Background and Goal

Service Discovery

1. Well organized logs
2. Actuator endpoints
3. Tools

Relevant Skills

1. Familiar with Java
2. Familiar with Microservice architecture

Potential Mentors

1. Jun Liu, Apache Dubbo PMC Chair, junliu@apache.org^{Image Added}

Background and Goal

Service Discovery

1. Well organized logs
2. Actuator endpoints
3. Tools

Relevant Skills

1. Familiar with Java
2. Familiar with Microservice architecture

Potential Mentors

1. Jun Liu, Apache Dubbo PMC Chair, junliu@apache.org^{Image Removed}
2. dev@dubbo.apache.org

    

Apache Beam's YAML DSL provides a powerful and declarative way to define data processing pipelines. In particular, many users want to use Beam for machine learning use cases like feature generation, embedding generation, and retrieval augmented generation (RAG). Today, however, Beam integrates with a relatively limited set of feature stores and vector DBs for these use cases. This project aims to build out a rich ecosystem of connectors to systems like Pinecone and Tecton to enable these ML use cases.

Background

 
Apache RocketMQ is a distributed messaging and streaming platform that supports various messaging protocols. One of the key features of RocketMQ is its orderly message consumption capability, which guarantees that messages are processed in the order they are sent. However, there are existing issues with the POP Orderly consumption process that need to be addressed to enhance its reliability and performance.

 Current Challenges

Currently, the POP Orderly feature faces several shortcomings, particularly in scenarios where network instability leads to the loss of the attemptId carried by the consumer from the previous round. This issue can result in message consumption getting stuck until the acknowledgment response (ack) for the previous message pull times out. Such situations hinder the efficient processing of messages and reduce the overall effectiveness of the messaging system.

 Objectives

The primary objectives of this project are as follows:
●Refactor the POP Orderly Code: Analyze and redesign the existing codebase to improve its structure, maintainability, and performance.
●Optimize Performance: Implement performance enhancements that allow the POP Orderly feature to cope with network fluctuations and reduce the likelihood of consumption halting.
●Elegant Process Resolution: Develop a more graceful approach to handling the issue of consumption stalling, ensuring that the system can recover more smoothly from failures.

 Recommended Skills

1. Proficiency in Java programming.
2. Strong understanding of concurrent programming.
3. Excellent logical thinking and problem-solving skills.
4. Familiarity with message queue systems, particularly Apache RocketMQ.

Mentor

Rongtong Jin, Apache RocketMQ PMC, jinrongtong@apache.org

Potential Mentor

Juntao Ji, 3160102420@zju.edu.cn

Difficulty: Major
Project Size: ~350 hours (large)

Background

Apache RocketMQ is renowned as a cloud-native messaging and streaming platform, enabling the creation of event-driven applications with simplicity and flexibility. The RocketMQ Dashboard is a crucial component that provides users with insight into system performance and client interactions through intuitive graphs and statistical data. Despite its fundamental role, the current user interface (UI) of the RocketMQ Dashboard is outdated, affecting user experience and interaction efficiency. Additionally, while the Dashboard offers valuable functionalities, there is a pressing need to enhance its usability and ensure robust security. This project aims to refactor the RocketMQ Dashboard by redesigning its UI with a more contemporary and user-friendly approach, improving overall usability, and introducing effective security measures to safeguard data and user interactions.

Relevant Skills

• Strong Java development skills.
• Experience with modern front-end technologies and frameworks
• Proficiency in Spring Boot development.
• Understanding of UX/UI design principles. - Knowledge of security best practices in web applications.
• A keen interest in open-source projects and a willingness to learn and adapt.

Tasks

• Launch and experiment with the RocketMQ Dashboard to understand current functionalities.
• Refactor the UI of the RocketMQ Dashboard to align with modern user interface standards, ensuring it is intuitive and visually appealing.  
• Improve usability by streamlining workflows, enhancing navigation, and incorporating responsive design. 
• Integrate security features to protect user data, prevent unauthorized access, and mitigate potential vulnerabilities.
• Maintain compatibility with existing RocketMQ functionalities while focusing on enhancements. 

Learning Material

• RocketMQ HomePage: https://rocketmq.apache.org([https://rocketmq.apache.org|https://rocketmq.apache.org/])
• RocketMQ GitHub Repository: https://github.com/apache/rocketmq([https://github.com/apache/rocketmq]) 
• RocketMQ Dashboard GitHub Repository: https://github.com/apache/rocketmq-dashboard([https://github.com/apache/rocketmq-dashboard]) 

Mentor

Rongtong Jin, Apache RocketMQ PMC, jinrongtong@apache.org

Potential Mentor

Juntao Ji, 3160102420@zju.edu.cn

Overview:
The current implementation of the remote.FS interface only supports a local file system (via the implementation in local.go). This GSOC2025 project proposes to extend remote.FS with popular object storage services—namely AWS S3, Google Cloud Storage, and Azure Blob Storage. This enhancement will allow the project to support robust cloud-based backup and restore operations in addition to local storage.

Proposed Features:

1. AWS S3 Implementation:
   • Implement methods for Upload, Download, List, and Delete operations using the AWS S3 API.
2. Google Cloud Storage Implementation:
   • Provide a module that integrates with Google Cloud Storage to perform similar operations.
3. Azure Blob Storage Implementation:
   • Develop functionality to access and manage Azure Blob Storage via the remote.FS interface.

Implementation Details:

• Interface Compliance:
  Each object storage implementation must adhere to the remote.FS interface defined in remote.go.

• Error Handling & Resilience:
  Implement robust error handling, logging, and retry mechanisms to ensure reliable operations across different cloud services.

• Testing:
  Develop comprehensive unit and integration tests to cover edge cases and guarantee compatibility and stability.

• Documentation:
  Update the project documentation to detail configuration, deployment, and usage of each cloud storage option

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