Linkis builds a computation middleware layer to facilitate connection, governance, and orchestration between the upper applications and the underlying data engines.
Linkis Official Site | Documentation
Introduction
Linkis serves as a computational middleware, acting as the bridge between upper applications and the underlying engines like MySQL, Spark, Hive, Presto, and Flink. Think of Linkis as a universal translator in a bustling market—facilitating communication between different vendors (data engines) and customers (applications) in real-time.
By employing standard interfaces such as REST, WS, and JDBC, Linkis allows upper applications to smoothly access various engines while handling unified resources, scripts, UDFs, and more. This function of linking high-level applications to low-level data engines not only simplifies the network integration but also helps in reducing development and maintenance costs, making it a highly sought-after tool in today’s data-driven marketplaces.
Features
- Diverse Engine Support: Seamless integration with various computation storage engines like Spark, Hive, Python, Shell, and more.
- Multi-language Support: Engages with numerous programming languages including SparkSQL, HiveSQL, Python, and Java.
- Robust Governance Capabilities: Facilitates task routing, load balancing, and resource control efficiently.
- Full-stack Support: Manages a wide range of computing tasks from batch to real-time streaming.
- Unified Context Service: Allows easy access and management of user resources across applications.
- Error Code Capabilities: Displays helpful error codes for common issues, assisting users in self-troubleshooting.
Engine Types
Linkis supports multiple engines, each tailored to handle specific types of tasks effectively. Below is a glimpse of the available engines:
| Engine Name | Support Underlying Component Version | Linkis Version Requirements | Included in Release Package |
|--------------|-------------------------------------|-----------------------------|------------------------------|
| Spark | Apache 2.0.0 | 1.0.3 | Yes |
| Hive | Apache 1.0.0 | 1.0.3 | Yes |
| Python | Python 2.6 | 1.0.3 | Yes |
| JDBC | MySQL 5.0 | 1.0.3 | No |
| Flink | Flink 1.12.2 | 1.0.2 | No |
| And more... | | | |
Imagine a toolbox (Linkis) that contains different tools (engines), with each tool designed for a specific task. Depending on your project needs—be it a screw or a nail—you can easily pick the right tool and get to work.
Download and Deployment
You can access a compiled distribution or source code package of Linkis through the Linkis Releases Page.
To compile and deploy:
- For the first-time setup, execute:
mvnw -N install. - Follow the instructions in the respective documentation for your operating system (Linux, MacOS, or Windows).
Examples and Guidance
For a better understanding of Linkis, you can refer to:
Troubleshooting Ideas
Being a multifaceted tool, users may encounter various challenges when implementing Linkis. Here are some troubleshooting tips:
- Ensure that you have the correct versions of the underlying components as per Linkis requirements.
- Consult the error codes provided by Linkis for insights on common issues.
- If you encounter persistent problems, visit our official channels for support or check the community forums.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
Apache Linkis is essentially the glue in the architecture of modern data solutions, allowing applications to seamlessly interact with varied engines while retaining governance and orchestration capabilities. This not only makes it easier for developers to build robust applications but also enhances efficiency across the board.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.