Distributed financial mechanisms handling multiple millions of transactions per day need highly intelligent observability solutions to be able to operate under demanding conditions with a high level of integrity. Deimos Cloud was faced with severe constraints of visibility upon serving its clients who included Mukuru and Optty in sensitive cross-border payments processing. The legacy monitoring systems did not provide sufficient error correlations between the microservices in the event of transaction failure, did not perform dimensional analysis in slowdowns of regional operation and used threshold-driven alerts which failed to signify anomalous behaviors in a transactional context. These deficiencies manifested severely during a fifteen-minute API latency incident occurring within the regulatory reporting cycles which delayed two hundred thousand transactions and threatened to breach regulatory compliance.
The technical solution was based on the introduction of a layered observability framework that includes Grafana, Prometheus, and Elastic Stack. To generate uniform traces, metrics, and logs across all the .NET Core and Node.js services, engineering teams standardized OpenTelemetry instrumentation in them all. Prometheus was chosen due to its dimensional data model which allows granular (or precise) queries like the failure rates of transactions based on geographic locations. Elasticsearch indexed structured JSON logs with indexed transaction identifiers while Grafana synthesized these datasets into actionable visualizations. This took place by defining stringent service-level targets such as three hundred millisecond latency at the ninety-fifth percentile of payment authorizations.
Technical execution followed a rigorous methodology beginning with semantic convention standardization before instrumentation. Prometheus exporters in Kubernetes pods collected application metrics every fifteen seconds and stored rules to save a precomputed query that was frequently used. Structured logs were being streamed by filebeat agents to Elasticsearch with automated life cycle management policies to deal with archivals. Three of the most important operational viewpoints, the transaction flow visualization mapping payment lifecycles, compliance health scorecards with jurisdictional rule evaluations, and infrastructure correlation boards with resource utilization in line with database contention, were combined into Grafana-dashboard. Alerting mechanisms evolved significantly through machine learning-driven anomaly detection using Prometheus’ statistical deviation analysis from established seasonal patterns.
The integration of observability produced revolutionary results after three months of entering into operation. The incident was resolved by the implementation of cross-service trace correlation and the detection of a misconfigured circuit breaker that resulted in frequent outages in the buy-now-pay-later integrations. Log files analyzed with the help of Elasticsearch identified the network issue with the .NET Core memory leak that was noticed when a certain pattern of transactions was used. Some measurable gains were an ability to resolve incidents sixty percent faster using single dashboards that correlated traces to metrics. False alerts as a result decreased by forty-five percent and root-cause analysis took three times so fast following machine learning anomaly detection when the payment processor went down. This operational visibility went further than engineering groups as settlement rates of finance groups were tracked on dedicated interfaces and compliance officers were provided with an automated review trail of rules assessments.
In this initiative, key technical learnings have occurred. Even modest levels of semantic consistency in the conventions of tagging were found to be critical to cross-dataset correlation of any validity. Prioritizing business transaction objectives over infrastructure metrics maintained user-centric focus during instrumentation design. Approaching observability as an iterative process of improvement based on quarterly deployment of dashboards to analyze use contributed to defining optimization steps in visualization efficiency. Such an in depth monitoring infrastructure is touted as a fundamental requirement of distributed financial systems that provide transaction integrity in face of failures. The documented sixty percent resolution time improvement and forty-five percent alert accuracy gain establish this implementation as a replicable framework for engineering teams operating high-stakes transactional platforms globally.
Technical implementation led by Malik Adeyemi, Senior Software Engineer & Technical Lead at Deimos Cloud. Systems support platforms processing 200M+ daily transactions across emerging markets.
