From 32af73f2182c8d8590032463610de423b1fc7823 Mon Sep 17 00:00:00 2001 From: donPain Date: Tue, 10 Dec 2024 10:32:47 -0300 Subject: [PATCH] Fix missing url in Avro Docs. --- .../connectors/datastream/formats/parquet.md | 18 +++++++++--------- .../connectors/table/formats/avro-confluent.md | 2 +- .../docs/connectors/table/formats/avro.md | 2 +- .../serialization/schema_evolution.md | 2 +- .../connectors/datastream/formats/parquet.md | 18 +++++++++--------- .../connectors/table/formats/avro-confluent.md | 2 +- .../docs/connectors/table/formats/avro.md | 2 +- .../avro/typeutils/AvroSerializerSnapshot.java | 3 ++- .../avro/AvroRecordInputFormatTest.java | 2 +- .../avro/AvroSplittableInputFormatTest.java | 2 +- 10 files changed, 27 insertions(+), 26 deletions(-) diff --git a/docs/content.zh/docs/connectors/datastream/formats/parquet.md b/docs/content.zh/docs/connectors/datastream/formats/parquet.md index 860a53131deb77..b16cf3a890c88b 100644 --- a/docs/content.zh/docs/connectors/datastream/formats/parquet.md +++ b/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++/getting-started-java) 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++/getting-started-java) 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 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" >}}： diff --git a/docs/content.zh/docs/connectors/table/formats/avro-confluent.md b/docs/content.zh/docs/connectors/table/formats/avro-confluent.md index fdc79540b0dd5c..70e568222fc04c 100644 --- a/docs/content.zh/docs/connectors/table/formats/avro-confluent.md +++ b/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。 diff --git a/docs/content.zh/docs/connectors/table/formats/avro.md b/docs/content.zh/docs/connectors/table/formats/avro.md index d2781212e063be..e97107b9b06c9a 100644 --- a/docs/content.zh/docs/connectors/table/formats/avro.md +++ b/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。 diff --git a/docs/content.zh/docs/dev/datastream/fault-tolerance/serialization/schema_evolution.md b/docs/content.zh/docs/dev/datastream/fault-tolerance/serialization/schema_evolution.md index f794d72e01f24b..2d7f29d18c1224 100644 --- a/docs/content.zh/docs/dev/datastream/fault-tolerance/serialization/schema_evolution.md +++ b/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。 diff --git a/docs/content/docs/connectors/datastream/formats/parquet.md b/docs/content/docs/connectors/datastream/formats/parquet.md index 706ad0a53d26dc..f01ed9c9338e3b 100644 --- a/docs/content/docs/connectors/datastream/formats/parquet.md +++ b/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++/getting-started-java): ```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++/getting-started-java/) 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 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" >}}: diff --git a/docs/content/docs/connectors/table/formats/avro-confluent.md b/docs/content/docs/connectors/table/formats/avro-confluent.md index 03a84771356f9c..c2fb1bc4717867 100644 --- a/docs/content/docs/connectors/table/formats/avro-confluent.md +++ b/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. diff --git a/docs/content/docs/connectors/table/formats/avro.md b/docs/content/docs/connectors/table/formats/avro.md index 0665271c555736..023f788107f3fa 100644 --- a/docs/content/docs/connectors/table/formats/avro.md +++ b/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. diff --git a/flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializerSnapshot.java b/flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializerSnapshot.java index 1643faf76d6db7..8c463f75a138a7 100644 --- a/flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializerSnapshot.java +++ b/flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSerializerSnapshot.java @@ -166,7 +166,8 @@ public TypeSerializer restoreSerializer() { * *

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 Schema + * rules (@see Schema * Resolution). */ @VisibleForTesting diff --git a/flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroRecordInputFormatTest.java b/flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroRecordInputFormatTest.java index a3e2e6c0914d3e..2792f7d0043627 100644 --- a/flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroRecordInputFormatTest.java +++ b/flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroRecordInputFormatTest.java @@ -73,7 +73,7 @@ /** * Test the avro input format. (The testcase is mostly the getting started tutorial of avro) - * http://avro.apache.org/docs/current/gettingstartedjava.html + * http://avro.apache.org/docs/current/getting-started-java */ public class AvroRecordInputFormatTest { diff --git a/flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroSplittableInputFormatTest.java b/flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroSplittableInputFormatTest.java index f6fe06342c2bb7..830b30ae75246e 100644 --- a/flink-formats/flink-avro/src/test/java/org/apache/flink/formats/avro/AvroSplittableInputFormatTest.java +++ b/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 {