klion26 commented on code in PR #27805:
URL: https://github.com/apache/flink/pull/27805#discussion_r2972512868
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
Review Comment:
Will `在运行流处理作业` -> `在流处理作业` be better?
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
-## Background
+## 背景
-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.
+当 Flink 流处理作业中所有顶点的并行度不一致时，Flink 部署 Task 的[默认策略]({{< ref
"docs/deployment/config" >}}#taskmanager-load-balance-mode)有时会导致某些 TaskManager
部署的 Task 较多，而其他 TaskManager 部署的 Task 较少，从而造成部署 Task 较多的 TaskManager
资源使用过度，成为整个作业处理的瓶颈。
-{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="任务调度倾斜示例" 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.
+如图（a）所示，假设有一个 Flink 作业包含两个顶点：`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）`，并行度分别为
`6` 和 `3`，且两个顶点属于同一个 Slot 共享组。在默认 Task 调度策略下，如图（b）所示，Task 在 TaskManager
之间的分布可能导致 Task 负载显著不均。具体来说，Task 数量最多的 TaskManager 可能承载 `4` 个任务，而负载最低的
TaskManager 可能只有 `2` 个任务。因此，承载 4 个 Task 的 TaskManager 容易成为整个作业的性能瓶颈。
-Therefore, Flink provides the 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`.
+因此，Flink 提供了基于 Task 数量的 Task 调度均衡能力。在作业的资源视图中，它旨在确保分配给每个 TaskManager
的任务数量尽可能接近，从而改善 TaskManager 之间的资源使用倾斜。
-<span class="label label-info">Note</span> The presence of inconsistent
parallelism does not imply that this strategy must be used, as this is not
always the case in practice.
+<span class="label label-info">注意</span> 并非并行度不一致就必须使用此策略，需根据实际情况判断。
-## Principle
+## 原理
-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
+基于 Task 数量的负载均衡调度策略将 Task 分配给 TaskManager 的过程分为两个阶段：
+- Task 到 Slot 的分配阶段
+- Slot 到 TaskManager 的分配阶段
-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.
+本节将通过两个示例对基于 Task 数量的调度策略在上述两个阶段中处理 Task 分配的简化流程及其原理加以说明。
-### The tasks-to-slots assignment phase
+### Task 到 Slot 的分配阶段
-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.
+以图（c）所示的作业为例，它包含五个作业顶点，并行度分别为 `1`、`4`、`4`、`2` 和 `3`。这五个作业顶点都属于默认 Slot 共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="The Tasks To Slots Allocation Principle Demo" class="offset" width="65%"
>}}
+{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="Task 到 Slot 分配原理示例" 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.
+在 Task 到 Slot 的分配阶段，该调度策略：
+- 首先直接将并行度最高的顶点的 Task 分配到第 `i` 个 Slot。
Review Comment:
`并行度最高的顶点` -> `并行度最高顶点`
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -1,5 +1,5 @@
---
-title: Balanced Tasks Scheduling
+title: Task 调度均衡
Review Comment:
And the other places.
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -1,5 +1,5 @@
---
-title: Balanced Tasks Scheduling
+title: Task 调度均衡
Review Comment:
`Task 调度均衡` -> `Task 均衡调度`
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
-## Background
+## 背景
-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.
+当 Flink 流处理作业中所有顶点的并行度不一致时，Flink 部署 Task 的[默认策略]({{< ref
"docs/deployment/config" >}}#taskmanager-load-balance-mode)有时会导致某些 TaskManager
部署的 Task 较多，而其他 TaskManager 部署的 Task 较少，从而造成部署 Task 较多的 TaskManager
资源使用过度，成为整个作业处理的瓶颈。
-{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="任务调度倾斜示例" 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.
+如图（a）所示，假设有一个 Flink 作业包含两个顶点：`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）`，并行度分别为
`6` 和 `3`，且两个顶点属于同一个 Slot 共享组。在默认 Task 调度策略下，如图（b）所示，Task 在 TaskManager
之间的分布可能导致 Task 负载显著不均。具体来说，Task 数量最多的 TaskManager 可能承载 `4` 个任务，而负载最低的
TaskManager 可能只有 `2` 个任务。因此，承载 4 个 Task 的 TaskManager 容易成为整个作业的性能瓶颈。
Review Comment:
`假设有一个 Flink 作业` -> `假设 Flink 作业`?
