liuzhuang2017 commented on code in PR #20660:
URL: https://github.com/apache/flink/pull/20660#discussion_r952036061
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**Note:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
Review Comment:
“整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。” 最好为 “整个 pipeline
需要的资源太大了以致不能和单一的 slot/task manager 相适应的场景。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**Note:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
Review Comment:
“在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。”
最好翻译为 “在它如何提高资源利用率 [它如何提高资源效率](#how-it-improves-resource-efficiency)
部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**注意:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在 DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
{{< img src="/fig/dynamic_slot_alloc.png" class="center" >}}
-With fine-grained resource management, the slots requests contain specific
resource profiles, which users can specify.
-Flink will respect those user-specified resource requirements and dynamically
cut an exactly-matched slot out of the TaskManager’s available
-resources. As shown above, there is a requirement for a slot with 0.25 Core
and 1GB memory, and Flink allocates *Slot 1* for it.
+对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。
{{< hint info >}}
-Previously in Flink, the resource requirement only contained the required
slots, without fine-grained resource
-profiles, namely **coarse-grained resource management**. The TaskManager had a
fixed number of identical slots to fulfill those requirements.
+Flink之前的资源申请只包含必须指定的slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下,
TaskManager以固定相同的slots的个数的方式来满足资源需求。
Review Comment:
“Flink之前的资源申请只包含必须指定的slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下,
TaskManager以固定相同的slots的个数的方式来满足资源需求。” 最好翻译为 “Flink 之前的资源申请只包含必须指定的
slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下, TaskManage r以固定相同的 slots
的个数的方式来满足资源需求。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**Note:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
Review Comment:
“如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,” 最好翻译为 “如 [Flink架构]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster) 中描述,”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**注意:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在 DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
{{< img src="/fig/dynamic_slot_alloc.png" class="center" >}}
-With fine-grained resource management, the slots requests contain specific
resource profiles, which users can specify.
-Flink will respect those user-specified resource requirements and dynamically
cut an exactly-matched slot out of the TaskManager’s available
-resources. As shown above, there is a requirement for a slot with 0.25 Core
and 1GB memory, and Flink allocates *Slot 1* for it.
+对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。
Review Comment:
"对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。"
最好翻译为 “对于细粒度资源管理,slot 资源请求包含用户指定的特定的资源配置文件。Flink 会遵从这些用户指定的资源请求并从 TaskManager
可用的资源中动态地切分出精确匹配的 slot。如上图所示，对于一个 slot，0.25 Core 和 1GB 内存的资源申请，Flink 为它分配 *slot
1* 。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**Note:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
Review Comment:
“slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.” 最好翻译为 “Slot 既是资源调度的基本单元,又是
Flink运行时申请资源的基本单元.”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**注意:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在 DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
{{< img src="/fig/dynamic_slot_alloc.png" class="center" >}}
-With fine-grained resource management, the slots requests contain specific
resource profiles, which users can specify.
-Flink will respect those user-specified resource requirements and dynamically
cut an exactly-matched slot out of the TaskManager’s available
-resources. As shown above, there is a requirement for a slot with 0.25 Core
and 1GB memory, and Flink allocates *Slot 1* for it.
+对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。
{{< hint info >}}
-Previously in Flink, the resource requirement only contained the required
slots, without fine-grained resource
-profiles, namely **coarse-grained resource management**. The TaskManager had a
fixed number of identical slots to fulfill those requirements.
+Flink之前的资源申请只包含必须指定的slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下,
TaskManager以固定相同的slots的个数的方式来满足资源需求。
{{< /hint >}}
-For the resource requirement without a specified resource profile, Flink will
automatically decide a resource profile.
-Currently, the resource profile of it is calculated from [TaskManager’s total
resource]({{< ref "docs/deployment/memory/mem_setup_tm" >}})
-and [taskmanager.numberOfTaskSlots]({{< ref "docs/deployment/config"
>}}#taskmanager-numberoftaskslots), just
-like in coarse-grained resource management. As shown above, the total resource
of TaskManager is 1 Core and 4 GB memory and the number of task slots
-is set to 2, *Slot 2* is created with 0.5 Core and 2 GB memory for the
requirement without a specified resource profile.
