本次以Redis为范例，阐述了有道基础架构团队在基础设施容器化道路上的实践。

希望通过本文让大家真正掌握跳表这种数据结构，掌握Redis的Sorted-Set底层实现的原理。

What if you could dramatically reduce the cost to operate on cloud providers? That’s what we found when we dug into Redis HASH vs SET.

Redis线程模型为什么要这么设计，有什么优点和缺点，有哪些思想是可以借鉴的...本文从网络IO的历史、Reactor模型的历史、到Redis线程模型的设计由浅入深，慢慢道来。

Jedis作为业内较通用的Redis客户端，通过深入剖析Jedis的多种模式的工作原理能够在日常工作中更好的使用。

本文介绍了一次大规模的Redis集群进行扩容操作遇到的性能问题，排查以及优化过程。

Uber’s GSS (Global Scaled Solutions) team runs scaled programs for diverse products and businesses, including but not limited to Eats, Rides, and Freight. The team transforms Uber’s ideas into agile, global solutions by designing and implementing scalable solutions. One of the areas of expertise within GSS is the Digitization vertical. The Digitization team efficiently converts physical signals into digital assets and provides services in labeling, in-field testing, data curation and validations for maps, product incubation, freight BOL (bill of lading), Eats menu uploads, etc.

All these digitization services are performed by thousands of humans (operators) working on our internal applications across many locations around the globe. While an operator is digitizing data, our backend collects a clickstream of all the user interactions in the form of raw events to the scale of 10 million events per day in AWS (Amazon Web Services) cloud infrastructure. Sometimes this data is also moved to Uber’s own data centers. Our data analytics team performs analysis on this data to improve/tweak the process, augment tooling infrastructure, address operator motivation, and improve operator skills. Analytics is usually performed by querying big data lakes and using different frontend tools for visualisation. Generally, any analytics setup has a latency (source to user) component to it and the latency of our existing (pre-COVID) infrastructure was 1 hour. With the onset of COVID-19 crisis, the digitization process had to be transitioned to work-from-home mode, leading to additional operational complexity of remotely managing a huge workforce of operators. This complexity created a gap in team’s communication, decision making, and collaboration. Where 1-hour latency of our analytics platform was previously acceptable, real-time analytics was needed to fill this gap. This blog describes how we improved latency of our data architecture by building a real-time analytics system.

While we researched approaches used for building real-time dashboards (example), we did not find an end-to-end solution, considering how rich visualization can be achieved at lower cost. We considered different visualization approaches and also looked at commercial solutions to come up with our choices. Another differentiating aspect was that our solution also addresses the need for a “single source of truth” on Amazon S3 (Amazon’s “simple storage service”), from which both streaming and batch processed dashboards would to be sourced, rather than hooking directly into the Amazon Kinesis Data Firehose stream itself. This intermediate storage lets us recover data (for the streaming window) with a replay. We production tested our visualizations with thousands of users for low load times and reliability.

大Key与热Key如果未能及时发现并进行处理，很可能会使服务性能下降、用户体验变差，甚至引发大面积故障。

最近优惠服务的redis经常会间歇性的抖动，具体表现为在短时间内redis rt上涨明显，RedisCommandTimeoutException异常陡增。

Redis 分布式锁的话题，很多文章已经写烂了，我为什么还要写这篇文章呢？

因为我发现网上 99% 的文章，并没有把这个问题真正讲清楚。导致很多读者看了很多文章，依旧云里雾里。

本文从实际使用场景出发，介绍了HyperLogLog算法的原理和Redis的实现。最后通过一系列实验，解释了文章最初的问题，以加深对HyperLogLog的理解。

笔者因项目需要，在一些场景下切换成 Lettuce 进行开发，并且希望能类似 Jedis pipeline 一样提升批量查询的性能。在对 Lettuce 进行了学习和踩坑后，笔者将过程中遇到的各种关键点总结成文章分享给大家。

REmote DIctionary Server(Redis) 是一个开源的使用 ANSI C 语言编写、遵守 BSD 协议、支持网络、可基于内存、分布式、可选持久性的键值对(Key-Value)存储数据库，并提供多种语言的 API。

Redis 通常被称为数据结构服务器，因为值（value）可以是字符串(String)、哈希(Hash)、列表(list)、集合(sets)和有序集合(sorted sets)等类型。

本文记录了一次线上服务，慢接口报警的解决流程，包括遇到线上问题的应急方案、分析问题的思路，主要集中在Redis响应比较慢时的分析和解决方案，通过对问题解决过程的总结，希望可以给大家一些参考。

为你详细讲解三种Redis双向同步方案机制，并根据业务特性及需求进行权衡选择。

Redis的AOF是类似的Log的机制，每次写操作都会写到硬盘上，当系统崩溃时，可以通过AOF来恢复数据。

每个带有写操作的命令被Redis服务器端收到运行时，该命令都会被记录到AOF文件上。由于只是一个append到文件操作(采用的是存储aof_buf中，并且采用异步方法fsync/fdatasync)，所以写到硬盘上的操作往往非常快。