yunqingmoswu commented on code in PR #7517:
URL: https://github.com/apache/inlong/pull/7517#discussion_r1126149065
##########
README.md:
##########
@@ -40,45 +40,74 @@
- [License](#license)
# What is Apache InLong?
-| **Stargazers Over Time**
|
**Contributors Over Time**
|
+
+| **Stargazers Over
+Time** |
**
+Contributors Over
+Time**
|
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [](https://starchart.cc/apache/inlong)
| [](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
|
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) 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.
-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.
+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.
-InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive data (data scale of more than 80 trillion
pieces of data per day) reporting services in big data scenarios. The entire
platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only needs to provide data sources, data
service quality, data landing clusters and data landing formats, that is, the
data can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service requirements in the business big data
scenario.
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -40,45 +40,74 @@
- [License](#license)
# What is Apache InLong?
-| **Stargazers Over Time**
|
**Contributors Over Time**
|
+
+| **Stargazers Over
+Time** |
**
+Contributors Over
+Time**
|
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [](https://starchart.cc/apache/inlong)
| [](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
|
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) 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.
-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.
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -40,45 +40,74 @@
- [License](#license)
# What is Apache InLong?
-| **Stargazers Over Time**
|
**Contributors Over Time**
|
+
+| **Stargazers Over
+Time** |
**
+Contributors Over
+Time**
|
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [](https://starchart.cc/apache/inlong)
| [](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
|
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) 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.
-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.
+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.
-InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive data (data scale of more than 80 trillion
pieces of data per day) reporting services in big data scenarios. The entire
platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only needs to provide data sources, data
service quality, data landing clusters and data landing formats, that is, the
data can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service requirements in the business big data
scenario.
+InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive
+data (data scale of more than 80 trillion pieces of data per day) reporting
services in big data scenarios. The entire
+platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only
+needs to provide data sources, data service quality, data landing clusters and
data landing formats, that is, the data
+can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service
+requirements in the business big data scenario.
For getting more information, please visit our project documentation at
https://inlong.apache.org/.
-
## Features
+
Apache InLong offers a variety of features:
-* **Ease of Use**: 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**: 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.
-* **Comprehensive Features**: 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**: 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**: 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.
+* **Ease of Use**: 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**: 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.
+* **Comprehensive Features**: 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**: 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**: 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.
## When should I use InLong?
-InLong is based on MQ and aims to provide a one-stop, practice-tested module
pluggable integration framework for massive data, based on this system, users
can easily build stream-based data applications. It is suitable for
environments that need to quickly build a data reporting platform, as well as
an ultra-large-scale data reporting environment that InLong is very suitable
for, and an environment that needs to automatically sort and land the reported
data.
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -40,45 +40,74 @@
- [License](#license)
# What is Apache InLong?
-| **Stargazers Over Time**
|
**Contributors Over Time**
|
+
+| **Stargazers Over
+Time** |
**
+Contributors Over
+Time**
|
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [](https://starchart.cc/apache/inlong)
| [](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
|
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) 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.
-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.
+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.
-InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive data (data scale of more than 80 trillion
pieces of data per day) reporting services in big data scenarios. The entire
platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only needs to provide data sources, data
service quality, data landing clusters and data landing formats, that is, the
data can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service requirements in the business big data
scenario.
+InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive
+data (data scale of more than 80 trillion pieces of data per day) reporting
services in big data scenarios. The entire
+platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only
+needs to provide data sources, data service quality, data landing clusters and
data landing formats, that is, the data
+can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service
+requirements in the business big data scenario.
For getting more information, please visit our project documentation at
https://inlong.apache.org/.
-
## Features
+
Apache InLong offers a variety of features:
-* **Ease of Use**: 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**: 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.
-* **Comprehensive Features**: 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**: 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**: 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.
+* **Ease of Use**: 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**: 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.
+* **Comprehensive Features**: 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**: 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**: 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.
