wuchong commented on a change in pull request #16928:
URL: https://github.com/apache/flink/pull/16928#discussion_r698166541
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File path: docs/content.zh/docs/deployment/elastic_scaling.md
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@@ -23,134 +23,132 @@ specific language governing permissions and limitations
under the License.
-->
-# Elastic Scaling
+# 弹性伸缩
-Apache Flink allows you to rescale your jobs. You can do this manually by
stopping the job and restarting from the savepoint created during shutdown with
a different parallelism.
+在 Apache Flink 中，可以通过手动停止 Job，然后从停止时创建的 Savepoint 恢复，最后重新指定并行度的方式来重新伸缩 Job。
-This page describes options where Flink automatically adjusts the parallelism
instead.
+这个文档描述的特性是 Flink 如何自动地调整并行度。
-## Reactive Mode
+## Reactive 模式
{{< hint info >}}
-Reactive mode is an MVP ("minimum viable product") feature. The Flink
community is actively looking for feedback by users through our mailing lists.
Please check the limitations listed on this page.
+Reactive 模式是一个 MVP （minimum viable product，最小可行产品）特性。目前 Flink
社区正在积极地从邮件列表中获取用户的使用反馈。请注意文中列举的一些限制。
{{< /hint >}}
-Reactive Mode configures a job so that it always uses all resources available
in the cluster. Adding a TaskManager will scale up your job, removing resources
will scale it down. Flink will manage the parallelism of the job, always
setting it to the highest possible values.
+在 Reactive 模式下，Job 会使用集群中所有的资源。当增加 TaskManager 时，Job 会自动扩容。当删除时，就会自动缩容。Flink
会管理 Job 的并行度，始终会尽可能地使用最大值。
-Reactive Mode restarts a job on a rescaling event, restoring it from the
latest completed checkpoint. This means that there is no overhead of creating a
savepoint (which is needed for manually rescaling a job). Also, the amount of
data that is reprocessed after rescaling depends on the checkpointing interval,
and the restore time depends on the state size.
+当发生伸缩时，Job 会被重启，并且会从最新的 Checkpoint 中恢复。这就意味着不需要花费额外的开销去创建
Savepoint。当然，所需要重新处理的数据量取决于 Checkpoint 的间隔时长，而恢复的时间取决于状态的大小。
-The Reactive Mode allows Flink users to implement a powerful autoscaling
mechanism, by having an external service monitor certain metrics, such as
consumer lag, aggregate CPU utilization, throughput or latency. As soon as
these metrics are above or below a certain threshold, additional TaskManagers
can be added or removed from the Flink cluster. This could be implemented
through changing the [replica
factor](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/#replicas)
of a Kubernetes deployment, or an [autoscaling
group](https://docs.aws.amazon.com/autoscaling/ec2/userguide/AutoScalingGroup.html)
on AWS. This external service only needs to handle the resource allocation and
deallocation. Flink will take care of keeping the job running with the
resources available.
-
-### Getting started
+借助 Reactive 模式，Flink 用户可以通过一些外部的监控服务产生的指标，例如：消费延迟、CPU
利用率汇总、吞吐量、延迟等，实现一个强大的自动伸缩机制。当上述的这些指标超出或者低于一定的阈值时，增加或者减少 TaskManager 的数量。在
Kubernetes 中，可以通过改变 Deployment 的[副本数（Replica
Factor）](https://kubernetes.io/zh/docs/concepts/workloads/controllers/deployment/#replicas)
实现。而在 AWS 中，可以通过改变 [Auto Scaling
组](https://docs.aws.amazon.com/zh_cn/autoscaling/ec2/userguide/AutoScalingGroup.html)
来实现。这类外部服务只需要负责资源的分配以及回收，而 Flink 则负责在这些资源上运行 Job。
-If you just want to try out Reactive Mode, follow these instructions. They
assume that you are deploying Flink on a single machine.
+<a name="getting-started"></a>
Review comment:
Yes, this line is needed if we would like to preserve the original tag
links.
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