wuchong commented on a change in pull request #9763: [FLINK-13037][docs-zh] Translate "Concepts -> Glossary" page into Chinese URL: https://github.com/apache/flink/pull/9763#discussion_r331749024
########## File path: docs/concepts/glossary.zh.md ########## @@ -25,142 +25,92 @@ under the License. #### Flink Application Cluster -A Flink Application Cluster is a dedicated [Flink Cluster](#flink-cluster) that only -executes a single [Flink Job](#flink-job). The lifetime of the -[Flink Cluster](#flink-cluster) is bound to the lifetime of the Flink Job. Formerly -Flink Application Clusters were also known as Flink Clusters in *job mode*. Compare to -[Flink Session Cluster](#flink-session-cluster). +Flink Application Cluster 是一个专用的 [Flink Cluster](#flink-cluster)，它仅用于执行单个 [Flink Job](#flink-job)。[Flink Cluster](#flink-cluster)的生命周期与 [Flink Job](#flink-job)的生命周期绑定在一起。以前，Flink Application Cluster 也称为*job mode*的 Flink Cluster。和 [Flink Session Cluster](#flink-session-cluster) 作对比。 #### Flink Cluster -A distributed system consisting of (typically) one [Flink Master](#flink-master) and one or more -[Flink TaskManager](#flink-taskmanager) processes. +一般情况下，Flink 集群是由一个 [Flink Master](#flink-master) 和一个或多个 [Flink TaskManager](#flink-taskmanager) 进程组成的分布式系统。 #### Event -An event is a statement about a change of the state of the domain modelled by the -application. Events can be input and/or output of a stream or batch processing application. -Events are special types of [records](#Record). +Event 是对应用程序建模的域的状态更改的声明。它可以同时为流或批处理应用程序的 input 和 output，也可以单独是 input 或者 output 中的一种。Event 是特殊类型的 [Record](#record)。 #### ExecutionGraph -see [Physical Graph](#physical-graph) +见 [Physical Graph](#physical-graph)。 #### Function -Functions are implemented by the user and encapsulate the -application logic of a Flink program. Most Functions are wrapped by a corresponding -[Operator](#operator). +Function 是由用户实现的，并封装了 Flink 程序的应用程序逻辑。大多数 Function 都由相应的 [Operator](#operator) 封装。 #### Instance -The term *instance* is used to describe a specific instance of a specific type (usually -[Operator](#operator) or [Function](#function)) during runtime. As Apache Flink is mostly written in -Java, this corresponds to the definition of *Instance* or *Object* in Java. In the context of Apache -Flink, the term *parallel instance* is also frequently used to emphasize that multiple instances of -the same [Operator](#operator) or [Function](#function) type are running in parallel. +Instance 常用于描述运行时的特定类型(通常是 [Operator](#operator) 或者 [Function](#function))的一个具体实例。由于 Apache Flink 主要是用 Java 编写的，所以，这与 Java 中的 *Instance* 或 *Object* 的定义相对应。在 Apache Flink 的上下文中，*parallel instance* 也常用于强调同一 [Operator](#operator) 或者 [Function](#function) 的多个 instance 以并行的方式运行。 #### Flink Job -A Flink Job is the runtime representation of a Flink program. A Flink Job can either be submitted -to a long running [Flink Session Cluster](#flink-session-cluster) or it can be started as a -self-contained [Flink Application Cluster](#flink-application-cluster). +Flink Job 代表运行时的 Flink 程序。Flink Job 可以提交到长时间运行的 [Flink Session Cluster](#flink-session-cluster)，也可以作为独立的 [Flink Application Cluster](#flink-application-cluster) 启动。 #### JobGraph -see [Logical Graph](#logical-graph) +见 [Logical Graph](#logical-graph)。 #### Flink JobManager -JobManagers are one of the components running in the [Flink Master](#flink-master). A JobManager is -responsible for supervising the execution of the [Tasks](#task) of a single job. Historically, the -whole [Flink Master](#flink-master) was called JobManager. +JobManager 是在 [Flink Master](#flink-master) 运行中的组件之一。JobManager 负责监督单个作业 [Task](#task) 的执行。以前，整个 [Flink Master](#flink-master) 都叫做 JobManager。 #### Logical Graph -A logical graph is a directed graph describing the high-level logic of a stream processing program. -The nodes are [Operators](#operator) and the edges indicate input/output-relationships or -data streams or data sets. +Logical Graph 是一种描述流处理程序的高阶逻辑有向图。节点是[Operator](#operator)，边代表输入/输出关系、数据流和数据集中的之一。 #### Managed State -Managed State describes application state which has been registered with the framework. For -Managed State, Apache Flink will take care about persistence and rescaling among other things. +Managed State 描述了已在框架中注册的应用程序的托管状态。对于托管状态，Apache Flink 会负责持久化和重伸缩等事宜。 #### Flink Master -The Flink Master is the master of a [Flink Cluster](#flink-cluster). It contains three distinct -components: Flink Resource Manager, Flink Dispatcher and one [Flink JobManager](#flink-jobmanager) -per running [Flink Job](#flink-job). +Flink Master 是 [Flink Cluster](#flink-cluster) 的主节点。它包含三个不同的组件：Flink Resource Manager、Flink Dispatcher、运行每个 [Flink Job](#flink-job) 的 [Flink JobManager](#flink-jobmanager)。 #### Operator -Node of a [Logical Graph](#logical-graph). An Operator performs a certain operation, which is -usually executed by a [Function](#function). Sources and Sinks are special Operators for data -ingestion and data egress. +[Logical Graph](#logical-graph) 的节点。算子执行某种操作，该操作通常由 [Function](#function) 执行。Source 和 Sink 是数据输入和数据输出的特殊算子。 #### Operator Chain -An Operator Chain consists of two or more consecutive [Operators](#operator) without any -repartitioning in between. Operators within the same Operator Chain forward records to each other -directly without going through serialization or Flink's network stack. +算子链由两个或多个连续的 [Operator](#operator) 组成，两者之间没有任何的重新分区。同一算子链内的算子可以彼此直接传递 record，而无需通过序列化或 Flink 的网络栈。 #### Partition -A partition is an independent subset of the overall data stream or data set. A data stream or -data set is divided into partitions by assigning each [record](#Record) to one or more partitions. -Partitions of data streams or data sets are consumed by [Tasks](#task) during runtime. A -transformation which changes the way a data stream or data set is partitioned is often called -repartitioning. +分区是整个数据流或数据集的独立子集。通过将每个 [Record](#record) 分配给一个或多个分区，来把数据流或数据集划分为多个分区。在运行期间，[Task](#task) 会消费数据流或数据集的分区。改变数据流或数据集分区方式的转换通常称为重分区。 #### Physical Graph -A physical graph is the result of translating a [Logical Graph](#logical-graph) for execution in a -distributed runtime. The nodes are [Tasks](#task) and the edges indicate input/output-relationships -or [partitions](#partition) of data streams or data sets. +Physical graph 是一个在分布式运行时，把 [Logical Graph](#logical-graph) 转换为可执行的结果。节点是 [Task](#task)，边表示数据流或数据集的输入/输出关系或 [partition](#partition)。 #### Record -Records are the constituent elements of a data set or data stream. [Operators](#operator) and -[Functions](#Function) receive records as input and emit records as output. +Record 是数据集或数据流的组成元素。[Operator](#operator) 和 [Function](#Function)接收 record 作为输入，并将 record 作为输出发出。 #### Flink Session Cluster -A long-running [Flink Cluster](#flink-cluster) which accepts multiple [Flink Jobs](#flink-job) for -execution. The lifetime of this Flink Cluster is not bound to the lifetime of any Flink Job. -Formerly, a Flink Session Cluster was also known as a Flink Cluster in *session mode*. Compare to -[Flink Application Cluster](#flink-application-cluster). +长时间运行的 [Flink Cluster](#flink-cluster)，它可以接受多个 [Flink Job](#flink-job) 的执行。此 [Flink Cluster](#flink-cluster) 的生命周期不受任何 [Flink Job](#flink-job) 生命周期的约束限制。以前，Flink Session Cluster 也称为 *session mode* 的 [Flink Cluster](#flink-cluster)，和 [Flink Application Cluster](#flink-application-cluster) 作对比。 #### State Backend -For stream processing programs, the State Backend of a [Flink Job](#flink-job) determines how its -[state](#managed-state) is stored on each TaskManager (Java Heap of TaskManager or (embedded) -RocksDB) as well as where it is written upon a checkpoint (Java Heap of -[Flink Master](#flink-master) or Filesystem). +对于流处理程序，[Flink Job](#flink-job) 的 State Backend 决定了其 [state](#managed-state) 是如何存储在每个 TaskManager 的 Java 堆栈（嵌入式）RocksDB 上，以及它在 checkpoint 时的写入位置（ [Flink Master](#flink-master) 的 Java 堆或者 Filesystem）。 Review comment: ```suggestion 对于流处理程序，[Flink Job](#flink-job) 的 State Backend 决定了其 [state](#managed-state) 是如何存储在每个 TaskManager 上的（ TaskManager 的 Java 堆栈或嵌入式 RocksDB），以及它在 checkpoint 时的写入位置（ [Flink Master](#flink-master) 的 Java 堆或者 Filesystem）。 ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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