+对于没有指定资源配置的资源请求，Flink会自动决定资源配置。粗粒度资源管理当前被计算的资源来自TaskManager总资源[TaskManager’s
total resource]({{< ref "docs/deployment/memory/mem_setup_tm"
>}})和TaskManager的总slot数[taskmanager.numberOfTaskSlots]({{< ref
"docs/deployment/config" >}}#taskmanager-numberoftaskslots)。
Review Comment:
“对于没有指定资源配置的资源请求，Flink会自动决定资源配置。粗粒度资源管理当前被计算的资源来自TaskManager总资源[TaskManager’s
total resource]({{< ref "docs/deployment/memory/mem_setup_tm"
>}})和TaskManager的总slot数[taskmanager.numberOfTaskSlots]({{< ref
"docs/deployment/config" >}}#taskmanager-numberoftaskslots)。” _这里最好翻译为_
“对于没有指定资源配置的资源请求，Flink 会自动决定资源配置。粗粒度资源管理当前被计算的资源来自 [TaskManager 总资源]({{< ref
"docs/deployment/memory/mem_setup_tm" >}}) 和 [TaskManager 的总 slot 数]({{< ref
"docs/deployment/config" >}}#taskmanager-numberoftaskslots)。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**Note:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
Review Comment:
"批处理作业，其中不同stage的task所需的资源差异明显。" 最好翻译为 “批处理作业，其中不同 stage 的 task 所需的资源差异明显。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**注意:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在 DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
{{< img src="/fig/dynamic_slot_alloc.png" class="center" >}}
-With fine-grained resource management, the slots requests contain specific
resource profiles, which users can specify.
-Flink will respect those user-specified resource requirements and dynamically
cut an exactly-matched slot out of the TaskManager’s available
-resources. As shown above, there is a requirement for a slot with 0.25 Core
and 1GB memory, and Flink allocates *Slot 1* for it.
+对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。
{{< hint info >}}
-Previously in Flink, the resource requirement only contained the required
slots, without fine-grained resource
-profiles, namely **coarse-grained resource management**. The TaskManager had a
fixed number of identical slots to fulfill those requirements.
+Flink之前的资源申请只包含必须指定的slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下,
TaskManager以固定相同的slots的个数的方式来满足资源需求。
{{< /hint >}}
-For the resource requirement without a specified resource profile, Flink will
automatically decide a resource profile.
-Currently, the resource profile of it is calculated from [TaskManager’s total
resource]({{< ref "docs/deployment/memory/mem_setup_tm" >}})
-and [taskmanager.numberOfTaskSlots]({{< ref "docs/deployment/config"
>}}#taskmanager-numberoftaskslots), just
-like in coarse-grained resource management. As shown above, the total resource
of TaskManager is 1 Core and 4 GB memory and the number of task slots
-is set to 2, *Slot 2* is created with 0.5 Core and 2 GB memory for the
requirement without a specified resource profile.
+对于没有指定资源配置的资源请求，Flink会自动决定资源配置。粗粒度资源管理当前被计算的资源来自TaskManager总资源[TaskManager’s
total resource]({{< ref "docs/deployment/memory/mem_setup_tm"
>}})和TaskManager的总slot数[taskmanager.numberOfTaskSlots]({{< ref
"docs/deployment/config" >}}#taskmanager-numberoftaskslots)。
+如上所示，TaskManager的总资源是1Core和4G内存，task的slot数设置为2，*Slot 2*
被创建，并申请0.5core和2G的内存而没有指定资源配置。
+在分配slot1和slot2后，在TaskManager留下0.25核和1G的内存作为未使用资源.
-After the allocation of *Slot 1* and *Slot 2*, there is 0.25 Core and 1 GB
memory remaining as the free resources in the
-TaskManager. These free resources can be further partitioned to fulfill the
following resource requirements.
