(){
  Collection<TaskItem<String>> items = fileLineItemProvider.getItems();
  while (CollectionUtils.isNotEmpty(items)){
    uuidPushTask.pushSync(items,3_000);
    items = fileLineItemProvider.getItems();
  }
}

分布式管理

采用集中式设计，集群始终仅存在一个leader，worker通过注册中心查询当前leader并发生心跳链接。使用事件驱动的处理方式，对于不同事件执行相应的操作，能够从各个不健康状态自动调整到健康状态。

启动过程

启动过程使用事件驱动，根据事件触发不同的启动操作，并能够对节点和通讯组件自己检测并调整到有效状态，事件类型和操作见下表：

事件 说明 触发场景 触发操作
BECOME_LEADER 成为Leader 本节点竞选为Leader后触发事件 启动server；更新本地Leader数据；
CHANGE_LEADER 变更Leader 监听到Leader节点数据发生变更时触发。 更新client端leader地址；更新本地Leade数据；检查节点状态；
REMOVE_LEADER 删除Leader 监听到Leader节点数据删除时触发。 更新client端leade地址为null；更新本地leader状态为GONE；
NOT_LEADER 不是Leader 本节点没有竞选到Leader触发。 更新client端leader地址；更新本地Leade数据；启动client客户端；
NOT_FOUND_LEADER 没有找到Leader 暂无 检查节点状态；
CHECK_LEADER 检查Leader 客户端创建链接Leader不是有效状态触发；客户端Channel建立连接连续10次超时触发；客户端连续10次无法建立有效Channel触发； 检查节点状态；
UPDATE_WORKER 更新Worker zk启动后同步所有worker节点数据时触发；监听到worker节点下数据变更时触发； 更新本地节点数据；检查节点状态；
REMOVE_WORKER 删除Worker 监听到worker节点删除时触发（worker掉线或宕机）； 删除本地节点数据；Leade节点执行任务resize；检查节点状态；
SERVER_STARTUP 服务端已启动 netty server启动完成后触发； 更新leader元数据port和状态为有效；更新本地leader数据；更新client端leader地址；启动client端；
CHANNEL_CONNECT 已连接 server端监听到有新channel建立时触发； 更新成功建立channel的worker本地数据和节点元数据port和状态为ACTIVE；
CHANNEL_CLOSE 已断开 server端监听到channel关闭或异常时触发； 更新已断开channel的worker本地数据和节点元数据状态为GONE；
CHANNEL_IDLE 已超时 server端监听到channel已超时触发； 更新已超时channel的worker本地数据和节点元数据状态为RECOVERING；
CLIENT_STARTUP 客户端已启动 client与server第一次成功建立channel时触发； 更新本worker节点元数据port和状态为ACTIVE；启动客户端心跳；
CLIENT_CHANNEL_CONNECT 客户端已连接 client与server建立channel变更时触发（重新建立channel时）； 更新本worker节点元数据port和状态为ACTIVE；启动客户端心跳；检查节点状态；
CLIENT_CHANNEL_IDLE 客户端已超时 client监听到channel已超时触发； 更新本worker节点元数据port和状态为RECOVERING；启动客户端心跳；
UPDATE_TASK_CONFIG 更新任务配置 暂无 暂无
REMOVE_TASK_CONFIG 删除任务配置 暂无 暂无
一个启动过程的图

从日志中可以看到在启动过程中的Event。

image-20201029235213728 image-20201029235040217

竞选领导者

所有节点启动时都会去参与竞选领导者，当竞选成功更新领导者元数据状态为准备，并启动服务端，服务端启动完成后更新为就绪状态。所有的worker都监听领导者元数据，当元数据变更为就绪，作业节点启动客户端或新建与领导者的连接并保持心跳。当领导者掉线或宕机，会执行新的领导者选举，所有的节点更新状态为恢复。新的领导者选举成功后，启动服务端并更新为就绪状态，作业节点监听到变更后，建立新的链接，集群恢复健康。

新增节点

向注册中心注册节点元数据，所有的worker加入集群后自己维持与leader的心跳。leader监听节点元数据新增到管理列表，初期状态为准备，当启动客户端并与leader成功建立连接后更新为就绪状态。

移除节点

领导者监听到节点链接关闭，更新节点状态为丢失，若节点恢复再次建立连接，更新状态为有效。若节点掉线或有重启，通过监听到节点移除，移除本地节点元数据。若节点心跳超时，更新节点状态为恢复，同时节点客户端会检查心跳连接，若连接失效会清除当前连接并重新建立，并更新节点数据。服务端接收到连接后，更新节点状态。节点状态恢复健康。

远程通信

分成两端，服务端和客户端。当竞选成为领导者后会启动服务端等待连接。客户端启动后会保持与服务端的心跳以保证自己状态健康。 服务端和客户端都支持同步，异步，单向请求。

