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new dde221832f [INLONG-8419][Doc] Update the description about InLong
(#796)
dde221832f is described below
commit dde221832f01e860703d9b17e7c0fa28ec175924
Author: Charles Zhang <[email protected]>
AuthorDate: Tue Jul 4 19:30:25 2023 +0800
[INLONG-8419][Doc] Update the description about InLong (#796)
---
.asf.yaml | 62 +++++++++++-----------
community/how-to-release.md | 2 +-
docs/design_and_concept/the_format_in_inlong.md | 2 +-
docs/introduction.md | 4 +-
docs/modules/manager/overview.md | 6 +--
docusaurus.config.js | 2 +-
.../design_and_concept/the_format_in_inlong.md | 2 +-
.../current/introduction.md | 4 +-
.../current/modules/manager/overview.md | 7 +--
src/pages/Home/config.json | 4 +-
10 files changed, 43 insertions(+), 52 deletions(-)
diff --git a/.asf.yaml b/.asf.yaml
index 06eeaf8db2..2587cef6a3 100644
--- a/.asf.yaml
+++ b/.asf.yaml
@@ -1,31 +1,31 @@
-github:
- description: "Apache InLong - a one-stop integration framework for massive
data"
- homepage: https://inlong.apache.org/
- features:
- # Enable issues management
- issues: true
- enabled_merge_buttons:
- # enable squash button:
- squash: true
- # disable merge button:
- merge: false
- # disable rebase button:
- rebase: false
-
-# Web site staging services:
-staging:
- profile: ~
- whoami: master
- foo: trigger
-
-publish:
- whoami: asf-site
-
-notifications:
- commits: [email protected]
- issues: [email protected]
- pullrequests_status: [email protected]
- pullrequests_comment: [email protected]
- issues_status: [email protected]
- issues_comment: [email protected]
- jira_options: link label worklog
+github:
+ description: "Apache InLong - a one-stop, full-scenario integration
framework for massive data"
+ homepage: https://inlong.apache.org/
+ features:
+ # Enable issues management
+ issues: true
+ enabled_merge_buttons:
+ # enable squash button:
+ squash: true
+ # disable merge button:
+ merge: false
+ # disable rebase button:
+ rebase: false
+
+# Web site staging services:
+staging:
+ profile: ~
+ whoami: master
+ foo: trigger
+
+publish:
+ whoami: asf-site
+
+notifications:
+ commits: [email protected]
+ issues: [email protected]
+ pullrequests_status: [email protected]
+ pullrequests_comment: [email protected]
+ issues_status: [email protected]
+ issues_comment: [email protected]
+ jira_options: link label worklog
diff --git a/community/how-to-release.md b/community/how-to-release.md
index a61178445a..d333df5510 100644
--- a/community/how-to-release.md
+++ b/community/how-to-release.md
@@ -410,7 +410,7 @@ Hi all,
The Apache InLong community is pleased to announce
that Apache InLong ${release_version} has been released!
-Apache InLong is a one-stop integration framework for massive data that
provides automatic, secure,
+Apache InLong is a one-stop, full-scenario integration framework for massive
data that supports `Data Ingestion`, `Data Synchronization` and `Data
Subscription`, and it provides automatic, secure,
distributed, and efficient data publishing and subscription capabilities.
This platform helps you easily build stream-based data applications.
diff --git a/docs/design_and_concept/the_format_in_inlong.md
b/docs/design_and_concept/the_format_in_inlong.md
index cba871428e..8e2f9d939e 100644
--- a/docs/design_and_concept/the_format_in_inlong.md
+++ b/docs/design_and_concept/the_format_in_inlong.md
@@ -20,7 +20,7 @@ Format provides two interfaces : SerializationSchema and
DeserializationSchema :
-InLong serves as a one-stop data integration platform , with MQ (the Cache
part in the picture) as the transmission channel , which decouples DataProxy
and Sort and provides better scalability . When DataProxy is reporting data ,
it needs to serialize the data into corresponding format (
`SerializationSchema#serialize` ) . When Sort receives data, it will
deserialize the MQ's data ( `DeserializationSchema#deserialize` ) into `Flink
Row` , and then write to the corresponding storage using [...]
+InLong serves as a one-stop, full-scenario data integration platform , with MQ
(the Cache part in the picture) as the transmission channel , which decouples
DataProxy and Sort and provides better scalability . When DataProxy is
reporting data , it needs to serialize the data into corresponding format (
`SerializationSchema#serialize` ) . When Sort receives data, it will
deserialize the MQ's data ( `DeserializationSchema#deserialize` ) into `Flink
Row` , and then write to the correspondin [...]
