The Dorivo backend is built on a fully asynchronous, event-driven runtime, eliminating traditional thread-per-request bottlenecks. This design is critical for handling thousands of concurrent connections with minimal memory overhead. The core loop processes I/O operations without blocking, allowing the system to maintain sub-10ms response times under load. By leveraging an actor model for state management, each request is isolated, preventing cascading failures. The entire stack is compiled to native code, reducing startup latency and runtime overhead. For a practical example of this architecture in a high-availability context, visit https://dorivo-belgium.com/.
Data flow is managed through a custom-built, lock-free ring buffer for inter-service communication. This eliminates mutex contention, a common source of latency in microservices. The backend uses a single-level cache with a write-behind strategy, ensuring that frequently accessed data is served from RAM while disk writes are batched and prioritized. This approach yields a 40% improvement in throughput compared to traditional caching layers.
Every external dependency-databases, third-party APIs-is wrapped in a circuit breaker pattern. If a downstream service responds slower than 50ms or returns errors, the circuit opens, and the backend falls back to a precomputed response or a local replica. This prevents cascading timeouts. The circuit resets automatically after a configurable cooldown, ensuring self-healing without manual intervention.
Dorivo uses a distributed SQL database with automatic horizontal sharding. Data is partitioned by a deterministic hash of the tenant ID, ensuring that no single node becomes a hot spot. Each shard is replicated synchronously to three nodes across different availability zones. Write operations are acknowledged only after at least two nodes confirm the commit, guaranteeing strong consistency without sacrificing write speed. Read requests are load-balanced across replicas, achieving an average latency of 3ms for point queries.
The schema is designed for denormalization. Join operations are avoided at query time; instead, related data is pre-joined during write operations and stored as aggregated documents. This reduces CPU overhead on read paths. Indexes are built using LSM trees, optimized for sequential writes and fast point lookups. The database layer automatically rebalances shards when nodes are added or removed, with zero downtime.
Every deployment is a blue-green rollout. The backend runs as a set of stateless containers orchestrated by a lightweight scheduler. When a new version is released, a full clone of the production environment is created, traffic is gradually shifted, and the old environment is terminated only after all health checks pass. This eliminates downtime and allows instant rollbacks. Network packets are routed via a custom eBPF-based proxy that inspects traffic at kernel level, reducing latency by 15% compared to user-space proxies.
Monitoring is passive and anomaly-based. The system collects over 200 metrics per node-CPU instruction retirement, memory bandwidth, disk I/O wait times, and GC pauses. Anomalies are detected using a statistical model that compares current metrics against a rolling window of historical data. Alerts are triggered only when deviations exceed three sigma, reducing false positives. The entire observability stack consumes less than 2% of CPU resources.
It uses synchronous replication with automatic failover. If a primary node fails, one of the two replicas is promoted within 200ms without data loss.
Under normal conditions, write latency is under 15ms for 99th percentile, thanks to the lock-free ring buffer and batched disk writes.
Yes. The scheduler can spin up new container instances within seconds, and the database shards auto-rebalance. No manual scaling is required.
Distributed transactions use a two-phase commit with a coordinator that logs to a write-ahead log. If a coordinator fails, another node takes over using the log.
It is written in Rust, chosen for its memory safety guarantees and zero-cost abstractions, which are essential for low-latency processing.
Alex K., DevOps Engineer
We migrated our payment processing to Dorivo. The circuit breaker logic saved us during a third-party API outage. No downtime, no data loss. The monitoring is incredibly precise.
Maria S., CTO
The sharding strategy is genius. We handle 50k writes per second with zero contention. The blue-green deployment pipeline simplified our release process-no more midnight rollouts.
James L., Software Architect
I analyzed the lock-free ring buffer implementation. It’s clean and efficient. The backend feels like a single-threaded system but scales linearly with cores. Impressive engineering.