The Role and Scale of Memcached in Facebook’s Data Infrastructure

Facebook's memcached cluster is a crucial component of its infrastructure, primarily used for caching frequently accessed data. This optimization boosts performance and reduces database load. Although the exact percentage of overall data cached in its memcached cluster is not publicly disclosed, it is known that Facebook leverages a substantial portion of memcached to handle its massive scale and traffic.

Common Caching Practices in Large-Scale Companies

In general, large-scale companies like Facebook cache a significant portion of their read-heavy data in memcached. This can potentially reach upwards of 80% for certain types of data. However, the exact figure can vary widely depending on the specific use case and data access patterns. For precise data, you may need to refer to Facebook's engineering blogs or technical papers where they discuss their caching strategies and infrastructure in detail.

Estimating the Data Size in Facebook's Memcached Cluster

To get a rough estimate of the data size in Facebook's memcached cluster, let's consider some key data points. Facebook operates with approximately 1 billion daily active users. On average, each user may post or comment on 20 items daily. Assuming an average post or comment size of 1KB, we can estimate the total size of posts and comments. We need to account for additional data such as user relations and the friends graph.

Estimating the Total Data Size

Posts/Comments Total: 1 billion users * 20 posts/comments/user * 1KB/post 20TB User Relations and Friends Graph: 1 billion users * (300 friends/user * 5 bytes/user 1000 readers/post * 5 bytes/post) 100TB

Given this, the total data size is approximately 120TB. Considering Facebook wants to have at least two replicas of this data, the grand total would be roughly 240TB.

Conclusion

While this is a rough estimation, it provides insight into the scale and complexity of Facebook's memcached infrastructure. Effective use of memcached is crucial for optimizing performance in large-scale applications. If you need precise data, refer to relevant engineering blogs or technical papers from Facebook.