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
-## Background
+## 背景
-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.
+当 Flink 流处理作业中所有顶点的并行度不一致时，Flink 部署 Task 的[默认策略]({{< ref
"docs/deployment/config" >}}#taskmanager-load-balance-mode)有时会导致某些 TaskManager
部署的 Task 较多，而其他 TaskManager 部署的 Task 较少，从而造成部署 Task 较多的 TaskManager
资源使用过度，成为整个作业处理的瓶颈。
-{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="任务调度倾斜示例" 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.
+如图（a）所示，假设有一个 Flink 作业包含两个顶点：`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）`，并行度分别为
`6` 和 `3`，且两个顶点属于同一个 Slot 共享组。在默认 Task 调度策略下，如图（b）所示，Task 在 TaskManager
之间的分布可能导致 Task 负载显著不均。具体来说，Task 数量最多的 TaskManager 可能承载 `4` 个任务，而负载最低的
TaskManager 可能只有 `2` 个任务。因此，承载 4 个 Task 的 TaskManager 容易成为整个作业的性能瓶颈。
-Therefore, Flink provides the 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`.
+因此，Flink 提供了基于 Task 数量的 Task 调度均衡能力。在作业的资源视图中，它旨在确保分配给每个 TaskManager
的任务数量尽可能接近，从而改善 TaskManager 之间的资源使用倾斜。
-<span class="label label-info">Note</span> The presence of inconsistent
parallelism does not imply that this strategy must be used, as this is not
always the case in practice.
+<span class="label label-info">注意</span> 并非并行度不一致就必须使用此策略，需根据实际情况判断。
-## Principle
+## 原理
-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
+基于 Task 数量的负载均衡调度策略将 Task 分配给 TaskManager 的过程分为两个阶段：
+- Task 到 Slot 的分配阶段
+- Slot 到 TaskManager 的分配阶段
-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.
+本节将通过两个示例对基于 Task 数量的调度策略在上述两个阶段中处理 Task 分配的简化流程及其原理加以说明。
-### The tasks-to-slots assignment phase
+### Task 到 Slot 的分配阶段
-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.
+以图（c）所示的作业为例，它包含五个作业顶点，并行度分别为 `1`、`4`、`4`、`2` 和 `3`。这五个作业顶点都属于默认 Slot 共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="The Tasks To Slots Allocation Principle Demo" class="offset" width="65%"
>}}
+{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="Task 到 Slot 分配原理示例" 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.
+在 Task 到 Slot 的分配阶段，该调度策略：
+- 首先直接将并行度最高的顶点的 Task 分配到第 `i` 个 Slot。
- That is, task `JV-Bi` is assigned directly to `sloti`, and task `JV-Ci` is
assigned directly to `sloti`.
+ 即将任务 `JV-Bi` 直接分配到 `sloti`，将任务 `JV-Ci` 也直接分配到 `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.
+- 接下来，对于属于次高并行度作业顶点的 Task ，以轮询方式分配给当前 Slot 共享组内的 Slot，直到所有 Task 分配完毕。
-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).
+如图（e）所示，在基于 Task 数量的分配策略下，每个 Slot 的任务数量范围（最大值与最小值之差）为 `1`，这优于图（d）所示默认策略下的范围
`3`。
-Thus, this ensures a more balanced distribution of the number of tasks across
slots.
+因此，这确保了 Slot 之间 Task 数量更加均衡地分布。
-### The slots-to-TaskManagers assignment phase
+### Slot 到 TaskManager 的分配阶段
-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.
+如图（f）所示，假设有一个 Flink 作业包含两个顶点 `JV-A` 和 `JV-B`，并行度分别为 `6` 和 `3`，且两个顶点属于同一个 Slot
共享组。
Review Comment:
`有一个 Flink 作业` -> `Flink 作业`
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
-## Background
+## 背景
-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.
+当 Flink 流处理作业中所有顶点的并行度不一致时，Flink 部署 Task 的[默认策略]({{< ref
"docs/deployment/config" >}}#taskmanager-load-balance-mode)有时会导致某些 TaskManager
部署的 Task 较多，而其他 TaskManager 部署的 Task 较少，从而造成部署 Task 较多的 TaskManager
资源使用过度，成为整个作业处理的瓶颈。
-{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="任务调度倾斜示例" 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.
