From 9c38c9ebbe4ad691482b6ae6b1fb75215be89b37 Mon Sep 17 00:00:00 2001
From: Yiran <cuiyiran3@gmail.com>
Date: Wed, 25 Dec 2024 16:59:38 +0800
Subject: [PATCH] fix: the configurations for Vector

---
 .../clients/vector-integration.md             | 19 -----------
 .../ingest-data/for-observerbility/vector.md  | 34 ++++++++++++-------
 .../clients/vector-integration.md             | 16 ---------
 .../ingest-data/for-observerbility/vector.md  | 34 +++++++++++--------
 4 files changed, 42 insertions(+), 61 deletions(-)
 delete mode 100644 docs/db-cloud-shared/clients/vector-integration.md
 delete mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/db-cloud-shared/clients/vector-integration.md

diff --git a/docs/db-cloud-shared/clients/vector-integration.md b/docs/db-cloud-shared/clients/vector-integration.md
deleted file mode 100644
index fa773c0f9..000000000
--- a/docs/db-cloud-shared/clients/vector-integration.md
+++ /dev/null
@@ -1,19 +0,0 @@
-
-Vector is [a high performance observability data
-pipeline](https://vector.dev). It has native support for GreptimeDB metrics data
-sink. With vector, you can ingest metrics data from various sources, including
-Prometheus, OpenTelemetry, StatsD and many more.
-GreptimeDB can be used as a Vector Sink component to receive metrics. 
-
-<InjectContent id="toml-config" content={props.children}/>
-
-Execute Vector with:
-
-```
-vector -c sample.toml
-```
-
-For more configuration options, see [Vector GreptimeDB
-Configuration](https://vector.dev/docs/reference/sinks/greptimedb/).
-
-<InjectContent id="data-model" content={props.children}/>
diff --git a/docs/user-guide/ingest-data/for-observerbility/vector.md b/docs/user-guide/ingest-data/for-observerbility/vector.md
index d965fdd18..781b02c00 100644
--- a/docs/user-guide/ingest-data/for-observerbility/vector.md
+++ b/docs/user-guide/ingest-data/for-observerbility/vector.md
@@ -3,16 +3,17 @@ keywords: [Vector, integration, configuration, data model, metrics]
 description: Instructions for integrating Vector with GreptimeDB, including configuration, data model mapping, and example configurations.
 ---
 
-import DocTemplate from '../../../db-cloud-shared/clients/vector-integration.md'
-
-
 # Vector
 
-<DocTemplate>
+Vector is [a high performance observability data
+pipeline](https://vector.dev). It has native support for GreptimeDB metrics data
+sink. With vector, you can ingest metrics data from various sources, including
+Prometheus, OpenTelemetry, StatsD and many more.
+GreptimeDB can be used as a Vector Sink component to receive metrics. 
 
-<div id="toml-config">
+## Collect metrics
 
-## Integration
+### Configuration
 
 A minimal configuration of when using your GreptimeDB instance can be:
 
@@ -24,7 +25,7 @@ type = "host_metrics"
 
 [sinks.my_sink_id]
 inputs = ["in"]
-type = "greptimedb"
+type = "greptimedb_metrics"
 endpoint = "<host>:4001"
 dbname = "<dbname>"
 username = "<username>"
@@ -35,11 +36,16 @@ new_naming = true
 GreptimeDB uses gRPC to communicate with Vector, so the default port for the Vector sink is `4001`.
 If you have changed the default gRPC port when starting GreptimeDB with [custom configurations](/user-guide/deployments/configuration.md#configuration-file), use your own port instead.
 
-</div>
+Execute Vector with:
+
+```
+vector -c sample.toml
+```
 
-<div id="data-model">
+For more configuration options, see [Vector GreptimeDB
+Configuration](https://vector.dev/docs/reference/configuration/sinks/greptimedb_metrics/).
 