## When should I use InLong?
-InLong is based on MQ and aims to provide a one-stop, practice-tested module
pluggable integration framework for massive data, based on this system, users
can easily build stream-based data applications. It is suitable for
environments that need to quickly build a data reporting platform, as well as
an ultra-large-scale data reporting environment that InLong is very suitable
for, and an environment that needs to automatically sort and land the reported
data.
+
+InLong is based on MQ and aims to provide a one-stop, practice-tested module
pluggable integration framework for massive
+data, based on this system, users can easily build stream-based data
applications. It is suitable for environments that
+need to quickly build a data reporting platform, as well as an
ultra-large-scale data reporting environment that InLong
+is very suitable for, and an environment that needs to automatically sort and
land the reported data.
You can use InLong in the following ways：
+
- Integrate InLong, manage data streams through
[SDK](https://inlong.apache.org/docs/next/sdk/manager-sdk/example).
-- Use [the InLong command-line
tool](https://inlong.apache.org/docs/next/user_guide/command_line_tools) to
view and create data streams.
+- Use [the InLong command-line
tool](https://inlong.apache.org/docs/next/user_guide/command_line_tools) to
view and
+ create data streams.
- Visualize your operations on [InLong
dashboard](https://inlong.apache.org/docs/next/user_guide/dashboard_usage).
## Supported Data Nodes (Updating)
+
| Type | Name | Version |
Architecture |
|--------------|-------------------|------------------------------|-----------------------|
| Extract Node | Auto Push | None | Standard
|
| | File | None | Standard
|
| | Kafka | 2.x |
Lightweight, Standard |
-| | MongoDB | >= 3.6 |
Lightweight, Standard |
-| | MQTT | >= 3.1 | Standard
|
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -40,45 +40,74 @@
- [License](#license)
# What is Apache InLong?
-| **Stargazers Over Time**
|
**Contributors Over Time**
|
+
+| **Stargazers Over
+Time** |
**
+Contributors Over
+Time**
|
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [](https://starchart.cc/apache/inlong)
| [](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
|
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) 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.
-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.
+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.
-InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive data (data scale of more than 80 trillion
pieces of data per day) reporting services in big data scenarios. The entire
platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only needs to provide data sources, data
service quality, data landing clusters and data landing formats, that is, the
data can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service requirements in the business big data
scenario.
+InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive
+data (data scale of more than 80 trillion pieces of data per day) reporting
services in big data scenarios. The entire
+platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only
+needs to provide data sources, data service quality, data landing clusters and
data landing formats, that is, the data
+can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service
+requirements in the business big data scenario.
For getting more information, please visit our project documentation at
https://inlong.apache.org/.
-
## Features
+
Apache InLong offers a variety of features:
-* **Ease of Use**: 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**: 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.
-* **Comprehensive Features**: 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**: 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**: 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.
+* **Ease of Use**: 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**: 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.
+* **Comprehensive Features**: 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**: 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**: 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.
## When should I use InLong?
-InLong is based on MQ and aims to provide a one-stop, practice-tested module
pluggable integration framework for massive data, based on this system, users
can easily build stream-based data applications. It is suitable for
environments that need to quickly build a data reporting platform, as well as
an ultra-large-scale data reporting environment that InLong is very suitable
for, and an environment that needs to automatically sort and land the reported
data.
+
+InLong is based on MQ and aims to provide a one-stop, practice-tested module
pluggable integration framework for massive
+data, based on this system, users can easily build stream-based data
applications. It is suitable for environments that
+need to quickly build a data reporting platform, as well as an
ultra-large-scale data reporting environment that InLong
+is very suitable for, and an environment that needs to automatically sort and
land the reported data.
You can use InLong in the following ways：
+
- Integrate InLong, manage data streams through
[SDK](https://inlong.apache.org/docs/next/sdk/manager-sdk/example).
-- Use [the InLong command-line
tool](https://inlong.apache.org/docs/next/user_guide/command_line_tools) to
view and create data streams.
+- Use [the InLong command-line
tool](https://inlong.apache.org/docs/next/user_guide/command_line_tools) to
view and
+ create data streams.