+如图（a）所示，假设有一个 Flink 作业包含两个顶点：`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）`，并行度分别为
`6` 和 `3`，且两个顶点属于同一个 Slot 共享组。在默认 Task 调度策略下，如图（b）所示，Task 在 TaskManager
之间的分布可能导致 Task 负载显著不均。具体来说，Task 数量最多的 TaskManager 可能承载 `4` 个任务，而负载最低的
TaskManager 可能只有 `2` 个任务。因此，承载 4 个 Task 的 TaskManager 容易成为整个作业的性能瓶颈。
-Therefore, Flink provides the 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`.
+因此，Flink 提供了基于 Task 数量的 Task 调度均衡能力。在作业的资源视图中，它旨在确保分配给每个 TaskManager
的任务数量尽可能接近，从而改善 TaskManager 之间的资源使用倾斜。
-<span class="label label-info">Note</span> The presence of inconsistent
parallelism does not imply that this strategy must be used, as this is not
always the case in practice.
+<span class="label label-info">注意</span> 并非并行度不一致就必须使用此策略，需根据实际情况判断。
-## Principle
+## 原理
-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
+基于 Task 数量的负载均衡调度策略将 Task 分配给 TaskManager 的过程分为两个阶段：
+- Task 到 Slot 的分配阶段
+- Slot 到 TaskManager 的分配阶段
-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.
+本节将通过两个示例对基于 Task 数量的调度策略在上述两个阶段中处理 Task 分配的简化流程及其原理加以说明。
-### The tasks-to-slots assignment phase
+### Task 到 Slot 的分配阶段
-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.
+以图（c）所示的作业为例，它包含五个作业顶点，并行度分别为 `1`、`4`、`4`、`2` 和 `3`。这五个作业顶点都属于默认 Slot 共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="The Tasks To Slots Allocation Principle Demo" class="offset" width="65%"
>}}
+{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="Task 到 Slot 分配原理示例" 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.
+在 Task 到 Slot 的分配阶段，该调度策略：
+- 首先直接将并行度最高的顶点的 Task 分配到第 `i` 个 Slot。
- That is, task `JV-Bi` is assigned directly to `sloti`, and task `JV-Ci` is
assigned directly to `sloti`.
+ 即将任务 `JV-Bi` 直接分配到 `sloti`，将任务 `JV-Ci` 也直接分配到 `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.
+- 接下来，对于属于次高并行度作业顶点的 Task ，以轮询方式分配给当前 Slot 共享组内的 Slot，直到所有 Task 分配完毕。
-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).
+如图（e）所示，在基于 Task 数量的分配策略下，每个 Slot 的任务数量范围（最大值与最小值之差）为 `1`，这优于图（d）所示默认策略下的范围
`3`。
-Thus, this ensures a more balanced distribution of the number of tasks across
slots.
+因此，这确保了 Slot 之间 Task 数量更加均衡地分布。
-### The slots-to-TaskManagers assignment phase
+### Slot 到 TaskManager 的分配阶段
-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.
+如图（f）所示，假设有一个 Flink 作业包含两个顶点 `JV-A` 和 `JV-B`，并行度分别为 `6` 和 `3`，且两个顶点属于同一个 Slot
共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/slots_to_taskmanagers_allocation_principle.svg"
alt="The Slots to TaskManagers Allocation Principle Demo" class="offset"
width="75%" >}}
+{{< img
src="/fig/deployments/tasks-scheduling/slots_to_taskmanagers_allocation_principle.svg"
alt="Slot 到 TaskManager 分配原理示例" 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.
+第一阶段的分配结果如图（g）所示，其中 `Slot0`、`Slot1` 和 `Slot2` 各包含 `2` 个 Task ，其余 Slot 各包含 `1`
个 Task 。
-Subsequently:
-- The strategy submits all slot requests and waits until all slot resources
required for the current job are ready.
+随后：
+- 策略提交所有 Slot 请求，并等待当前作业所需的所有 Slot 资源准备就绪。
-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.
-Afterward, 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.
+Slot 资源准备就绪后：
+- 策略首先根据每个请求包含的 Task 数量按降序对所有 Slot 请求进行排序。然后，依次将每个 Slot 请求分配给当前 Task 负载最小的
TaskManager。此过程持续进行，直到所有 Slot 请求分配完毕。
Review Comment:
`策略首先` -> `首先`
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
-## Background
+## 背景
-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.