+详情请参考资源分配策略 [Resource Allocation Strategy](#resource-allocation-strategy)。
-Please refer to [Resource Allocation Strategy](#resource-allocation-strategy)
for more details.
-## Usage
+## 用法
-To use fine-grained resource management, you need to:
+为了可以使用细粒度的资源管理,需要做以下步骤:
- - Configure to enable fine-grained resource management.
+- 配置细粒度的资源管理
- - Specify the resource requirement.
+- 指定资源请求
-### Enable Fine-Grained Resource Management
-
-To enable fine-grained resource management, you need to configure the
[cluster.fine-grained-resource-management.enabled]({{< ref
"docs/deployment/config" >}}#cluster-fine-grained-resource-management-enabled)
to true.
+### Enable 细粒度资源管理
Review Comment:
"Enable 细粒度资源管理" _最好翻译为_ “启用细粒度资源管理” 后面的 “enable” 也统一翻译为 “启用”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**注意:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在 DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
{{< img src="/fig/dynamic_slot_alloc.png" class="center" >}}
-With fine-grained resource management, the slots requests contain specific
resource profiles, which users can specify.
-Flink will respect those user-specified resource requirements and dynamically
cut an exactly-matched slot out of the TaskManager’s available
-resources. As shown above, there is a requirement for a slot with 0.25 Core
and 1GB memory, and Flink allocates *Slot 1* for it.
+对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。
{{< hint info >}}
-Previously in Flink, the resource requirement only contained the required
slots, without fine-grained resource
-profiles, namely **coarse-grained resource management**. The TaskManager had a
fixed number of identical slots to fulfill those requirements.
+Flink之前的资源申请只包含必须指定的slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下,
TaskManager以固定相同的slots的个数的方式来满足资源需求。
{{< /hint >}}
-For the resource requirement without a specified resource profile, Flink will
automatically decide a resource profile.
-Currently, the resource profile of it is calculated from [TaskManager’s total
resource]({{< ref "docs/deployment/memory/mem_setup_tm" >}})
-and [taskmanager.numberOfTaskSlots]({{< ref "docs/deployment/config"
>}}#taskmanager-numberoftaskslots), just
-like in coarse-grained resource management. As shown above, the total resource
of TaskManager is 1 Core and 4 GB memory and the number of task slots
-is set to 2, *Slot 2* is created with 0.5 Core and 2 GB memory for the
requirement without a specified resource profile.
+对于没有指定资源配置的资源请求，Flink会自动决定资源配置。粗粒度资源管理当前被计算的资源来自TaskManager总资源[TaskManager’s
total resource]({{< ref "docs/deployment/memory/mem_setup_tm"
>}})和TaskManager的总slot数[taskmanager.numberOfTaskSlots]({{< ref
"docs/deployment/config" >}}#taskmanager-numberoftaskslots)。
+如上所示，TaskManager的总资源是1Core和4G内存，task的slot数设置为2，*Slot 2*
被创建，并申请0.5core和2G的内存而没有指定资源配置。
+在分配slot1和slot2后，在TaskManager留下0.25核和1G的内存作为未使用资源.
-After the allocation of *Slot 1* and *Slot 2*, there is 0.25 Core and 1 GB
memory remaining as the free resources in the
-TaskManager. These free resources can be further partitioned to fulfill the
following resource requirements.
+详情请参考资源分配策略 [Resource Allocation Strategy](#resource-allocation-strategy)。
Review Comment:
"详情请参考资源分配策略 [Resource Allocation Strategy](#resource-allocation-strategy)。"
_这里最好翻译为_ “详情请参考 [资源分配策略](#resource-allocation-strategy)。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**注意:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在 DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
{{< img src="/fig/dynamic_slot_alloc.png" class="center" >}}
-With fine-grained resource management, the slots requests contain specific
resource profiles, which users can specify.
