[hotfix][docs] Fix typos in documentation

docs/content.zh/docs/connectors/datastream/formats/text_files.md

@@ -73,7 +73,7 @@ stream = env.from_source(source, WatermarkStrategy.no_watermarks(), "file-source
 在此示例中，我们创建了一个 DataStream，随着新文件被添加到目录中，其中包含的文本文件行的字符串流将无限增长。我们每秒会进行新文件监控。
 此处不需要水印策略，因为记录不包含事件时间戳。
 
-{{< tabs "continous" >}}
+{{< tabs "continuous" >}}
 {{< tab "Java" >}}
 ```java
 final FileSource<String> source =

docs/content.zh/docs/connectors/table/formats/protobuf.md

@@ -132,7 +132,7 @@ Format Options
       <td>no</td>
       <td style="word-wrap: break-word;">(none)</td>
       <td>String</td>
-      <td>The full name of a Protobuf generated class. The name must match the message name in the proto definition file. <code>$</code> is supported for inner class names, like 'com.exmample.OuterClass$MessageClass'</td>
+      <td>The full name of a Protobuf generated class. The name must match the message name in the proto definition file. <code>$</code> is supported for inner class names, like 'com.example.OuterClass$MessageClass'</td>
     </tr>
     <tr>
       <td><h5>protobuf.ignore-parse-errors</h5></td>

docs/content.zh/docs/deployment/adaptive_batch.md

@@ -58,7 +58,7 @@ Adaptive Batch Scheduler 支持自动推导算子并行度，如果算子未设
     - [`execution.batch.adaptive.auto-parallelism.min-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-min-parallelism): 允许自动设置的并行度最小值。
     - [`execution.batch.adaptive.auto-parallelism.max-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-max-parallelism): 允许自动设置的并行度最大值，如果该配置项没有配置将使用通过 [`parallelism.default`]({{< ref "docs/deployment/config" >}}) 或者 `StreamExecutionEnvironment#setParallelism()` 设置的默认并行度作为允许自动设置的并行度最大值。
     - [`execution.batch.adaptive.auto-parallelism.avg-data-volume-per-task`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-avg-data-volume-per-ta): 期望每个任务平均处理的数据量大小。请注意，当出现数据倾斜，或者确定的并行度达到最大并行度（由于数据过多）时，一些任务实际处理的数据可能会远远超过这个值。
-    - [`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-paralle): source 算子可动态推导的最大并行度，若该配置项没有配置将优先使用 [`execution-batch-adaptive-auto-parallelism-max-parallelism`]({{< ref "docs/deployment/config" >}})作为允许动态推导的并行度最大值，若该配置项也没有配置，将使用 [`parallelism.default`]({{< ref "docs/deployment/config" >}}) 或者 `StreamExecutionEnvironment#setParallelism()` 设置的默认并行度。
+    - [`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-parallelism): source 算子可动态推导的最大并行度，若该配置项没有配置将优先使用 [`execution-batch-adaptive-auto-parallelism-max-parallelism`]({{< ref "docs/deployment/config" >}})作为允许动态推导的并行度最大值，若该配置项也没有配置，将使用 [`parallelism.default`]({{< ref "docs/deployment/config" >}}) 或者 `StreamExecutionEnvironment#setParallelism()` 设置的默认并行度。
 - 不要指定算子的并行度：
 
   Adaptive Batch Scheduler 只会为用户未指定并行度的算子推导并行度。 所以如果你想算子的并行度被自动推导，需要避免通过算子的 `setParallelism()` 方法来为其指定并行度。
@@ -78,7 +78,7 @@ public interface DynamicParallelismInference {
 其中 Context 会提供可推导并行度上界、期望每个任务平均处理的数据量大小、动态过滤信息来协助并行度推导。
 Adaptive Batch Scheduler 将会在调度 Source 节点之前调用上述接口，需注意实现中应尽量避免高耗时的操作。
 
-若 Source 未实现上述接口，[`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-paralle) 将会作为 Source 节点的并行度。
+若 Source 未实现上述接口，[`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-parallelism) 将会作为 Source 节点的并行度。
 
 需注意，Source 动态并行度推导也只会为用户未指定并行度的 Source 算子推导并行度。
 

docs/content.zh/docs/deployment/filesystems/azure.md

@@ -47,7 +47,7 @@ wasb://<your-container>@$<your-azure-account>.blob.core.windows.net/<object-path
 // WASB SSL encrypted access
