chore: logs before events (GreptimeTeam#1088)

Signed-off-by: tison <wander4096@gmail.com>

docs/nightly/en/index.md

@@ -4,15 +4,15 @@
     <img src="/logo-greptimedb.png" alt="GreptimeDB Logo" width="400">
 </p>
 
-**GreptimeDB** is an open-source unified time-series database for **Metrics**, **Events**, and **Logs** (also **Traces** in plan). You can gain real-time insights from Edge to Cloud at any scale.
+**GreptimeDB** is an open-source unified time-series database for **Metrics**, **Logs**, and **Events** (also **Traces** in plan). 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 time-series database service that features serverless scalability,
 seamless integration with ecoystem and improved Prometheus compatibility.
 
 Our core developers have been building time-series data platforms for years. Based on their best-practices, GreptimeDB is born to give you:
 
-- Unified all kinds of time series; GreptimeDB treats all time series as contextual events with timestamp, and thus unifies the processing of metrics and events. It supports analyzing metrics and events with SQL and PromQL, and doing streaming with continuous aggregation.
+- Unified all kinds of time series; GreptimeDB treats all time series as contextual events with timestamp, and thus unifies the processing of metrics and logs. It supports analyzing metrics and logs with SQL and PromQL, and doing streaming with continuous aggregation.
 - Optimized columnar layout for handling time-series data; compacted, compressed, and stored on various storage backends, particularly cloud object storage with 50x cost efficiency.
 - Fully open-source distributed cluster architecture that harnesses the power of cloud-native elastic computing resources.
 - Seamless scalability from a standalone binary at edge to a robust, highly available distributed cluster in cloud, with a transparent experience for both developers and administrators.

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

@@ -3,7 +3,7 @@
 ## Model
 
 GreptimeDB uses the time-series table to guide the organization, compression, and expiration management of data.
-The data model is mainly based on the table model in relational databases while considering the characteristics of metrics, logs and events data.
+The data model is mainly based on the table model in relational databases while considering the characteristics of metrics, logs, and events data.
 
 All data in GreptimeDB is organized into tables with names. Each data item in a table consists of three types of columns: `Tag`, `Timestamp`, and `Field`.
 
@@ -12,7 +12,7 @@ All data in GreptimeDB is organized into tables with names. Each data item in a
   The values in `Tag` columns are labels attached to the collected sources,
   generally used to describe a particular characteristic of these sources.
   `Tag` columns are indexed, making queries on tags performant.
-- `Timestamp` is the root of a metrics, logs and events database.
+- `Timestamp` is the root of a metrics, logs, and events database.
   It represents the date and time when the data was generated.
   Timestamps are indexed, making queries on timestamps performant.
   A table can only have one timestamp column, which is called time index.
@@ -86,7 +86,7 @@ Of course, you can place metrics and logs in a single table at any time, which i
 
 GreptimeDB is designed on top of Table for the following reasons:
 
-- The Table model has a broad group of users and it's easy to learn, that we just introduced the concept of time index to the metrics, logs and events.
+- The Table model has a broad group of users and it's easy to learn, that we just introduced the concept of time index to the metrics, logs, and events.
 - Schema is meta-data to describe data characteristics, and it's more convenient for users to manage and maintain. By introducing the concept of schema version, we can better manage data compatibility.
 - Schema brings enormous benefits for optimizing storage and computing with its information like types, lengths, etc., on which we could conduct targeted optimizations.
 - When we have the Table model, it's natural for us to introduce SQL and use it to process association analysis and aggregation queries between various tables, offsetting the learning and use costs for users.

docs/nightly/en/user-guide/concepts/overview.md

@@ -1,6 +1,6 @@
 # Overview
 
-- [Why GreptimeDB](./why-greptimedb.md): This document outlines the features and benefits of GreptimeDB, including its unified design for metrics, logs and events; Cloud-Native and flexible architecture that allows for deployment in various environments, from embedded to cloud. GreptimeDB is also cost-effective, high-performance, and user-friendly.
+- [Why GreptimeDB](./why-greptimedb.md): This document outlines the features and benefits of GreptimeDB, including its unified design for metrics, logs, and events; Cloud-Native and flexible architecture that allows for deployment in various environments, from embedded to cloud. GreptimeDB is also cost-effective, high-performance, and user-friendly.
 - [Data Model](./data-model.md): This document describes the data model of GreptimeDB, including table schema, time index constraint, etc.
 - [Architecture](./architecture.md): Get the cloud-native architecture of GreptimeDB.
 - [Storage Location](./storage-location.md): This document describes the storage location of GreptimeDB, including local disk, HDFS, and cloud object storage such as S3, Azure Blob Storage, etc.

