Load Testing & Performance Benchmarking (JMeter & k6) · 课时

解读 k6 指标

了解 k6 的详细指标和自定义标签，以及如何将其可视化以获取洞察

第 3 / 4 课12 个步骤

解读 k6 指标 是 CoddyKit 上的免费 Load Testing & Performance Benchmarking (JMeter & k6) 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Load Testing & Performance Benchmarking (JMeter & k6) 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Load Testing & Performance Benchmarking (JMeter & k6) 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

What are k6 Metrics?

When you run a k6 performance test, it generates various metrics. These are numerical values that describe your system's behavior under load.

Understanding these metrics is crucial for identifying performance bottlenecks, verifying service level agreements (SLAs), and ensuring your application can handle expected traffic.

Key Metric: Request Duration

One of the most important built-in k6 metrics is http_req_duration. This measures the total time it takes for an HTTP request to complete.

It starts when the request is sent.
It ends when the full response is received.
It directly reflects the response time users experience.

Key Metrics: VUs & Iterations

Other fundamental metrics help you understand the load:

vus (Virtual Users): Shows the number of virtual users currently active during the test.
iterations: Counts how many times your test script's default function has been executed.

These metrics help you see if your test is applying the intended load.

Key Metrics: Data Transfer

Understanding network usage is also vital. k6 provides metrics for data transfer:

data_sent: The total number of bytes sent by k6 during the test.
data_received: The total number of bytes received by k6.

These metrics help analyze bandwidth consumption and identify potentially large responses.

Understanding Metric Aggregations

k6 doesn't just give raw values; it aggregates them. Common aggregations include:

avg (average)
min (minimum)
max (maximum)
p(XX) (percentile, e.g., p(90), p(95))

Percentiles are especially important as they represent the experience of a certain percentage of your users, ignoring extreme outliers.

Beyond Built-in: Custom Metrics

While k6's built-in metrics are powerful, you might need to track specific application-level events or durations. This is where custom metrics come in.

You can create your own metrics to gain deeper insights into your system's unique behavior. Common types are Counter, Gauge, Rate, and Trend.

Adding a Custom Metric

Here's how to add a Trend custom metric to measure the duration of a specific operation within your script. This helps track performance beyond just HTTP requests.

import { Trend } from 'k6/metrics';
import http from 'k6/http';
import { sleep } from 'k6';

const myCustomTrend = new Trend('custom_operation_duration');

export default function () {
  const startTime = Date.now();
  http.get('https://test.k6.io/contacts.php');
  const endTime = Date.now();
  myCustomTrend.add(endTime - startTime); // Add duration in milliseconds

  sleep(1);
}

Categorizing with k6 Tags

Tags are key-value pairs that provide metadata for your metrics. They allow you to categorize and filter your results for more granular analysis.

k6 automatically adds tags like url and status. You can also add custom tags to differentiate metrics for different parts of your application or specific user flows.

Applying Custom Tags

Custom tags are very useful for breaking down metrics. For example, you can tag requests based on the page or API endpoint they hit, allowing you to analyze performance per feature.

import http from 'k6/http';
import { sleep } from 'k6';

export default function () {
  // Tagging requests to different pages
  http.get('https://test.k6.io/', {
    tags: { page_name: 'homepage' },
  });

  http.get('https://test.k6.io/news.php', {
    tags: { page_name: 'news_page' },
  });

  sleep(1);
}

Visualizing Your k6 Metrics

While k6's console output is useful, visualizing your metrics makes analysis much easier. Tools like Grafana (often paired with InfluxDB or Prometheus) are excellent for this.

k6 can export results directly to these systems, allowing you to create dashboards that show trends, spikes, and performance regressions over time.

Metric Interpretation Challenge

Consider the k6 output snippet below:

http_req_duration{status=200, scenario=default, my_tag=checkout}......: avg=350ms min=100ms med=300ms p(90)=550ms p(95)=600ms max=1200ms

What does p(95)=600ms primarily tell you about the checkout requests?

在交互式课程中回答

Recap: Interpreting k6 Metrics

You've learned how to interpret k6's powerful metrics!

Built-in metrics like http_req_duration, vus, and data_sent/received give foundational insights.
Aggregations, especially percentiles, provide a realistic view of user experience.
Custom metrics and tags allow for deep, granular analysis of specific application behaviors.
Visualization tools are essential for making sense of complex test results.

Mastering metric interpretation is key to successful performance testing!

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常见问题解答

「解读 k6 指标」课时是免费的吗？

是的 — 「解读 k6 指标」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Load Testing & Performance Benchmarking (JMeter & k6) 课程的其余内容，请升级到 CoddyKit PRO。 Load Testing & Performance Benchmarking (JMeter & k6) 课程共包含 4 节课。

「解读 k6 指标」这节课中我会学到什么？

了解 k6 的详细指标和自定义标签，以及如何将其可视化以获取洞察你通过在浏览器中直接运行的动手代码来练习 Load Testing & Performance Benchmarking (JMeter & k6)，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Load Testing & Performance Benchmarking (JMeter & k6) 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Load Testing & Performance Benchmarking (JMeter & k6) 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「解读 k6 指标」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 Load Testing & Performance Benchmarking (JMeter & k6) 课中编写并运行代码吗？

能。每节 Load Testing & Performance Benchmarking (JMeter & k6) 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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