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commit a6767d321a30eed5ef193f48bc1dcd93fe7dbd8a Author: Jark Wu <[email protected]> AuthorDate: Mon Aug 25 19:30:10 2025 +0800 fix broken url links --- website/blog/2024-11-29-fluss-open-source.md | 2 +- website/blog/2025-06-01-partial-updates.md | 4 ++-- website/blog/releases/0.7.md | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/2024-11-29-fluss-open-source.md b/website/blog/2024-11-29-fluss-open-source.md index ec8d307a0..3e3effb41 100644 --- a/website/blog/2024-11-29-fluss-open-source.md +++ b/website/blog/2024-11-29-fluss-open-source.md @@ -40,7 +40,7 @@ Make sure to keep an eye on the project, give it a try and if you like it, don ### Getting Started - Visit the [GitHub repository](https://github.com/apache/fluss). -- Check out the [quickstart guide](/docs/quickstart/flink.md). +- Check out the [quickstart guide](/docs/quickstart/flink/). ### Additional Resources - Announcement Blog Post: [Introducing Fluss: Unified Streaming Storage For Next-Generation Data Analytics](https://www.ververica.com/blog/introducing-fluss) diff --git a/website/blog/2025-06-01-partial-updates.md b/website/blog/2025-06-01-partial-updates.md index 08de6d53c..55317235e 100644 --- a/website/blog/2025-06-01-partial-updates.md +++ b/website/blog/2025-06-01-partial-updates.md @@ -265,7 +265,7 @@ Flink SQL> SELECT * FROM user_rec_wide; Now let's switch to `batch` mode and query the current snapshot of the `user_rec_wide` table. -But before that, let's start the [Tiering Service](/docs/maintenance/tiered-storage/lakehouse-storage.md#start-the-datalake-tiering-service) that allows offloading the tables as `Lakehouse` tables. +But before that, let's start the [Tiering Service](/docs/maintenance/tiered-storage/lakehouse-storage/#start-the-datalake-tiering-service) that allows offloading the tables as `Lakehouse` tables. **Step 7:** Open a new terminal 💻 in the `Coordinator Server` and run the following command to start the `Tiering Service`: ```shell @@ -297,7 +297,7 @@ Flink SQL> SELECT * FROM user_rec_wide; ### Conclusion Partial updates in Fluss enable an alternative approach in how we design streaming data pipelines for enriching or joining data. -When all your sources share a primary key - otherwise you can mix & match [streaming lookup joins](/docs/engine-flink/lookups.md#lookup) - you can turn the problem on its head: update a unified table incrementally, rather than joining streams on the fly. +When all your sources share a primary key - otherwise you can mix & match [streaming lookup joins](/docs/engine-flink/lookups/#lookup) - you can turn the problem on its head: update a unified table incrementally, rather than joining streams on the fly. The result is a more scalable, maintainable, and efficient pipeline. Engineers can spend less time wrestling with Flink’s state, checkpoints and join mechanics, and more time delivering fresh, integrated data to power real-time analytics and applications. diff --git a/website/blog/releases/0.7.md b/website/blog/releases/0.7.md index ce57ed808..a8ebf1bc0 100644 --- a/website/blog/releases/0.7.md +++ b/website/blog/releases/0.7.md @@ -155,7 +155,7 @@ DataStreamSource<Order> stream = env.fromSource( ); ``` -For usage examples and configuration parameters, see the [DataStream Connector documentation](/docs/engine-flink/datastream.md). +For usage examples and configuration parameters, see the [DataStream Connector documentation](/docs/engine-flink/datastream/). ## Fluss Java Client @@ -164,7 +164,7 @@ In this version, we officially release the Fluss Java Client, a client library d * **Table API:** For table-based data operations, supporting streaming reads/writes, updates, deletions, and point queries. * **Admin API:** For metadata management, including cluster management, table lifecycle, and access control. -The client supports forward and backward compatibility, ensuring smooth upgrades across Fluss versions. With the Fluss Java Client, developers can build online applications and data ingestion services based on Fluss, as well as enterprise-level components such as Fluss management platforms and operations monitoring systems. For detailed usage instructions, please refer to the official documentation: [Fluss Java Client User Guide](/docs/apis/java-client.md). +The client supports forward and backward compatibility, ensuring smooth upgrades across Fluss versions. With the Fluss Java Client, developers can build online applications and data ingestion services based on Fluss, as well as enterprise-level components such as Fluss management platforms and operations monitoring systems. For detailed usage instructions, please refer to the official documentation: [Fluss Java Client User Guide](/docs/apis/java-client/). Fluss uses Apache Arrow as its underlying storage format, enabling efficient cross-language extensions. A **Fluss Python Client** is planned for future releases, leveraging the rich ecosystem of **PyArrow** to integrate with popular data analysis tools such as **Pandas** and **DuckDB**. This will further lower the barrier for real-time data exploration and analytics.
