知鸦日报2024-11-13

How and Why We Migrated Airbnb’s Large-Scale Web Monorepo to Bazel.

In today’s fast-paced digital landscape, the ability to adapt quickly to user needs and handle large-scale demands is paramount. That’s why…

Cryptographic monitoring at scale has been instrumental in helping our engineers understand how cryptography is used at Meta. Monitoring has given us a distinct advantage in our efforts to proactiv…

In our previous blog post, we introduced Netflix’s TimeSeries Abstraction, a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction. This counting service, built on top of the TimeSeries Abstraction, enables distributed counting at scale while maintaining similar low latency performance. As with all our abstractions, we use our Data Gateway Control Plane to shard, configure, and deploy this service globally.

Distributed counting is a challenging problem in computer science. In this blog post, we’ll explore the diverse counting requirements at Netflix, the challenges of achieving accurate counts in near real-time, and the rationale behind our chosen approach, including the necessary trade-offs.

Note: When it comes to distributed counters, terms such as ‘accurate’ or ‘precise’ should be taken with a grain of salt. In this context, they refer to a count very close to accurate, presented with minimal delays.

优惠券是电商常见的营销手段，是营销平台中的一个重要组成部分，既可以作为促销活动的载体，也是重要的引流入口。在刚刚过去的电商大促周期内，各大电商平台都有配置不同类目、价位的优惠券，吸引用户下单购买。

优惠券系统主要涵盖四个核心能力：创建、派发、使用、统计。本篇主要针对派发这部分，在系统设计和落地过程中遇到和解决的一些问题做一个简单记录，以便后来补缺。

本文主要记录了自己通过查阅相关资料，一步步排查问题，最后通过优化Docerfile文件将docker镜像构建从十几分钟降低到1分钟左右，效率提高了10倍左右。