Modularity
Separating critical services can help isolate faults and make individual components easier to maintain and scale.
Technology
Technology Designed Around Operational Resilience
A reliable platform depends on more than peak performance. Architecture, monitoring, controlled changes, and recovery planning all contribute to operational resilience.
Architecture overview
Services separated by design
Separating critical services can help isolate faults and make individual components easier to maintain and scale. The diagram is conceptual: it illustrates the principle of separation rather than a specific deployment.
Interfaces, APIs, core services, and data and monitoring are described as distinct layers so that a failure or change in one layer does not automatically become a failure in another.
Interfaces
Web applicationMobile web
API layer
RESTWebSocketAuthentication
Core services
MatchingAccountsMarket data
Data & logs
LogsMetricsAlertsAudit trails
Conceptual illustration of service separation only. It does not represent a verified production architecture, deployment topology, or performance claim.
Four pillars
What resilience is made of
These are operating principles. Each should be confirmed against the deployed service, including the scope and frequency of testing.
Availability
Redundancy and failover planning reduce dependence on any single component or location.
Monitoring
Service metrics, logs, and alerts make abnormal conditions visible before they become incidents.
Recovery
Tested backups and documented recovery procedures matter more than untested assumptions.
Infrastructure
Built to be maintained, not just launched.
Service separation, observability, and recovery planning are ongoing work — the operating environment does not stand still.
Matching system
Predictability is a feature
A matching system is judged less by its best-case throughput than by whether participants can predict how it behaves under stress.
- 01
Deterministic order handling
Order acceptance, sequencing, and cancellation follow documented rules so that participants can reason about behaviour in advance.
- 02
Stable matching logic
Order-priority rules should remain consistent over time. Frequent mechanism changes invalidate the assumptions users build strategies on.
- 03
Controlled change
Changes to critical paths are reviewed, tested, and released deliberately, with rollback considered before deployment.
- 04
Capacity planning
Peak load, extreme conditions, and sudden volatility are treated as expected operating states rather than exceptional events.
Availability & recovery
Continuity is planned, not assumed
Redundancy, tested backups, recovery procedures, and incident planning are important parts of a resilient operating model.
Redundancy
Critical services are designed to avoid single points of failure, with failover paths documented and exercised rather than only described.
Backups and restoration
Backups are only useful if restoration is tested. Recovery objectives should be defined per system and reviewed as the platform changes.
Incident planning
Roles, escalation paths, and communication steps are defined in advance so that response does not depend on improvisation.
Monitoring & observability
Visibility across the operating stack
Logs, service metrics, alerts, and audit trails help teams identify abnormal conditions, investigate incidents, and improve systems over time.
Continuous signals
Latency, error rates, queue depth, and settlement status are examples of signals used to detect degradation.
Audit trails
Sensitive operations should leave a complete record, supporting both internal review and independent examination.
Continuous improvement
Technology practices should be reviewed regularly as infrastructure, threats, product requirements, and market conditions evolve.
Change management
How changes reach production
Most outages are introduced by change. The sequence below is deliberately unglamorous, and it is the one that matters.
01
Review before build
Changes to critical paths are reviewed for risk, reversibility, and the customer-visible effect before work starts.
02
Test before release
Behaviour is verified in a representative environment, including failure paths rather than only the happy path.
03
Release with rollback
Releases are observable and staged; the rollback decision is made before deployment, not during an incident.
04
Verify after release
Post-release checks confirm that the intended change took effect and that no unrelated behaviour moved.
Data handling
Data is treated as an operating asset
Encryption, access control, retention, and auditability are baseline requirements rather than optional hardening.
Encryption
Data is encrypted in transit and at rest, with keys managed separately from application data.
Least privilege
Access to production data is limited by role, time-bounded where possible, and logged.
Retention
Retention periods are defined per data category, balancing regulatory obligations against minimisation.
Auditability
Administrative actions on data leave a record that can be reviewed independently of the operator.