docs: revisit concepts (#2214)

Signed-off-by: Dennis Zhuang <[email protected]>
Co-authored-by: Ning Sun <[email protected]>
Co-authored-by: Copilot <[email protected]>

Author: 3 people

docs/index.md

@@ -10,15 +10,11 @@ description: Introduction to GreptimeDB, an open-source unified observability da
 
 **GreptimeDB** is an open-source, cloud-native, unified observability database for metrics, logs and traces. You can gain real-time insights from edge to cloud—at any scale.
 
-GreptimeDB is also on cloud as [GreptimeCloud](https://greptime.com/product/cloud),
-a fully-managed observability database service that features serverless scalability,
-seamless integration with rich ecosystems.
-
 Our core developers have been building observability data platforms for years. Based on their best-practices, GreptimeDB is born to give you:
 
 - **All-in-One Observability Database**: Process metrics, logs, and traces in real-time through a unified database with native [SQL](/user-guide/query-data/sql.md), [PromQL](/user-guide/query-data/promql.md), and [streaming processing](/user-guide/flow-computation/overview.md) support. It replaces complex legacy data stacks with a high-performance single solution.
 - **High-Performance Engine**: Built with Rust for high performance and reliability. Rich [indexing options](/user-guide/manage-data/data-index.md) (inverted, full-text, skip list, and vector indexing) accelerate queries, enabling sub-second responses on petabyte-scale datasets and handling hundreds of thousands of concurrent requests.
-- **Significant Cost Reduction**: Achieve 50x lower operational and storage costs through a compute-storage separation [architecture](/user-guide/concepts/architecture.md). Scale flexibly across cloud storage systems (e.g., S3, Azure Blob Storage) with a fully-managed cloud service [GreptimeCloud](https://greptime.com/product/cloud), for simplified management.
+- **Significant Cost Reduction**: Achieve up to 50x lower operational and storage costs through a compute‑storage separation [architecture](/user-guide/concepts/architecture.md). Scale flexibly across cloud storage systems (e.g., S3, Azure Blob Storage) for simplified management, dramatic cost efficiency, and no vendor lock‑in.
 - **Infinity Scalability**: Purpose-built for [Kubernetes](/user-guide/deployments-administration/deploy-on-kubernetes/greptimedb-operator-management.md) and cloud environments with industry-leading compute-storage separation. Enables unlimited scaling across cloud environments while efficiently managing cardinality explosion at a massive scale.
 - **Developer-Friendly**: Features standardized SQL and PromQL interfaces, a built-in web dashboard, REST API, and support for MySQL/PostgreSQL protocols. Widely compatible with popular data [ingestion protocols](/user-guide/protocols/overview.md) for seamless migration and integration.
 - **Flexible Deployment Options**: Deploy anywhere, from ARM-based edge devices to cloud environments, with unified APIs and bandwidth-efficient data synchronization. Query edge and cloud data seamlessly using identical APIs.

docs/user-guide/concepts/data-model.md

@@ -62,6 +62,8 @@ This is very similar to the table model everyone is familiar with. The differenc
   These columns contain the actual data.
 - The table sorts and deduplicates rows by `host`, `idc`, `ts`. So `select count(*) from system_metrics` will scan all rows.
 
+To learn how GreptimeDB maps Prometheus metrics to this model, see [the documentation](/user-guide/ingest-data/for-observability/prometheus/#data-model).
+
 ### Logs
 
 Another example is creating a table for logs like web server access logs:
@@ -105,7 +107,6 @@ GreptimeDB is designed on top of the table model for the following reasons:
   The multi-value model is used to model data sources where a metric can have multiple values represented by fields.
   The advantage of the multi-value model is that it can write or read multiple values to the database at once, reducing transfer traffic and simplifying queries. In contrast, the single-value model requires splitting the data into multiple records. Read the [blog](https://greptime.com/blogs/2024-05-09-prometheus) for more detailed benefits of the multi-value mode.
 
