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commit 3949e8ead77d67881d08b0f5bb828dd23036266e Author: Zmm <[email protected]> AuthorDate: Wed Jul 23 15:15:28 2025 +0800 [doc] fix typos, formatting and consistency in architecture.md (#1390) --- website/docs/concepts/architecture.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/concepts/architecture.md b/website/docs/concepts/architecture.md index fb04cdd16..7f852c5ab 100644 --- a/website/docs/concepts/architecture.md +++ b/website/docs/concepts/architecture.md @@ -32,12 +32,12 @@ Additionally, it coordinates critical operations such as: - Managing data migration and service node switching in the event of node failures. - Overseeing table management tasks, including creating or deleting tables and updating bucket counts. -As the **brain** of the cluster, the **Coordinator Server** ensures efficient cluster operation and seamless management of resources. +As the **brain** of the cluster, the **CoordinatorServer** ensures efficient cluster operation and seamless management of resources. ## TabletServer The **TabletServer** is responsible for data storage, persistence, and providing I/O services directly to users. It comprises two key components: **LogStore** and **KvStore**. - For **PrimaryKey Tables** which support updates, both **LogStore** and **KvStore** are activated. The KvStore is used to support updates and point lookup efficiently. LogStore is used to store the changelogs of the table. -- For **Log Tables** which only supports appends, only the **LogStore** is activated, optimizing performance for write-heavy workloads. +- For **Log Tables** which only support appends, only the **LogStore** is activated, optimizing performance for write-heavy workloads. This architecture ensures the **TabletServer** delivers tailored data handling capabilities based on table types. @@ -71,4 +71,4 @@ In upcoming releases, **ZooKeeper will be replaced** by **KvStore** for metadata Additionally, **Remote Storage** allows clients to perform bulk read operations on Log and Kv data, enhancing data analysis efficiency and reduce the overhead on Fluss servers. In the future, it will also support bulk write operations, optimizing data import workflows for greater scalability and performance. ## Client -Fluss clients/sdks support streaming reads/writes, batch read/writes, DDL and point queries. Currently, the main implementation of client is Flink Connector. Users can use Flink SQL to easily operate Fluss tables and data. \ No newline at end of file +Fluss clients/SDKs support streaming reads/writes, batch reads/writes, DDL and point queries. Currently, the main implementation of client is Flink Connector. Users can use Flink SQL to easily operate Fluss tables and data. \ No newline at end of file
