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_r339286218
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File path: docs/dev/stream/state/checkpointing.zh.md
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@@ -25,146 +25,138 @@ under the License.
* ToC
{:toc}
-Every function and operator in Flink can be **stateful** (see [working with
state](state.html) for details).
-Stateful functions store data across the processing of individual
elements/events, making state a critical building block for
-any type of more elaborate operation.
+Flink 中的每个方法或算子都能够是**有状态的**（阅读 [working with state](state.html) 查看详细）。
+状态化的方法在处理单个 元素/事件 的时候存储数据，让状态成为使各个类型的算子更加精细的重要部分。
+为了让状态容错，Flink 需要为状态添加**Checkpoint（检查点）**。Checkpoint 使得 Flink
能够恢复状态和在流中的位置，从而向应用提供和无故障执行时一样的语义。
-In order to make state fault tolerant, Flink needs to **checkpoint** the
state. Checkpoints allow Flink to recover state and positions
-in the streams to give the application the same semantics as a failure-free
execution.
+[Documentation on streaming fault tolerance]({{ site.baseurl
}}/zh/internals/stream_checkpointing.html) 介绍了 Flink 流计算容错机制内部的技术原理。
-The [documentation on streaming fault tolerance]({{ site.baseurl
}}/internals/stream_checkpointing.html) describes in detail the technique
behind Flink's streaming fault tolerance mechanism.
+## 前提条件
-## Prerequisites
+Flink 的 Checkpoint 机制会和持久化存储进行交互，交换流与状态。一般需要：
-Flink's checkpointing mechanism interacts with durable storage for streams and
state. In general, it requires:
+ - 一个能够回放一段时间内数据的持久化数据源，例如持久化消息队列（例如 Apache Kafka、RabbitMQ、 Amazon Kinesis、
Google PubSub 等）或文件系统（例如 HDFS、 S3、 GFS、 NFS、 Ceph 等）。
+ - 存放状态的持久化存储，通常为分布式文件系统（比如 HDFS、 S3、 GFS、 NFS、 Ceph 等）。
- - A *persistent* (or *durable*) data source that can replay records for a
certain amount of time. Examples for such sources are persistent messages
queues (e.g., Apache Kafka, RabbitMQ, Amazon Kinesis, Google PubSub) or file
systems (e.g., HDFS, S3, GFS, NFS, Ceph, ...).
- - A persistent storage for state, typically a distributed filesystem (e.g.,
HDFS, S3, GFS, NFS, Ceph, ...)
+## 激活与配置 Checkpoint
+默认情况下，Checkpoint 是禁用的。通过调用 `StreamExecutionEnvironment` 的
`enableCheckpointing(n)` 来激活 Checkpoint，里面的 *n* 是进行 Checkpoint 的间隔，单位毫秒。
-## Enabling and Configuring Checkpointing
+Checkpoint 其他的属性包括：
-By default, checkpointing is disabled. To enable checkpointing, call
`enableCheckpointing(n)` on the `StreamExecutionEnvironment`, where *n* is the
checkpoint interval in milliseconds.
-
-Other parameters for checkpointing include:
-
- - *exactly-once vs. at-least-once*: You can optionally pass a mode to the
`enableCheckpointing(n)` method to choose between the two guarantee levels.
- Exactly-once is preferable for most applications. At-least-once may be
relevant for certain super-low-latency (consistently few milliseconds)
applications.
-
- - *checkpoint timeout*: The time after which a checkpoint-in-progress is
aborted, if it did not complete by then.
-
- - *minimum time between checkpoints*: To make sure that the streaming
application makes a certain amount of progress between checkpoints,
- one can define how much time needs to pass between checkpoints. If this
value is set for example to *5000*, the next checkpoint will be
- started no sooner than 5 seconds after the previous checkpoint completed,
regardless of the checkpoint duration and the checkpoint interval.
- Note that this implies that the checkpoint interval will never be smaller
than this parameter.
+ - *精确一次（exactly-once） 对比 至少一次（at-least-once）*：你可以选择向
`enableCheckpointing(n)` 方法中传入一个模式来选择使用两种保证等级中的哪一种。
+ 对于大多数应用来说，精确一次是较好的选择。至少一次可能与某些延迟超低（始终只有几毫秒）的应用的关联较大。
+
+ - *Checkpoint超时（checkpoint timeout）*：如果过了这个时间，还在进行中的 checkpoint 操作就会被抛弃。
+
+ - *checkpoint 之间的最小时间（minimum time between checkpoints）*： 为了确保流应用在
checkpoint 之间有足够的进展，可以定义在 checkpoint 之间需要多久的时间。如果值设置为了 *5000*，
+ 无论 checkpoint 持续时间与间隔是多久，在前一个 checkpoint 完成的五秒后才会开始下一个 checkpoint。
- It is often easier to configure applications by defining the "time between
checkpoints" than the checkpoint interval, because the "time between
checkpoints"
- is not susceptible to the fact that checkpoints may sometimes take longer
than on average (for example if the target storage system is temporarily slow).
