Reflecting back on his 15-year experience and deep knowledge in coding, Vladimir utilized a rule engine to aggregate incoming request parameters, calculate them, and trigger call-to-actions based on filter and rule triggers.
The rule can be segmented into two distinct sections: "header" and "body" (taking inspiration from the HTML language specification). The "header" outlines whether the rule matches incoming request parameters, while the "body" contains several filters or any relevant data that should be returned if the rule matches the request. To optimize the speed of rule lookup, hashes can be utilized, formed from header parameter/value pairs, and compared to those formed from the request. Upon receiving a request, matching rules are identified, and aggregated/calculated parameter values are added to pre-defined tables, and stored in a lightweight, small database. This allows for easy retrieval of values using a hash lookup method based on the rules' header parameters.
Overall, this approach appears to be flexible, reliable, transparent, and adaptable to any modifications, making it suitable for most business processes found in complex transactional environments, such as payment systems.