This paper presents an analysis of network traffic patterns in Facebook's datacenters, highlighting differences from previously studied datacenter traffic. Facebook operates a variety of services, including Hadoop, Web servers, and cache infrastructure, which exhibit distinct traffic behaviors compared to the literature. The traffic in Facebook's datacenters is characterized by stable locality, predictability, and a mix of short-lived and long-lived flows. Unlike previous studies that often assume all-to-all traffic patterns, Facebook's traffic is more localized and stable over time, with significant portions of traffic directed to a small number of racks. The paper also discusses the implications of these findings for network architecture, traffic engineering, and switch design. It shows that traffic patterns in Facebook's datacenters are not representative of the general datacenter traffic, and that many previous proposals based on these assumptions may not be applicable. The paper also highlights the importance of traffic predictability for network design and the need for flexible, scalable solutions that can adapt to varying traffic demands. The study provides insights into the traffic characteristics of Facebook's datacenters, which can inform the design of more efficient and effective network architectures.This paper presents an analysis of network traffic patterns in Facebook's datacenters, highlighting differences from previously studied datacenter traffic. Facebook operates a variety of services, including Hadoop, Web servers, and cache infrastructure, which exhibit distinct traffic behaviors compared to the literature. The traffic in Facebook's datacenters is characterized by stable locality, predictability, and a mix of short-lived and long-lived flows. Unlike previous studies that often assume all-to-all traffic patterns, Facebook's traffic is more localized and stable over time, with significant portions of traffic directed to a small number of racks. The paper also discusses the implications of these findings for network architecture, traffic engineering, and switch design. It shows that traffic patterns in Facebook's datacenters are not representative of the general datacenter traffic, and that many previous proposals based on these assumptions may not be applicable. The paper also highlights the importance of traffic predictability for network design and the need for flexible, scalable solutions that can adapt to varying traffic demands. The study provides insights into the traffic characteristics of Facebook's datacenters, which can inform the design of more efficient and effective network architectures.