Production Architecture and Integration Patterns

Multi Region Architecture

Production distributed SQL deployments fit within a larger system architecture. Stateless application servers in multiple regions connect to a distributed SQL cluster spanning those regions. A typical pattern places API gateways in North America, Europe, and Asia, each forwarding to regional application clusters. Those clusters connect to a database deployment with data partitioned by customer or account ID and replicated across regions, ensuring each region can serve local traffic with low latency while maintaining global consistency.

Data placement strategies optimize for locality. Commonly accessed data partitions to the regions where users primarily access them, reducing cross-region reads. Global data (configuration, reference tables) replicates everywhere with read-from-local and write-to-primary patterns. The database automatically handles placement based on configured constraints, relocating ranges as traffic patterns shift.

Caching Integration

Caching layers absorb hot read traffic, serving requests in under 1 millisecond compared to 5 to 10 milliseconds for database reads. In-memory caches (using key-value stores) sit between application servers and the database, storing frequently accessed data. However, cache invalidation introduces complexity: after a write commits to the database, the cache may remain stale until TTL (Time-To-Live, the duration after which cached data expires) forces a refresh.

Applications must choose invalidation strategies. Cache-aside (also called lazy loading) reads from cache first, fetches from database on miss, and caches the result; writes go directly to database and invalidate cache entries. Write-through writes to both cache and database atomically, ensuring cache is always current but adding write latency. Read-through combines both, with the cache handling database communication. Most distributed SQL integrations use cache-aside with short TTLs (30 to 300 seconds), accepting bounded staleness for dramatically reduced database load.

Change Data Capture Pipelines

Data pipelines stream changes from distributed SQL into analytics systems. CDC (Change Data Capture) mechanisms export committed transaction logs to message buses. Stream processors consume these events, transforming and aggregating data before loading into columnar stores optimized for analytics queries. This architecture separates OLTP (Online Transaction Processing, the operational database handling user transactions) from OLAP (Online Analytical Processing, systems optimized for complex queries over historical data).

Separating workloads prevents heavy reporting queries from impacting user-facing latencies. A dashboard querying millions of rows would directly slow transaction processing if both ran on the same system. With CDC pipelines, the transactional database handles only OLTP, while analytics systems handle OLAP with different performance characteristics (optimized for scan throughput rather than transaction latency).

Observability and Orchestration

Monitoring distributed SQL requires tracking consensus health metrics. Leader lease duration (how long a leader holds leadership before re-election) indicates stability; frequent leader changes suggest network issues or overloaded nodes. Log lag (the number of committed entries or bytes that followers have not yet applied) shows replication health. Transaction restart rates reveal contention where transactions conflict and must retry. Lock wait times expose hotspots where queries block on the same data.

Container orchestration platforms manage database pods, handling node failures by rescheduling replicas and maintaining desired counts across zones. Automated runbooks respond to metric degradation: relocating replicas when nodes approach capacity, rebalancing ranges when traffic shifts, or alerting operators when conditions require human judgment. At scale, teams monitor thousands of ranges across hundreds of nodes, requiring aggregation and intelligent alerting to surface problems before users notice.