 wasbs://<your-container>@$<your-azure-account>.blob.core.windows.net/<object-path>
 
-// ABFS unecrypted access
+// ABFS unencrypted access
 abfs://<your-container>@$<your-azure-account>.dfs.core.windows.net/<object-path>
 
 // ABFS SSL encrypted access

docs/content.zh/docs/deployment/metric_reporters.md

@@ -186,7 +186,7 @@ InfluxDB 发送器会使用 http 协议按照将指标发送到 InfluxDB 服务
 参数：
 
 - `port` - （可选的）Prometheus 发送器监听的端口，默认为 [9249](https://github.com/prometheus/prometheus/wiki/Default-port-allocations)。如果需要在一台机器上运行多个发送器示例进行监控时（比如 TaskManger 与 JobManager 在一台机器上运行时），建议将端口号配置为 `9250-9260` 这样的区间。
-- `filterLabelValueCharacters` -（可选的）指定是否过滤 label 中的非法字符。如果设置过滤，所有没有按照 \[a-zA-Z0-9:_\] 匹配的字符都会被过滤掉，如果设置不过滤，则不会有字符被过滤掉。设置不过滤前，请确保你的 label 符合 [Prometheus 的 lable 命名规范](https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels)。
+- `filterLabelValueCharacters` -（可选的）指定是否过滤 label 中的非法字符。如果设置过滤，所有没有按照 \[a-zA-Z0-9:_\] 匹配的字符都会被过滤掉，如果设置不过滤，则不会有字符被过滤掉。设置不过滤前，请确保你的 label 符合 [Prometheus 的 label 命名规范](https://prometheus.io/docs/concepts/data_model/#metric-names-and-labels)。
 
 配置示例：
 

docs/content.zh/docs/deployment/resource-providers/standalone/kubernetes.md

@@ -45,7 +45,7 @@ under the License.
 
 ### 准备
 
-本指南假设存在一个 Kubernets 的运行环境。你可以通过运行 `kubectl get nodes` 命令来确保 Kubernetes 环境运行正常，该命令展示所有连接到 Kubernets 集群的 node 节点信息。
+本指南假设存在一个 Kubernetes 的运行环境。你可以通过运行 `kubectl get nodes` 命令来确保 Kubernetes 环境运行正常，该命令展示所有连接到 Kubernetes 集群的 node 节点信息。
 
 如果你想在本地运行 Kubernetes，建议使用 [MiniKube](https://minikube.sigs.k8s.io/docs/start/)。
 
@@ -156,7 +156,7 @@ $ ./bin/flink run -m localhost:8081 ./examples/streaming/TopSpeedWindowing.jar
 
 <a name="accessing-flink-in-kubernetes"></a>
 
-### 在 Kubernets 上访问 Flink
+### 在 Kubernetes 上访问 Flink
 
 接下来可以访问 Flink UI 页面并通过不同的方式提交作业：
 
@@ -207,7 +207,7 @@ flink-taskmanager-64847444ff-nnd6m   1/1     Running            3          3m28s
 
 ### 高可用的 Standalone Kubernetes
 
-对于在 Kubernetes 上实现HA，可以参考当前的 [Kubernets 高可用服务]({{< ref "docs/deployment/ha/overview" >}})。
+对于在 Kubernetes 上实现HA，可以参考当前的 [Kubernetes 高可用服务]({{< ref "docs/deployment/ha/overview" >}})。
 
 <a name="kubernetes-high-availability-services"></a>
 

docs/content.zh/docs/deployment/security/security-ssl.md

@@ -61,7 +61,7 @@ added to the container images, or attached to the YARN deployment.
 
   - In an environment where operators are constrained to use firm-wide Internal CA (cannot generated self-signed certificates),
     the recommendation is to still have a dedicated key pair / certificate for the Flink deployment, signed by that CA.
-    However, the TrustStore must then also contain the CA's public certificate tho accept the deployment's certificate
+    However, the TrustStore must then also contain the CA's public certificate to accept the deployment's certificate
     during the SSL handshake (requirement in JDK TrustStore implementation).
 
     **NOTE:** Because of that, it is critical that you specify the fingerprint of the deployment certificate

docs/content.zh/docs/getting-started/flink-operations-playground.md

@@ -575,7 +575,7 @@ docker compose scale taskmanager=2
 
 **请求**
 ```bash
-curl "localhost:8081/jobs/<jod-id>/metrics?get=lastCheckpointSize"
+curl "localhost:8081/jobs/<job-id>/metrics?get=lastCheckpointSize"
 ```
 **预期响应 (结果已格式化且去除了占位符)**
 ```json
@@ -592,7 +592,7 @@ REST API 不仅可以用于查询指标，还可以用于获取正在运行中
 **请求**
 ```bash
 # 可以从结果中获取感兴趣的 vertex-id
-curl localhost:8081/jobs/<jod-id>
+curl localhost:8081/jobs/<job-id>
 ```
 
