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Easy Scheduler是一个分布式工作流任务调度系统,主要解决数据研发ETL错综复杂的依赖关系,而不能直观监控任务健康状态等问题。Easy Scheduler以DAG流式的方式将Task组装起来,可实时监控任务的运行状态,同时支持重试、从指定节点恢复失败、暂停及Kill任务等操作。EasyScheduler由在工作流调度方面工作多年的多位小伙伴研发而成,致力于成为大数据平台的中流砥柱,使调度变得更加容易,更可以从其中文名“易调度”看出我们的初衷... spread retract

https://analysys.github.io/easyscheduler_docs_cn/

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README.md

Easy Scheduler

License

Easy Scheduler for Big Data

Design features:

A distributed and easy-to-expand visual DAG workflow scheduling system. Dedicated to solving the complex dependencies in the data processing process, making the scheduling system out of the box in the data processing process. Its main objectives are as follows:

  • Associate the Tasks according to the dependencies of the tasks in a DAG graph, which can visualize the running state of task in real time.
  • Support for many task types: Shell, MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Sub_Process, Procedure, etc.
  • Support process scheduling, dependency scheduling, manual scheduling, manual pause/stop/recovery, support for failed retry/alarm, recovery from specified nodes, Kill task, etc.
  • Support process priority, task priority and task failover and task timeout alarm/failure
  • Support process global parameters and node custom parameter settings
  • Support online upload/download of resource files, management, etc. Support online file creation and editing
  • Support task log online viewing and scrolling, online download log, etc.
  • Implement cluster HA, decentralize Master cluster and Worker cluster through Zookeeper
  • Support online viewing of Master/Worker cpu load, memory, cpu
  • Support process running history tree/gantt chart display, support task status statistics, process status statistics
  • Support for complement
  • Support for multi-tenant
  • Support internationalization
  • There are more waiting partners to explore

Comparison with similar scheduler systems

  EasyScheduler Azkaban Airflow
Stability      
Single point of failure Decentralized multi-master and multi-worker Yes Single Web and Scheduler Combination Node Yes. Single Scheduler
Additional HA requirements Not required (HA is supported by itself) DB Celery / Dask / Mesos + Load Balancer + DB
Overload processing Task queue mechanism, the number of schedulable tasks on a single machine can be flexibly configured, when too many tasks will be cached in the task queue, will not cause machine jam. Jammed the server when there are too many tasks Jammed the server when there are too many tasks
Easy to use      
DAG Monitoring Interface Visualization process defines key information such as task status, task type, retry times, task running machine, visual variables and so on at a glance. Only task status can be seen Can't visually distinguish task types
Visual process definition Yes All process definition operations are visualized, dragging tasks to draw DAGs, configuring data sources and resources. At the same time, for third-party systems, the api mode operation is provided. No DAG and custom upload via custom DSL No DAG is drawn through Python code, which is inconvenient to use, especially for business people who can't write code.
Quick deployment One-click deployment Complex clustering deployment Complex clustering deployment
Features      
Suspend and resume Support pause, recover operation No Can only kill the workflow first and then re-run No Can only kill the workflow first and then re-run
Whether to support multiple tenants Users on easyscheduler can achieve many-to-one or one-to-one mapping relationship through tenants and Hadoop users, which is very important for scheduling large data jobs. " Supports traditional shell tasks, while supporting large data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process No No
Task type Supports traditional shell tasks, and also support big data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process shell、gobblin、hadoopJava、java、hive、pig、spark、hdfsToTeradata、teradataToHdfs BashOperator、DummyOperator、MySqlOperator、HiveOperator、EmailOperator、HTTPOperator、SqlOperator
Compatibility Support the scheduling of big data jobs like spark, hive, Mr. At the same time, it is more compatible with big data business because it supports multiple tenants. Because it does not support multi-tenant, it is not flexible enough to use business in big data platform. Because it does not support multi-tenant, it is not flexible enough to use business in big data platform.
Scalability      
Whether to support custom task types Yes Yes Yes
Is Cluster Extension Supported? Yes The scheduler uses distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. Master and Worker support dynamic online and offline. Yes, but complicated Executor horizontal extend Yes, but complicated Executor horizontal extend

System partial screenshot

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Document

More documentation please refer to [EasyScheduler online documentation]

Recent R&D plan

Work plan of Easy Scheduler: R&D plan, where In Develop card is the features of 1.1.0 version , TODO card is to be done (including feature ideas)

How to contribute code

Welcome to participate in contributing code, please refer to the process of submitting the code: https://github.com/analysys/EasyScheduler/blob/master/CONTRIBUTING.md

Thanks

Easy Scheduler uses a lot of excellent open source projects, such as google guava, guice, grpc, netty, ali bonecp, quartz, and many open source projects of apache, etc. It is because of the shoulders of these open source projects that the birth of the Easy Scheduler is possible. We are very grateful for all the open source software used! We also hope that we will not only be the beneficiaries of open source, but also be open source contributors, so we decided to contribute to easy scheduling and promised long-term updates. I also hope that partners who have the same passion and conviction for open source will join in and contribute to open source!

Help

The fastest way to get response from our developers is to submit issues, or add our wechat : 510570367

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