Distributed Services Architecture & Event-Driven Systems
We design, build and rescue distributed systems: microservices and event-driven architectures that process orders, payments and telemetry without losing an event. You get systems that degrade predictably under load and partial failure, with tracing, replay and recovery tooling so nothing is silently lost.
A distributed system that fails one component at a time, not all at once
Failures in distributed systems are usually contract failures: an event lost between the database and the broker, a retry applied twice, a consumer far behind. We design the message boundaries, the idempotency rules and the recovery paths first, so a spike or an outage degrades the system instead of collapsing it.
What this looks like in the codebase
After the workflow is clear, we lock the technical choices that keep the product maintainable. These are the ones we use here:
- Typed service contracts with gRPC and GraphQL, so a breaking schema change fails in CI and not in production
- Event schema registry with versioning, so old producers and new consumers stay compatible across deploys
- Transactional outbox pattern, so the business write and its event commit or roll back together
- Idempotent handlers, retries with backoff and saga orchestration for multi-service workflows
- Partitioning and sharding strategies with distributed tracing, so hot keys and slow spans surface before users feel them
Stack we ship with
Chosen because we have run it in production, not because it is fashionable.
The work inside this service
From the first data model to a production deploy. Here is what we hand over.
Event-Driven Architecture & Messaging
Events as the integration between services: one write, many consumers, and agreed rules for ordering, retry and replay. A broker is a promise, not a guarantee, so we make the promise hold.
- Event schema registry and versioning
- Transactional outbox pattern with idempotent consumers
- Saga orchestration or choreography for multi-step flows
- Dead-letter queues, lag monitoring and replay tooling
Microservices & Service Contracts
Service splits only where ownership, traffic or independent scaling demands them, with contracts written before code and resilience built into every call.
- Service decomposition along data ownership boundaries
- Contract-first gRPC and REST APIs with versioned schemas
- Retries, timeouts, circuit breakers and bulkheads on every dependency
- Polyglot runtimes only where the workload justifies them
Data Partitioning & Distributed Data
Data placement decides latency, consistency and cost. We design partitions, replicas and caches around the access pattern, then write down the trade-offs so they are decisions, not accidents.
- Partitioning and sharding strategy matched to access patterns
- Read replicas and caching layers with invalidation rules
- Distributed locks and idempotency keys for once-only effects
- Consistency and isolation trade-offs decided and documented
A call we made in production
Zero duplicate orders through a payment retry storm
Zero duplicate orders through a payment retry storm
An ordering platform asked us to rescue an event pipeline that could not survive a bad day. A broker outage left consumers reprocessing old offsets, retries fired on top of retries, and 1.4% of orders were placed twice, with payment p99 at 9 seconds. We rebuilt the write path around a transactional outbox. The order row and its outbox row commit in the same database transaction, a relay publishes in commit order, and every consumer deduplicates on an idempotency key stored with the order. The payment step became a saga with bounded retries and a compensation path. After the change, duplicate orders stayed at zero across three months of peak traffic, order p99 fell to 340ms, and replaying one bad day of backlog takes minutes instead of a weekend.
Questions before you write to us
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