The architectural diagram shows 3 clusters. Each consist of 3 pods and storages. In order to do that there must be synchronization across clusters. How do they achieve that?
Loved how they tackled the trade-offs between consistency and performance while still keeping developer velocity high. It’s a great example of how infra teams can blend cloud-native principles with real-world production demands.
The local volume strategy with EBS snapshots is elegant - basically trading off some recovery time for low-latency reads. What's interesting is they're accepting the state managment complexity of K8s for databases to get better resource utilization and standardized tooling. The drain coordination with StatefulSet hooks is crucial here - prevents data loss during node replacement. I wonder how they handle schema migrations across the multi-cluster setup though. Also curious about their monitoring approach - tracking replication lag and data consistency across 3 AZs must require sophisticated observability. Great case study of pragmatic trade-offs in production systems.
How much cost savings from paying a large team to maintain this vs running off of an existing distributed database?
The architectural diagram shows 3 clusters. Each consist of 3 pods and storages. In order to do that there must be synchronization across clusters. How do they achieve that?
Loved how they tackled the trade-offs between consistency and performance while still keeping developer velocity high. It’s a great example of how infra teams can blend cloud-native principles with real-world production demands.
The local volume strategy with EBS snapshots is elegant - basically trading off some recovery time for low-latency reads. What's interesting is they're accepting the state managment complexity of K8s for databases to get better resource utilization and standardized tooling. The drain coordination with StatefulSet hooks is crucial here - prevents data loss during node replacement. I wonder how they handle schema migrations across the multi-cluster setup though. Also curious about their monitoring approach - tracking replication lag and data consistency across 3 AZs must require sophisticated observability. Great case study of pragmatic trade-offs in production systems.