Fix missing url in Avro Docs.

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

@@ -198,14 +198,14 @@ ds = env.from_source(source, WatermarkStrategy.no_watermarks(), "file-source")
 
 Flink 支持三种方式来读取 Parquet 文件并创建 Avro records （PyFlink 只支持 Generic record）：
 
-- [Generic record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
-- [Specific record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
-- [Reflect record](https://avro.apache.org/docs/1.10.0/api/java/org/apache/avro/reflect/package-summary.html)
+- [Generic record](https://avro.apache.org/docs/++version++/api/java/index.html)
+- [Specific record](https://avro.apache.org/docs/++version++/api/java/index.html)
+- [Reflect record](https://avro.apache.org/docs/++version++/api/java/org/apache/avro/reflect/package-summary.html)
 
 ### Generic record
 
-使用 JSON 定义 Avro schemas。你可以从 [Avro specification](https://avro.apache.org/docs/1.10.0/spec.html) 获取更多关于 Avro schemas 和类型的信息。
-此示例使用了一个在 [official Avro tutorial](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html) 中描述的示例相似的 Avro schema：
+使用 JSON 定义 Avro schemas。你可以从 [Avro specification](https://avro.apache.org/docs/++version++/spec.html) 获取更多关于 Avro schemas 和类型的信息。
+此示例使用了一个在 [official Avro tutorial](https://avro.apache.org/docs/++version++/gettingstartedjava.html) 中描述的示例相似的 Avro schema：
 
 ```json lines
 {"namespace": "example.avro",
@@ -219,11 +219,11 @@ Flink 支持三种方式来读取 Parquet 文件并创建 Avro records （PyFlin
 }
 ```
 这个 schema 定义了一个具有三个属性的的 user 记录：name，favoriteNumber 和 favoriteColor。你可以
-在 [record specification](https://avro.apache.org/docs/1.10.0/spec.html#schema_record) 找到更多关于如何定义 Avro schema 的详细信息。
+在 [record specification](https://avro.apache.org/docs/++version++/spec.html#schema_record) 找到更多关于如何定义 Avro schema 的详细信息。
 
 在此示例中，你将创建包含由 Avro Generic records 格式构成的 Parquet records 的 DataStream。
 Flink 会基于 JSON 字符串解析 Avro schema。也有很多其他的方式解析 schema，例如基于 java.io.File 或 java.io.InputStream。
-请参考 [Avro Schema](https://avro.apache.org/docs/1.10.0/api/java/org/apache/avro/Schema.html) 以获取更多详细信息。
+请参考 [Avro Schema](https://avro.apache.org/docs/++version++/api/java/org/apache/avro/Schema.html) 以获取更多详细信息。
 然后，你可以通过 `AvroParquetReaders` 为 Avro Generic 记录创建 `AvroParquetRecordFormat`。
 
 {{< tabs "GenericRecord" >}}
@@ -286,7 +286,7 @@ stream = env.from_source(source, WatermarkStrategy.no_watermarks(), "file-source
 基于之前定义的 schema，你可以通过利用 Avro 代码生成来生成类。
 一旦生成了类，就不需要在程序中直接使用 schema。
 你可以使用 `avro-tools.jar` 手动生成代码，也可以直接使用 Avro Maven 插件对配置的源目录中的任何 .avsc 文件执行代码生成。
-请参考 [Avro Getting Started](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html) 获取更多信息。
+请参考 [Avro Getting Started](https://avro.apache.org/docs/++version++/gettingstartedjava.html) 获取更多信息。
 
 此示例使用了样例 schema {{< gh_link file="flink-formats/flink-parquet/src/test/resources/avro/testdata.avsc" name="testdata.avsc" >}}：
 
@@ -335,7 +335,7 @@ final DataStream<GenericRecord> stream =
 
 除了需要预定义 Avro Generic 和 Specific 记录， Flink 还支持基于现有 Java POJO 类从 Parquet 文件创建 DateStream。
 在这种场景中，Avro 会使用 Java 反射为这些 POJO 类生成 schema 和协议。
-请参考 [Avro reflect](https://avro.apache.org/docs/1.10.0/api/java/index.html) 文档获取更多关于 Java 类型到 Avro schemas 映射的详细信息。
+请参考 [Avro reflect](https://avro.apache.org/docs/++version++/api/java/index.html) 文档获取更多关于 Java 类型到 Avro schemas 映射的详细信息。
 