-Flink will respect those user-specified resource requirements and dynamically
cut an exactly-matched slot out of the TaskManager’s available
-resources. As shown above, there is a requirement for a slot with 0.25 Core
and 1GB memory, and Flink allocates *Slot 1* for it.
+对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。
{{< hint info >}}
-Previously in Flink, the resource requirement only contained the required
slots, without fine-grained resource
-profiles, namely **coarse-grained resource management**. The TaskManager had a
fixed number of identical slots to fulfill those requirements.
+Flink之前的资源申请只包含必须指定的slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下,
TaskManager以固定相同的slots的个数的方式来满足资源需求。
{{< /hint >}}
-For the resource requirement without a specified resource profile, Flink will
automatically decide a resource profile.
-Currently, the resource profile of it is calculated from [TaskManager’s total
resource]({{< ref "docs/deployment/memory/mem_setup_tm" >}})
-and [taskmanager.numberOfTaskSlots]({{< ref "docs/deployment/config"
>}}#taskmanager-numberoftaskslots), just
-like in coarse-grained resource management. As shown above, the total resource
of TaskManager is 1 Core and 4 GB memory and the number of task slots
-is set to 2, *Slot 2* is created with 0.5 Core and 2 GB memory for the
requirement without a specified resource profile.
+对于没有指定资源配置的资源请求，Flink会自动决定资源配置。粗粒度资源管理当前被计算的资源来自TaskManager总资源[TaskManager’s
total resource]({{< ref "docs/deployment/memory/mem_setup_tm"
>}})和TaskManager的总slot数[taskmanager.numberOfTaskSlots]({{< ref
"docs/deployment/config" >}}#taskmanager-numberoftaskslots)。
+如上所示，TaskManager的总资源是1Core和4G内存，task的slot数设置为2，*Slot 2*
被创建，并申请0.5core和2G的内存而没有指定资源配置。
Review Comment:
“TaskManager的总资源是1Core和4G内存，task的slot数设置为2，*Slot 2*
被创建，并申请0.5core和2G的内存而没有指定资源配置。” _这里最好翻译为_ “ TaskManager 的总资源是 1 Core 和 4 GB
内存，task 的 slot 数设置为 2，*Slot 2* 被创建，并申请 0.5 Core 和 2 GB 的内存而没有指定资源配置。”
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**注意:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在 DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
+slot既是资源调度的基本单元,又是flink运行时申请资源的基本单元.
{{< img src="/fig/dynamic_slot_alloc.png" class="center" >}}
-With fine-grained resource management, the slots requests contain specific
resource profiles, which users can specify.
-Flink will respect those user-specified resource requirements and dynamically
cut an exactly-matched slot out of the TaskManager’s available
-resources. As shown above, there is a requirement for a slot with 0.25 Core
and 1GB memory, and Flink allocates *Slot 1* for it.
+对于细粒度资源管理,Slot资源请求包含用户指定的特定的资源配置文件。Flink会遵从这些用户指定的资源请求并从TaskManager可用的资源中动态地切分出精确匹配的slot。如上图所示，对于一个slot，0.25core和1G内存的资源申请，Flink为它分配一个slot。
{{< hint info >}}
-Previously in Flink, the resource requirement only contained the required
slots, without fine-grained resource
-profiles, namely **coarse-grained resource management**. The TaskManager had a
fixed number of identical slots to fulfill those requirements.
+Flink之前的资源申请只包含必须指定的slots,但没有精细化的资源配置,这是一种粗粒度的资源管理.在这种管理方式下,
TaskManager以固定相同的slots的个数的方式来满足资源需求。
{{< /hint >}}
-For the resource requirement without a specified resource profile, Flink will
automatically decide a resource profile.
-Currently, the resource profile of it is calculated from [TaskManager’s total
resource]({{< ref "docs/deployment/memory/mem_setup_tm" >}})
-and [taskmanager.numberOfTaskSlots]({{< ref "docs/deployment/config"
>}}#taskmanager-numberoftaskslots), just
-like in coarse-grained resource management. As shown above, the total resource
of TaskManager is 1 Core and 4 GB memory and the number of task slots
-is set to 2, *Slot 2* is created with 0.5 Core and 2 GB memory for the
requirement without a specified resource profile.