-
 GreptimeDB uses SQL to manage table schema. Please refer to [table management](/user-guide/deployments-administration/manage-data/basic-table-operations.md) for more information.
 However, our definition of schema is not mandatory and leans towards a **schemaless** approach, similar to MongoDB.
 For more details, see [Automatic Schema Generation](/user-guide/ingest-data/overview.md#automatic-schema-generation).

docs/user-guide/concepts/features-that-you-concern.md

@@ -9,7 +9,7 @@ description: Answers common questions about GreptimeDB's features, including sup
 
 Yes. Since v0.9.0, GreptimeDB treats metrics, logs and traces as contextual wide events with timestamps, and thus unifies the processing of metrics, logs, and traces. It supports analyzing metrics, logs, and traces with SQL, PromQL, and streaming with continuous aggregation.
 
-Please read the [log user guide](/user-guide/logs/overview.md).
+Please read the [log user guide](/user-guide/logs/overview.md) and [traces user guide](/user-guide/traces/overview.md).
 
 ## Does GreptimeDB support updates?
 
@@ -49,11 +49,9 @@ Since 0.8, GreptimeDB added a new function called `Flow`, which is used for cont
 Yes, GreptimeDB's data access layer is based on [OpenDAL](https://github.com/apache/incubator-opendal), which supports most kinds of object storage services.
 The data can be stored in cost-effective cloud storage services such as AWS S3 or Azure Blob Storage, please refer to storage configuration guide [here](/user-guide/deployments-administration/configuration.md#storage-options).
 
-GreptimeDB also offers a fully-managed cloud service [GreptimeCloud](https://greptime.com/product/cloud) to help you manage data in the cloud.
-
 ## How is GreptimeDB's performance compared to other solutions?
 
-[GreptimeDB archives 1 billion cold run #1 in JSONBench!](https://greptime.com/blogs/2025-03-18-jsonbench-greptimedb-performance)
+[GreptimeDB achieves 1 billion cold run #1 in JSONBench!](https://greptime.com/blogs/2025-03-18-jsonbench-greptimedb-performance)
 
 Please read the performance benchmark reports:
 

docs/user-guide/concepts/why-greptimedb.md

@@ -21,7 +21,7 @@ It replaces complex legacy data stacks with a high-performance single solution.
 
 ## Cost-Effective with Object Storage
 
-GreptimeDB leverages cloud object storage (like AWS S3 and Azure Blob Storage etc.) as its storage layer, dramatically reducing costs compared to traditional storage solutions. Its optimized columnar storage and advanced compression algorithms achieve up to 50x cost efficiency, while the pay-as-you-go model (via [GreptimeCloud](https://greptime.com/product/cloud)) ensures you only pay for what you use.
+GreptimeDB leverages cloud object storage (like AWS S3 and Azure Blob Storage etc.) as its storage layer, dramatically reducing costs compared to traditional storage solutions. Its optimized columnar storage and advanced compression algorithms achieve up to 50x cost efficiency. Scale flexibly across cloud storage systems (e.g., S3, Azure Blob Storage) for simplified management, dramatic cost efficiency, and **no vendor lock‑in**.
 
 ## High Performance
 
@@ -59,17 +59,16 @@ From embedded and standalone deployments to cloud-native clusters, GreptimeDB ad
 
 GreptimeDB simplifies deployment and maintenance with tools like:
 - [K8s Operator](https://github.com/GreptimeTeam/greptimedb-operator)
+- [Helm Charts](https://github.com/GreptimeTeam/helm-charts)
 - [Command-line Tool](https://github.com/GreptimeTeam/gtctl)
 - Embedded [Dashboard](https://github.com/GreptimeTeam/dashboard)
 
-For an even simpler experience, check out the fully managed [GreptimeCloud](https://greptime.com/product/cloud).
-
 ### Easy to Integrate
 
 GreptimeDB supports multiple data ingestion protocols, making integration with existing observability stacks seamless:
 - **Database protocols**: MySQL, PostgreSQL
-- **Time-series protocols**: InfluxDB, OpenTSDB, Prometheus RemoteStorage
-- **Observability protocols**: OpenTelemetry, Loki, ElasticSearch
+- **Time-series protocols**: InfluxDB, OpenTSDB
+- **Observability protocols**: OpenTelemetry, Loki, ElasticSearch, Prometheus RemoteStorage
 - **gRPC with SDKs**: Java, Go, Erlang, etc.
 