 本例使用了一个简单的 Java POJO 类 {{< gh_link file="flink-formats/flink-parquet/src/test/java/org/apache/flink/formats/parquet/avro/Datum.java" name="Datum" >}}：
 

docs/content.zh/docs/connectors/table/formats/avro-confluent.md

@@ -291,4 +291,4 @@ Format 参数
 
 除了此处列出的类型之外，Flink 还支持读取/写入可为空（nullable）的类型。 Flink 将可为空的类型映射到 Avro `union(something, null)`, 其中 `something` 是从 Flink 类型转换的 Avro 类型。
 
-您可以参考 [Avro Specification](https://avro.apache.org/docs/current/spec.html) 以获取有关 Avro 类型的更多信息。
+您可以参考 [Avro Specification](https://avro.apache.org/docs/++version++/specification/) 以获取有关 Avro 类型的更多信息。

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

@@ -204,4 +204,4 @@ Format 参数
 
 除了上面列出的类型，Flink 支持读取/写入 nullable 的类型。Flink 将 nullable 的类型映射到 Avro `union(something, null)`，其中 `something` 是从 Flink 类型转换的 Avro 类型。
 
-您可以参考 [Avro 规范](https://avro.apache.org/docs/current/spec.html) 获取更多有关 Avro 类型的信息。
+您可以参考 [Avro 规范](https://avro.apache.org/docs/++version++/specification/) 获取更多有关 Avro 类型的信息。

docs/content.zh/docs/dev/datastream/fault-tolerance/serialization/schema_evolution.md

@@ -91,7 +91,7 @@ Flink 基于下面的规则来支持 [POJO 类型]({{< ref "docs/dev/datastream/
 ### Avro 类型
 
 Flink 完全支持 Avro 状态类型的升级，只要数据结构的修改是被
-[Avro 的数据结构解析规则](http://avro.apache.org/docs/current/spec.html#Schema+Resolution)认为兼容的即可。
+[Avro 的数据结构解析规则](https://avro.apache.org/docs/++version++/specification/_print/#schema-resolution)认为兼容的即可。
 
 一个例外是如果新的 Avro 数据 schema 生成的类无法被重定位或者使用了不同的命名空间，在作业恢复时状态数据会被认为是不兼容的。
 

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

@@ -196,14 +196,14 @@ ds = env.from_source(source, WatermarkStrategy.no_watermarks(), "file-source")
 
 Flink supports producing three types of Avro records by reading Parquet files (Only Generic record is supported in PyFlink):
 
-- [Generic record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
-- [Specific record](https://avro.apache.org/docs/1.10.0/api/java/index.html)
-- [Reflect record](https://avro.apache.org/docs/1.10.0/api/java/org/apache/avro/reflect/package-summary.html)
+- [Generic record](https://avro.apache.org/docs/++version++/api/java/index.html)
+- [Specific record](https://avro.apache.org/docs/++version++/api/java/index.html)
+- [Reflect record](https://avro.apache.org/docs/++version++/api/java/org/apache/avro/reflect/package-summary.html)
 
 ### Generic record
 
-Avro schemas are defined using JSON. You can get more information about Avro schemas and types from the [Avro specification](https://avro.apache.org/docs/1.10.0/spec.html).
-This example uses an Avro schema example similar to the one described in the [official Avro tutorial](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html):
+Avro schemas are defined using JSON. You can get more information about Avro schemas and types from the [Avro specification](https://avro.apache.org/docs/++version++/spec.html).
+This example uses an Avro schema example similar to the one described in the [official Avro tutorial](https://avro.apache.org/docs/++version++/gettingstartedjava.html):
 
 ```json lines
 {"namespace": "example.avro",
@@ -217,10 +217,10 @@ This example uses an Avro schema example similar to the one described in the [of
 }
 ```
 
-This schema defines a record representing a user with three fields: name, favoriteNumber, and favoriteColor. You can find more details at [record specification](https://avro.apache.org/docs/1.10.0/spec.html#schema_record) for how to define an Avro schema.
+This schema defines a record representing a user with three fields: name, favoriteNumber, and favoriteColor. You can find more details at [record specification](https://avro.apache.org/docs/++version++/spec.html#schema_record) for how to define an Avro schema.
 
