Browse Source

Update README.md

pull/2/head
easyscheduler 5 years ago committed by GitHub
parent
commit
c2020437d9
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
  1. 8
      README.md

8
README.md

@ -30,20 +30,20 @@ Its main objectives are as follows:
  | EasyScheduler | Azkaban | Airflow   | EasyScheduler | Azkaban | Airflow
-- | -- | -- | -- -- | -- | -- | --
Stability |   |   |   **Stability** |   |   |  
Single point of failure | Decentralized multi-master and multi-worker | Yes Single Web and Scheduler Combination Node | Yes. Single Scheduler 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 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 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 |   |   |   **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 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. 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 Quick deployment | One-click deployment | Complex clustering deployment | Complex clustering deployment
Features |   |   |   **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 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 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 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. 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 |   |   |   **Scalability** |   |   |  
Whether to support custom task types | Yes | Yes | Yes 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 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

Loading…
Cancel
Save