-## Data Model
+### Data Model
 
 The following rules are used when storing Vector metrics into GreptimeDB:
 
@@ -54,6 +60,10 @@ The following rules are used when storing Vector metrics into GreptimeDB:
   - For AggregatedSummary metrics, the values of each percentile are stored in the `pxx` column, where xx is the percentile, and the `sum/count` columns are also stored;
   - For Sketch metrics, the values of each percentile are stored in the `pxx` column, where xx is the percentile, and the `min/max/avg/sum` columns are also stored;
 
-</div>
+## Collect logs
+
+### Configuration
+
+### Data Model
+
 
-</DocTemplate>
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/db-cloud-shared/clients/vector-integration.md b/i18n/zh/docusaurus-plugin-content-docs/current/db-cloud-shared/clients/vector-integration.md
deleted file mode 100644
index 498bff093..000000000
--- a/i18n/zh/docusaurus-plugin-content-docs/current/db-cloud-shared/clients/vector-integration.md
+++ /dev/null
@@ -1,16 +0,0 @@
-Vector 是高性能的可观测数据管道。
-它原生支持 GreptimeDB 指标数据接收端。
-通过 Vector，你可以从各种来源接收指标数据，包括 Prometheus、OpenTelemetry、StatsD 等。
-GreptimeDB 可以作为 Vector 的 Sink 组件来接收指标数据。
-
-<InjectContent id="toml-config" content={props.children}/>
-
-启动 Vector:
-
-```
-vector -c sample.toml
-```
-
-请前往 [Vector GreptimeDB Configuration](https://vector.dev/docs/reference/sinks/greptimedb/) 查看更多配置项。
-
-<InjectContent id="data-model" content={props.children}/>
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/user-guide/ingest-data/for-observerbility/vector.md b/i18n/zh/docusaurus-plugin-content-docs/current/user-guide/ingest-data/for-observerbility/vector.md
index bb18cfa4b..4bc6b6cde 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/current/user-guide/ingest-data/for-observerbility/vector.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/current/user-guide/ingest-data/for-observerbility/vector.md
@@ -3,18 +3,16 @@ keywords: [Vector, 数据写入, gRPC 通信, 数据模型, 配置示例]
 description: 介绍如何使用 Vector 将数据写入 GreptimeDB，包括最小配置示例和数据模型的映射规则。
 ---
 
-import DocTemplate from '../../../db-cloud-shared/clients/vector-integration.md'
-
-
 # Vector
 
-<DocTemplate>
-
-[Vector](https://vector.dev/) 是一种高性能的可以帮助工程师控制可观测性数据的通道工具。我们的 Vector 集成页面在[这里](https://vector.dev/docs/reference/configuration/sinks/greptimedb/)。
+Vector 是高性能的可观测数据管道。
+它原生支持 GreptimeDB 指标数据接收端。
+通过 Vector，你可以从各种来源接收指标数据，包括 Prometheus、OpenTelemetry、StatsD 等。
+GreptimeDB 可以作为 Vector 的 Sink 组件来接收指标数据。
 
-<div id="toml-config">
+## 收集指标
 
-## 集成
+### 配置
 
 使用 GreptimeDB 的 Vector 集成的最小配置如下：
 
@@ -26,7 +24,7 @@ type = "host_metrics"
 
 [sinks.my_sink_id]
 inputs = ["in"]
-type = "greptimedb"
+type = "greptimedb_metrics"
 endpoint = "<host>:4001"
 dbname = "<dbname>"
 username = "<username>"
@@ -37,11 +35,15 @@ new_naming = true
 GreptimeDB 使用 gRPC 与 Vector 进行通信，因此 Vector sink 的默认端口是 `4001`。
 如果你在使用 [自定义配置](/user-guide/deployments/configuration.md#configuration-file) 启动 GreptimeDB 时更改了默认的 gRPC 端口，请使用你自己的端口。
 
-</div>
+启动 Vector:
 
-<div id="data-model">
+```
+vector -c sample.toml
+```
 
-## 数据模型
+请前往 [Vector GreptimeDB Configuration](https://vector.dev/docs/reference/configuration/sinks/greptimedb_metrics/) 查看更多配置项。
+
+### 数据模型
 
 我们使用这样的规则将 Vector 指标存入 GreptimeDB：
 
@@ -56,6 +58,10 @@ GreptimeDB 使用 gRPC 与 Vector 进行通信，因此 Vector sink 的默认端
   - AggregatedSummary 类型，各个百分位数值点分别存入 `pxx` 列，其中 xx 是 quantile 数值，此外我们还会记录 `sum/count` 列；
   - Sketch 类型，各个百分位数值点分别存入 `pxx` 列，其中 xx 是 quantile 数值，此外我们还会记录 `min/max/avg/sum` 列；
 
-</div>
 
-</DocTemplate>
+## 收集日志
+
+### 配置
+
+### 数据模型
+