ptyp commented on a change in pull request #4374:
URL:
https://github.com/apache/incubator-dolphinscheduler/pull/4374#discussion_r552470089
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File path: README.md
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@@ -41,9 +41,9 @@ Its main objectives are as follows:
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 DolphinScheduler 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. " | 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 | |
+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 Dolphin Scheduler 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. | 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.
Review comment:
Hi, actually I can't quite understand these sentences or phrases and
this chart is missing in the Chinese version. Hence, I just roughly had a look
and modified this part. I will fix the third person singular later but sorry I
can't go deeper.
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