+当 Flink 流处理作业中所有顶点的并行度不一致时，Flink 部署 Task 的[默认策略]({{< ref
"docs/deployment/config" >}}#taskmanager-load-balance-mode)有时会导致某些 TaskManager
部署的 Task 较多，而其他 TaskManager 部署的 Task 较少，从而造成部署 Task 较多的 TaskManager
资源使用过度，成为整个作业处理的瓶颈。
-{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="任务调度倾斜示例" 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.
+如图（a）所示，假设有一个 Flink 作业包含两个顶点：`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）`，并行度分别为
`6` 和 `3`，且两个顶点属于同一个 Slot 共享组。在默认 Task 调度策略下，如图（b）所示，Task 在 TaskManager
之间的分布可能导致 Task 负载显著不均。具体来说，Task 数量最多的 TaskManager 可能承载 `4` 个任务，而负载最低的
TaskManager 可能只有 `2` 个任务。因此，承载 4 个 Task 的 TaskManager 容易成为整个作业的性能瓶颈。
Review Comment:
`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）` -> `JobVertex-A(JV-A)` 和
`JobVertex-B(JV-B)`
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
-## Background
+## 背景
-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.
+当 Flink 流处理作业中所有顶点的并行度不一致时，Flink 部署 Task 的[默认策略]({{< ref
"docs/deployment/config" >}}#taskmanager-load-balance-mode)有时会导致某些 TaskManager
部署的 Task 较多，而其他 TaskManager 部署的 Task 较少，从而造成部署 Task 较多的 TaskManager
资源使用过度，成为整个作业处理的瓶颈。
-{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="任务调度倾斜示例" 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.
+如图（a）所示，假设有一个 Flink 作业包含两个顶点：`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）`，并行度分别为
`6` 和 `3`，且两个顶点属于同一个 Slot 共享组。在默认 Task 调度策略下，如图（b）所示，Task 在 TaskManager
之间的分布可能导致 Task 负载显著不均。具体来说，Task 数量最多的 TaskManager 可能承载 `4` 个任务，而负载最低的
TaskManager 可能只有 `2` 个任务。因此，承载 4 个 Task 的 TaskManager 容易成为整个作业的性能瓶颈。
-Therefore, Flink provides the 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`.
+因此，Flink 提供了基于 Task 数量的 Task 调度均衡能力。在作业的资源视图中，它旨在确保分配给每个 TaskManager
的任务数量尽可能接近，从而改善 TaskManager 之间的资源使用倾斜。
-<span class="label label-info">Note</span> The presence of inconsistent
parallelism does not imply that this strategy must be used, as this is not
always the case in practice.
+<span class="label label-info">注意</span> 并非并行度不一致就必须使用此策略，需根据实际情况判断。
-## Principle
+## 原理
-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
+基于 Task 数量的负载均衡调度策略将 Task 分配给 TaskManager 的过程分为两个阶段：
+- Task 到 Slot 的分配阶段
+- Slot 到 TaskManager 的分配阶段
-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.
+本节将通过两个示例对基于 Task 数量的调度策略在上述两个阶段中处理 Task 分配的简化流程及其原理加以说明。
-### The tasks-to-slots assignment phase
+### Task 到 Slot 的分配阶段
-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.
+以图（c）所示的作业为例，它包含五个作业顶点，并行度分别为 `1`、`4`、`4`、`2` 和 `3`。这五个作业顶点都属于默认 Slot 共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="The Tasks To Slots Allocation Principle Demo" class="offset" width="65%"
>}}
+{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="Task 到 Slot 分配原理示例" 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.
+在 Task 到 Slot 的分配阶段，该调度策略：
+- 首先直接将并行度最高的顶点的 Task 分配到第 `i` 个 Slot。
- That is, task `JV-Bi` is assigned directly to `sloti`, and task `JV-Ci` is
assigned directly to `sloti`.
+ 即将任务 `JV-Bi` 直接分配到 `sloti`，将任务 `JV-Ci` 也直接分配到 `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.
+- 接下来，对于属于次高并行度作业顶点的 Task ，以轮询方式分配给当前 Slot 共享组内的 Slot，直到所有 Task 分配完毕。
-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).
+如图（e）所示，在基于 Task 数量的分配策略下，每个 Slot 的任务数量范围（最大值与最小值之差）为 `1`，这优于图（d）所示默认策略下的范围
`3`。
-Thus, this ensures a more balanced distribution of the number of tasks across
slots.