 For data querying, GreptimeDB provides:

i18n/zh/docusaurus-plugin-content-docs/current/index.md

@@ -11,15 +11,12 @@ description: 介绍了 GreptimeDB，一个开源的统一可观测性数据库
 
 **GreptimeDB** 是一个开源、云原生、统一的可观测性数据库，用于存储指标、日志和链路追踪数据。您可以从边缘到云端获得实时洞察——无论规模大小。
 
-GreptimeDB 经由 [GreptimeCloud](https://greptime.cn/product/cloud) 提供云服务。
-GreptimeCloud 是一个完全托管的可观测性数据库服务，具有无服务器（Serverless）的可扩展性、与丰富的 IoT 和可观测软件生态系统的无缝集成。
-
 我们的核心开发人员多年深耕于建立可观测（监控）数据平台。基于他们的丰富经验，GreptimeDB 应运而生，并为用户提供：
 
 
 - **All-in-One 的可观测性数据库**：通过统一数据库实时处理指标、日志和链路追踪，原生支持 [SQL](/user-guide/query-data/sql.md)、[PromQL](/user-guide/query-data/promql.md) 和 [流式处理](/user-guide/flow-computation/overview.md)。用高性能单一解决方案取代复杂的传统数据堆栈。
 - **高性能引擎**：采用 Rust 语言打造，具备卓越的性能和可靠性。丰富的[索引选择](/user-guide/manage-data/data-index.md)（倒排、全文、调数和向量索引）加速查询，实现 PB 级数据的亚秒级响应，并能处理数十万并发请求。
-- **显著的成本降低**：凭借计算与存储分离的[架构](/user-guide/concepts/architecture.md)，运营和存储成本降低 50 倍。可灵活扩展至各类云存储系统（如 S3、Azure Blob Storage），配合全托管云服务 [GreptimeCloud](https://greptime.cn/product/cloud) ，极大简化运维管理。
+- **显著的成本降低**：凭借计算与存储分离的[架构](/user-guide/concepts/architecture.md)，运营和存储成本降低高达 50 倍。可灵活扩展至各类云存储系统（如 S3、Azure Blob Storage），简化运维管理、大幅降低成本，**无厂商锁定**。
 - **无限扩展性**：专为 [Kubernetes](/user-guide/deployments-administration/deploy-on-kubernetes/greptimedb-operator-management.md) 和云环境而设计，采用业界领先的计算与存储分离架构，实现无限制的跨云扩展。高效应对高基数爆炸问题。
 - **开发者友好**：支持标准 SQL、PromQL 接口、内置 Web 仪表盘、REST API，并兼容 MySQL/PostgreSQL 协议。广泛适配主流数据 [接入协议](/user-guide/protocols/overview.md)，轻松迁移与集成。
 - **灵活的部署选项**：可部署于任意环境，从 ARM 边缘设备到云端，提供统一 API 和高效带宽的数据同步。通过相同的 API 无缝查询边缘和云端数据。

i18n/zh/docusaurus-plugin-content-docs/current/user-guide/concepts/data-model.md

@@ -47,6 +47,8 @@ CREATE TABLE IF NOT EXISTS system_metrics (
 - `Field` 列中的 `cpu_util`、`memory_util`、`disk_util` 列分别表示机器的 CPU 利用率、内存利用率和磁盘利用率。这些列包含实际的数据。
 - 表按 `host`、`idc`、`ts` 对行进行排序和去重。因此，查询 `select count(*) from system_metrics` 需要扫描所有的行做统计。
 
+要了解 GreptimeDB 如何将 Prometheus 指标映射到此模型，请参阅[文档](/user-guide/ingest-data/for-observability/prometheus/#数据模型)。
+
 ### 日志
 
