
Fluid
Fluid, elastic data abstraction and acceleration for BigData/AI applications in cloud. (Project under CNCF)
- 3
- Projects
- 1
- Years active
- 3
- In 2026 T1
- Fluid
- Go
- CLI tool development
- LangChain(or other alternative LLM frameworks)
- +5
Projects
32026
3 projectsTerm 1 · Mar-May · 3 projects
Design and implement a CLI tool to help easily use Fluid
LFX Mentorship · Listed in the official cohort
Fluid manages Kubernetes resources (Statefulsets, PersistentVolumeClaims & PersistentVolume, etc.) under two Fluid custom resources CR called Dataset and Runtime. Given a pair of Dataset and Runtime CR, users may want to inspect the underlying resources, check their status and diagnose which part is going wrong. A CLI tool (e.g. a kubectl plugin) for Fluid would be a straightforward way for Fluid's users to easily get such information.
- Fluid
- Go
- CLI tool development
- LangChain(or other alternative LLM frameworks)
Extend Cache Runtime Interface to Support Full Data Lifecycle and In-Place Operations
LFX Mentorship · Listed in the official cohort
CNCF - Fluid: Extend cache runtime interface for full data lifecycle (2026 Term 1) Description:Fluid’s existing Generic Cache Runtime interface will be extended to support the complete lifecycle of data operations, including data loading, data processing workflows, and cache-aware data mutations. Additionally, the interface will be enhanced to enable in-place cache upgrades and in-place cache rebuilds—allowing runtime updates and recovery without disrupting workloads or requiring dataset re-provisioning.
- Fluid
- Go
- kubernetes operator development
Unify and Modernize Fluid’s Unit Testing Framework and enhance testing coverage
LFX Mentorship · Listed in the official cohort
To enhance code quality, maintainability, and developer experience, Fluid plans to migrate its unit testing framework from Testify to Ginkgo + Gomega—a more expressive and behavior-driven testing stack widely adopted in the Go ecosystem. Concurrently, we aim to significantly improve unit test (UT) coverage, raising it from the current 57% to at least 75%, thereby reducing regression risks and strengthening overall system reliability
- Fluid
- Go
- unit testing frameworks (gomonkey
- ginkgo
- gomega
- testify)