# Metrics

Inspector offers several ways to view and aggregate data: Execution time, Memory usage, Performance distribution. 

What do they do, and how should you use them? 

### Data aggregations

Data aggregation is when multiple values of a specific parameter are grouped together to give a single summary value. This is especially useful when you want to extract simple but meaningful information from your application data that consists of thousands, or even millions of measurements.

### Execution time & Memory Usage

These metrics are calculated using the 75th percentile. It is the value where 75% of all measurements are under it, and 25% of measurements are over it. It is [the percentile that Google research team recommends](https://web.dev/defining-core-web-vitals-thresholds/#choice-of-percentile) using when monitoring Web Vitals.

![](https://2873457055-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ51SrLj6qvIRejC1XD%2Fuploads%2F280UZOZsF4ylRcEmSJL0%2Fc5a3c0a-75th.png?alt=media\&token=53773de7-6cc4-4fc0-a969-92fcce2b1870)

The chart above shows the page load histogram with all of the measurements below the 75th percentile coloured in blue.

#### Why use the 75th percentile

The 75th percentile is a good balance of representing the vast majority of measurements, and not being impacted by outliers. While not as stable as the median (50th pecentile), the 75th percentile is a good choice for seeing medium - to long term trends. We also think the 75th percentile is the best value to use when setting performance budgets.

### Performance Distribution

You can make a deeper investigation on specific transactions using the frequency distribution histogram.

That's a fancy way of describing a bar chart where the X (horizontal) axis shows the value of a measurement and the Y (vertical) axis shows the number of measurements that had that value. 

Take this chart for example:

![Performance Distribution Chart](https://2873457055-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-LQ51SrLj6qvIRejC1XD%2Fuploads%2Fq2Jkei2CsqIu6FoC3KNP%2Fperformance-distribution.png?alt=media\&token=4cb87f45-63a8-45bc-a305-99e6843b3b53)

The chart above shows clusters of execution times on the X axis. The height of the bars represents how many measurements had execution times that fell within each cluster. We can see that the majority of execution times were between 0 and 407 milliseconds, with a smaller number of execution times on either side.

Notice how there are many clusters to the right of the chart, but they all have a small number of measurements. This is called the "long tail" and represents the times the transaction had the worst performances.

{% hint style="info" %}
This particular chart is "**clickable**". So you can view and compare two samples of the transaction at different performance. One from a good performance cluster (left side of the chart), versus one from bad performance (right side of the chart).
{% endhint %}