Bug Report: Slow Response Time In Price Tool

The Problem: A Laggy Experience

We've encountered a BUG-1763418345415 related to a slow_response in our price tool. This issue occurred on November 17, 2025, at 22:25:45 UTC, within session_1762886112219_hsfrkbje9. The user's query, "Ok show me price for alwinton," was met with a frustratingly slow response, eventually leading to a "Sorry, I encountered a connection error. Please try again." message. This kind of delay can significantly impact user experience, making it feel like the tool is unresponsive or broken. In the world of e-commerce and service applications, speed is paramount. Users expect instant feedback, and when that's not delivered, they're likely to abandon the task or seek alternatives. This report delves into the technical details captured during this incident to pinpoint the root cause and implement a swift solution.

Diving Deep: Debugging the Slowness

To understand why the slow_response occurred, we've meticulously analyzed the provided debug data. The environment where this bug manifested was a Safari browser on an iPhone, with a screen size of 399x675. While the user interaction logs show a sequence of clicks and input changes, the critical clues lie within the console logs and network requests.

Console Logs: A Glimpse into Errors

The console logs reveal some interesting, though not immediately critical, warnings. At 2:24:19 PM, there's a warn message indicating that the user attempted voice input on an unsupported browser, accompanied by a MediaRecorder failed to start: {"name":"NotSupportedError"} error. While this points to a potential feature limitation, it doesn't directly explain the core slow_response issue. More pertinent are the subsequent warn messages related to network fetch attempts. Between 2:25:12 PM and 2:25:21 PM, we see multiple warnings about [Fetch] Attempt 1 failed: Request timed out after 30 seconds. The server and [Fetch] Attempt 2 failed: Load failed. These timeouts and load failures are strong indicators that the backend service was not responding within the expected timeframe, leading to the user-facing error.

Network Requests: The Culprit Revealed

The network request logs provide the most compelling evidence. We can see a series of POST requests to europe-west2-sofa-project-v3-new.cloudfunctions.net/sofa-price-calculator-v3/chat. The first request, initiated around 2:23:50 PM, completed successfully with a duration of 192ms. However, the subsequent requests paint a different picture. The second request, which seems to be a re-attempt or part of the process, took a staggering 3502ms to complete, with a status code of 200 OK. While it eventually succeeded, this duration is significantly longer than expected for a typical API call. The real problem emerges with the third and fourth requests. The third request, at 2:25:12 PM, failed with an error status after a massive 30008ms (over 30 seconds), explicitly mentioning Fetch is aborted. This is the 30-second timeout seen in the console logs. The fourth request at 2:25:21 PM also failed with an error status, a Load failed, after 7556ms. These prolonged request times and outright failures directly correlate with the user experiencing a slow_response and ultimately receiving an error message. The backend service, for some reason, was struggling to process the request and return a timely response, leading to these network failures.

User Interactions and DOM State

The user interaction logs show a typical flow: searching for a product, initiating a chat, and then attempting to get pricing information. The voice input attempt at 2:24:19 PM, although failing, seems to have occurred after the initial network issues had already begun. The DOM state analysis indicates that elements like .message-feedback and .pricing-card were present and visible, suggesting the UI itself was mostly functional. However, crucial elements like .discovery-buttons-container and .pricing-card-price-amount were not found, which might indicate that the system was unable to populate the results correctly due to the underlying network problem.

Performance and Environment Details

Performance metrics show a load time of -1763418208746ms, which appears to be an anomaly or a data logging error. However, DOM Ready at 332ms and Response Time at 131ms for the initial successful request suggest that the client-side setup is generally quick. The browser and environment details confirm it was a mobile Safari instance. Notably, Service Workers are not supported in this environment, which could be a factor in how requests are handled, though it's unlikely to be the primary cause of the backend slow_response.

Conclusion and Next Steps

The primary cause of the slow_response bug (BUG-1763418345415) appears to be the backend service's inability to respond within acceptable time limits. The network requests show timeouts and load failures stemming from excessive processing time. While the client-side might have minor issues (like voice input on unsupported browsers), the core problem lies in the server-side performance of the sofa-price-calculator-v3 cloud function.

To address this, we recommend the following actions:

1. Backend Performance Optimization: Investigate the sofa-price-calculator-v3 cloud function for bottlenecks. This could involve optimizing database queries, improving algorithm efficiency, or scaling resources.
2. API Timeout Configuration: Review and potentially adjust API gateway or load balancer timeouts to prevent abrupt failures, though the goal should be to fix the underlying slowness rather than just extending timeouts.
3. Error Handling and User Feedback: Enhance the error handling to provide more specific feedback to the user when timeouts occur, and implement more robust retry mechanisms.

By focusing on the backend performance, we can ensure a snappier and more reliable experience for our users when they are looking for their perfect sofa.

For further insights into API performance and debugging, you can refer to resources on Google Cloud Functions documentation or explore general web performance optimization techniques on web.dev.