# Investigating CPU Usage With Profiles

> Step-by-step walkthrough for finding the functions behind a CPU-heavy service with Last9 Profiles, plus best practices.

Source: https://last9.io/docs/profiles/investigating-cpu-usage/

This walkthrough uses the [services view](/docs/profiles/services/), [flamegraph](/docs/profiles/flamegraph/), and [top functions](/docs/profiles/top-functions/) together to find what is driving CPU usage in a service.

## Walkthrough

1. Open **Profiles**, select the environment, and set the time range to cover the period of interest.

2. Sort by **CPU share** and pick the service at the top, or the one whose share is higher than you expect.

3. In the flamegraph, find the widest application frames below the HTTP or RPC server frames. These are the handlers that use the most CPU.

4. Click a handler to zoom in and follow its widest children down to where the width narrows. The frames where width drops sharply, or where **Self** is high, are where the time is spent.

5. Switch to **Top functions** and sort by **Self samples** to confirm the hottest functions across all call paths.

6. Turn on **Hide runtime** to check how much of the cost is in your code versus the runtime.

## Example

![Flamegraph for a service](../../../../assets/content/docs/profiles/profiles-flamegraph.png)

In this service, `getAlertsDetailsHandler` accounts for most of the CPU. Zooming in shows the time split between `encoding/json.Unmarshal` and database row reads. **Top functions** shows `runtime.scanobject` and `runtime.mallocgc` near the top.

Together these suggest two fixes: decode less JSON per request, and reduce allocations to lower garbage collection cost.

## Best Practices

- **Profile a symbolizable workload first.** Validate your setup with a Go, Java, or similar service that has symbols, before profiling stripped or minimal images.
- **Pick a time range that matches the problem.** Use a short range around a spike to isolate it, and a longer range to find steady-state hot spots.
- **Compare Self and Total.** Optimize frames with high **Self**; use high **Total** frames to understand which entry points lead there.
- **Check the runtime before tuning code.** A large share in garbage collection or allocation frames usually means reducing allocations will help more than optimizing logic.
- **Set environment and cluster attributes.** Consistent `deployment.environment.name` and `k8s.cluster.name` values keep filters and the environment selector useful.

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## Troubleshooting

See [Troubleshooting in the Profiles overview](/docs/profiles/#troubleshooting) for missing data and setup issues.

Please get in touch with us on [Discord](https://discord.com/invite/Q3p2EEucx9) or [Email](mailto:cs@last9.io) if you have any questions.