协议设计

请求和响应消息为请求消息和响应消息结构类型，通过类型标示是请求还是响应结构，在编解码时可通过标示位进行识别消息类型。消息主体类型根据不同的场景和请求分别为不同的协议结构，通过编码成二进制流进行传输，同时使用主体类型属性识别类型二进制流进行解码。 通信消息和编码设计为：

MessageEncoder

image-20201025113242375
RequestMessage
Message字段 类型 说明
id int 请求ID，响应ID与请求一一对应
action int 请求Action，根据不同的Action执行不同的处理。
body byte[] 请求的主体信息，将主体信息的对象编码成byte[]传输
bodyClass Class 请求主体信息的类型，通过该类型对body进行解码
ResponseMessage
Message字段 类型 说明
id int 直接复制请求ID，通过ID找到该请对应响应的处理。
status int 响应状态
success boolean 响应成功标示
message String 响应成功或失败消息
body byte[] 响应主体信息，将主体信息的对象编码成byte[]传输
bodyClass Class 响应主体信息的类型，通过该类型对body进行解码
RequestAction说明
Action Code 说明
PULL_TASK 0 Worker拉取任务请求,Leader使用PullItemProviderHandler处理。
PUSH_TASK 1 1.客户端推送数据到Leader，Leader使用PushItemLeaderHandler接收数据并执行分发推送；2.服务端推送数据到Worker，Worker使用PushItemWorkerHandler接收数据并执行。
SYNC_DATA 2 节点相互同步备份任务数据以防止数据丢失和容错（计划实现）。
META_DATA 3 节点相互同步备份元数据，与Zookeeper丢失连接仍可继续（计划实现）。
COMMIT_STATUS 4 Worker完后任务后提交任务请求，Leader使用CommitStatusHandler处理。
HEARTBEAT 5 Worker发送心跳请求，单向请求Leader无需处理。
RequestAction对应请求和相同Message的body结构说明
PULL_TASK
  • 请求Body：PullItemRequest
字段 类型 说明
taskName String 请求拉取数据的任务名称
size Integer 拉取的数量
nodeId String 请求的Worker节点id
timeMillis Long 请求时间戳
  • 响应Body：PullItemResponse
字段 类型 说明
taskName String 响应数据任务名称
index Integer 响应数据开始任务索引
items Collection<TaskItem> 响应的任务集合
  • 任务项TaskItem结构说明
字段 类型 说明
id long 任务唯一id，自动生成
status TaskStatus 任务状态：READY,DISTRIBUTED,PROCESSING,SUCCESS,FAILURE,COMMIT_FAILURE
failedCount int 失败次数
data Object 任务业务数据
PUSH_TASK
  • 请求Body：PushItemRequest
字段 类型 说明
taskName String 推送的任务名称
items Collection<TaskItem> 推送的任务数据集合
  • 响应Body：PushItemResponse
字段 类型 说明
itemIds Collection<Long> 接收到的任务id集合
success Boolean 是否成功
message String 失败消息
COMMIT_STATUS
  • 请求body：CommitRequest
字段 类型 说明
taskName String 提交数据的任务名称
items List<TaskItem> 提交的任务列表
nodeId String 请求的Worker节点id
  • 响应Body：CommitResponse
字段 类型 说明
results List<CommitResult> 提交的处理结果
  • 提交结果CommitResult结构说明
字段 类型 说明
itemId Long 任务ID
success Boolean 是否成功
message String 失败消息

元数据说明

使用Zookeeper进行节点协同和元数据同步。元数据结构如下

/ws-task/{namespace}/leader-data节点数据
// leader-data节点存储的数据，解码后元数据示例（实际存储的为编码后的byte[]）
{
  "active":true,	// 是否有效
  "address":"127.0.0.1:8258",	// 地址
  "host":"127.0.0.1",	// host
  "id":"03e6135e-7550-4594-8e2c-fbb99b5d25a9",	// 节点ID
  "leader":true,	// 是否为leader
  "port":8258,	// server绑定端口
  "role":"LEADER",	// 角色
  "status":"ACTIVE",	// 节点状态
  "updateTime":1604232336496	// 更新时间
}
/ws-task/{namespace}/workers/node/{worker-1}节点数据
// {worker-1}节点存储的数据，解码后元数据示例（实际存储的为编码后的byte[]）
{
  "active":true,	// 是否有效
  "address":"127.0.0.1:1251",	// 地址
  "host":"127.0.0.1",	// host
  "id":"03e6135e-7550-4594-8e2c-fbb99b5d25a9",	// 节点ID
  "leader":false,	// 是否为leader
  "port":1251,	// 与server的通讯端口
  "role":"WORKER",	// 角色
  "status":"ACTIVE",	// 节点状态
  "updateTime":1604232336627	// 更新时间
}

