From df9d89f4f7442a330dce4ffb98ab4a58279d9ba7 Mon Sep 17 00:00:00 2001 From: easyscheduler Date: Thu, 18 Jul 2019 16:30:31 +0800 Subject: [PATCH] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 6506b16da..12eb925dc 100644 --- a/README.md +++ b/README.md @@ -31,21 +31,21 @@ Its main objectives are as follows:   | EasyScheduler | Azkaban | Airflow -- | -- | -- | -- **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 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 +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 +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 +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 -- GitLab