 另一个例子是创建一个用于日志（如 Server 访问日志）的表：

i18n/zh/docusaurus-plugin-content-docs/current/user-guide/concepts/features-that-you-concern.md

@@ -9,7 +9,7 @@ description: 介绍 GreptimeDB 的关键特性，并解答用户关心的常见
 
 是的。从 v0.9.0 版本开始，GreptimeDB 将指标、日志和链路追踪视为带有时间戳的上下文“宽”事件（Wide Events），从而统一了指标、日志和链路追踪的处理。它支持使用 SQL、PromQL 和通过连续聚合进行流式处理来分析指标、日志和追踪。
 
-请阅读[日志处理使用指南](/user-guide/logs/overview.md)。
+请阅读[日志处理使用指南](/user-guide/logs/overview.md)和[链路追踪使用指南](/user-guide/traces/overview.md)。
 
 ## GreptimeDB 支持更新数据吗？
 
@@ -48,8 +48,6 @@ GreptimeDB 通过以下方式解决这个问题：
 可以，GreptimeDB 的数据访问层基于 [OpenDAL](https://github.com/apache/incubator-opendal)，它支持大多数类型的对象存储服务。
 数据可以存储在如 AWS S3 或 Azure Blob Storage 等性价比高的云存储服务中，请参考这里的存储[配置指南](/user-guide/deployments-administration/configuration.md#storage-options)。
 
-GreptimeDB 还提供一个完全托管的云服务 [GreptimeCloud](https://greptime.cn/product/cloud) 来帮助您管理云中的数据。
-
 ## GreptimeDB 对比其他存储或时序数据库的性能如何？
 
 GreptimeDB 在 [ClickHouse 的 JSONBench 测试中 Cold Run 斩获第一](https://greptime.cn/blogs/2025-03-18-json-benchmark-greptimedb)！

i18n/zh/docusaurus-plugin-content-docs/current/user-guide/concepts/why-greptimedb.md

@@ -21,7 +21,7 @@ GreptimeDB 通过以下方式统一处理指标、日志和链路追踪：
 
 ## 基于对象存储的成本优势
 
-GreptimeDB 采用云对象存储（如 AWS S3、阿里云 OSS 和 Azure Blob Storage 等）作为存储层，与传统存储方案相比显著降低了成本。通过优化的列式存储和先进的压缩算法，实现了高达 50 倍的成本效率，而按需付费模式的 [GreptimeCloud](https://greptime.com/product/cloud) 确保您只需为实际使用的资源付费。
+GreptimeDB 采用云对象存储（如 AWS S3、阿里云 OSS 和 Azure Blob Storage 等）作为存储层，与传统存储方案相比显著降低了成本。通过优化的列式存储和先进的压缩算法，实现了高达 50 倍的成本效率。可灵活扩展至各类云存储系统（如 S3、Azure Blob Storage），简化运维管理、大幅降低成本，**无厂商锁定**。
 
 ## 高性能
 
@@ -60,17 +60,16 @@ GreptimeDB的模块化架构允许不同的组件根据需要独立运行或协
 
 GreptimeDB 通过以下工具简化了部署和维护：
 - [K8s Operator](https://github.com/GreptimeTeam/greptimedb-operator)
+- [Helm Charts](https://github.com/GreptimeTeam/helm-charts)
 - [命令行工具](https://github.com/GreptimeTeam/gtctl)
 - 内嵌[仪表盘](https://github.com/GreptimeTeam/dashboard)
 
-为了获得更简便的体验，请查看完全托管的 [GreptimeCloud](https://greptime.cn/product/cloud)。
-
 ### 易于集成
 
 GreptimeDB 支持多种数据摄入协议，从而实现与现有可观测性技术栈的无缝集成：
 - **数据库协议**：MySQL、PostgreSQL
-- **时序数据协议**：InfluxDB、OpenTSDB、Prometheus RemoteStorage
-- **可观测数据协议**：OpenTelemetry、Loki、ElasticSearch
+- **时序数据协议**：InfluxDB、OpenTSDB
+- **可观测数据协议**：OpenTelemetry、Loki、ElasticSearch、Prometheus RemoteStorage
 - **高性能 gRPC 协议及客户端 SDK**（Java、Go、Erlang 等）
 
