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Recommendation model scoring loop running O(n²) cosine similarity over full product catalog (780s duration). Missing LRU cache causes redundant ML inference on repeated product IDs, saturating the service at peak request volume.

Memory leak or unbounded cache growth in cartservice-2 caused heap utilization to spike from 42% to 88% baseline, likely due to accumulated shopping cart objects not being garbage collected or evicted from in-memory data structures. The 540-second duration suggests a gradual memory accumulation pattern rather than a sudden spike, pointing to a resource exhaustion issue in cart session management or product cache.

The shippingservice pod experienced resource exhaustion with CPU utilization spiking to 95% (3x baseline), indicating either a memory leak, runaway process, or increased load without proper resource limits. The pod failure was triggered by Kubernetes eviction due to resource constraints, causing the container runtime to terminate the pod after 540 seconds of sustained high resource consumption.

The Redis pod (redis-cart-0) experienced a controlled fault injection that triggered pod eviction, causing the container to be terminated and restarted. The 95% CPU/Memory spike indicates the pod was resource-constrained during the failure window, likely due to increased load attempting to reconnect or recover state after the pod crash.

The currencyservice pod experienced a failure injection that caused resource exhaustion, with CPU utilization spiking from baseline 30% to 95%, likely due to inefficient request handling or a memory leak under load conditions. The 840-second duration suggests the service instance became unresponsive and required pod restart or eviction to recover, causing service degradation.

A CPU-intensive workload or infinite loop was triggered in the paymentservice process, causing CPU utilization to spike from baseline 35% to 91%. This is consistent with fault injection testing that artificially increased CPU load on the paymentservice-1 instance for 540 seconds.

Stripe webhook retry handler spawning unbounded goroutines on payment validation timeout. Each timed-out transaction triggers 3 retries with no backoff, causing goroutine pile-up at peak transaction volume (540s duration, 91.0% CPU).

A memory leak or unbounded allocation occurred on the frontend-1 instance, causing memory usage to spike from 42% to 88% baseline over the 600-second window. This was triggered by fault injection that likely exhausted available heap space, forcing increased garbage collection pressure and eventual performance degradation.

A memory leak or uncontrolled memory allocation occurred on the frontend-1 instance, causing heap memory usage to spike from the baseline 42% to 88% over the 600-second window. This was likely triggered by fault injection that either prevented proper garbage collection, introduced a circular reference pattern, or caused unbounded cache growth without eviction.

Network packet loss was injected on the frontend-1 instance, causing TCP retransmissions and increased latency as the application repeatedly resends lost packets. The elevated CPU usage (78% vs 10% baseline) results from increased kernel network stack processing and application-level retry logic consuming additional resources during the 600-second fault injection period.

Network packet loss was injected on the frontend-1 instance, causing TCP retransmissions and increased latency as the application waits for acknowledgments and resends lost packets. The elevated CPU (78% vs 10% baseline) resulted from increased kernel network stack processing overhead and application-level retry logic during packet loss conditions.

No root cause recorded for this pipeline run.

Frontend Envoy sidecar missing retry budget configuration. Service mesh injects 500ms delay for 10% of requests per chaos injection; without circuit breaker, cascading timeouts propagate to all upstream services.

Network latency was injected on the frontend-1 pod, causing outbound request delays that cascaded into increased CPU utilization (78%) as the service attempted retries and queued pending connections. The delay injection remained active for 540 seconds, during which the frontend service experienced degraded performance due to timeouts on downstream service calls and connection pool exhaustion.

Network delay was artificially injected on checkoutservice-0 pod, causing request timeouts and connection backlog which led to CPU utilization spike from 10% baseline to 78% as the service attempted retries and queued incoming requests. The 720-second delay injection overwhelmed the service's thread pool and connection buffers, creating a cascading resource exhaustion.