Why existing approaches to root cause analysis fall short

Modern distributed architectures introduce staggering complexity when issues arise. Traditional monitoring tools detect problems, but cannot trace causality across interconnected infrastructure, network, and application layers. And rapid manual investigation is nearly impossible at cloud scale.  

Let’s examine why existing troubleshooting practices fall painfully short, focusing on a detailed incident response scenario. 

The nightmare of manual troubleshooting

Jasmine, a senior site reliability engineer at a travel website, wakes up to alerts blaring. KPI trackers show a dramatic increase in the rate of users leaving the site after starting a session. The CEO wants all hands investigating.

Step 1:
Jasmine starts checking the KPI dashboards and finds that customers in a specific region are experiencing a significant slowdown in searches.

Step 2:
Metrics dashboards spotlight heavy memory utilization across containers in the search caching services. Jasmine scales out pods and optimizes resource allocations, but issues spread to booking processing services.

Step 3:
As more teams jump in, they find that a recent Envoy sidecar injection changed label selectors, affecting the applicability of Network Policies and causing communication issues between the cluster and the Redis cluster used for caching historical search results. Developers must scramble to update the label selectors to accommodate the changes. 

This scenario displays the disorientation of Kubernetes container troubleshooting, as ephemeral instances rapidly change state across a complex orchestration fabric. Each failure clue leads responders down isolated rat holes. Engineers waste countless hours chasing ghosts.

There must be a better way – but standard tools also demonstrate limitations.

Most enterprises employ observability platforms to escape dependency on raw detective work. Solutions from vendors like Datadog, New Relic, and Dynatrace (or open source stacks like Prometheus/Grafana) ingest metrics, traces, and logs – supposedly making failures easier to unravel. 

But while helpful for monitoring known issues, these tools falter in tracing causality for complex, rapidly evolving architectures.

ChallengeImpact on ability to do root cause analysis
Data silos obscure connectionsLegacy solutions compartmentalize data into pillars. Metrics monitor utilization and performance for specific apps or infrastructure. Traces track request flows within services. Logs capture outputs.  

This siloing means no single source provides end-to-end visibility across layers when chasing failures. Engineers must manually comb through dozens of dashboards to spot correlations – a tall order during time-sensitive incidents.
Incomplete, inflexible topology limits contextTo infer causation accurately, observability platforms require an understanding of runtime topology – how thousands of discrete components interact together.

Traditional tools feature dynamic dependency mapping limited only to configuration or DNS resolution, leaving engineers to manually define dependencies across other components. This bespoke mapping then stretches inaccurately across dynamic infrastructure.
Alert noise burdens users and lacks cohesionBasic anomaly detection algorithms trigger alerts when utilization patterns deviate from baselines anywhere in an environment. But lacking topology context, these tools cannot distinguish between trivial anomalies versus critical service degradations.

Engineers thus get bombarded with thousands of low signal alerts daily across unrelated domains. It can be difficult to group together relevant alerts to identify a cohesive system problem.
Surface-level symptoms, not causesFinally, most observability platforms only spotlight symptoms, not underlying root causes. Traces might show where application latency emerges while metrics indicate overloaded servers. But the originating issue frequently lies elsewhere – impossible to isolate from siloed data.

Production issues increasingly involve root causes spanning infrastructure, network, and application or API layers. This makes manual debugging time-consuming and resource-intensive at scale. And it also presents challenges for traditional observability platforms, which are built for isolated system monitoring and lack higher-level context into user and business flows.