+因此，这确保了 Slot 之间 Task 数量更加均衡地分布。
-### The slots-to-TaskManagers assignment phase
+### Slot 到 TaskManager 的分配阶段
-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.
+如图（f）所示，假设有一个 Flink 作业包含两个顶点 `JV-A` 和 `JV-B`，并行度分别为 `6` 和 `3`，且两个顶点属于同一个 Slot
共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/slots_to_taskmanagers_allocation_principle.svg"
alt="The Slots to TaskManagers Allocation Principle Demo" class="offset"
width="75%" >}}
+{{< img
src="/fig/deployments/tasks-scheduling/slots_to_taskmanagers_allocation_principle.svg"
alt="Slot 到 TaskManager 分配原理示例" 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.
+第一阶段的分配结果如图（g）所示，其中 `Slot0`、`Slot1` 和 `Slot2` 各包含 `2` 个 Task ，其余 Slot 各包含 `1`
个 Task 。
-Subsequently:
-- The strategy submits all slot requests and waits until all slot resources
required for the current job are ready.
+随后：
+- 策略提交所有 Slot 请求，并等待当前作业所需的所有 Slot 资源准备就绪。
-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.
-Afterward, 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.
+Slot 资源准备就绪后：
+- 策略首先根据每个请求包含的 Task 数量按降序对所有 Slot 请求进行排序。然后，依次将每个 Slot 请求分配给当前 Task 负载最小的
TaskManager。此过程持续进行，直到所有 Slot 请求分配完毕。
-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`.
+最终分配结果如图（i）所示，每个 TaskManager 最终恰好承载 `3` 个 Task ，TaskManager 之间的 Task 数量差异为
`0`。相比之下，默认策略下的调度结果如图（h）所示，TaskManager 之间的 Task 数量差异为 `2`。
-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.
+因此，如果你遇到上述描述的性能瓶颈问题，那么使用这种 Task
负载均衡调度策略可以改善性能。请注意，如果你没有遇到这些瓶颈问题，则不应使用此策略，因为这可能导致性能下降。
-## Usage
+## 使用方法
-You can enable balanced tasks scheduling through the following configuration
item:
+你可以通过以下配置项启用 Task 调度均衡：
-- `taskmanager.load-balance.mode`: `tasks`
+- `taskmanager.load-balance.mode`：`tasks`
Review Comment:
`：` -> `:`
This needs to be half-width character.
##########
docs/content.zh/docs/deployment/tasks-scheduling/balanced_tasks_scheduling.md:
##########
@@ -23,101 +23,73 @@ specific language governing permissions and limitations
under the License.
-->
-# Balanced Tasks Scheduling
+# Task 调度均衡
-This page describes the background and principle of balanced tasks scheduling,
-how to use it when running streaming jobs.
+本文档描述了 Task 调度均衡的背景和原理，以及如何在运行流处理作业时使用它。
-## Background
+## 背景
-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.
+当 Flink 流处理作业中所有顶点的并行度不一致时，Flink 部署 Task 的[默认策略]({{< ref
"docs/deployment/config" >}}#taskmanager-load-balance-mode)有时会导致某些 TaskManager
部署的 Task 较多，而其他 TaskManager 部署的 Task 较少，从而造成部署 Task 较多的 TaskManager
资源使用过度，成为整个作业处理的瓶颈。
-{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="The Skew Case of Tasks Scheduling" class="offset" width="50%" >}}
+{{< img src="/fig/deployments/tasks-scheduling/tasks_scheduling_skew_case.svg"
alt="任务调度倾斜示例" 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.
+如图（a）所示，假设有一个 Flink 作业包含两个顶点：`JobVertex-A（JV-A）`和`JobVertex-B（JV-B）`，并行度分别为
`6` 和 `3`，且两个顶点属于同一个 Slot 共享组。在默认 Task 调度策略下，如图（b）所示，Task 在 TaskManager
之间的分布可能导致 Task 负载显著不均。具体来说，Task 数量最多的 TaskManager 可能承载 `4` 个任务，而负载最低的
TaskManager 可能只有 `2` 个任务。因此，承载 4 个 Task 的 TaskManager 容易成为整个作业的性能瓶颈。
-Therefore, Flink provides the 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`.
