走进Netflix Conductor之微服务编排 |
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走进Netflix Conductor之微服务编排
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服务编排 以现有分布式微服务为基础数据，搭建分布式服务编排平台。目标是现有服务无需特殊改造即能灵活装配出新的业务能力，更好地发挥现有服务的业务价值，减少冗余的服务数量。提供丰富的编排能力，适应客户丰富的业务场景，并保障配置的业务流程能高效运行。除了以上基本功能外，还保证分布式数据的一致性、高并发、跨可用区高可用。 价值服务编排系统可以帮助我们编排现有的微服务，以提高现有业务的整合能力，极大程度实现服务重用、服务增强，减少了开发成本，从而实现企业的降本增效，以及加速企业新业务研发。 场景 奈飞 - 内容工程 内容提取与传送 内容本地化 内容质量控制 现有业务增强，比如资产交易形态的转变 打通现有孤立业务，比如打通HR/CMS/OA独立的系统功能 根据现有业务编织新业务，比如复用现有的CMS 工程解析 ArtifactDescriptionconductor-commonCommon models used by various conductor modulesconductor-coreCore Conductor moduleconductor-redis-persistencePersistence and queue using Redis/Dynomiteconductor-cassandra-persistencePersistence using Cassandraconductor-mysql-persistencePersistence and queue using MySQLconductor-postgres-persistencePersistence and queue using Postgresconductor-es6-persistenceIndexing using Elasticsearch 6.Xconductor-restSpring MVC resources for the core servicesconductor-uinode.js based UI for Conductorconductor-contribsOptional contrib package that holds extended workflow tasks and support for SQS, AMQP, etcconductor-clientJava client for Conductor that includes helpers for running worker tasksconductor-client-springClient starter kit for Springconductor-serverSpring Boot Web Applicationconductor-azureblob-storageExternal payload storage implementation using AzureBlobconductor-redis-lockWorkflow execution lock implementation using Redisconductor-zookeeper-lockWorkflow execution lock implementation using Zookeeperconductor-grpcProtobuf models used by the server and clientconductor-grpc-clientgRPC server Applicationconductor-grpc-servergRPC client to interact with the gRPC serverconductor-test-harnessIntegration and regression tests 核心亮点 无状态 多数据中心 分布式锁 基于中间件的任务队列 可插拔组件 模块化加载 Worker实现 * 实现Worker Interface public class SampleWorker implements Worker { private String taskDefName; public SampleWorker(String taskDefName) { this.taskDefName = taskDefName; } @Override public String getTaskDefName() { return taskDefName; } @Override public TaskResult execute(Task task) { TaskResult result = new TaskResult(task); result.setStatus(Status.COMPLETED); //Register the output of the task result.getOutputData().put("outputKey1", "value"); result.getOutputData().put("oddEven", 1); result.getOutputData().put("mod", 4); return result; } } 轮询任务队列 public static void main(String[] args) { TaskClient taskClient = new TaskClient(); taskClient.setRootURI("http://localhost:8080/api/"); //Point this to the server API int threadCount = 2; //number of threads used to execute workers. To avoid starvation, should be same or more than number of workers Worker worker1 = new SampleWorker("task_1"); Worker worker2 = new SampleWorker("task_5"); // Create TaskRunnerConfigurer TaskRunnerConfigurer configurer = new TaskRunnerConfigurer.Builder(taskClient, Arrays.asList(worker1, worker2)) .withThreadCount(threadCount) .build(); // Start the polling and execution of tasks configurer.init(); } Demo展示 |
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