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Global Distribution & Multi-Region Architecture

Objective: Evolve the solution to support 100,000+ concurrent users with global distribution.

✅ Required: How do you handle cross-region collaboration with acceptable latency?

✅ Required: Compare active-active vs active-passive multi-region strategies

✅ Required: How do you ensure data locality while maintaining global consistency?

✅ Optional: What's your approach to handling regional failures and failover?

✅ Optional: How do you manage distributed caching across regions?

1. Cross-Region Collaboration

CRDT operations based on LSEQ allow edits to be performed locally and merged asynchronously across regions without requiring centralized coordination. Each user interacts with the region closest to them, minimizing latency and avoiding global locks.

DynamoDB Global Tables replicate events automatically across regions, allowing each region to maintain its own event log while staying eventually consistent with others. Kafka clusters use cross-region replication to ensure that event streams remain durable and ordered per document, even when traffic spans multiple continents.

Clients always synchronize local operations with the nearest regional endpoint, using AWS Global Accelerator to route traffic optimally through AWS backbone network. When offline, operations are stored locally and merged seamlessly once the client reconnects, preserving eventual consistency and maintaining the global state in sync without conflicts.

This design enables real-time collaboration across continents while keeping write latency low and throughput high, essential for scaling to thousands of concurrent editors.

1.1 Data Locality

The system follows an active-passive architecture with warm failover, where only the primary region handles write operations. Passive regions act as replicas, ready to take over in case of failure but not serving writes under normal conditions.

Users are routed to the nearest region via AWS Global Accelerator, ensuring low-latency access for reads and failover readiness.

Edits are first processed and stored in the primary region's DynamoDB table and Kafka topic partition. Passive regions maintain up-to-date replicas through DynamoDB Global Tables and Kafka cross-region replication, ensuring data durability and eventual consistency in the event of a failover.

Key points:

  • Writes are centralized in the primary region, simplifying conflict resolution.

  • Passive regions are read-ready and can quickly take over as primary if the active region fails.

  • Replication is incremental, transmitting only deltas to minimize bandwidth and cost.

  • The combination of centralized writes and replicated passive regions ensures resilience, global availability, and predictable performance without sacrificing consistency.

2. Multi-Region Strategy

The system adopts a warm standby (active-passive) strategy, optimized for cost efficiency, operational simplicity, and resilience, leveraging CRDT-based eventual consistency to tolerate temporary regional outages without data loss.

In this model, a primary region actively handles both read and write traffic, while one or more standby regions maintain asynchronous replicas of state and event data.

If the primary region experiences a failure, traffic is redirected to a standby region, which is promoted to primary within minutes through automated failover orchestration (Route 53 health checks or AWS Global Accelerator).

This approach provides strong availability guarantees with minimal operational overhead:

Data replication:

  • DynamoDB Global Tables replicate event streams asynchronously across standby regions.

  • PostgreSQL logical replication ensures snapshots remain nearly up to date.

  • S3 cross-region replication maintains object durability and geo-distribution.

Consistency and conflict resolution:

  • Because CRDT operations are commutative and idempotent, concurrent updates from disconnected nodes or offline clients merge deterministically after recovery,eliminating manual conflict resolution.

Latency trade-offs:

  • Write latency is slightly higher for remote users since writes occur only in the primary region, but read latency remains low thanks to regional replicas and caching layers.

Cost-efficiency:

  • Unlike an active-active topology, which doubles infrastructure and coordination costs, the warm standby model achieves high availability and near-real-time recovery (low RPO, moderate RTO) at a fraction of the cost.

Overall, this active-passive with warm standby strategy aligns with the system's distributed and offline-tolerant design, providing global durability, predictable recovery, and efficient resource utilization without the complexity of full multi-master replication.

3. Global consistency

Global consistency is achieved through CRDT-based state management, ensuring eventual convergence even in the presence of network partitions or regional outages.

  • Conflict-free writes: All operations are idempotent and commutative, enabling automatic resolution without coordination.

  • Efficient partitioning: Documents are sharded by document ID (and optionally by region for latency optimization) to distribute load and reduce cross-region traffic.

  • Replication: Each operation is propagated asynchronously via DynamoDB Global Tables and Kafka cross-region replication, guaranteeing durability and ordered delivery.

  • Recovery points: Periodic snapshots in PostgreSQL and S3 allow deterministic recovery of document state for auditing, backups, or disaster recovery scenarios.

  • Optimized network usage: Only delta changes are transmitted across regions, minimizing bandwidth and cost while maintaining global consistency.

4. Regional failures and failover

The system is designed to handle regional outages gracefully without compromising data integrity or availability.

  • Client routing is managed through AWS Global Accelerator, automatically directing traffic to the nearest healthy active region.

  • Passive regions maintain fully synchronized replicas of all data, ready to be promoted to active in case of failure. They do not serve reads under normal conditions, ensuring consistency and avoiding stale data.

  • Data durability: DynamoDB Global Tables and Kafka replication ensure no data loss occurs during regional failures.

  • Automated failover: Managed via Auto Scaling Groups (ASG) or Kubernetes, failover procedures are incremental, reducing downtime and avoiding cascading failures.

  • Critical coordination: Optional leader election via Zookeeper or MSK ensures that region-level critical operations, like batch processing or global snapshots, maintain consistency and avoid split-brain scenarios.

5. Distributed caching across regions

Caching is implemented to reduce read latency and offload primary storage while maintaining reasonable consistency:

  • Local caching: Redis or Memcached instances colocated in each region store hot documents, minimizing read latency.

  • Event-driven invalidation: Cache updates are triggered by DynamoDB Streams or Kafka events, ensuring that stale reads are minimized.

  • Multi-region caching is designed to preload data in passive regions, ensuring minimal delay once a failover occurs.

  • Consistency controls: TTL policies and versioned snapshots manage eventual consistency for read-heavy workloads, balancing freshness with performance.

6. Observability and metrics

Comprehensive observability ensures operational reliability and rapid issue resolution:

  • Metrics monitoring: Track replication lag, write/read latency, cache hit ratios, and regional health continuously.

  • Alerting and notifications: Automated alerts for hot partitions, replication delays, failover events, or regional unavailability enable proactive response.

  • Logging: Detailed logs capture event propagation, CRDT merges, cache invalidations, and failover actions, supporting auditing, debugging, and forensic analysis.

  • Dashboards and analytics: Real-time dashboards provide insights into system performance, traffic patterns, and operational bottlenecks.

7. Expected results

The system delivers a robust, globally-distributed architecture optimized for consistency, resilience, and scalability:

  • Centralized reads and writes are always handled by the active region, ensuring deterministic CRDT convergence and preventing conflicts.

  • Global Accelerator routes users to the active region with minimal latency, providing the closest network path to the primary operations region.

  • Passive regions maintain warm replicas, ready to take over automatically during failover, without serving reads in normal operation.

  • Eventual consistency is guaranteed through CRDTs and deterministic conflict resolution, with incremental replication minimizing bandwidth usage.

  • Independent regional scaling allows the active region to handle peak loads while passive regions remain synchronized for failover readiness.

  • Resilience and fault tolerance are achieved via automated failover orchestration, cross-region replication, and pre-warmed caches, enabling the system to recover quickly from regional outages.

  • The architecture is capable of supporting 100,000+ concurrent users globally, balancing performance, cost-effectiveness, and operational simplicity.