Apache Linkis
Apache Linkis is a computation middleware and data infrastructure layer that provides a unified entry point for accessing and managing various compute engines in big data and analytics platforms (data infrastructure / data platform middleware).
- Unified service layer for accessing heterogeneous computation engines such as Structured Query Language (SQL), Spark, and Python (data processing orchestration).
- Resource and context management for multi-tenant, multi-engine big data environments (data platform governance).
- Pluggable architecture for integrating diverse engines and tools through a standard interface (platform extensibility).
- Job submission, execution, and lifecycle management via Representational State Transfer (REST) APIs, SDKs, and user interfaces (workflow and job orchestration).
- Support for shared services such as metadata, UDFs, variables, and result sets across engines (data platform shared services).
More About Apache Linkis
Apache Linkis operates as a computation middleware layer designed to connect upper-layer applications to various underlying compute engines in big data and analytics environments (data infrastructure / data platform middleware). It provides a unified service entry, so users and applications can submit tasks such as SQL queries, Spark jobs, or Python scripts without coupling directly to specific engines or clusters. This architecture targets enterprises that run multiple heterogeneous engines and need consistent access, management, and governance across them.
At its core, Apache Linkis exposes a standard protocol and set of services for job submission, execution, and result retrieval (workflow and job orchestration). Upper-layer tools, including data analysis workbenches or BI systems, communicate with Linkis through REST APIs, SDKs, or UI components, and Linkis translates these requests into engine-specific operations. This decouples applications from engine details such as cluster endpoints, execution parameters, and resource configurations.
The project provides a pluggable engine connection framework (platform extensibility) that supports integration with multiple compute backends. Typical categories include SQL engines, batch processing frameworks, interactive computation engines, and script execution environments. Engine plug-ins conform to the Linkis engine connection protocol, allowing operators to add or upgrade engines without changing calling applications. This extensibility is positioned for organizations that maintain evolving big data stacks.
Apache Linkis also includes resource and context management capabilities (data platform governance). It manages user sessions, execution contexts, variables, UDFs, and metadata so that these elements can be reused across tasks and engines. Multi-tenant support, permission control, and resource configuration help platform teams allocate and monitor computational resources across different business units or applications. Shared services for result sets and metadata enable cross-tool collaboration and reduce data duplication.
In enterprise deployments, Linkis typically runs as a cluster of microservices that handle user requests, route jobs to engines, track execution states, and manage engine lifecycles (microservices architecture / platform services). Operations teams can deploy these services on existing infrastructure, connect them to authentication and authorization systems, and integrate them with monitoring and logging stacks. This positions Linkis as a central gateway and management layer for big data computation services within an organization.
From a taxonomy perspective, Apache Linkis fits into categories such as data platform middleware, compute engine orchestration, and multi-tenant big data service governance. It is relevant for organizations that operate diverse data processing engines and want a unified control plane for job submission, engine integration, and shared data services.