การตีความตัวชี้วัดของ k6

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Consider the k6 output snippet below:

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

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!