klion26 commented on a change in pull request #8750: [FLINK-11606] Translate
the "Distributed Runtime Environment" page in to Chinese
URL: https://github.com/apache/flink/pull/8750#discussion_r294129669
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File path: docs/concepts/runtime.zh.md
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@@ -26,102 +26,74 @@ under the License.
* This will be replaced by the TOC
{:toc}
-## Tasks and Operator Chains
+## 任务和算子链
-For distributed execution, Flink *chains* operator subtasks together into
*tasks*. Each task is executed by one thread.
-Chaining operators together into tasks is a useful optimization: it reduces
the overhead of thread-to-thread
-handover and buffering, and increases overall throughput while decreasing
latency.
-The chaining behavior can be configured; see the [chaining
docs](../dev/stream/operators/#task-chaining-and-resource-groups) for details.
+分布式计算中，Flink 将算子（operator）的 subtask *链接（chain）*成 task。每个 task 由一个线程执行。把算子链接成
tasks 是一个很好的优化：它减少了线程间切换和缓冲的开销，并在降低延迟的同时提高了整体吞吐量。链接操作的配置详情可参考：[chaining
docs](../dev/stream/operators/#task-chaining-and-resource-groups)
-The sample dataflow in the figure below is executed with five subtasks, and
hence with five parallel threads.
+下图的 dataflow 由五个 subtasks 执行，因此具有五个并行线程。
<img src="{{ site.baseurl }}/fig/tasks_chains.svg" alt="Operator chaining into
Tasks" class="offset" width="80%" />
{% top %}
-## Job Managers, Task Managers, Clients
+## Job Managers、Task Managers、客户端（Clients）
-The Flink runtime consists of two types of processes:
+Flink 运行时包含两类进程：
- - The **JobManagers** (also called *masters*) coordinate the distributed
execution. They schedule tasks, coordinate
- checkpoints, coordinate recovery on failures, etc.
+ - **JobManagers** （也称为 *masters*）协调分布式计算。它们负责调度任务、协调 checkpoints、协调故障恢复等。
- There is always at least one Job Manager. A high-availability setup will
have multiple JobManagers, one of
- which one is always the *leader*, and the others are *standby*.
+ 至少需要一个 JobManager。高可用部署下会有多个 JobManagers，其中一个作为 *leader*，其余处于 *standby* 状态。
- - The **TaskManagers** (also called *workers*) execute the *tasks* (or more
specifically, the subtasks) of a dataflow,
- and buffer and exchange the data *streams*.
+ - **TaskManagers**（也称为 *workers*）执行 dataflow 中的 *tasks*（准确来说是 subtasks
），并且缓存和交换数据 *streams*。
- There must always be at least one TaskManager.
+ 至少需要一个 TaskManager。
-The JobManagers and TaskManagers can be started in various ways: directly on
the machines as a [standalone cluster](../ops/deployment/cluster_setup.html), in
-containers, or managed by resource frameworks like
[YARN](../ops/deployment/yarn_setup.html) or
[Mesos](../ops/deployment/mesos.html).
-TaskManagers connect to JobManagers, announcing themselves as available, and
are assigned work.
+JobManagers 和 TaskManagers 可由多种方式启动：可以直接在机器上启动（该集群称为 [standalone
cluster](../ops/deployment/cluster_setup.html)），也可以在容器或资源管理框架，如
[YARN](../ops/deployment/yarn_setup.html) 或
[Mesos](../ops/deployment/mesos.html)，中启动。TaskManagers 连接到
JobManagers，通知后者自己可用，之后 TaskManagers 便可以被分配工作了。
-The **client** is not part of the runtime and program execution, but is used
to prepare and send a dataflow to the JobManager.
-After that, the client can disconnect, or stay connected to receive progress
reports. The client runs either as part of the
-Java/Scala program that triggers the execution, or in the command line process
`./bin/flink run ...`.