 **预期响应 (结果已格式化)**

docs/content.zh/docs/sql/hive-compatibility/hive-dialect/alter.md

@@ -250,7 +250,7 @@ ALTER TABLE table_name [PARTITION partition_spec] CHANGE [COLUMN] col_old_name c
 
 ```sql
 ALTER TABLE t1 CHANGE COLUMN c1 new_c1 STRING FIRST;
-ALTER TABLE t1 CHANGE COLUMN c1 new_c1 STRING AFRER c2;
+ALTER TABLE t1 CHANGE COLUMN c1 new_c1 STRING AFTER c2;
 ```
 
 #### Add/Replace Columns

docs/content.zh/docs/sql/interfaces/jdbc-driver.md

@@ -231,6 +231,6 @@ public class Sample {
 
 ### Use with Others
 
-In addition to java, Flink JDBC driver can be used by any JVM language such as scala, kotlin and ect, you can add the dependency of Flink JDBC driver in your project and use it directly.
+In addition to java, Flink JDBC driver can be used by any JVM language such as scala, kotlin, etc., you can add the dependency of Flink JDBC driver in your project and use it directly.
 
 Most applications may use data access frameworks to access data, for example, JOOQ, MyBatis and Spring Data. You can config Flink JDBC driver in them to perform Flink queries on an exist Flink cluster, just like a regular database.

docs/content.zh/docs/sql/reference/queries/joins.md

@@ -472,7 +472,7 @@ Because every probe-side row is joined against the final, complete build side, t
 Lookup Join
 --------------
 
-lookup join 通常用于使用从外部系统查询的数据来丰富表。join 要求一个表具有处理时间属性，另一个表由查找源连接器（lookup source connnector）支持。
+lookup join 通常用于使用从外部系统查询的数据来丰富表。join 要求一个表具有处理时间属性，另一个表由查找源连接器（lookup source connector）支持。
 
 lookup join 和上面的 [处理时间 Temporal Join](#processing-time-temporal-join) 语法相同，右表使用查找源连接器支持。
 

docs/content.zh/docs/sql/reference/queries/window-agg.md

@@ -307,7 +307,7 @@ SELECT window_start, window_end, SUM(partial_price) AS total_price
         <code>SESSION_END(time_attr, interval)</code><br/>
       </td>
       <td><p>返回相应滚动窗口，跳跃窗口或会话窗口的上限的时间戳（exclusive），即窗口结束时间。</p>
-        <p><b>注意:</b> 上限时间戳（exlusive）<i>不能</i>作为 <a href="{{< ref "docs/concepts/sql-table-concepts/time_attributes" >}}">rowtime attribute</a> 用于后续基于时间的操作，例如：<a href="{{< ref "docs/sql/reference/queries/joins" >}}#interval-joins">interval joins</a> 和 <a href="{{< ref "docs/sql/reference/queries/window-agg" >}}">group window</a> 或 <a href="{{< ref "docs/sql/reference/queries/over-agg" >}}">over window aggregations</a>。</p></td>
+        <p><b>注意:</b> 上限时间戳（exclusive）<i>不能</i>作为 <a href="{{< ref "docs/concepts/sql-table-concepts/time_attributes" >}}">rowtime attribute</a> 用于后续基于时间的操作，例如：<a href="{{< ref "docs/sql/reference/queries/joins" >}}#interval-joins">interval joins</a> 和 <a href="{{< ref "docs/sql/reference/queries/window-agg" >}}">group window</a> 或 <a href="{{< ref "docs/sql/reference/queries/over-agg" >}}">over window aggregations</a>。</p></td>
     </tr>
     <tr>
       <td>

