Fluid

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. Expected Outcome: - Design and implement a CLI tool for Fluid - Support `inspect` subcommand: list resource status given a Fluid Dataset CR. - Support `diagnose` subcommand: collect related information (e.g. logs, pod status, etc.) to help diagnose what's going wrong. - Implement a framework to diagnose Fluid with LLM/AI. (The collected information can be put into the context of an AI inference request)

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. Expected Outcomes: 1. Extended Cache Runtime interface covering data load, data operation lifecycle, and state transitions. 2. Working reference adapters for Curvine and Alluxio 3. Support for in-place upgrade (e.g., engine version update) and in-place cache rebuild (e.g., after node failure or config change).

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 Expected Outcome: 1. Deliver comprehensive migration guidelines, coding best practices, and hands-on team training for Ginkgo + Gomega adoption. 2. Achieve a measurable increase in unit test coverage—from 57% to 75%—across core modules of the Fluid codebase.