+**客户端**不是运行时（runtime）和作业执行时的一部分，但它是被用来准备和发送 dataflow 到 JobManager
的。在那之后，客户端可以断开连接，也可以保持连接来接收进度报告。客户端既可以作为触发执行的 Java / Scala
程序的一部分，也可以在命令行进程中运行`./bin/flink run ...`。
<img src="{{ site.baseurl }}/fig/processes.svg" alt="The processes involved in
executing a Flink dataflow" class="offset" width="80%" />
{% top %}
-## Task Slots and Resources
+## Task Slots 和资源
-Each worker (TaskManager) is a *JVM process*, and may execute one or more
subtasks in separate threads.
-To control how many tasks a worker accepts, a worker has so called **task
slots** (at least one).
+每个 worker（TaskManager）都是一个 *JVM 进程*，并且可以在不同的线程中执行一个或多个 subtasks。为了控制 worker 接收
task 的数量，worker 拥有所谓的 **task slots** （至少一个）。
-Each *task slot* represents a fixed subset of resources of the TaskManager. A
TaskManager with three slots, for example,
-will dedicate 1/3 of its managed memory to each slot. Slotting the resources
means that a subtask will not
-compete with subtasks from other jobs for managed memory, but instead has a
certain amount of reserved
-managed memory. Note that no CPU isolation happens here; currently slots only
separate the managed memory of tasks.
+每个 *task slots*代表 TaskManager 的一份固定资源子集。例如，具有三个 slots 的 TaskManager
会将其管理的内存资源分成三等份给每个 slot。 划分资源意味着 subtask 之间不会竞争资源，但是也意味着它们只拥有固定的资源。注意这里并没有 CPU
隔离，当前 slots 之间只是划分任务的内存资源。
-By adjusting the number of task slots, users can define how subtasks are
isolated from each other.
-Having one slot per TaskManager means each task group runs in a separate JVM
(which can be started in a
-separate container, for example). Having multiple slots
-means more subtasks share the same JVM. Tasks in the same JVM share TCP
connections (via multiplexing) and
-heartbeat messages. They may also share data sets and data structures, thus
reducing the per-task overhead.
+通过调整 slot 的数量，用户可以决定 subtasks 的隔离方式。每个 TaskManager 有一个 slot 意味着每组 task 在一个单独的
JVM 中运行（例如，在一个单独的容器中启动）。拥有多个 slots 意味着多个 subtasks 共享同一个 JVM。 Tasks 在同一个 JVM 中共享
TCP 连接（通过多路复用技术）和心跳信息（heartbeat messages）。它们还可能共享数据集和数据结构，从而降低每个 task 的开销。
<img src="{{ site.baseurl }}/fig/tasks_slots.svg" alt="A TaskManager with Task
Slots and Tasks" class="offset" width="80%" />
-By default, Flink allows subtasks to share slots even if they are subtasks of
different tasks, so long as
-they are from the same job. The result is that one slot may hold an entire
pipeline of the
-job. Allowing this *slot sharing* has two main benefits:
+默认情况下，Flink 允许 subtasks 共享 slots，即使它们是不同 tasks 的 subtasks，只要它们来自同一个 job。因此，一个
slot 可能会负责这个 job 的整个管道（pipeline）。允许 *slot sharing* 有两个好处：
- - A Flink cluster needs exactly as many task slots as the highest
parallelism used in the job.
- No need to calculate how many tasks (with varying parallelism) a program
contains in total.
+ - Flink 集群需要与 job 中使用的最高并行度一样多的 slots。这样不需要计算作业总共包含多少个 tasks（具有不同并行度）。
- - It is easier to get better resource utilization. Without slot sharing, the
non-intensive
- *source/map()* subtasks would block as many resources as the resource
intensive *window* subtasks.
- With slot sharing, increasing the base parallelism in our example from two
to six yields full utilization of the
- slotted resources, while making sure that the heavy subtasks are fairly
distributed among the TaskManagers.
+ - 更好的资源利用率。在没有 slot sharing 的情况下，轻松的 subtasks（*source/map()*）将会占用和繁重的
subtasks （*window*）一样多的资源。通过 slot sharing，将示例中的并行度从 2 增加到 6 可以充分利用 slot
的资源，同时确保繁重的 subtask 在 TaskManagers 之间公平地获取资源。
Review comment:
Seems "轻松的" and "繁重的" here is not good enought, Do you have any other ideas?
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