docs/content/docs/connectors/datastream/formats/text_files.md

@@ -74,7 +74,7 @@ In this example, we create a DataStream containing the lines of text files as St
 as new files are added to the directory. We monitor for new files each second.
 There is no need for a watermark strategy as records do not contain event timestamps.
 
-{{< tabs "continous" >}}
+{{< tabs "continuous" >}}
 {{< tab "Java" >}}
 ```java
 final FileSource<String> source =

docs/content/docs/connectors/table/formats/protobuf.md

@@ -132,7 +132,7 @@ Format Options
       <td>no</td>
       <td style="word-wrap: break-word;">(none)</td>
       <td>String</td>
-      <td>The full name of a Protobuf generated class. The name must match the message name in the proto definition file. <code>$</code> is supported for inner class names, like 'com.exmample.OuterClass$MessageClass'</td>
+      <td>The full name of a Protobuf generated class. The name must match the message name in the proto definition file. <code>$</code> is supported for inner class names, like 'com.example.OuterClass$MessageClass'</td>
     </tr>
     <tr>
       <td><h5>protobuf.ignore-parse-errors</h5></td>

docs/content/docs/deployment/adaptive_batch.md

@@ -59,7 +59,7 @@ To automatically decide parallelisms for operators with Adaptive Batch Scheduler
     - [`execution.batch.adaptive.auto-parallelism.min-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-min-parallelism): The lower bound of allowed parallelism to set adaptively.
     - [`execution.batch.adaptive.auto-parallelism.max-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-max-parallelism): The upper bound of allowed parallelism to set adaptively. The default parallelism set via [`parallelism.default`]({{< ref "docs/deployment/config" >}}) or `StreamExecutionEnvironment#setParallelism()` will be used as upper bound of allowed parallelism if this configuration is not configured.
     - [`execution.batch.adaptive.auto-parallelism.avg-data-volume-per-task`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-avg-data-volume-per-ta): The average size of data volume to expect each task instance to process. Note that when data skew occurs, or the decided parallelism reaches the max parallelism (due to too much data), the data actually processed by some tasks may far exceed this value.
-    - [`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-paralle): The default parallelism of data source or the upper bound of source parallelism to set adaptively. The upper bound of allowed parallelism set via [`execution.batch.adaptive.auto-parallelism.max-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-max-parallelism) will be used if this configuration is not configured. If the upper bound of allowed parallelism is also not configured, the default parallelism set via [`parallelism.default`]({{< ref "docs/deployment/config" >}}) or `StreamExecutionEnvironment#setParallelism()` will be used instead.
+    - [`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-parallelism): The default parallelism of data source or the upper bound of source parallelism to set adaptively. The upper bound of allowed parallelism set via [`execution.batch.adaptive.auto-parallelism.max-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-max-parallelism) will be used if this configuration is not configured. If the upper bound of allowed parallelism is also not configured, the default parallelism set via [`parallelism.default`]({{< ref "docs/deployment/config" >}}) or `StreamExecutionEnvironment#setParallelism()` will be used instead.
 
 - Avoid setting the parallelism of operators:
 
@@ -77,7 +77,7 @@ The Context will provide the upper bound for the inferred parallelism, the expec
 
 The Adaptive Batch Scheduler will invoke the interface before scheduling the source vertices, and it should be noted that implementations should avoid time-consuming operations as much as possible.
 
-If the Source does not implement the interface, the configuration setting [`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-paralle) will be used as the parallelism of the source vertices.
+If the Source does not implement the interface, the configuration setting [`execution.batch.adaptive.auto-parallelism.default-source-parallelism`]({{< ref "docs/deployment/config" >}}#execution-batch-adaptive-auto-parallelism-default-source-parallelism) will be used as the parallelism of the source vertices.
 
 Note that the dynamic source parallelism inference only decides the parallelism for source operators which do not already have a specified parallelism.
 

docs/content/docs/deployment/filesystems/azure.md

@@ -50,7 +50,7 @@ wasb://<your-container>@$<your-azure-account>.blob.core.windows.net/<object-path
 // WASB SSL encrypted access
 wasbs://<your-container>@$<your-azure-account>.blob.core.windows.net/<object-path>
 
-// ABFS unecrypted access
+// ABFS unencrypted access
 abfs://<your-container>@$<your-azure-account>.dfs.core.windows.net/<object-path>
 
 // ABFS SSL encrypted access

docs/content/docs/deployment/security/security-ssl.md

@@ -61,7 +61,7 @@ added to the container images, or attached to the YARN deployment.
 
   - In an environment where operators are constrained to use firm-wide Internal CA (cannot generated self-signed certificates),
     the recommendation is to still have a dedicated key pair / certificate for the Flink deployment, signed by that CA.
-    However, the TrustStore must then also contain the CA's public certificate tho accept the deployment's certificate
+    However, the TrustStore must then also contain the CA's public certificate to accept the deployment's certificate
     during the SSL handshake (requirement in JDK TrustStore implementation).
 
     **NOTE:** Because of that, it is critical that you specify the fingerprint of the deployment certificate

docs/content/docs/dev/datastream/java_lambdas.md

@@ -64,7 +64,7 @@ org.apache.flink.api.common.functions.InvalidTypesException: The generic type pa
     Otherwise the type has to be specified explicitly using type information.
 ```
 