## What are the formats?
diff --git a/docs/introduction.md b/docs/introduction.md
index f39152e2e8..e8d79bbc0a 100644
--- a/docs/introduction.md
+++ b/docs/introduction.md
@@ -10,9 +10,9 @@ import TabItem from '@theme/TabItem';
> and it is regarded as a metaphor of the InLong system for reporting data
> streams.
## About InLong
-[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data ,it provides automatic, safe, reliable, and high-performance
data transmission capabilities to
+[Apache InLong](https://inlong.apache.org) is a one-stop, full-scenario
integration framework for massive data that supports `Data Ingestion`, `Data
Synchronization` and `Data Subscription`, and it provides automatic, safe,
reliable, and high-performance data transmission capabilities to
facilitate the construction of streaming-based data analysis, modeling, and
applications. The Apache InLong project was originally called TubeMQ, focusing
on high-performance,
-low-cost message queuing services. To further release the surrounding
ecological capabilities of TubeMQ, the community upgraded the project to
InLong, focusing on creating a one-stop integration framework for massive data.
+low-cost message queuing services. To further release the surrounding
ecological capabilities of TubeMQ, the community upgraded the project to
InLong, focusing on creating a one-stop, full-scenario integration framework
for massive data.
Apache InLong relies on 10 trillion-level data ingestion and processing
capabilities to integrate the entire process of data collection, aggregation,
storage, and sorting data processing. It is simple, flexible, stable, and
reliable.
The project was initially donated to the Apache incubator by the Tencent Big
Data team in November 2019 and officially graduated as an Apache top-level
project in June 2022. Currently, InLong is widely used in various industries
such as advertising,
payment, social networking, games, artificial intelligence, etc., to provide
efficient and convenient customer services in multiple fields.
diff --git a/docs/modules/manager/overview.md b/docs/modules/manager/overview.md
index 58564a1950..da458ead74 100644
--- a/docs/modules/manager/overview.md
+++ b/docs/modules/manager/overview.md
@@ -5,11 +5,7 @@ sidebar_position: 1
## Introduction
-- Target positioning: Apache InLong is positioned as a one-stop mass data
integration framework, providing technical capabilities for big data from
collection -> transmission -> sorting -> landing.
-
-- Platform value: Users can complete task configuration, management and
indicator monitoring through the management and configuration platform in the
platform, and at the same time, through the plug-in expansion capability
provided by the platform, they can realize customized expansion on demand.
-
-- InLong Manager is a unified management platform for the Apache InLong
project. The platform provides maintenance portals for various basic
configurations (such as data flow configuration, consumption configuration,
cluster management, etc.). Users can create data collection tasks and view
indicator data.
+InLong Manager is a unified management platform for the Apache InLong project.
The platform provides maintenance portals for various basic configurations
(such as data flow configuration, consumption configuration, cluster
management, etc.). Users can create data collection tasks and view indicator
data.
## Module
diff --git a/docusaurus.config.js b/docusaurus.config.js
index 41e190f1ea..5dcd5b7282 100644
--- a/docusaurus.config.js
+++ b/docusaurus.config.js
@@ -6,7 +6,7 @@ const darkCodeTheme =
require('prism-react-renderer/themes/dracula');
(module.exports = {
// omit unrelated docusaurus options
title: 'Apache InLong',
- tagline: 'Apache InLong is a one-stop integration framework for massive data
that provides automatic, secure and reliable data transmission capabilities.',
+ tagline: 'Apache InLong is a one-stop, full-scenario integration framework
