Tell me about a time you worked with limited information

We're interested in understanding how you handle ambiguity and uncertainty in real-world engineering projects. Tell me about a time when you had to work on a project or task where you were given limited information or context upfront. What was the situation, what actions did you take to gather necessary information or make progress despite the lack of clarity, and what was the outcome?

Okay, sure. Let me tell you about a time I worked with limited information.

Background I was working as a Senior Software Engineer at Google, focusing on improving the performance of a core internal service that handled a large volume of requests. This service was critical for many other teams, so any performance degradation had a ripple effect across the company.

Situation One day, we received an alert that the service's latency had spiked significantly. The monitoring dashboards showed a clear increase in response times, but the available logs and metrics provided very little insight into the root cause. The error rate was normal, CPU and memory usage were within acceptable limits, and there were no recent code deployments that could explain the sudden change. The initial information was very limited; all we knew was that performance had degraded, but we had no idea why.

Task My task, along with a few other engineers, was to diagnose and resolve the performance issue as quickly as possible, minimizing the impact on dependent services. Given the limited information, we needed to approach the problem systematically and creatively.

Action Here's what we did, following a process of information gathering and hypothesis testing:

1. Expanded Logging and Monitoring: We immediately added more granular logging and monitoring to the service. This included logging request parameters, timing individual function calls, and tracking resource usage at a more detailed level. We deployed these changes with a gradual rollout to minimize any further impact.

2. Hypothesis Generation and Testing: Based on our understanding of the system, we generated several potential hypotheses. These included:

   • A sudden increase in request volume or complexity.
   • A change in the behavior of a downstream dependency.
   • A resource leak or contention issue.
   • A code path that was performing poorly under specific conditions.

   We then designed tests to validate or refute each hypothesis. For example, we analyzed request patterns to look for changes in volume or complexity. We monitored the performance of downstream dependencies to see if they were contributing to the latency. We used profiling tools to identify any resource leaks or contention issues within our service.

3. Collaboration and Communication: Because of the high impact, we kept stakeholders informed. We regularly updated the team and stakeholders on our progress, sharing our findings and proposed solutions. This was crucial for aligning expectations and ensuring that everyone was aware of the situation.

4. Root Cause Analysis: After a period of intense data gathering we discovered that a change was made to a downstream service that had increased the response size of its payloads. Our service was not equipped to deal with larger payloads at that scale, which created a bottleneck.

Result By following this systematic approach, we were able to identify the root cause of the performance issue within a few hours. We then implemented a fix to optimize the way our service handled the larger payloads. After deploying the fix, the service's latency returned to normal, and the impact on dependent services was resolved.

Lessons Learned This experience taught me the importance of:

• Systematic Problem Solving: When faced with limited information, it's crucial to have a structured approach to problem-solving.
• Collaboration and Communication: Working with other engineers and keeping stakeholders informed is essential for resolving complex issues effectively.
• Continuous Monitoring and Logging: Having robust monitoring and logging in place is critical for quickly diagnosing and resolving performance issues.
• Flexibility and Adaptability: Being able to adjust your approach based on new information is vital when dealing with uncertainty.

I think the most valuable takeaway from that situation was the importance of not jumping to conclusions and, instead, methodically gathering data to inform our decision-making process.