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Posted to commits@inlong.apache.org by "GanfengTan (via GitHub)" <gi...@apache.org> on 2023/03/05 15:22:12 UTC
[GitHub] [inlong] GanfengTan opened a new pull request, #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
GanfengTan opened a new pull request, #7517:
URL: https://github.com/apache/inlong/pull/7517
Changing Flink's version management.
1.My personal idea for this PR is to migrate Flink’s version management to each module, and the public dependencies will be placed under the root POM.
2.The following PR will extract common capabilities for different versions of Flink such as Sort-format, Sort-core, etc.
3.The final idea is to put the Flink version that Sort depends on in Sort-common, and only need to modify the POM of Sort-common when switching versions.
- Fixes #7516
### Motivation
Decoupling Flink version dependencies for multiple versions of Apache Flink
### Modifications
Modified the POM
### Verifying this change
*(Please pick either of the following options)*
- [x] This change is a trivial rework/code cleanup without any test coverage.
- [ ] This change is already covered by existing tests, such as:
*(please describe tests)*
- [ ] This change added tests and can be verified as follows:
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[GitHub] [inlong] GanfengTan commented on a diff in pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "GanfengTan (via GitHub)" <gi...@apache.org>.
GanfengTan commented on code in PR #7517:
URL: https://github.com/apache/inlong/pull/7517#discussion_r1133480527
##########
inlong-agent/pom.xml:
##########
@@ -36,4 +36,54 @@
<module>agent-release</module>
<module>agent-docker</module>
</modules>
+
+ <properties>
+ <debezium.version>1.8.0.Final</debezium.version>
Review Comment:
Each component depends on a different version of debezium. In the past, a version was managed under the root POM, but sort has its own version, which is very misleading.
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[GitHub] [inlong] healchow commented on a diff in pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "healchow (via GitHub)" <gi...@apache.org>.
healchow commented on code in PR #7517:
URL: https://github.com/apache/inlong/pull/7517#discussion_r1133595876
##########
inlong-agent/pom.xml:
##########
@@ -36,4 +36,54 @@
<module>agent-release</module>
<module>agent-docker</module>
</modules>
+
+ <properties>
+ <debezium.version>1.8.0.Final</debezium.version>
Review Comment:
Get it.
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[GitHub] [inlong] healchow commented on a diff in pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "healchow (via GitHub)" <gi...@apache.org>.
healchow commented on code in PR #7517:
URL: https://github.com/apache/inlong/pull/7517#discussion_r1133373065
##########
inlong-agent/pom.xml:
##########
@@ -36,4 +36,54 @@
<module>agent-release</module>
<module>agent-docker</module>
</modules>
+
+ <properties>
+ <debezium.version>1.8.0.Final</debezium.version>
Review Comment:
Is this modification related to the Flink version?
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[GitHub] [inlong] gong commented on a diff in pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "gong (via GitHub)" <gi...@apache.org>.
gong commented on code in PR #7517:
URL: https://github.com/apache/inlong/pull/7517#discussion_r1133528899
##########
inlong-sort/pom.xml:
##########
@@ -80,6 +83,17 @@
<artifactId>kudu-client</artifactId>
<version>${kudu.version}</version>
</dependency>
+ <dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-shaded-jackson</artifactId>
+ <version>${flink.jackson.version}</version>
+ </dependency>
+ <!--ojdbc8 is FUTC license, we use it test only-->
+ <dependency>
+ <groupId>com.oracle.database.jdbc</groupId>
+ <artifactId>ojdbc8</artifactId>
+ <version>${oracle.jdbc.version}</version>
+ </dependency>
Review Comment:
It need add <scope>test</scope> ?
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[GitHub] [inlong] GanfengTan commented on a diff in pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "GanfengTan (via GitHub)" <gi...@apache.org>.
GanfengTan commented on code in PR #7517:
URL: https://github.com/apache/inlong/pull/7517#discussion_r1133655638
##########
inlong-sort/pom.xml:
##########
@@ -80,6 +83,17 @@
<artifactId>kudu-client</artifactId>
<version>${kudu.version}</version>
</dependency>
+ <dependency>
+ <groupId>org.apache.flink</groupId>
+ <artifactId>flink-shaded-jackson</artifactId>
+ <version>${flink.jackson.version}</version>
+ </dependency>
+ <!--ojdbc8 is FUTC license, we use it test only-->
+ <dependency>
+ <groupId>com.oracle.database.jdbc</groupId>
+ <artifactId>ojdbc8</artifactId>
+ <version>${oracle.jdbc.version}</version>
+ </dependency>
Review Comment:
done
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[GitHub] [inlong] yunqingmoswu commented on pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "yunqingmoswu (via GitHub)" <gi...@apache.org>.
yunqingmoswu commented on PR #7517:
URL: https://github.com/apache/inlong/pull/7517#issuecomment-1455791236
Please fix the unit test error. @GanfengTan
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[GitHub] [inlong] yunqingmoswu commented on a diff in pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "yunqingmoswu (via GitHub)" <gi...@apache.org>.
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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[GitHub] [inlong] dockerzhang merged pull request #7517: [INLONG-7516][Manager][Sort][Agent] Decoupling Flink version dependencies for multiple versions of Apache Flink
Posted by "dockerzhang (via GitHub)" <gi...@apache.org>.
dockerzhang merged PR #7517:
URL: https://github.com/apache/inlong/pull/7517
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