- Visualize your operations on [InLong
dashboard](https://inlong.apache.org/docs/next/user_guide/dashboard_usage).
## Supported Data Nodes (Updating)
+
| Type | Name | Version |
Architecture |
|--------------|-------------------|------------------------------|-----------------------|
| Extract Node | Auto Push | None | Standard
|
| | File | None | Standard
|
| | Kafka | 2.x |
Lightweight, Standard |
-| | MongoDB | >= 3.6 |
Lightweight, Standard |
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -40,45 +40,74 @@
- [License](#license)
# What is Apache InLong?
-| **Stargazers Over Time**
|
**Contributors Over Time**
|
+
+| **Stargazers Over
+Time** |
**
+Contributors Over
+Time**
|
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [](https://starchart.cc/apache/inlong)
| [](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
|
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) 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.
-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.
+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.
-InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive data (data scale of more than 80 trillion
pieces of data per day) reporting services in big data scenarios. The entire
platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only needs to provide data sources, data
service quality, data landing clusters and data landing formats, that is, the
data can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service requirements in the business big data
scenario.
+InLong was originally built at Tencent, which has served online businesses for
more than 8 years, to support massive
+data (data scale of more than 80 trillion pieces of data per day) reporting
services in big data scenarios. The entire
+platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting,
and Management, so that the business only
+needs to provide data sources, data service quality, data landing clusters and
data landing formats, that is, the data
+can be continuously pushed from the source to the target cluster, which
greatly meets the data reporting service
+requirements in the business big data scenario.
For getting more information, please visit our project documentation at
https://inlong.apache.org/.
-
## Features
+
Apache InLong offers a variety of features:
-* **Ease of Use**: a SaaS-based service platform. Users can easily and quickly
report, transfer, and distribute data by publishing and subscribing to data
based on topics.
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -40,45 +40,74 @@
- [License](#license)
# What is Apache InLong?
-| **Stargazers Over Time**
|
**Contributors Over Time**
|
+
+| **Stargazers Over
+Time** |
**
+Contributors Over
+Time**
|
|:-----------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [](https://starchart.cc/apache/inlong)
| [](https://git-contributor.com?chart=contributorOverTime&repo=apache/inlong)
|
-[Apache InLong](https://inlong.apache.org) 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.
+[Apache InLong](https://inlong.apache.org) is a one-stop integration framework
for massive data that provides automatic,
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -100,58 +129,81 @@ You can use InLong in the following ways：
| | PostgreSQL | 9.6, 10, 11, 12 |
Lightweight, Standard |
| | SQLServer | 2012, 2014, 2016, 2017, 2019 |
Lightweight, Standard |
| | TDSQL-PostgreSQL | 10.17 |
Lightweight, Standard |
-| | Doris | >= 0.13 |
Lightweight, Standard |
Review Comment:
Keep the origin style?
##########
README.md:
##########
@@ -100,58 +129,81 @@ You can use InLong in the following ways：
| | PostgreSQL | 9.6, 10, 11, 12 |
Lightweight, Standard |
| | SQLServer | 2012, 2014, 2016, 2017, 2019 |
Lightweight, Standard |
| | TDSQL-PostgreSQL | 10.17 |
Lightweight, Standard |
-| | Doris | >= 0.13 |
Lightweight, Standard |
-| | StarRocks | >= 2.0 |
Lightweight, Standard |
-| | Kudu | >= 1.12.0 |
Lightweight, Standard |
-| | Redis | >= 3.0 |
Lightweight, Standard |
+| | Doris | > = 0.13 |
Lightweight, Standard |
+| | StarRocks | > = 2.0 |
Lightweight, Standard |
+| | Kudu | > = 1.12.0 |
Lightweight, Standard |
+| | Redis | > = 3.0 |
Lightweight, Standard |
## Build InLong
-More detailed instructions can be found at [Quick
Start](https://inlong.apache.org/docs/next/quick_start/how_to_build) section in
the documentation.
Review Comment:
Keep the origin style?
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