wuchong commented on a change in pull request #9415: [FLINK-12939][docs-zh]
Translate "Apache Kafka Connector" page into C…
URL: https://github.com/apache/flink/pull/9415#discussion_r312738797
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File path: docs/dev/connectors/kafka.zh.md
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@@ -342,74 +300,51 @@
myConsumer.setStartFromSpecificOffsets(specificStartOffsets)
</div>
</div>
-The above example configures the consumer to start from the specified offsets
for
-partitions 0, 1, and 2 of topic `myTopic`. The offset values should be the
-next record that the consumer should read for each partition. Note that
-if the consumer needs to read a partition which does not have a specified
-offset within the provided offsets map, it will fallback to the default
-group offsets behaviour (i.e. `setStartFromGroupOffsets()`) for that
-particular partition.
+上面的例子中使用的配置是指定从 `myTopic` 主题的 0 、1 和 2 分区的指定偏移量开始消费。 offset 值是 consumer
应该为每个分区读取的下一条消息。请注意：如果 consumer 需要读取在提供的 offsets 映射中没有指定 offsets
的分区，那么它将回退到该特定分区的默认组偏移行为（即 `setStartFromGroupOffsets()` ）。
+
-Note that these start position configuration methods do not affect the start
position when the job is
-automatically restored from a failure or manually restored using a savepoint.
-On restore, the start position of each Kafka partition is determined by the
-offsets stored in the savepoint or checkpoint
-(please see the next section for information about checkpointing to enable
-fault tolerance for the consumer).
+请注意：当 Job 从故障中自动恢复或使用 savepoint 手动恢复时，这些起始位置配置方法不会影响消费的起始位置。在恢复时，每个 Kafka
分区的起始位置由存储在 savepoint 或 checkpoint 中的 offsets 确定（有关 checkpointing
的信息，请参阅下一节，以便为 consumer 启用容错功能）。
-### Kafka Consumers and Fault Tolerance
+### Kafka Consumers 和容错
-With Flink's checkpointing enabled, the Flink Kafka Consumer will consume
records from a topic and periodically checkpoint all
-its Kafka offsets, together with the state of other operations, in a
consistent manner. In case of a job failure, Flink will restore
-the streaming program to the state of the latest checkpoint and re-consume the
records from Kafka, starting from the offsets that were
-stored in the checkpoint.
+伴随着启用 Flink 的 checkpointing 后，Flink Kafka Consumer 将使用 topic
中的记录，并以一致的方式定期检查其所有 Kafka offsets 和其他算子的状态。``如果 Job 失败， Flink 会将流式程序恢复到最新
checkpoint 的状态，并从存储在 checkpoint 中的 offsets 开始重新消费 Kafka 中的消息。
-The interval of drawing checkpoints therefore defines how much the program may
have to go back at most, in case of a failure.
+因此，设置 checkpoints 的间隔定义了程序在发生故障时最多需要返回多少。
-To use fault tolerant Kafka Consumers, checkpointing of the topology needs to
be enabled at the execution environment:
+要使用容错的 Kafka Consumers ，需要在执行环境中启用拓扑的 checkpointing 。
<div class="codetabs" markdown="1">
<div data-lang="java" markdown="1">
{% highlight java %}
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
-env.enableCheckpointing(5000); // checkpoint every 5000 msecs
+env.enableCheckpointing(5000); // 每隔 5000 毫秒 执行一次 checkpoint
{% endhighlight %}
</div>
<div data-lang="scala" markdown="1">
{% highlight scala %}
val env = StreamExecutionEnvironment.getExecutionEnvironment()
-env.enableCheckpointing(5000) // checkpoint every 5000 msecs
+env.enableCheckpointing(5000) // 每隔 5000 毫秒 执行一次 checkpoint
{% endhighlight %}
</div>
</div>
-Also note that Flink can only restart the topology if enough processing slots
are available to restart the topology.
-So if the topology fails due to loss of a TaskManager, there must still be
enough slots available afterwards.
-Flink on YARN supports automatic restart of lost YARN containers.
+另请注意，如果有足够的处理 solts 是可用于重新启动拓扑的，那么 Flink 只能重新启动拓扑计划。因此，如果拓扑由于丢失了 TaskManager
而失败，那么之后必须要一直有足够可用的 solts 。 Flink on YARN 支持自动重启丢失的 YARN 容器。
Review comment:
```suggestion
另请注意，Flink 只有在有足够的可用 solts 情况下才重新启动拓扑。因此，如果拓扑由于丢失了 TaskManager
而失败，那么之后必须要有足够可用的 solts 。 Flink on YARN 支持自动重启丢失的 YARN 容器。
```
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