-
- Note that this value also implies that the number of concurrent
checkpoints is *one*.
-
- - *number of concurrent checkpoints*: By default, the system will not
trigger another checkpoint while one is still in progress.
- This ensures that the topology does not spend too much time on checkpoints
and not make progress with processing the streams.
- It is possible to allow for multiple overlapping checkpoints, which is
interesting for pipelines that have a certain processing delay
- (for example because the functions call external services that need some
time to respond) but that still want to do very frequent checkpoints
- (100s of milliseconds) to re-process very little upon failures.
-
- This option cannot be used when a minimum time between checkpoints is
defined.
-
- - *externalized checkpoints*: You can configure periodic checkpoints to be
persisted externally. Externalized checkpoints write their meta data out to
persistent storage and are *not* automatically cleaned up when the job fails.
This way, you will have a checkpoint around to resume from if your job fails.
There are more details in the [deployment notes on externalized checkpoints]({{
site.baseurl }}/ops/state/checkpoints.html#externalized-checkpoints).
-
- - *fail/continue task on checkpoint errors*: This determines if a task will
be failed if an error occurs in the execution of the task's checkpoint
procedure. This is the default behaviour. Alternatively, when this is disabled,
the task will simply decline the checkpoint to the checkpoint coordinator and
continue running.
+ 往往使用" checkpoint 之间的时间"来配置应用会比 checkpoint间隔 容易很多，因为" checkpoint 之间的时间"在
checkpoint 的执行时间在超过平均值时不会受到影响（例如如果目标的存储系统忽然变得很慢）。
+
+ 注意这个值也意味着并发 checkpoint 的数目是*一*。
- - *prefer checkpoint for recovery*: This determines if a job will fallback
to latest checkpoint even when there are more recent savepoints available to
potentially reduce recovery time.
+ - *并发 checkpoint 的数目*: 默认情况下，系统不会在有一个 checkpoint 在进行时触发另一个
checkpoint。这确保了拓扑不会在 checkpoint 上花费太多时间，并且不会和流处理同时进行。
+ 允许多个 checkpoint 重叠进行是可行的，对于有确定的处理延迟（例如是因为某方法所调用的外部服务需要些时间来回复），但是仍然想进行频繁的
checkpoint 去最小化故障重跑的 pipelines 来说，是有意义的。
+
+ 该选项在 checkpoint 间的最小时间被定义时不能使用。
+
+ - *外部 checkpoint（externalized checkpoints）*: 你可以配置周期 checkpoint 存储到外部系统中。外部
checkpoint 将他们的元数据写到持久化存储上并且在 job 失败的时候*不会*被自动删除。
+ 这种方式下，如果你的 job 失败，你将会有一个现有的 checkpoint 去恢复。更多的细节请看 [deployment notes on
externalized checkpoints]({{ site.baseurl
}}/zh/ops/state/checkpoints.html#externalized-checkpoints)。
+
+ - *在 checkpoint 出错时 置错/继续 task*：他决定了在 task checkpoint 的过程中发生错误时，一个 task
是否会被置为失败。这个是默认的行为。
+ 或者被禁用时，这个任务将会简单的吧 checkpoint 削减为 checkpoint 协调器并继续运行。
+
+ - *选择 checkpoint 进行恢复（prefer checkpoint for recovery）*：此属性将确定作业是否回退到最新
checkpoint，即使有更多最近的 savepoint 可用来潜在地减少恢复时间。
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
-// start a checkpoint every 1000 ms
+// 每 1000ms 开始一次 checkpoint
env.enableCheckpointing(1000);
-// advanced options:
+// 高级选项：
-// set mode to exactly-once (this is the default)
+// 设置模式为精确一次 (这是默认值)
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
-// make sure 500 ms of progress happen between checkpoints
+// 确认 checkpoint 之间的时间会进行 500 ms
env.getCheckpointConfig().setMinPauseBetweenCheckpoints(500);
-// checkpoints have to complete within one minute, or are discarded
+// Checkpoint 必须在一分钟内完成，否则就会被放弃
Review comment:
所有"弃" 都统一改成了 “抛弃”。
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