Distributed flight-pricing systems rely on layered caches to balance low latency and fresh data. In practice, caches often use short TTL
s (minutes to hours) supplemented by event-driven invalidation. However, concurrent cache writes – for example when multiple instances update fares simultaneously – can trigger subtle race conditions. These manifest as stale or inconsistent prices, duplicate cache entries, or “split-brain” behavior across regions. To diagnose and prevent these issues, experienced teams use end-to-end observability and proven patterns. In particular, embedding correlation ID
s in every log and trace, combined with Datadog
‘s metrics
/trace
/log
stack, lets engineers pinpoint exactly where a fare-update went wrong. The key is to instrument cache operations thoroughly (hits
, misses
, writes
, expirations
) and watch for anomalies in real telemetry such as cache hit rate
or TTL variance
.
Observability: Traces, Logs, and Correlation IDs
Every flight search or booking request should carry a unique transaction or correlation ID
across services. In airline data standards, for example, a Correlation ID
is a UUID
included by the seller and echoed by the airline to link related messages. In modern systems, that ID is logged by each microservice and also attached to traces. Datadog
recommends injecting trace/span IDs
and env
/service
/version
into structured logs so that logs and traces automatically correlate. With this in place, an engineer can query “show me all logs for request X” and see cache lookups, price calculations, rule-engine calls, etc. in one timeline. This end-to-end view is critical for spotting race conditions: for instance, two cache-write
spans with the same timestamp but different data hints at a write-write conflict. Teams should also set up Datadog
alerts on slow cache write latencies
or abnormal request paths. For example, if a cache refresh suddenly takes much longer than usual (as seen in traces), that can indicate contention or serialization issues.