大规模迁移：在不宕机的情况下将营销云缓存从 Memcached 移动到 Redis，达到 1.5M RPS

By Paladi Sandhya Madhuri, Rakesh Chhabra, Piyush Pruthi, Sumit Sahrawat, Ankit Jain, and Basaveshwar Hiremath.

作者：Paladi Sandhya Madhuri, Rakesh Chhabra, Piyush Pruthi, Sumit Sahrawat, Ankit Jain, 和 Basaveshwar Hiremath。

In our Engineering Energizers Q&A series, we highlight the engineering minds driving innovation across Salesforce. Today’s edition features Paladi Sandhya Madhuri, a Senior Software Engineer on the Marketing Cloud Caching team, whose work involves evolving the platform’s core caching infrastructure to support high-volume, latency-sensitive workloads, including a live migration handling approximately 1.5 million cache events per second across over 50 applications.

在我们的工程激励者问答系列中，我们突出了推动Salesforce创新的工程人才。今天的版本介绍了Paladi Sandhya Madhuri，她是营销云缓存团队的高级软件工程师，她的工作涉及发展平台的核心缓存基础设施，以支持高流量、对延迟敏感的工作负载，包括处理每秒约150万缓存事件的实时迁移，涉及超过50个应用程序。

Explore how the team executed a zero-downtime migration under live production traffic, preserving application behavior while changing the underlying cache engine, managing hot-key pressure from Redis at scale, and validating stable performance and reliability by sustaining end-to-end P50 latency near 1 millisecond and P99 latency around 20 milliseconds throughout the transition.

探索团队如何在实时生产流量下执行零停机迁移，保持应用程序行为的同时更改底层缓存引擎，管理来自Redis的热键压力，并通过在整个过渡过程中保持接近1毫秒的端到端P50延迟和约20毫秒的P99延迟来验证稳定的性能和可靠性。

The team embarked on a mission to modernize the Marketing Cloud’s core caching layer. Their goal was to maintain availability, security, and performance at scale, which was a significant undertaking.

团队开始了一项任务，旨在现代化Marketing Cloud的核心缓存层。他们的目标是在规模上保持可用性、安全性和性能，这是一项重大任务。

The existing Memcached-based system, while supporting a vast application ecosystem, presented challenges with its lack of native replication and built-in user authentication, making it difficult to meet evolving uptime, security, and maintenance demands. The absence of replication meant that a failure of a Memcached node required rebuilding the entire cache, leading to increased latency and significant load on the underlying databas...