GitHub Issue: Performance Benchmark For Asset Queries

by Marco 54 views

Hey guys! Let's dive into this GitHub issue template designed to tackle a crucial aspect of our project: performance. We're focusing on implementing a performance benchmark for asset queries. This is super important because it helps us validate and document our API latency, ensuring everything runs smoothly and efficiently. Think of it as giving our system a health check-up!

1. Issue Type

  • [x] Test
  • [ ] Feature Request
  • [ ] Enhancement
  • [ ] Documentation
  • [ ] Security
  • [ ] Compliance
  • [ ] Other: Please specify

2. Summary

Our main goal here is to implement a performance benchmark for asset queries. This means we need to set up a system where we can measure how quickly our API responds to requests for assets. This benchmark will help us validate and document the latency, ensuring we meet our performance goals. Essentially, we want to make sure that when someone asks for an asset, they get it fast. This is crucial for a great user experience and the overall health of our application.

To make this happen, we’ll be creating scripts and running tests to simulate real-world scenarios. We’ll then analyze the results and compare them against our performance requirements. This process will not only help us identify potential bottlenecks but also provide a clear record of our API’s performance over time. We’ll be looking at metrics like response time, throughput, and error rates to get a comprehensive understanding of our system’s capabilities. The idea is to proactively address any performance issues before they impact our users.

This benchmark isn't just a one-time thing; it's something we'll want to run regularly to monitor performance trends and catch any regressions. By having this in place, we can ensure that our application remains fast and responsive as we continue to add features and scale our infrastructure. Plus, having solid documentation of our API’s performance is incredibly valuable for troubleshooting and planning future enhancements. So, let’s get this done and keep our asset queries running like a well-oiled machine!

3. Context & Impact

  • Related files/modules: tests/performance/api-performance.test.ts
  • Environment: Node.js, Linux/Ubuntu
  • Priority: Medium
  • Blast Radius: Asset query performance and related test coverage
  • Deadline/Target Release: 2025-08-28

Let's break down the context and impact of this task. First off, the related files/modules point us to tests/performance/api-performance.test.ts. This is where the magic will happen – the script where we'll define and run our performance tests. Knowing this upfront helps us focus our efforts and keeps everyone on the same page. Our environment is specified as Node.js running on Linux/Ubuntu, which gives us a clear understanding of the technical landscape. This ensures that our tests are relevant and reflective of the actual production environment.

The priority is set to Medium, indicating that while this is important, it's not an immediate fire drill. We have some time to plan and execute, but it's still a crucial task. The Blast Radius is Asset query performance and related test coverage. This means that the impact of this task will primarily be on how quickly and efficiently we can query assets, as well as the robustness of our performance testing framework. If we do this right, we'll have a system that's not only fast but also well-monitored. Conversely, if we drop the ball, we risk performance bottlenecks and a lack of visibility into our system's behavior.

Our deadline/target release is 2025-08-28, giving us a specific timeframe to work within. This helps us plan our work, allocate resources, and track progress. By setting a deadline, we create a sense of accountability and ensure that this task doesn't get lost in the shuffle. Ultimately, understanding the context and impact of this task is key to its successful execution. It helps us prioritize, make informed decisions, and ensure that our efforts are aligned with the overall goals of the project. So, let’s keep these factors in mind as we move forward and nail this performance benchmark!

4. Steps to Reproduce / Implementation Plan

  1. Create benchmark script for asset queries in api-performance.test.ts.
  2. Run and document results, compare to NFRs.
  3. Ensure latency meets target (<500ms p95).

Alright, let's get into the nitty-gritty of our implementation plan. Our first step is to create a benchmark script specifically for asset queries. This script will live in api-performance.test.ts, as mentioned earlier. The idea here is to simulate various scenarios and loads to see how our system handles real-world conditions. We'll need to use tools and techniques that allow us to measure response times, throughput, and error rates accurately. This might involve writing code to generate synthetic requests, setting up load testing frameworks, or even using performance monitoring tools.

Next up, we're going to run and document the results. This isn't just about running the script once; it's about running it multiple times, under different conditions, and capturing all the data. We'll need to record things like average response time, maximum response time, number of requests processed, and any errors encountered. Once we have this data, we need to document it in a clear and understandable way. This could involve creating graphs, charts, or even just writing a detailed report. The key is to make the information accessible and actionable. We also need to compare these results to our Non-Functional Requirements (NFRs). NFRs are the performance targets we've set for our system. By comparing our benchmark results to these targets, we can see if we're on track or if we need to make some adjustments.

Finally, we need to ensure that our latency meets the target of less than 500ms at the 95th percentile (p95). This means that 95% of our requests should be processed in under 500 milliseconds. This is a critical performance metric, and it's our primary goal for this task. If our latency is higher than this, we'll need to investigate and identify the bottlenecks. This could involve optimizing our code, upgrading our infrastructure, or even tweaking our database queries. The key is to systematically address any performance issues until we meet our target. By following these steps, we'll be well on our way to having a robust and performant asset query system.

5. Screenshots / Evidence

Attach relevant screenshots, logs, diagrams, or links.

6. Acceptance Criteria

  • Benchmark script runs and documents results.
  • Latency meets NFR (<500ms p95).
  • No regression in asset query performance.

Okay, let's talk about the acceptance criteria for this task. These are the specific conditions that need to be met for us to say, "Yep, we nailed it!" First off, our benchmark script needs to run successfully and document its results. This means we should be able to execute the script without errors and capture all the relevant performance metrics. The documentation part is crucial too. We need to have a clear and understandable record of the test results, so we can analyze them and track progress over time. This documentation should include things like average response time, peak response time, error rates, and any other relevant data points.

Next up, and this is a big one, our latency needs to meet the NFR (Non-Functional Requirement) of less than 500ms at the 95th percentile (p95). As we discussed earlier, this means that 95% of our asset query requests should be processed in under 500 milliseconds. This is a critical performance target, and it's essential for a smooth user experience. If we can't meet this target, we need to dig deeper and figure out why. It could be a code issue, an infrastructure problem, or something else entirely. Whatever the cause, we need to address it to ensure our system is performing optimally.

Finally, we need to ensure that there's no regression in asset query performance. This means that our changes shouldn't make things worse than they were before. We need to compare our performance metrics before and after implementing the benchmark script to make sure we haven't introduced any new bottlenecks or issues. This is where having solid documentation and historical data becomes really valuable. If we see any regressions, we need to investigate and fix them before we consider the task complete. By meeting these acceptance criteria, we can be confident that we've not only implemented a performance benchmark but also ensured that our asset query system is running smoothly and efficiently. Let’s make sure we hit these targets, guys!

Estimated Timeline

  • Estimated Start Date: 2025-08-21
  • Estimated End Date: 2025-08-28

Project Metadata

  • Related Project/Milestone: MaintAInPro Performance
  • Priority: Medium
  • Assignees: Copilot
  • Dependencies: None
  • Labels: type:test, size:S, parallelizable, no-conflict

7. Additional Notes / References

  • NFRs
  • [Performance Report](performance report)

8. Checklist

  • [x] Issue reviewed for sensitive data
  • [x] Impact/risk assessed
  • [x] Linked to relevant compliance/privacy requirements
  • [x] Stakeholders notified
  • [x] CI gates considered
  • [x] Runbook updated (if needed)

Please assign appropriate labels and reviewers.

Assigned to Copilot: @github-copilot[bot] (automatic assignment not supported by GitHub API for bots)