Example说明

ws-task-example-batch

model路径/ws-task-example/ws-task-example-batch

在SpringBatch中使用拉数据模式获取任务数据并进行处理。

若没有任务文件请执行test下的UUIDTest生成。

如何运行
  • 启动本地zookeeper，端口为2181，可编辑application.yml中ws-task.zookeeper.address修改成指定的地址。
  • 运行test目录下org.ws.task.example.batch.UUIDTest，其中FILE_MAX_LINE可以修改，最大不要超过5000万（1.9G），否则文件size会超出限制。
    • 文件的生成和读取都是采用nio方式，可以随意生成或读取不超出文件size限制的任意大小文件。
  • 启动
    • 单节点启动：可以直接启动spring-boot:run，默认端口为8888
    • 多节点启动
      • 进入目录： cd ws-task-example/ws-task-example-batch；
      • 执行mvn package，若执行clean可能会将测试文件删除导致打包错误；
      • 进入ws-task-example-batch/bin，可以执行startNodeX.bat或startNodeX.sh，运行几个节点。
  • 运行
    • 直接请求地址如：http://127.0.0.1:8888/run执行拉取任务；
    • 若存在多个节点，执行Leader的run不会处理任务，执行worker的run会执行拉取并处理；
    • 若多节点下，只执行一个worker则只会执行分配到该节点的任务，其他节点任务在leader的内存中，一直到其他节点执行拉取处理。
下图为执行一个100万的uuid数据拉取处理大约10s

采用一个Leader节点负责从文件中读取任务数据并分发，Leader不处理任务。两个worker节点处理分配的任务。

image-20201031174009610

ws-task-example-mq

model路径/ws-task-example/ws-task-example-mq

在推数据模式下Leader获取任意节点推送的任务数据并负责分发推送到worker进行处理，类似一个内存型的MQ。

若没有任务文件请执行test下的UUIDTest生成。

如何运行
  • 启动本地zookeeper，端口为2181，可编辑application.yml中ws-task.zookeeper.address修改成指定的地址。
  • 运行test目录下org.ws.task.example.mq.UUIDTest，其中FILE_MAX_LINE可以修改，最大不要超过5000万（1.9G），否则文件size会超出限制。
    • 文件的生成和读取都是采用nio方式，可以随意生成或读取不超出文件size限制的任意大小文件。
  • 启动
    • 单节点启动：可以直接启动spring-boot:run，默认端口为8888
    • 多节点启动
      • 进入目录： cd ws-task-example/ws-task-example-mq；
      • 执行mvn package，若执行clean可能会将测试文件删除导致打包错误；
      • 进入ws-task-example-mq/bin，可以执行startNodeX.bat或startNodeX.sh，运行几个节点。
  • 运行
下图为执行一个100万的uuid数据推数据处理大约10s

采用一个Leader节点负责从接收需要分发的数据并进行分发任务数据，Leader不处理任务。两个worker节点轮询分配任务。

image-20201031175019857

PS：每一个任务数据在执行过程中只有提交成功才算是执行完成，若出现提交超时或其他错误，将再次分发重试，以确保任务执行成功。所以任务至少被执行一次，若执行失败为提交会多次重试，最多重试3次。

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简介

一个分布式任务分发框架核心包，一个内存型的MQ。将`ws-task-core`引入到自己的application，你就可以将自己的普通application转变成一个分布式任务分发application。`ws-task-core`主要提供了Leader节点自动选举，动态增删节点，task数据分发。提供了两种类型的任务，拉数据模式，推数据模式。数据分发支持轮询，随机，HASH。可配置leader是否参与任务处理，可配置节点是否参与leader选举（见下面示例）。 展开 收起
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开源评估指数源自 OSS-Compass 评估体系，评估体系围绕以下三个维度对项目展开评估：

1. 开源生态

  • 生产力：来评估开源项目输出软件制品和开源价值的能力。
  • 创新力：用于评估开源软件及其生态系统的多样化程度。
  • 稳健性：用于评估开源项目面对多变的发展环境，抵御内外干扰并自我恢复的能力。

2. 协作、人、软件

  • 协作：代表了开源开发行为中协作的程度和深度。
  • 人：观察开源项目核心人员在开源项目中的影响力，并通过第三方视角考察用户和开发者对开源项目的评价。
  • 软件：从开源项目对外输出的制品评估其价值最终落脚点。也是开源评估最“古老”的主流方向之一“开源软件” 的具体表现。

3. 评估模型

    基于“开源生态”与“协作、人、软件”的维度，找到与该目标直接或间接相关的可量化指标，对开源项目健康与生态进行量化评估，最终形成开源评估指数。

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