Sure thing, here’s the low‑down on the worst‑case scenarios for eventual consistency when you push latency to the extreme. In the CAP world, you’re basically trading off **Consistency** (C) and **Availability** (A) while **Latency** (L) is the cost you pay to get data “eventually.” If you want the absolute worst‑case latency, you set the **partition tolerance** (P) to stay up, keep the system highly available, and allow the consistency window to stretch until all replicas have caught up. That window is bounded by the *maximum replication lag* plus any *network partitions* that may delay sync. Mathematically, you can bound the worst‑case latency, \(L_{max}\), as: \(L_{max} = T_{rep} + T_{part}\) where \(T_{rep}\) is the longest time it takes for a write to propagate to the slowest replica, and \(T_{part}\) is the maximum duration of a network partition that keeps that replica isolated. If you’re using a gossip‑based sync, \(T_{rep}\) might be a few seconds, but with heavy write amplification or a low‑bandwidth link, it could balloon to minutes. Add a partition that lasts, say, 10 minutes, and you’ve got a 15‑minute worst‑case lag before that replica sees the latest state. In practice, the worst case shows up when you have a **single point of failure** in the replication topology—like a central coordinator that pushes updates to all replicas. If that coordinator stalls or the network splits, the lag explodes. The “eventual” promise collapses to “eventual after X minutes,” and you get stale reads for anyone querying the lagging node. So, to map it out: 1. **Identify the slowest replication path** (the bottleneck replica). 2. **Measure the maximum round‑trip time** for updates to that replica (T_rep). 3. **Determine the worst‑case partition duration** (T_part) in your environment. 4. **Add them up** for the worst‑case latency. If you want to keep the worst‑case under, say, 5 seconds, you’ll need to ensure both T_rep and T_part stay below that, which usually means redundant paths, sharding the data, or accepting a tighter consistency model. If you can live with 30‑second latency, you can afford a single point of failure and a more relaxed network topology. Hope that helps. If you need the actual numbers for your system, just plug in your observed propagation times and the longest split you’ve seen, and you’ll get a pretty good upper bound. Happy debugging, and don’t forget to document that one edge case that broke the test suite on Friday!