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

@@ -8,7 +8,7 @@ To gain insight into the motivations that led to the development of GreptimeDB,
 
 In these documents, we delve into the reasons behind Greptime's high performance and some highlighted features.
 
-## Unified metrics, logs and events
+## Unified metrics, logs, and events
 
 Through the model design of [time series tables](./data-model), native support for SQL, and the hybrid workload brought by the storage-computation separation architecture, GreptimeDB can handle metrics, logs, and events together, enhance the correlation analysis between different time series data and simplify the architecture, deployment and APIs for users.
 

docs/nightly/en/user-guide/overview.md

@@ -2,7 +2,7 @@
 
 Welcome to the user guide for GreptimeDB.
 
-GreptimeDB is the unified time series database for metrics, events, and logs,
+GreptimeDB is the unified time series database for metrics, logs, and events,
 providing real-time insights from Edge to Cloud at any scale.
 This guide will help you explore each powerful feature of GreptimeDB.
 

docs/nightly/zh/index.md

@@ -4,7 +4,7 @@
     <img src="/logo-greptimedb.png" alt="GreptimeDB Logo" width="400">
 </p>
 
-GreptimeDB 是开源的统一时序数据库，能同时处理**指标**（Metrics）、**事件**（Events）、**日志**（Logs）和**追踪**（Traces）。从云到端，GreptimeDB 能从任意规模的时序数据中获取实时数据洞察。
+GreptimeDB 是开源的统一时序数据库，能同时处理**指标**（Metrics）、**日志**（Logs）、**事件**（Events）和**追踪**（Traces）。从云到端，GreptimeDB 能从任意规模的时序数据中获取实时数据洞察。
 
 GreptimeDB 经由 [GreptimeCloud](https://greptime.cn/product/cloud) 提供云服务。
 GreptimeCloud 是一个完全托管的时序数据库服务，具有无服务器的可扩展性、与生态系统的无缝集成和 Prometheus 兼容性。

docs/release-notes/release-0-9-0.md

@@ -4,7 +4,7 @@ Release date: July 16, 2024
 
 ## 👍 Highlights
 
-* [Log Engine](https://docs.greptime.com/user-guide/logs/overview): GreptimeDB is now a unified time-series database for both metrics, events, and logs (trace in plan).
+* [Log Engine](https://docs.greptime.com/user-guide/logs/overview): GreptimeDB is now a unified time-series database for both metrics, logs, and events (trace in plan).
 * [Remote WAL](https://docs.greptime.com/user-guide/operations/remote-wal/quick-start) is significantly improved and is now recommended to turn on.
 * Table View: You can now `CREATE VIEW` on tables and treat them as logical table.
 * [Short interval literal](https://docs.greptime.com/reference/sql/data-types#interval-type) for user experience.

docs/v0.9/en/index.md

@@ -4,7 +4,7 @@
     <img src="/logo-greptimedb.png" alt="GreptimeDB Logo" width="400">
 </p>
 
-**GreptimeDB** is an open-source unified time-series database for **Metrics**, **Events**, and **Logs** (also **Traces** in plan). You can gain real-time insights from Edge to Cloud at any scale.
+**GreptimeDB** is an open-source unified time-series database for **Metrics**, **Logs**, and **Events** (also **Traces** in plan). 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 time-series database service that features serverless scalability,

docs/v0.9/en/user-guide/concepts/data-model.md

@@ -3,7 +3,7 @@
 ## Model
 
 GreptimeDB uses the time-series table to guide the organization, compression, and expiration management of data.
-The data model is mainly based on the table model in relational databases while considering the characteristics of metrics, logs and events data.
+The data model is mainly based on the table model in relational databases while considering the characteristics of metrics, logs, and events data.
 
 All data in GreptimeDB is organized into tables with names. Each data item in a table consists of three types of columns: `Tag`, `Timestamp`, and `Field`.
 
