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new 1985d7af4 [INLONG-4698][Doc] Update the definitions and features to
make them accurate (#424)
1985d7af4 is described below
commit 1985d7af4cdada9e358f7cf7f4189e1bdc13bd34
Author: Charles Zhang <[email protected]>
AuthorDate: Sat Jun 18 21:14:26 2022 +0800
[INLONG-4698][Doc] Update the definitions and features to make them
accurate (#424)
---
docs/introduction.md | 27 +++++++++-------------
.../current/introduction.md | 2 +-
2 files changed, 12 insertions(+), 17 deletions(-)
diff --git a/docs/introduction.md b/docs/introduction.md
index fe0bdd45f..f6e7e87a6 100644
--- a/docs/introduction.md
+++ b/docs/introduction.md
@@ -3,39 +3,34 @@ title: InLong Introduction
sidebar_position: 1
---
-> InLong (应龙) is a divine beast in Chinese mythology who guides river into the
sea,
-> it is regarded as a metaphor of the InLong system for reporting streams of
data.
+> InLong (应龙) is a divine beast in Chinese mythology who guides the river into
the sea,
+> 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 donated by Tencent to the Apache community. 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. In order to further
release the surrounding ecological capabilities of TubeMQ, we upgraded the
project to InLong, focusing on creating a one-stop integration framework for
massive data.
-Apache InLong uses TDBank internally used by Tencent as the prototype, and
relies on trillion-level data access and processing capabilities to integrate
the entire process of data collection, aggregation, storage, and sorting
data processing. It is simple to use, flexible to expand, stable and
reliable characteristic.
+[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data donated by Tencent to the Apache community. 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.
+Apache InLong relies on 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.
## Features
- Ease of Use
- Apache InLong is a SaaS-based service platform. You can easily and quickly
report, transfer, and distribute data by publishing and subscribing to data
based on topics
+ InLong is a SaaS-based service platform. Users can easily and quickly
report, transfer, and distribute data by publishing and subscribing to data
based on topics.
- Stability & Reliability
- Apache InLong is derived from the actual online production environment,
- it delivers high-performance processing capabilities for 10 trillion-level
data streams and highly reliable services for 100 billion-level data streams
+ InLong is derived from the actual online production environment. It delivers
high-performance processing capabilities for 100 trillion-level data streams
and highly reliable services for 100 billion-level data streams.
- Comprehensive Features
- Apache InLong supports various types of data access methods and can be
integrated with different types of Message Queue (MQ) services. It also
provides real-time data extract, transform,
- and load (ETL) and sorting capabilities based on rules. Apache InLong also
allows you to plug features to extend system capabilities
+ InLong supports various types of data access methods and can be integrated
with different types of Message Queue (MQ). It also provides real-time data
extract, transform, and load (ETL) and sorting capabilities based on rules.
InLong also allows users to plug features to extend system capabilities.
- Service Integration
- Apache InLong provides unified system monitoring and alert services. It
provides fine-grained metrics to facilitate data visualization.
- You can view the running status of queues and topic-based data statistics in
a unified data metric platform.
- You can also configure the alert service based on your business requirements
so that users can be alerted when errors occur
+ InLong provides unified system monitoring and alert services. It provides
fine-grained metrics to facilitate data visualization. Users can view the
running status of queues and topic-based data statistics in a unified data
metric platform. Users can also configure the alert service based on their
business requirements so that users can be alerted when errors occur.
- Scalability
- Apache InLong adopts a pluggable architecture that allows you to plug
modules into the system based on specific protocols.
- You can replace components and add features based on your business
requirements
+ InLong adopts a pluggable architecture that allows you to plug modules into
the system based on specific protocols. Users can replace components and add
features based on their business requirements.
## Architecture
<img src="/img/inlong-structure-en.png" align="center" alt="Apache InLong"/>
@@ -76,4 +71,4 @@ Apache InLong serves the entire life cycle from data
collection to landing, and
| | Greenplum | 4.x, 5.x, 6.x |
Lightweight, Standard |
| | Elasticsearch | 6.x, 7.x |
Lightweight, Standard |
| | SQLServer | 2012, 2014, 2016, 2017, 2019 |
Lightweight, Standard |
-| | HDFS | 2.x, 3.x |
Lightweight, Standard |
\ No newline at end of file
+| | HDFS | 2.x, 3.x |
Lightweight, Standard |
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 6feb46dea..8f5cfbbe6 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/introduction.md
@@ -8,7 +8,7 @@ sidebar_position: 1
## 关于 InLong
[Apache InLong（应龙）](https://inlong.apache.org)是腾讯捐献给 Apache
社区的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，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 以腾讯内部使用的 TDBank
为原型，依托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。
+Apache InLong 依托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。
## 特性
- 简单易用