-In this case, the type information needs to be *specified explicitly*, otherwise the output will be treated as type `Object` which leads to unefficient serialization.
+In this case, the type information needs to be *specified explicitly*, otherwise the output will be treated as type `Object` which leads to inefficient serialization.
 
 ```java
 DataStream<Integer> input = env.fromData(1, 2, 3);

docs/content/docs/getting-started/flink-operations-playground.md

@@ -546,7 +546,7 @@ The endpoint depends on the scope of these metrics. Metrics scoped to a Job can
 
 **Request**
 ```bash
-curl "localhost:8081/jobs/<jod-id>/metrics?get=lastCheckpointSize"
+curl "localhost:8081/jobs/<job-id>/metrics?get=lastCheckpointSize"
 ```
 **Expected Response (pretty-printed; no placeholders)**
 ```json
@@ -564,7 +564,7 @@ about the status of a running Job.
 **Request**
 ```bash
 # find the vertex-id of the vertex of interest
-curl localhost:8081/jobs/<jod-id>
+curl localhost:8081/jobs/<job-id>
 ```
 
 **Expected Response (pretty-printed)**

docs/content/docs/sql/hive-compatibility/hive-dialect/alter.md

@@ -250,7 +250,7 @@ ALTER TABLE table_name [PARTITION partition_spec] CHANGE [COLUMN] col_old_name c
 
 ```sql
 ALTER TABLE t1 CHANGE COLUMN c1 new_c1 STRING FIRST;
-ALTER TABLE t1 CHANGE COLUMN c1 new_c1 STRING AFRER c2;
+ALTER TABLE t1 CHANGE COLUMN c1 new_c1 STRING AFTER c2;
 ```
 
 #### Add/Replace Columns

docs/content/docs/sql/reference/queries/vector-search.md

@@ -93,7 +93,7 @@ input_table, LATERAL VECTOR_SEARCH(
   CONFIG => MAP['async', 'true', 'timeout', '100s']
 );
 
--- Searching with contant value
+-- Searching with constant value
 SELECT * 
 FROM TABLE(VECTOR_SEARCH(
   TABLE vector_table,