Introduction

Linkis builds a layer of computation middleware between upper applications and underlying engines. By using standard interfaces such as REST/WS/JDBC provided by Linkis, the upper applications can easily access the underlying engines such as MySQL/Spark/Hive/Presto/Flink, etc., and achieve the intercommunication of user resources like unified variables, scripts, UDFs, functions and resource files，and provides data source and metadata management services through REST standard interface. at the same time.

As a computation middleware, Linkis provides powerful connectivity, reuse, orchestration, expansion, and governance capabilities. By decoupling the application layer and the engine layer, it simplifies the complex network call relationship, and thus reduces the overall complexity and saves the development and maintenance costs as well.

Since the first release of Linkis in 2019, it has accumulated more than 700 trial companies and 1000+ sandbox trial users, which involving diverse industries, from finance, banking, tele-communication, to manufactory, internet companies and so on. Lots of companies have already used Linkis as a unified entrance for the underlying computation and storage engines of the big data platform.

• Support for diverse underlying computation storage engines. Currently supported computation/storage engines: Spark, Hive, Python, Presto, ElasticSearch, MLSQL, TiSpark, JDBC, Shell, etc; Computation/storage engines to be supported: Flink(Supported in version >=1.0.2), Impala, etc; Supported scripting languages: SparkSQL, HiveQL, Python, Shell, Pyspark, R, Scala and JDBC, etc.
• Powerful task/request governance capabilities. With services such as Orchestrator, Label Manager and customized Spring Cloud Gateway, Linkis is able to provide multi-level labels based, cross-cluster/cross-IDC fine-grained routing, load balance, multi-tenancy, traffic control, resource control, and orchestration strategies like dual-active, active-standby, etc.
• Support full stack computation/storage engine. As a computation middleware, it will receive, execute and manage tasks and requests for various computation storage engines, including batch tasks, interactive query tasks, real-time streaming tasks and storage tasks;
• Resource management capabilities. ResourceManager is not only capable of managing resources for Yarn and Linkis EngineManger as in Linkis 0.X, but also able to provide label-based multi-level resource allocation and recycling, allowing itself to have powerful resource management capabilities across multiple Yarn clusters and multiple computation resource types;
• Unified Context Service. Generate Context ID for each task/request, associate and manage user and system resource files (JAR, ZIP, Properties, etc.), result set, parameter variable, function, etc., across user, system, and computing engine. Set in one place, automatic reference everywhere;
• Unified materials. System and user-level unified material management, which can be shared and transferred across users and systems.
• Unified Data Source Manage. Provides functions such as adding, deleting, checking, and modifying data sources of hive, es, mysql, and kafka types, version control, and connection testing.
• Unified MetaData Manage. Provides database, table, and partition queries for hive, es, mysql, and kafka metadata.

Please go to the Linkis releases page to download a compiled distribution or a source code package of Linkis.

Please follow Compile Guide to compile Linkis from source code. Please refer to Deployment_Documents to do the deployment.

You can find examples and guidance for how to use and manage Linkis in User_Manual, engine-usage_Documents and API_Documents.

The documentation of linkis is in Linkis-WebSite

Linkis services could be divided into three categories: computation governance services, public enhancement services and microservice governance services.

• The computation governance services, support the 3 major stages of processing a task/request: submission -> preparation -> execution;
• The public enhancement services, including the material library service, context service, and data source service;
• The microservice governance services, including Spring Cloud Gateway, Eureka and Open Feign.

Below is the Linkis architecture diagram. You can find more detailed architecture docs in Architecture.

Based on Linkis the computation middleware, we’ve built a lot of applications and tools on top of it in the big data platform suite WeDataSphere. Below are the currently available open-source projects.

• DataSphere Studio - Data Application Integration& Development Framework
• Scriptis - Data Development IDE Tool
• Visualis - Data Visualization Tool
• Schedulis - Workflow Task Scheduling Tool
• Qualitis - Data Quality Tool
• MLLabis - Machine Learning Notebook IDE

More projects upcoming, please stay tuned.

Contributions are always welcomed, we need more contributors to build Linkis together. either code, or doc, or other supports that could help the community. For code and documentation contributions, please follow the contribution guide.

Any questions or suggestions please kindly submit an issue. You can scan the QR code below to join our WeChat group to get more immediate response.

Meetup videos on Bilibili.

We opened an issue for users to feedback and record who is using Linkis. Since the first release of Linkis in 2019, it has accumulated more than 700 trial companies and 1000+ sandbox trial users, which involving diverse industries, from finance, banking, tele-communication, to manufactory, internet companies and so on.