The anatomy of a production incident – resolution and recovery

Imagine you’re part of the on-call team for a bustling e-commerce platform. In the middle of the night, your phone rings with notifications about a significant drop in sales. Panic ensues as the team realizes that the checkout page, the lifeblood of the platform, is inaccessible. The race against time begins.

  • SRE Team: The first line of defense in this incident is the Site Reliability Engineering team. They are responsible for maintaining system reliability and performance. They also analyze monitoring tools, functioning as the eyes and ears of the operation. Their initial response is to gather information about the incident and start triaging.
  • DevOps Team: Working closely with SREs, the DevOps team focuses on the deployment pipeline, infrastructure provisioning, and automating operational tasks. They also configure and maintain monitoring tools. Their role in this incident is to check if any recent deployments or changes have impacted the checkout page.
  • Development Team: Developers who maintain the checkout page and the services it relies on are crucial. They might need to dive deep into the application code to uncover potential issues.

As the teams assemble, they start gathering data from various sources:

  • Logs: DevOps and SRE teams start poring over logs from the checkout page and the dependent services. They look for error messages, stack traces, and any unusual behavior.
  • Monitoring dashboards: The monitoring team checks their dashboards, which show that the web pods are indeed under heavy load. However, it’s not clear why this is happening.
  • Infrastructure metrics: The DevOps team examines infrastructure metrics to ensure that resource limitations or misconfigurations aren’t causing the issue.
  • Codebase Review: DevOps and SRE teams get eyes on the codebase to look for recent changes that may have inadvertently caused the problem. The development team is brought in to assist with the error analysis.
The rabbit hole

Initial investigations reveal that the web pods are indeed overloaded, but this is merely the tip of the iceberg. The root cause is far from obvious. Despite traditional monitoring tools pointing to the web tier, the real issue lies elsewhere.

Upon closer inspection, it becomes apparent that the cache service, which the web tier depends on, is in trouble. But here’s the twist: the cache service pods are spread across different nodes, and they are all showing signs of degradation. This puts extra strain on the web tier, making it appear as if the issue is confined to the web pods.

The incident response teams now need to piece together the puzzle.

Ultimately the root cause comes into focus: a degradation of the cache service. This in turn is causing full calls to the database, which is upping the workload across all nodes. 

With the root cause identified, the incident response teams swing into action:

  • Cache Service Fix: The networking team quickly resolves the network misconfiguration affecting the cache service pods.
  • Load Balancer Tuning: The load balancer configuration is adjusted to distribute traffic more evenly across nodes.
  • Web Tier Optimization: The development team optimizes the web tier to handle increased load more gracefully, reducing the dependency on the cache service.
  • Communication: Throughout the incident, clear and continuous communication between teams is maintained to ensure everyone is on the same page.

Lessons Learned

This incident highlights the complexity of modern production environments. It underscores the importance of having a robust incident response process, cross-functional collaboration, and the right tools to uncover hidden issues.

It also reveals the need for an AIOps platform with automated service discovery and topology mapping. This would allow users to quickly trace degradation from web to cache tiers based on real-time dependency maps. Machine learning detection of anomalies like the cache hit drop could also speed up problem identification and resolution.

How Senser helps

While traditional monitoring tools are essential for detecting symptoms, they often fall short in identifying the root cause, especially when the issue spans multiple layers of infrastructure and services. In such cases, having an AIOps platform like Senser, which employs eBPF for lightweight data collection and uses machine learning to automatically identify root causes, can be a game-changer. Senser’s topology mapping capabilities would have quickly pinpointed the cache service degradation and saved valuable time.

Leveraging an AIOps solution like Senser to autocorrelate metrics across services enables companies to massively reduce the time to detect and remediate critical system issues. This would reduce downtime, improve customer experience, and let engineers get back to sleep sooner!

Learn more about how Senser helps SRE and DevOps teams go from production chaos to intelligence.