Major Description
Background
Apache RocketMQ is a cloud-native messaging and streaming platform, streamlining the process of creating event-driven applications. Over the years, with the iteration of RocketMQ, a significant amount of code has been written to leverage multicore processors, enhancing program efficiency through concurrency. Consequently, managing concurrent performance has become vitally important. Locks are essential for ensuring multiple execution threads synchronize safely when accessing shared resources. Although locks are indispensable in ensuring mutual exclusion in multicore systems, their use can also pose optimization challenges. As concurrent systems grow more complex internally, deploying effective lock management strategies is key to preserving performance.
The adoption of locks in the concurrent code of RocketMQ may have room for optimization. For instance, the current usage of locks, while critical for ensuring consistency and preventing race conditions, could potentially be refined to improve overall message throughput without significantly impacting performance. In practice, we have demonstrated that adjusting the lock strategy can impact the message-sending performance of RocketMQ. Merely altering the backoff strategy of SpinLock can result in a performance difference of 20% (or even more) between the best and worst cases. Therefore, we hope to delve deeper into exploring the potential for performance optimization in this area. The concept of an adaptive lock mechanism could be introduced to enhance these synchronization points.
An adaptive lock could dynamically adjust its behavior based on runtime conditions, such as lock contention levels and the number of threads competing for the same resource. This could lead to improved performance by minimizing the overhead associated with lock acquisition and release, especially in scenarios with high contention. By monitoring the system's performance metrics in real time, an adaptive lock could switch between different locking strategies, such as spinning versus blocking or using a queue-based lock versus a contention-free mechanism.
To implement such a system, a lock profiling tool could be employed to analyze the lock's performance, provide insights into lock contention, and suggest the optimal lock configuration tailored to RocketMQ's specific workload patterns. This approach would ensure that the locking mechanism remains both efficient and responsive to the changing dynamics of the system, ultimately enhancing the performance of message passing while maintaining the necessary safety guarantees.
Relevant Skills
- Java Concurrent Programming Skills: Understanding how to write and optimize code that can run in parallel across multiple processor cores is a must. This includes knowledge of synchronization mechanisms, such as locks, semaphores, and barriers
- Familiarity with Locking Mechanisms: A deep understanding of various locking strategies and their trade-offs. This includes mutexes, spinlocks, reader-writer locks, and potentially more advanced lock-free and wait-free algorithms
- Expertise in System Performance Analysis: The ability to analyze system performance, identify bottlenecks, and interpret metrics such as lock contention, CPU utilization, and thread performance.
Tasks
- Examine the locking mechanism in RocketMQ and analyze any potential performance bottlenecks it may cause.
- Enhance the message sending and processing performance through flexible lock optimization strategies
- Condense the research findings into a report or article, and submit our discoveries to academic journals or conferences.
Learning Material
- RocketMQ HomePage (https://rocketmq.apache.org
- Github: https://github.com/apache/rocketmq
- T. E. Anderson, "The performance of spin lock alternatives for shared-money multiprocessors," in IEEE Transactions on Parallel and Distributed Systems, vol. 1, no. 1, pp. 6-16, Jan. 1990, doi: 10.1109/71.80120
- Y. Woo, S. Kim, C. Kim and E. Seo, "Catnap: A Backoff Scheme for Kernel Spinlocks in Many-Core Systems," in IEEE Access, vol. 8, pp. 29842-29856, 2020, doi: 10.1109/ACCESS.2020.2970998
- L. Li, P. Wagner, A. Mayer, T. Wild and A. Herkersdorf, "A non-intrusive, operating system independent spinlock profiler for embedded multicore systems," Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, Lausanne, Switzerland, 2017, pp. 322-325, doi: 10.23919/DATE.2017.7927009.
Mentor
Lei Ding, PMC Member of Apache RocketMQ, dinglei@apache.org
Rongtong Jin, PMC Member of Apache RocketMQ, jinrongtong@apache.org
Juntao Ji, Contributor of Apache RocketMQ, 3160102420@zju.edu.cn
Yinyou Gu, Contributor of Apache RocketMQ, guyinyou.gyy@alibaba-inc.com
