Leeviiii commented on a change in pull request #8300: [FLINK-11638][docs-zh]
Translate Savepoints page into Chinese
URL: https://github.com/apache/flink/pull/8300#discussion_r279328665
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File path: docs/ops/state/savepoints.zh.md
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@@ -25,32 +25,24 @@ under the License.
* toc
{:toc}
-## What is a Savepoint? How is a Savepoint different from a Checkpoint?
+## 什么是 Savepoint ？ Savepoint 与 Checkpoint 有什么不同？
-A Savepoint is a consistent image of the execution state of a streaming job,
created via Flink's [checkpointing mechanism]({{ site.baseurl
}}/internals/stream_checkpointing.html). You can use Savepoints to
stop-and-resume, fork,
-or update your Flink jobs. Savepoints consist of two parts: a directory with
(typically large) binary files on stable storage (e.g. HDFS, S3, ...) and a
(relatively small) meta data file. The files on stable storage represent the
net data of the job's execution state
-image. The meta data file of a Savepoint contains (primarily) pointers to all
files on stable storage that are part of the Savepoint, in form of absolute
paths.
+Savepoint 是依据 Flink [检查点机制]({{ site.baseurl
}}/internals/stream_checkpointing.html)所创建的流作业执行状态的一致镜像。 您可以使用 Savepoint
来停止并恢复，fork，或更新您的 Flink 工作。 Savepoint 由两部分组成：具有稳定存储（例如
HDFS，S3，...）上的（通常是大的）二进制文件的目录和（相对较小的）元数据文件。 稳定存储上的文件表示作业执行状态的净数据图片。 Savepoint
的元数据文件以（绝对路径）的形式包含（主要）指向作为 Savepoint 一部分的稳定存储上的所有文件的指针。
<div class="alert alert-warning">
-<strong>Attention:</strong> In order to allow upgrades between programs and
Flink versions, it is important to check out the following section about <a
href="#assigning-operator-ids">assigning IDs to your operators</a>.
+<strong>注意:</strong> 为了允许程序和 Flink 版本之间的升级，请务必查看以下有关<a
href="#assigning-operator-ids">分配算子 ID </a>的部分 。
</div>
+从概念上讲，Flink 的 Savepoint 与 Checkpoint 的不同之处在于备份与传统数据库系统中的恢复日志不同。
检查点的主要目的是提供恢复机制，以防万一
+出乎意料的失业。 Checkpoint 的生命周期由Flink管理，即 Flink 创建，拥有和发布 Checkpoint - 无需用户交互。
作为一种恢复和定期触发的方法，主要有两个检查点实现的设计目标是：i）轻量级创建和 ii）尽可能快地恢复。 针对这些目标的优化可以利用某些属性，例如，
那份工作代码在执行尝试之间不会改变。 在用户终止作业后，通常会删除检查点（除非明确配置为保留的检查点）。
-Conceptually, Flink's Savepoints are different from Checkpoints in a similar
way that backups are different from recovery logs in traditional database
systems. The primary purpose of Checkpoints is to provide a recovery mechanism
in case of
-unexpected job failures. A Checkpoint's lifecycle is managed by Flink, i.e. a
Checkpoint is created, owned, and released by Flink - without user interaction.
As a method of recovery and being periodically triggered, two main
-design goals for the Checkpoint implementation are i) being as lightweight to
create and ii) being as fast to restore from as possible. Optimizations towards
those goals can exploit certain properties, e.g. that the job code
-doesn't change between the execution attempts. Checkpoints are usually dropped
after the job was terminated by the user (except if explicitly configured as
retained Checkpoints).
+与此相反，Savepoint 由用户创建，拥有和删除。 他们的用例是计划的，手动备份和恢复。 例如，这可能是您的Flink
版本的更新，更改您的工作图，改变并行性，分配第二个工作，如红色/蓝色部署，等等。 当然，Savepoint 必须在终止工作后继续存在。
从概念上讲，Savepoint 的生成，恢复和关注成本可能更高一些
+更多关于可移植性和对前面提到的工作变化的支持。
-In contrast to all this, Savepoints are created, owned, and deleted by the
user. Their use-case is for planned, manual backup and resume. For example,
this could be an update of your Flink version, changing your job graph,
-changing parallelism, forking a second job like for a red/blue deployment, and
so on. Of course, Savepoints must survive job termination. Conceptually,
Savepoints can be a bit more expensive to produce and restore and focus
-more on portability and support for the previously mentioned changes to the
job.
+除去这些概念上的差异，Checkpoint 和 Savepoint
的当前实现基本上使用相同的代码并生成相同的格式。然而，目前有一个例外，我们可能会在未来引入更多的差异。例外情况是使用 RocksDB
状态后端的增量检查点。他们使用了一些 RocksDB 内部格式，而不是 Flink 的本机Savepoint格式。这使他们成为了与 Savepoint
相比，更轻量级的检查点机制的第一个实例。
Review comment:
的本机Savepoint格式 -> 的本机Savepoint 格式
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