@ -27,27 +27,14 @@ Its main objectives are as follows:
- 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 <br/> Single Web and Scheduler Combination Node | Yes <br/> 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 <br/> 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 <br/> DAG and custom upload via custom DSL | No <br/> DAG is drawn through Python code, which is inconvenient to use, especially for business people who can't write code.
Suspend and resume | Support pause, recover operation | No <br/> Can only kill the workflow first and then re-run | No <br/> 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 <br/> 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 <br/> but complicated Executor horizontal extend | Yes <br/> but complicated Executor horizontal extend
### what's in the scheduler systems
| Stability | Easy to use | Features | Scalability |
-- | -- | -- | -- | --
Decentralized multi-master and multi-worker | Visualization process defines key information such as task status, task type, retry times, task running machine, visual variables and so on at a glance. | Support pause, recover operation | support custom task types
HA is supported by itself | 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. | 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 | 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.
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. | One-click deployment | Supports traditional shell tasks, and also support big data platform task scheduling: MR, Spark, SQL (mysql, postgresql, hive, sparksql), Python, Procedure, Sub_Process | |
lenboo
5 years ago