 在数据查询方面，GreptimeDB 提供：

i18n/zh/docusaurus-plugin-content-docs/version-1.0/index.md

@@ -11,15 +11,12 @@ description: 介绍了 GreptimeDB，一个开源的统一可观测性数据库
 
 **GreptimeDB** 是一个开源、云原生、统一的可观测性数据库，用于存储指标、日志和链路追踪数据。您可以从边缘到云端获得实时洞察——无论规模大小。
 
-GreptimeDB 经由 [GreptimeCloud](https://greptime.cn/product/cloud) 提供云服务。
-GreptimeCloud 是一个完全托管的可观测性数据库服务，具有无服务器（Serverless）的可扩展性、与丰富的 IoT 和可观测软件生态系统的无缝集成。
-
 我们的核心开发人员多年深耕于建立可观测（监控）数据平台。基于他们的丰富经验，GreptimeDB 应运而生，并为用户提供：
 
 
 - **All-in-One 的可观测性数据库**：通过统一数据库实时处理指标、日志和链路追踪，原生支持 [SQL](/user-guide/query-data/sql.md)、[PromQL](/user-guide/query-data/promql.md) 和 [流式处理](/user-guide/flow-computation/overview.md)。用高性能单一解决方案取代复杂的传统数据堆栈。
 - **高性能引擎**：采用 Rust 语言打造，具备卓越的性能和可靠性。丰富的[索引选择](/user-guide/manage-data/data-index.md)（倒排、全文、调数和向量索引）加速查询，实现 PB 级数据的亚秒级响应，并能处理数十万并发请求。
-- **显著的成本降低**：凭借计算与存储分离的[架构](/user-guide/concepts/architecture.md)，运营和存储成本降低 50 倍。可灵活扩展至各类云存储系统（如 S3、Azure Blob Storage），配合全托管云服务 [GreptimeCloud](https://greptime.cn/product/cloud) ，极大简化运维管理。
+- **显著的成本降低**：凭借计算与存储分离的[架构](/user-guide/concepts/architecture.md)，运营和存储成本降低高达 50 倍。可灵活扩展至各类云存储系统（如 S3、Azure Blob Storage），简化运维管理、大幅降低成本，**无厂商锁定**。
 - **无限扩展性**：专为 [Kubernetes](/user-guide/deployments-administration/deploy-on-kubernetes/greptimedb-operator-management.md) 和云环境而设计，采用业界领先的计算与存储分离架构，实现无限制的跨云扩展。高效应对高基数爆炸问题。
 - **开发者友好**：支持标准 SQL、PromQL 接口、内置 Web 仪表盘、REST API，并兼容 MySQL/PostgreSQL 协议。广泛适配主流数据 [接入协议](/user-guide/protocols/overview.md)，轻松迁移与集成。
 - **灵活的部署选项**：可部署于任意环境，从 ARM 边缘设备到云端，提供统一 API 和高效带宽的数据同步。通过相同的 API 无缝查询边缘和云端数据。

i18n/zh/docusaurus-plugin-content-docs/version-1.0/user-guide/concepts/data-model.md

@@ -47,6 +47,8 @@ CREATE TABLE IF NOT EXISTS system_metrics (
 - `Field` 列中的 `cpu_util`、`memory_util`、`disk_util` 列分别表示机器的 CPU 利用率、内存利用率和磁盘利用率。这些列包含实际的数据。
 - 表按 `host`、`idc`、`ts` 对行进行排序和去重。因此，查询 `select count(*) from system_metrics` 需要扫描所有的行做统计。
 
+要了解 GreptimeDB 如何将 Prometheus 指标映射到此模型，请参阅[文档](/user-guide/ingest-data/for-observability/prometheus/#数据模型)。
+
 ### 日志
 
 另一个例子是创建一个用于日志（如 Server 访问日志）的表：