for massive data that supports Data Ingestion, Data Synchronization and Data
Subscription, and it provides automatic, secure and reliable data transmission
capabilities.',
url: 'https://inlong.apache.org',
baseUrl: '/',
onBrokenLinks: 'throw',
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/design_and_concept/the_format_in_inlong.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/design_and_concept/the_format_in_inlong.md
index 228901cbd2..faa148f236 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/design_and_concept/the_format_in_inlong.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/design_and_concept/the_format_in_inlong.md
@@ -22,7 +22,7 @@ Flink 的 Format 提供了两种接口：SerializationSchema 和 Deserialization
-InLong 作为一站式的数据集成平台，将 MQ（图中 Cache 部分）作为传输通道，同时实现 DataProxy 与 Sort 的解耦，扩展性会更强：
+InLong 作为一站式、全场景的数据集成平台，将 MQ（图中 Cache 部分）作为传输通道，同时实现 DataProxy 与 Sort
的解耦，扩展性会更强：
- DataProxy 上报数据时，需要将数据序列化成对应的格式（`SerializationSchema#serialize`）。
- Sort 接收到数据，将 MQ 的数据反序列化（`DeserializationSchema#deserialize`）成 `Flink Row`
，通过 Flink SQL 写入到对应的存储。
diff --git a/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
index f966b9fea0..fc7572acfd 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
@@ -9,8 +9,8 @@ import TabItem from '@theme/TabItem';
> InLong(应龙)，中国神话故事里的神兽，引流入海，借喻 InLong 系统提供数据集成能力。
## 关于 InLong
-[Apache
InLong（应龙）](https://inlong.apache.org)是一站式的海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
-InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式海量数据集成框架。
+[Apache
InLong（应龙）](https://inlong.apache.org)是一站式、全场景的海量数据集成框架，同时支持数据接入、数据同步和数据订阅，提供自动、安全、可靠和高性能的数据传输能力，方便业务构建基于流式的数据分析、建模和应用。
+InLong 项目原名 TubeMQ ，专注于高性能、低成本的消息队列服务。为了进一步释放 TubeMQ 周边的生态能力，我们将项目升级为
InLong，专注打造一站式、全场景海量数据集成框架。
Apache InLong 依托 10 万亿级别的数据接入和处理能力，整合了数据采集、汇聚、存储、分拣数据处理全流程，拥有简单易用、灵活扩展、稳定可靠等特性。
该项目最初于 2019 年 11 月由腾讯大数据团队捐献到 Apache 孵化器，2022 年 6 月正式毕业成为 Apache 顶级项目。目前
InLong 正广泛应用于广告、支付、社交、游戏、人工智能等各个行业领域，为多领域客户提供高效化便捷化服务。
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
index b1eb827ee3..7053e362f0 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/modules/manager/overview.md
@@ -5,12 +5,7 @@ sidebar_position: 1
## 介绍
-- 目标定位：Apache InLong 定位为一站式海量数据集成框架，提供大数据从采集 -> 传输 -> 分拣 -> 落地的技术能力。
-
-- 平台价值：用户可以通过平台中的管理、配置平台完成任务的配置、管理以及指标的监控，同时通过平台提供的插件化扩展能力，按需实现自定义的扩展。
-
-- InLong Manager 是 Apache InLong
项目的统一管理平台，平台提供各基础配置（如数据流配置、消费配置、集群管理等）的维护入口，用户可创建数据采集任务、查看指标数据等。
-
+InLong Manager 是 Apache InLong
项目的统一管理平台，平台提供各基础配置（如数据流配置、消费配置、集群管理等）的维护入口，用户可创建数据采集任务、查看指标数据等。
## 模块
diff --git a/src/pages/Home/config.json b/src/pages/Home/config.json
index 6828cbec2d..ee17ec7ddc 100644
--- a/src/pages/Home/config.json
+++ b/src/pages/Home/config.json
@@ -3,7 +3,7 @@
"brand": {
"brandName": "Apache",
"projectName": "InLong",
- "briefIntroduction": "Apache
InLong（应龙）是一个一站式海量数据集成框架，提供自动、安全、可靠和高性能的数据传输能力，同时支持批和流，方便业务构建基于流式的数据分析、建模和应用。",
+ "briefIntroduction": "Apache
InLong（应龙）是一个一站式、全场景海量数据集成框架，同时支持数据接入、数据同步和数据订阅，提供自动、安全、可靠和高性能的数据传输能力，同时支持批和流，方便业务构建基于流式的数据分析、建模和应用。",
"features": [
"自动",
"安全",
@@ -67,7 +67,7 @@
"High performance",
"Distributed"
],
- "briefIntroduction": "Apache InLong is a one-stop integration
framework for massive data that provides automatic, secure and reliable data
transmission capabilities. InLong supports both batch and stream data
processing at the same time, which offers great power to build data analysis,
modeling and other real-time applications based on streaming data.",
+ "briefIntroduction": "Apache InLong is a one-stop, full-scenario
integration framework for massive data that supports Data Ingestion, Data
Synchronization and Data Subscription, and it provides automatic, secure and
reliable data transmission capabilities. InLong also supports both batch and
stream data processing at the same time, which offers great power to build data
analysis, modeling and other real-time applications based on streaming data.",
"buttons": [
{
"text": "Quick Start",