+对于没有指定资源配置的资源请求，Flink会自动决定资源配置。粗粒度资源管理当前被计算的资源来自TaskManager总资源[TaskManager’s
total resource]({{< ref "docs/deployment/memory/mem_setup_tm"
>}})和TaskManager的总slot数[taskmanager.numberOfTaskSlots]({{< ref
"docs/deployment/config" >}}#taskmanager-numberoftaskslots)。
+如上所示，TaskManager的总资源是1Core和4G内存，task的slot数设置为2，*Slot 2*
被创建，并申请0.5core和2G的内存而没有指定资源配置。
+在分配slot1和slot2后，在TaskManager留下0.25核和1G的内存作为未使用资源.
Review Comment:
在分配 *Slot 1* 和 *Slot 2* 后，在 TaskManager 留下 0.25 Core 和 1 GB 的内存作为未使用资源.
##########
docs/content.zh/docs/deployment/finegrained_resource.md:
##########
@@ -23,97 +23,86 @@ specific language governing permissions and limitations
under the License.
-->
-# Fine-Grained Resource Management
-Apache Flink works hard to auto-derive sensible default resource requirements
for all applications out of the box.
-For users who wish to fine-tune their resource consumption, based on knowledge
of their specific scenarios, Flink offers **fine-grained resource management**.
+# 细粒度资源管理
-This page describes the fine-grained resource management’s usage, applicable
scenarios, and how it works.
+Apache Flink 努力为所有开箱即用的应用程序自动派生合理的默认资源需求。对于希望更精细化调节资源消耗的用户，基于对特定场景的了解，Flink
提供了**细粒度资源管理**。
+本文介绍了细粒度资源管理的使用、适用场景以及工作原理。
{{< hint warning >}}
-**Note:** This feature is currently an MVP (“minimum viable product”) feature
and only available to [DataStream API]({{< ref "docs/dev/datastream/overview"
>}}).
+**Note:** 本特性是当前的一个最简化产品(版本)的特性，它支持只在DataStream API [DataStream API]({{< ref
"docs/dev/datastream/overview" >}})中使用。
{{< /hint >}}
-## Applicable Scenarios
+## 使用场景
-Typical scenarios that potentially benefit from fine-grained resource
management are where:
+可能从细粒度资源管理中受益的典型场景包括：
- - Tasks have significantly different parallelisms.
+- Tasks 有显著不同的并行度的场景。
- - The resource needed for an entire pipeline is too much to fit into a
single slot/task manager.
+- 整个pipeline需要的资源太大了以致不能和单一的slot/task Manager相适应的场景。
- - Batch jobs where resources needed for tasks of different stages are
significantly different
+- 批处理作业，其中不同stage的task所需的资源差异明显。
-An in-depth discussion on why fine-grained resource management can improve
resource efficiency for the above scenarios is presented in [How it improves
resource efficiency](#how-it-improves-resource-efficiency).
+在它如何提高资源利用率 [How it improves resource
efficiency](#how-it-improves-resource-efficiency)部分将会对细粒度资源管理为什么在以上使用场景中可以提高资源利用率作深入的讨论。
-## How it works
-As described in [Flink Architecture]({{< ref
"docs/concepts/flink-architecture" >}}#anatomy-of-a-flink-cluster),
-task execution resources in a TaskManager are split into many slots.
-The slot is the basic unit of both resource scheduling and resource
requirement in Flink's runtime.
+## 工作原理
+如Flink架构 [Flink Architecture]({{< ref "docs/concepts/flink-architecture"
>}}#anatomy-of-a-flink-cluster)中描述,
+在一个TaskManager中,执行task时使用的资源被分割成许多个slots.
Review Comment:
"在一个TaskManager中,执行task时使用的资源被分割成许多个slots." 最好翻译为 “在一个 TaskManager 中,执行 task
时所使用的资源被分割成许多个 slots。”
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