Henvealf commented on a change in pull request #9805: [FLINK-11635][docs-zh]
translate dev/stream/state/checkpointing into Chinese
URL: https://github.com/apache/flink/pull/9805#discussion_r339286584
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File path: docs/dev/stream/state/checkpointing.zh.md
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@@ -173,30 +165,26 @@ Some more parameters and/or defaults may be set via
`conf/flink-conf.yaml` (see
## Selecting a State Backend
-Flink's [checkpointing mechanism]({{ site.baseurl
}}/internals/stream_checkpointing.html) stores consistent snapshots
-of all the state in timers and stateful operators, including connectors,
windows, and any [user-defined state](state.html).
-Where the checkpoints are stored (e.g., JobManager memory, file system,
database) depends on the configured
-**State Backend**.
-
-By default, state is kept in memory in the TaskManagers and checkpoints are
stored in memory in the JobManager. For proper persistence of large state,
-Flink supports various approaches for storing and checkpointing state in other
state backends. The choice of state backend can be configured via
`StreamExecutionEnvironment.setStateBackend(…)`.
+Flink 的 [checkpointing mechanism]({{ site.baseurl
}}/zh/internals/stream_checkpointing.html) 存储在定时器与状态操作里的持久化快照，
+包括连接器（connectors），窗口（windows）以及任何用户[自定义的状态](state.html)
+checkpoint 存储在那里取决于所配置的 **State Backend**（比如 JobManager memory、 file system、
database）。
-See [state backends]({{ site.baseurl }}/ops/state/state_backends.html) for
more details on the available state backends and options for job-wide and
cluster-wide configuration.
+默认情况下，状态是保持在 TaskManagers 的内存中，checkpoint 保存在 JobManager
的内存中。为了体量大的状态的能完全恰当的持久化，
+Flink 支持各种各样的途径去存储，来 checkpoint 状态到其他的 state backends。通过
`StreamExecutionEnvironment.setStateBackend(…)` 来配置所选的 state backends。
+阅读 [state backends]({{ site.baseurl }}/zh/ops/state/state_backends.html)
来查看在可用 state backends 上的更多细节，选择 job范围 与 集群返回 的配置。
-## State Checkpoints in Iterative Jobs
+## 在 Iterative Jobs 中的状态 checkpoint
-Flink currently only provides processing guarantees for jobs without
iterations. Enabling checkpointing on an iterative job causes an exception. In
order to force checkpointing on an iterative program the user needs to set a
special flag when enabling checkpointing: `env.enableCheckpointing(interval,
CheckpointingMode.EXACTLY_ONCE, force = true)`.
+Flink 现在只提供没有 iterations 的 job 的处理保证。在 iterative job 上激活 checkpoint
会导致异常。为了在迭代程序中强制进行 checkpoint，用于需要在激活 checkpoint 时设置一个特殊的标志：
`env.enableCheckpointing(interval, CheckpointingMode.EXACTLY_ONCE, force =
true)`。
-Please note that records in flight in the loop edges (and the state changes
associated with them) will be lost during failure.
+请注意在环形边上游走的记录（以及与之相关的状态变化）在故障时会丢失。
{% top %}
+## 重启策略
-## Restart Strategies
-
-Flink supports different restart strategies which control how the jobs are
restarted in case of a failure. For more
-information, see [Restart Strategies]({{ site.baseurl
}}/dev/restart_strategies.html).
+Flink 支持不同的重启策略，来控制 job 万一故障时该如何重启。更多信息请阅读 [Restart Strategies]({{
site.baseurl }}/zh/dev/restart_strategies.html)。
Review comment:
看了下会跳转。
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