zhuzhurk commented on a change in pull request #18757:
URL: https://github.com/apache/flink/pull/18757#discussion_r809623828
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File path: docs/content.zh/docs/deployment/elastic_scaling.md
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@@ -149,5 +149,44 @@ Adaptive 调度器可以通过[所有在名字包含 `adaptive-scheduler` 的配
- **空闲 Slot**: 如果 Slot 共享组的最大并行度不相等，提供给 Adaptive 调度器所使用的的 Slot 可能不会被使用。
- 扩缩容事件会触发 Job 和 Task 重启，Task 重试的次数也会增加。
+## Adaptive Batch Scheduler
+
+Adaptive Batch Scheduler
是一种可以自动推导每个算子并行度的批作业处理调度器。如果算子未设置并行度，调度器将根据其消费的数据量的大小来推导其并行度。这可以带来诸多好处：
Review comment:
批作业处理 -> 批处理作业 or 批作业
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File path: docs/content/docs/deployment/adaptive_batch_scheduler.md
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@@ -0,0 +1,63 @@
+---
+title: Adaptive Batch Scheduler
+weight: 5
+type: docs
+
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+## Adaptive Batch Scheduler
+
+The Adaptive Batch Scheduler can automatically decide parallelisms of job
vertices for batch jobs. If a job vertex is not set with a parallelism, the
scheduler will decide parallelism for the job vertex according to the size of
its consumed datasets. This can bring many benefits:
+- Batch job users can be relieved from parallelism tuning
+- Automatically tuned parallelisms can be vertex level and can better fit
consumed datasets which have a varying volume size every day
+- Vertices from SQL batch jobs can be assigned with different parallelisms
which are automatically tuned
Review comment:
+ 1 for `operator`. It is the concept that users can/must understand. I
think adaptively deciding parallelisms does mean to adaptively deciding
parallelisms for operators. We just do not want to break beneficial operator
chaining, so that parallelisms are decided for OperatorChain/JobVertex.
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File path: docs/content/docs/deployment/adaptive_batch_scheduler.md
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@@ -0,0 +1,63 @@
+---
+title: Adaptive Batch Scheduler
+weight: 5
+type: docs
+
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+## Adaptive Batch Scheduler
+
+The Adaptive Batch Scheduler can automatically decide parallelisms of job
vertices for batch jobs. If a job vertex is not set with a parallelism, the
scheduler will decide parallelism for the job vertex according to the size of
its consumed datasets. This can bring many benefits:
+- Batch job users can be relieved from parallelism tuning
+- Automatically tuned parallelisms can be vertex level and can better fit
consumed datasets which have a varying volume size every day
+- Vertices from SQL batch jobs can be assigned with different parallelisms
which are automatically tuned
Review comment:
+1 for `operator`. It is the concept that users can/must understand. I
think adaptively deciding parallelisms does mean to adaptively deciding
parallelisms for operators. We just do not want to break beneficial operator
chaining, so that parallelisms are decided for OperatorChain/JobVertex.
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