+因此，Flink 提供了基于 Task 数量的 Task 调度均衡能力。在作业的资源视图中，它旨在确保分配给每个 TaskManager
的任务数量尽可能接近，从而改善 TaskManager 之间的资源使用倾斜。
-<span class="label label-info">Note</span> The presence of inconsistent
parallelism does not imply that this strategy must be used, as this is not
always the case in practice.
+<span class="label label-info">注意</span> 并非并行度不一致就必须使用此策略，需根据实际情况判断。
-## Principle
+## 原理
-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
+基于 Task 数量的负载均衡调度策略将 Task 分配给 TaskManager 的过程分为两个阶段：
+- Task 到 Slot 的分配阶段
+- Slot 到 TaskManager 的分配阶段
-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.
+本节将通过两个示例对基于 Task 数量的调度策略在上述两个阶段中处理 Task 分配的简化流程及其原理加以说明。
-### The tasks-to-slots assignment phase
+### Task 到 Slot 的分配阶段
-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.
+以图（c）所示的作业为例，它包含五个作业顶点，并行度分别为 `1`、`4`、`4`、`2` 和 `3`。这五个作业顶点都属于默认 Slot 共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="The Tasks To Slots Allocation Principle Demo" class="offset" width="65%"
>}}
+{{< img
src="/fig/deployments/tasks-scheduling/tasks_to_slots_allocation_principle.svg"
alt="Task 到 Slot 分配原理示例" 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.
+在 Task 到 Slot 的分配阶段，该调度策略：
+- 首先直接将并行度最高的顶点的 Task 分配到第 `i` 个 Slot。
- That is, task `JV-Bi` is assigned directly to `sloti`, and task `JV-Ci` is
assigned directly to `sloti`.
+ 即将任务 `JV-Bi` 直接分配到 `sloti`，将任务 `JV-Ci` 也直接分配到 `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.
+- 接下来，对于属于次高并行度作业顶点的 Task ，以轮询方式分配给当前 Slot 共享组内的 Slot，直到所有 Task 分配完毕。
-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).
+如图（e）所示，在基于 Task 数量的分配策略下，每个 Slot 的任务数量范围（最大值与最小值之差）为 `1`，这优于图（d）所示默认策略下的范围
`3`。
-Thus, this ensures a more balanced distribution of the number of tasks across
slots.
+因此，这确保了 Slot 之间 Task 数量更加均衡地分布。
-### The slots-to-TaskManagers assignment phase
+### Slot 到 TaskManager 的分配阶段
-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.
+如图（f）所示，假设有一个 Flink 作业包含两个顶点 `JV-A` 和 `JV-B`，并行度分别为 `6` 和 `3`，且两个顶点属于同一个 Slot
共享组。
-{{< img
src="/fig/deployments/tasks-scheduling/slots_to_taskmanagers_allocation_principle.svg"
alt="The Slots to TaskManagers Allocation Principle Demo" class="offset"
width="75%" >}}
+{{< img
src="/fig/deployments/tasks-scheduling/slots_to_taskmanagers_allocation_principle.svg"
alt="Slot 到 TaskManager 分配原理示例" 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.
+第一阶段的分配结果如图（g）所示，其中 `Slot0`、`Slot1` 和 `Slot2` 各包含 `2` 个 Task ，其余 Slot 各包含 `1`
个 Task 。
-Subsequently:
-- The strategy submits all slot requests and waits until all slot resources
required for the current job are ready.
+随后：
+- 策略提交所有 Slot 请求，并等待当前作业所需的所有 Slot 资源准备就绪。
-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.
-Afterward, 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.
+Slot 资源准备就绪后：
+- 策略首先根据每个请求包含的 Task 数量按降序对所有 Slot 请求进行排序。然后，依次将每个 Slot 请求分配给当前 Task 负载最小的
TaskManager。此过程持续进行，直到所有 Slot 请求分配完毕。
-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`.
+最终分配结果如图（i）所示，每个 TaskManager 最终恰好承载 `3` 个 Task ，TaskManager 之间的 Task 数量差异为
`0`。相比之下，默认策略下的调度结果如图（h）所示，TaskManager 之间的 Task 数量差异为 `2`。
-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.
+因此，如果你遇到上述描述的性能瓶颈问题，那么使用这种 Task
负载均衡调度策略可以改善性能。请注意，如果你没有遇到这些瓶颈问题，则不应使用此策略，因为这可能导致性能下降。
Review Comment:
`那么使用这种 ` -> `使用这种 `
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