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fix: the configurations for Vector #1406

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Dec 25, 2024
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fix: the configurations for Vector #1406

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9c38c9e
fix: the configurations for Vector
nicecui Dec 25, 2024
5dee25a
fix the front matter checker
nicecui Dec 25, 2024
8babecc
remove the log content
nicecui Dec 25, 2024
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4 changes: 3 additions & 1 deletion .github/scripts/check-front-matter.js
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Expand Up @@ -46,7 +46,9 @@ async function checkMarkdownFiles() {
});

// Filter to get only markdown files
const markdownFiles = files.filter(file => file.filename.endsWith('.md'));
const markdownFiles = files.filter(file =>
file.filename.endsWith('.md') && file.status !== 'removed'
);

let allValid = true;

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19 changes: 0 additions & 19 deletions docs/db-cloud-shared/clients/vector-integration.md
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29 changes: 16 additions & 13 deletions docs/user-guide/ingest-data/for-observerbility/vector.md
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Expand Up @@ -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:

Expand All @@ -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>"
Expand All @@ -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:

Expand All @@ -54,6 +60,3 @@ 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>

</DocTemplate>
16 changes: 0 additions & 16 deletions ...aurus-plugin-content-docs/current/db-cloud-shared/clients/vector-integration.md
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30 changes: 14 additions & 16 deletions ...plugin-content-docs/current/user-guide/ingest-data/for-observerbility/vector.md
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Expand Up @@ -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 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。

[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/)。
## 收集指标

<div id="toml-config">

## 集成
### 配置

使用 GreptimeDB 的 Vector 集成的最小配置如下：

Expand All @@ -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>"
Expand All @@ -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:

```
vector -c sample.toml
```

<div id="data-model">
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
### 数据模型

我们使用这样的规则将 Vector 指标存入 GreptimeDB：

Expand All @@ -55,7 +57,3 @@ GreptimeDB 使用 gRPC 与 Vector 进行通信，因此 Vector sink 的默认端
- AggregatedHistoragm 类型，每个 bucket 的数值将被存入 `bxx` 列，其中 xx 是 bucket 数值的上限，此外我们还会记录 `sum/count` 列；
- 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>
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