{"id":9963,"date":"2025-04-28T10:07:56","date_gmt":"2025-04-28T02:07:56","guid":{"rendered":"https:\/\/www.dkmeco.com\/en\/?p=9963"},"modified":"2025-04-28T10:21:12","modified_gmt":"2025-04-28T02:21:12","slug":"using-redis-for-real-time-analytics-the-choice-of-many-leading-companies","status":"publish","type":"post","link":"https:\/\/www.dkmeco.com\/en\/using-redis-for-real-time-analytics-the-choice-of-many-leading-companies\/","title":{"rendered":"Using Redis for real-time analytics \u2014 the choice of many leading companies!"},"content":{"rendered":"

Smart and delightful customer experiences put great demands on the performance of interactive applications. This requires high-performance databases that can handle a wide variety of application scenarios with minimal complexity and cost, without compromising performance. Redis is exactly such a database, widely used by enterprises for real-time analytics.<\/p>\n

High-Performance Business Drivers<\/h2>\n

Real-Time Analytics<\/h3>\n

In everyday life, when users interact with applications, the new generation of apps often tell users things like:
“We noticed you like X, we think you might also like Y.”<\/em>
Or when users make a selection or a purchase, they receive suggestions like:
“Many of our users who chose A were happiest when they also chose C. We thought you should know.”<\/em>
Or even:
“You just booked Q \u2014 here are some reviews for R, which you might want to check out afterward.”<\/em><\/p>\n

<\/p>\n

These smart applications offer timely and contextually relevant suggestions while users are still engaged, providing opportunities to enhance user experience and satisfaction. Offers made at this moment are much more effective than cold emails or calls made days later.<\/p>\n

This level of smart interaction also greatly enhances user experience \u2014 for instance, a user who just booked a ticket to “Second City Chicago” might appreciate being shown discounted dining options nearby before the show. From a business perspective, delivering a superior experience can retain loyal customers who might otherwise be lured away by competitors offering better service.<\/p>\n

The “smartness” of an application can also include detecting unusual behaviors that signal various types of fraud, from gaming fraud to identity fraud or promotional abuse. Successful detection must happen before<\/strong> users finish fraudulent actions, which demands real-time behavioral analysis.<\/p>\n

<\/p>\n

Thanks to the increasing availability of inexpensive computing power, data processing tools, and learning frameworks, integrating “intelligence” into applications is more achievable than ever. However, adding intelligence without sacrificing application performance or responsiveness is critical.<\/strong><\/p>\n

Especially for customer-facing applications, users expect responses within 100 milliseconds<\/strong>. Considering that Internet round-trip times can take up to 50 ms<\/strong>, the application must complete processing, data access, and response generation within 50 ms<\/strong>.<\/p>\n

<\/p>\n

To meet this requirement at scale, the database must deliver sub-millisecond response times under any load<\/strong>. In addition, real-time analysis must happen at the same speed<\/strong> and in the same operations<\/strong> as transactional processing \u2014 meaning in-database analytics<\/strong> is essential.<\/p>\n

\n

Redis\u2019 Analytical Capabilities<\/h3>\n

Redis is an in-memory database platform renowned for its high performance, extreme versatility of data structures, and modular extensibility for all types of processing, analysis, and storage use cases.<\/p>\n

Performance:<\/strong>
Redis has been benchmarked to process one million operations per second<\/strong> with sub-millisecond latency on ordinary AWS instances. When it comes to end-to-end application throughput and latency, Redis easily outperforms every other NoSQL database on the market with minimal resource usage.<\/p>\n

<\/p>\n

Data Structures:<\/strong>
The real power behind Redis\u2019 analytics lies in its rich data structures: Sets, Sorted Sets, Hashes, Lists, Strings, Bitmaps, and HyperLogLogs<\/strong>. These structures not only allow flexible data storage but also provide built-in operations for performing complex in-memory analytics<\/strong>.<\/p>\n

Example: Sorted Sets<\/strong>
Sorted Sets store members ordered by scores. Retrieving members by score range is trivial, making them ideal for time-series data<\/strong>, real-time bidding<\/strong>, purchase volume rankings<\/strong>, most viewed articles<\/strong>, highest scores<\/strong>, and more.<\/p>\n

Thanks to their native implementation in Redis, Sorted Sets enable highly efficient operations like sorting, range queries, range counting, and set operations (intersection, union, etc.) \u2014 all performed with maximum simplicity and speed<\/strong>.
Using Sorted Sets for analytics, such as time-series analysis, can be 10\u2013100x faster<\/strong> than using traditional key-value or disk-based databases.<\/p>\n

<\/p>\n

Other Redis data structures offer similar capabilities for analytics:<\/p>\n