RocMarshal commented on code in PR #27128:
URL: https://github.com/apache/flink/pull/27128#discussion_r2501507379


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docs/content/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
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+---
+title: Balanced Tasks Scheduling
+weight: 5
+type: docs
+
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
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+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.
+-->
+
+# Balanced Tasks Scheduling
+
+This page describes the background and principle of balanced tasks scheduling, 
+how to use it when running streaming jobs.
+
+## Background
+
+When the parallelism of all vertices within a Flink streaming job is 
inconsistent,
+the [default strategy]({{< ref "docs/deployment/config" 
>}}#taskmanager-load-balance-mode)
+of Flink to deploy tasks sometimes leads some `TaskManagers` have more tasks 
while others have fewer tasks, 
+resulting in excessive resource utilization at some `TaskManagers` 
+that contain more tasks and becoming a bottleneck for the entire job 
processing.
+
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg" 
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+
+As shown in figure (a), given a Flink job comprising two vertices, 
`JobVertex-A (JV-A)` and `JobVertex-B (JV-B)`, 
+with parallelism degrees of `6` and `3` respectively,
+and both vertices sharing the same slot sharing group.
+Under the default tasks scheduling strategy, as illustrated in figure (b), 
+the distribution of tasks across `TaskManagers` may result in significant 
disparities in task load. 
+Specifically, the `TaskManager`s with the highest number of tasks may host `4` 
tasks, 
+while the one with the lowest load may have only `2` tasks. 
+Consequently, the `TaskManager`s bearing 4 tasks is prone to become a 
performance bottleneck for the entire job.
+
+Therefore, Flink provides a task-quantity-based balanced tasks scheduling 
capability. 
+Within the job's resource view, it aims to ensure that the number of tasks 
+scheduled to each `TaskManager` as close as possible to, thereby improving the 
resource usage skew among `TaskManagers`.
+
+## Principle
+
+The task-quantity-based load balancing tasks scheduling strategy completes the 
assignment of tasks to `TaskManagers` in two phases: 
+- The tasks-to-slots assignment phase 
+- The slots-to-TaskManagers assignment phase
+
+This section will use two examples to illustrate the simplified process and 
principle of 
+how the task-quantity-based tasks scheduling strategy handles the assignments 
in these two phases.
+
+### The tasks-to-slots assignment phase
+
+Taking the job shown in figure (c) as an example, it contains five job 
vertices with parallelism degrees of `1`, `4`, `4`, `2`, and `3`, respectively.
+All five job vertices belong to the default slot sharing group.  
+
+{{< img 
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg" 
alt="The Tasks To Slots Allocation Principle Demo" class="offset" width="65%" 
>}}
+
+During the tasks-to-slots assignment phase, this tasks scheduling strategy:  
+- First directly assigns the tasks of the vertices with the highest 
parallelism to the `i-th` slot. 
+
+  That is, task `JV-Bi` is assigned directly to `sloti`, and task `JV-Ci` is 
assigned directly to `sloti`.
+
+- Next, for tasks belonging to job vertices with sub-maximal parallelism, they 
are assigned in a round-robin fashion across the slots within the current
+slot sharing group until all tasks are allocated.
+
+As shown in figure (e), under the task-quantity-based assignment strategy, the 
range (max-min difference) of the number of tasks per slot is `1`, 
+which is better than the range of `3` under the default strategy shown in 
figure (d).
+
+Thus, this ensures a more balanced distribution of the number of tasks across 
slots.
+
+### The slots-to-TaskManagers assignment phase
+
+As shown in figure (f), given a Flink job comprising two vertices, `JV-A` and 
`JV-B`, with parallelism of `6` and `3` respectively,
+and both vertices sharing the same slot sharing group.
+
+{{< img 
src="/fig/deployments/tasks-scheduling/slots_to_taskmanagers_allocation_principle.svg"
 alt="The Slots to TaskManagers Allocation Principle Demo" class="offset" 
width="75%" >}}
+
+The assignment result after the first phase is shown in figure (g), 
+where `Slot0`, `Slot1`, and `Slot2` each contain `2` tasks, while the 
remaining slots contain `1` task each.
+
+Subsequently:
+- The strategy submits all slot requests and waits until all slot resources 
required for the current job are ready.
+
+Once the slot resources are ready:  
+- The strategy then sorts all slot requests in descending order based on the 
number of tasks contained in each request. 
+Afterwards, it sequentially assigns each slot request to the `TaskManager` 
with the smallest current tasks loading. 
+This process continues until all slot requests have been allocated.
+
+The final assignment result is shown in figure (i), where each `TaskManager` 
ends up with exactly `3` tasks, 
+resulting in a task count difference of `0` between `TaskManagers`. In 
contrast, the scheduling result under the default strategy, 
+shown in figure (h), has a task count difference of `2` between 
`TaskManagers`. 
+
+Therefore, theoretically, using this load balancing tasks scheduling strategy 
could effectively mitigate the issue of 

Review Comment:
   @davidradl 
   Thanks
   
   Updated as:
   
   ```
   Therefore, if you are seeing performance bottlenecks of the sort described 
above,
   then using this load balancing tasks scheduling strategy can improve 
performance.
   Be aware that you should not use this strategy, if you are not seeing these 
bottlenecks,
   as you may experience performance degradation.
   ```



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