 In the following example, you will create a DataStream containing Parquet records as Avro Generic records. 
-It will parse the Avro schema based on the JSON string. There are many other ways to parse a schema, e.g. from java.io.File or java.io.InputStream. Please refer to [Avro Schema](https://avro.apache.org/docs/1.10.0/api/java/org/apache/avro/Schema.html) for details.
+It will parse the Avro schema based on the JSON string. There are many other ways to parse a schema, e.g. from java.io.File or java.io.InputStream. Please refer to [Avro Schema](https://avro.apache.org/docs/++version++/api/java/org/apache/avro/Schema.html) for details.
 After that, you will create an `AvroParquetRecordFormat` via `AvroParquetReaders` for Avro Generic records.
 
 {{< tabs "GenericRecord" >}}
@@ -284,7 +284,7 @@ Based on the previously defined schema, you can generate classes by leveraging A
 Once the classes have been generated, there is no need to use the schema directly in your programs. 
 You can either use `avro-tools.jar` to generate code manually or you could use the Avro Maven plugin to perform 
 code generation on any .avsc files present in the configured source directory. Please refer to 
-[Avro Getting Started](https://avro.apache.org/docs/1.10.0/gettingstartedjava.html) for more information.
+[Avro Getting Started](https://avro.apache.org/docs/++version++/gettingstartedjava.html) for more information.
 
 The following example uses the example schema {{< gh_link file="flink-formats/flink-parquet/src/test/resources/avro/testdata.avsc" name="testdata.avsc" >}}:
 
@@ -334,7 +334,7 @@ final DataStream<GenericRecord> stream =
 Beyond Avro Generic and Specific record that requires a predefined Avro schema, 
 Flink also supports creating a DataStream from Parquet files based on existing Java POJO classes.
 In this case, Avro will use Java reflection to generate schemas and protocols for these POJO classes.
-Java types are mapped to Avro schemas, please refer to the [Avro reflect](https://avro.apache.org/docs/1.10.0/api/java/index.html) documentation for more details.
+Java types are mapped to Avro schemas, please refer to the [Avro reflect](https://avro.apache.org/docs/++version++/api/java/index.html) documentation for more details.
 
 This example uses a simple Java POJO class {{< gh_link file="flink-formats/flink-parquet/src/test/java/org/apache/flink/formats/parquet/avro/Datum.java" name="Datum" >}}:
 

docs/content/docs/connectors/table/formats/avro-confluent.md

@@ -298,4 +298,4 @@ See the [Apache Avro Format]({{< ref "docs/connectors/table/formats/avro" >}}#da
 
 In addition to the types listed there, Flink supports reading/writing nullable types. Flink maps nullable types to Avro `union(something, null)`, where `something` is the Avro type converted from Flink type.
 
-You can refer to [Avro Specification](https://avro.apache.org/docs/current/spec.html) for more information about Avro types.
+You can refer to [Avro Specification](https://avro.apache.org/docs/++version++/specification/) for more information about Avro types.

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

@@ -218,4 +218,4 @@ So the following table lists the type mapping from Flink type to Avro type.
 
 In addition to the types listed above, Flink supports reading/writing nullable types. Flink maps nullable types to Avro `union(something, null)`, where `something` is the Avro type converted from Flink type.
 
-You can refer to [Avro Specification](https://avro.apache.org/docs/current/spec.html) for more information about Avro types.
+You can refer to [Avro Specification](https://avro.apache.org/docs/++version++/specification/) for more information about Avro types.

flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializerSnapshot.java

@@ -166,7 +166,8 @@ public TypeSerializer<T> restoreSerializer() {
      *
      * <p>Checks whenever a new version of a schema (reader) can read values serialized with the old
      * schema (writer). If the schemas are compatible according to {@code Avro} schema resolution
-     * rules (@see <a href="https://avro.apache.org/docs/current/spec.html#Schema+Resolution">Schema
+     * rules (@see <a
+     * href="https://avro.apache.org/docs/++version++/specification/_print/#schema-resolution">Schema
      * Resolution</a>).
      */
     @VisibleForTesting

flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroSplittableInputFormatTest.java

@@ -51,7 +51,7 @@
 
 /**
  * Test the avro input format. (The testcase is mostly the getting started tutorial of avro)
- * http://avro.apache.org/docs/current/gettingstartedjava.html
+ * https://avro.apache.org/docs/++version++/getting-started-java/
  */
 class AvroSplittableInputFormatTest {