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所在平台: Udemy |
课程主页: https://www.udemy.com/course/how-to-handling-1-million-requests-per-minute-with-golang/
课程评论：没有评论
**课程概述：** 本课程名为“如何使用 Golang 构建每分钟百万请求系统”，旨在解决在匿名遥测和分析系统中处理海量 POST 请求的挑战。核心问题在于，传统的解决方案通常需要构建复杂的、多层级的后台 worker 架构，例如使用 Sidekiq、Resque、Delayed Job、Elastic Beanstalk Worker Tier、RabbitMQ 等，并维护独立的 Web 前端和 Worker 集群以应对高负载。 **课程亮点与结论：** 该课程的开发者团队选择 Golang，是因为他们预见到这是一个可能面临巨大流量的系统。尽管有过使用 Golang 开发系统的经验，但从未达到如此高的负载水平。 最终，课程强调了“**简单性总是获胜**”的理念。他们没有选择设计一个包含大量队列、后台 worker 和复杂部署的臃肿系统。相反，他们巧妙地结合了 **Elastic Beanstalk 的自动伸缩能力** 和 **Golang 内置的强大并发处理能力**。 **成果：** 课程展示了一个令人瞩目的成果：一个仅由 4 台机器组成的集群，这些机器的配置可能远不如开发者的 MacBook Pro，却能够每分钟成功处理 100 万次 POST 请求，并将数据写入 Amazon S3 存储桶。这充分体现了 Golang 在高并发场景下的效率和简单性带来的巨大优势。
The ProblemWhile working on a piece of our anonymous telemetry and analytics system, our goal was to be able to handle a large amount of POST requests from millions of endpoints. The web handler would receive a JSON document that may contain a collection of many payloads that needed to be written to Amazon S3, in order for our map-reduce systems to later operate on this data.Traditionally we would look into creating a worker-tier architecture, utilizing things such as:SidekiqResqueDelayedJobElasticbeanstalk Worker TierRabbitMQand so on…And setup 2 different clusters, one for the web front-end and another for the workers, so we can scale up the amount of background work we can handle.But since the beginning, our team knew that we should do this in Go because during the discussion phases we saw this could be potentially a very large traffic system. I have been using Go for about 2 years or so, and we had developed a few systems here at work but none that would get this amount of load.ConclusionSimplicity always wins in my book. We could have designed a complex system with many queues, background workers, complex deployments, but instead we decided to leverage the power of Elasticbeanstalk auto-scaling and the efficiency and simple approach to concurrency that Golang provides us out of the box.It's not everyday that you have a cluster of only 4 machines, that are probably much less powerful than my current MacBook Pro, handling POST requests writing to an Amazon S3 bucket 1 million times every minute.
