docs: add zomato case study (#6848)

This commit is contained in:
Ayush Chauhan 2024-08-21 13:24:10 +05:30 committed by GitHub
parent 535a9ed059
commit af54ddea23
No known key found for this signature in database
GPG key ID: B5690EEEBB952194

View file

@ -41,6 +41,7 @@ where you can chat with VictoriaMetrics users to get additional references, revi
- [Xiaohongshu](#xiaohongshu) - [Xiaohongshu](#xiaohongshu)
- [Zerodha](#zerodha) - [Zerodha](#zerodha)
- [zhihu](#zhihu) - [zhihu](#zhihu)
- [Zomato](#zomato)
You can also read [articles about VictoriaMetrics from our users](https://docs.victoriametrics.com/articles/#third-party-articles-and-slides-about-victoriametrics). You can also read [articles about VictoriaMetrics from our users](https://docs.victoriametrics.com/articles/#third-party-articles-and-slides-about-victoriametrics).
@ -663,3 +664,28 @@ Numbers:
- Index size: ~600 GB - Index size: ~600 GB
- The average query rate is ~3k per second (mostly alert queries). - The average query rate is ~3k per second (mostly alert queries).
- Query duration: median is ~40ms, 99th percentile is ~100ms. - Query duration: median is ~40ms, 99th percentile is ~100ms.
## Zomato
### Who We Are
At [Zomato](https://www.zomato.com/), our mission statement is better food for more people, We connect millions of users with restaurants, delivering meals to their doorsteps while offering a variety of services, including restaurant discovery, online ordering, and table reservations.
### The Challenge
As we scaled, our existing observability stack (Prometheus and Thanos) began to show its limitations. We faced challenges like high memory usage, slow query response times, and rising costs, particularly due to the high cardinality of our metrics. Managing this setup became increasingly complex, impacting our ability to maintain reliable service and effectively troubleshoot issues.
### Our Solution
To address these challenges, we decided to migrate to VictoriaMetrics. We were drawn to its reputation for high performance, low resource usage, and scalability. The migration process was carefully planned to ensure a smooth transition with minimal disruption. We focused on:
- **Data Optimization**: We reduced unnecessary metrics to minimize data ingestion and storage needs.
- **Performance Enhancements**: VictoriaMetrics efficient query processing allowed us to achieve significantly faster query response times.
- **Cost Efficiency**: The optimized storage format in VictoriaMetrics led to a noticeable reduction in our storage and operational costs.
### The Results
Post-migration, we successfully scaled our monitoring infrastructure to handle billions of data points daily, all while experiencing faster query performance and 60% reduction in yearly infra cost. The improved observability has enhanced our ability to deliver reliable service, allowing us to troubleshoot issues more quickly and effectively.
Read more about the migration journey in our blog - https://blog.zomato.com/migrating-to-victoriametrics-a-complete-overhaul-for-enhanced-observability