@@ -12,7 +12,7 @@ All data in GreptimeDB is organized into tables with names. Each data item in a
   The values in `Tag` columns are labels attached to the collected sources,
   generally used to describe a particular characteristic of these sources.
   `Tag` columns are indexed, making queries on tags performant.
-- `Timestamp` is the root of a metrics, logs and events database.
+- `Timestamp` is the root of a metrics, logs, and events database.
   It represents the date and time when the data was generated.
   Timestamps are indexed, making queries on timestamps performant.
   A table can only have one timestamp column, which is called time index.
@@ -86,7 +86,7 @@ Of course, you can place metrics and logs in a single table at any time, which i
 
 GreptimeDB is designed on top of Table for the following reasons:
 
-- The Table model has a broad group of users and it's easy to learn, that we just introduced the concept of time index to the metrics, logs and events.
+- The Table model has a broad group of users and it's easy to learn, that we just introduced the concept of time index to the metrics, logs, and events.
 - Schema is meta-data to describe data characteristics, and it's more convenient for users to manage and maintain. By introducing the concept of schema version, we can better manage data compatibility.
 - Schema brings enormous benefits for optimizing storage and computing with its information like types, lengths, etc., on which we could conduct targeted optimizations.
 - When we have the Table model, it's natural for us to introduce SQL and use it to process association analysis and aggregation queries between various tables, offsetting the learning and use costs for users.

docs/v0.9/en/user-guide/concepts/overview.md

@@ -1,6 +1,6 @@
 # Overview
 
-- [Why GreptimeDB](./why-greptimedb.md): This document outlines the features and benefits of GreptimeDB, including its unified design for metrics, logs and events; Cloud-Native and flexible architecture that allows for deployment in various environments, from embedded to cloud. GreptimeDB is also cost-effective, high-performance, and user-friendly.
+- [Why GreptimeDB](./why-greptimedb.md): This document outlines the features and benefits of GreptimeDB, including its unified design for metrics, logs, and events; Cloud-Native and flexible architecture that allows for deployment in various environments, from embedded to cloud. GreptimeDB is also cost-effective, high-performance, and user-friendly.
 - [Data Model](./data-model.md): This document describes the data model of GreptimeDB, including table schema, time index constraint, etc.
 - [Architecture](./architecture.md): Get the cloud-native architecture of GreptimeDB.
 - [Storage Location](./storage-location.md): This document describes the storage location of GreptimeDB, including local disk, HDFS, and cloud object storage such as S3, Azure Blob Storage, etc.

docs/v0.9/en/user-guide/concepts/why-greptimedb.md

@@ -8,7 +8,7 @@ To gain insight into the motivations that led to the development of GreptimeDB,
 
 In these documents, we delve into the reasons behind Greptime's high performance and some highlighted features.
 
-## Unified metrics, logs and events
+## Unified metrics, logs, and events
 
 Through the model design of [time series tables](./data-model), native support for SQL, and the hybrid workload brought by the storage-computation separation architecture, GreptimeDB can handle metrics, logs, and events together, enhance the correlation analysis between different time series data and simplify the architecture, deployment and APIs for users.
 

docs/v0.9/en/user-guide/overview.md

@@ -2,7 +2,7 @@
 
 Welcome to the user guide for GreptimeDB.
 
-GreptimeDB is the unified time series database for metrics, events, and logs,
+GreptimeDB is the unified time series database for metrics, logs, and events,
 providing real-time insights from Edge to Cloud at any scale.
 This guide will help you explore each powerful feature of GreptimeDB.
 

docs/v0.9/zh/index.md

@@ -4,7 +4,7 @@
     <img src="/logo-greptimedb.png" alt="GreptimeDB Logo" width="400">
 </p>
 
-GreptimeDB 是开源的统一时序数据库，能同时处理**指标**（Metrics）、**事件**（Events）、**日志**（Logs）和**追踪**（Traces）。从云到端，GreptimeDB 能从任意规模的时序数据中获取实时数据洞察。
+GreptimeDB 是开源的统一时序数据库，能同时处理**指标**（Metrics）、**日志**（Logs）、**事件**（Events）和**追踪**（Traces）。从云到端，GreptimeDB 能从任意规模的时序数据中获取实时数据洞察。
 
 GreptimeDB 经由 [GreptimeCloud](https://greptime.cn/product/cloud) 提供云服务。
 GreptimeCloud 是一个完全托管的时序数据库服务，具有无服务器的可扩展性、与生态系统的无缝集成和 Prometheus 兼容性。