# Dec 5th, 2024: \[EN\] Keep track of your Steam Deck gaming with the Elastic Agent **URL:** \ **Category:** Advent Calendar\ **Created:** [December 5, 2024, 8:00am UTC](https://discuss.elastic.co/t/dec-5th-2024-en-keep-track-of-your-steam-deck-gaming-with-the-elastic-agent/371088 "2024-12-05T08:00:51Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![AndersonQ](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/andersonq/32/112214_2.png) [@AndersonQ](https://discuss.elastic.co/u/AndersonQ)\ **Post date:** [December 5, 2024, 8:00am UTC](https://discuss.elastic.co/t/dec-5th-2024-en-keep-track-of-your-steam-deck-gaming-with-the-elastic-agent/371088/1 "2024-12-05T08:00:51Z")
Esse artigo também está disponível em [Português](https://discuss.elastic.co/t/dez-21-pt-monitore-seus-jogos-no-steam-deck-com-o-elastic-agent/371224). Let's take Observability to a different spin and and use our favorite tools to monitor our gaming performance. Today we'll explore how to use the Elastic Agent to monitor a Steam Deck so we can see the games we play the most, how much resources they consume and how the GPU is performing. We'll cover: - Install a Elastic Agent - Set up integrations - Use the _Custom API_ integration to collect custom data - Build Kibana dashboards - Export and Import dashboards ## Process data The [System Integration](https://www.elastic.co/docs/current/integrations/system) gives us all the data we need about the running processes, their names, how much CPU and memory they're using. We'll use this to see how much resources the process are using as well as to see which games we play the most. ## GPU data In general Observability data does not include how the CPU or GPU cores are performing. While it's possible to [collect NVIDIA GPU](https://www.elastic.co/blog/how-to-monitor-nvidia-gpu-metrics-with-elastic-observability) data, the Steam Deck uses a custom AMD GPU and we want to get our hands dirty and do some work ourselves. Linux distros usually include [lm\_sensors](https://wiki.archlinux.org/title/Lm_sensors) and it's present on Steam OS giving us all the information we need. Running `sensors` on my Steam Deck I get the following: ```auto nvme-pci-0100 Adapter: PCI adapter Composite: +45.9°C (low = -273.1°C, high = +82.8°C) (crit = +84.8°C) Sensor 1: +45.9°C (low = -273.1°C, high = +65261.8°C) BAT1-acpi-0 Adapter: ACPI interface in0: 8.40 V curr1: 1.74 A amdgpu-pci-0400 Adapter: PCI adapter vddgfx: 650.00 mV vddnb: 655.00 mV edge: +44.0°C slowPPT: 7.12 W (avg = 11.10 W, cap = 15.00 W) fastPPT: 11.10 W (cap = 15.00 W) steamdeck_hwmon-isa-0000 Adapter: ISA adapter PD Contract Voltage: 5.00 V System Fan: 0 RPM Battery Temp: +25.0°C PD Contract Current: 1000.00 mA acpitz-acpi-0 Adapter: ACPI interface temp1: +54.0°C (crit = +105.0°C) ``` All we need is there, but not quite in the best format to ingest this data, let's get a JSON then with `sensors -j`: ```auto { "nvme-pci-0100":{ "Adapter": "PCI adapter", "Composite":{ "temp1_input": 31.850, "temp1_max": 82.850, "temp1_min": -273.150, "temp1_crit": 84.850, "temp1_alarm": 0.000 }, "Sensor 1":{ "temp2_input": 31.850, "temp2_max": 65261.850, "temp2_min": -273.150 } }, "BAT1-acpi-0":{ "Adapter": "ACPI interface", "in0":{ "in0_input": 8.656 }, "curr1":{ "curr1_input": 0.159 } }, "amdgpu-pci-0400":{ "Adapter": "PCI adapter", "vddgfx":{ "in0_input": 0.650 }, "vddnb":{ "in1_input": 0.655 }, "edge":{ "temp1_input": 41.000 }, "slowPPT":{ "power1_average": 2.072, "power1_input": 2.040, "power1_cap": 15.000 }, "fastPPT":{ "power2_average": 2.072, "power2_cap": 15.000 } }, "steamdeck_hwmon-isa-0000":{ "Adapter": "ISA adapter", "PD Contract Voltage":{ "in0_input": 5.000 }, "System Fan":{ "fan1_input": 1522.000, "fan1_fault": 0.000 }, "Battery Temp":{ "temp1_input": 25.000 }, "PD Contract Current":{ "curr1_input": 1.000 } }, "acpitz-acpi-0":{ "Adapter": "ACPI interface", "temp1":{ "temp1_input": 42.000, "temp1_crit": 105.000 } } } ``` We can't just have the Elastic Agent running arbitrary commands and parsing the output, therefore, in order to make this data available we'll need a wee HTTP server which can fetch this data. I've built one in Go, go ahead to [GitHub - AndersonQ/steamdeck-sensors-api](https://github.com/AndersonQ/steamdeck-sensors-api) and install it or build your own version of it. With this server we can configure the Elastic Agent to collect data from the API we just created. To make sure it's always up and running, `steamdeck-sensors-api` can install and register itself as a `systemd` service. Now we have all the data we need, let's collect it. ## Collecting data Go ahead and [install an Elastic Agent](https://www.elastic.co/guide/en/fleet/current/install-fleet-managed-elastic-agent.html). By default the `System` integration is added to any Elastic Agent policy. Go ahead create a policy, I'll call mine `Steam Deck`, and add an Elastic Agent to your Steam Deck. Check the [FAQ](https://help.steampowered.com/en/faqs/view/671A-4453-E8D2-323C) to see how to disable the read only mode with `sudo steamos-readonly disable`. Btw, did you forget your sudo password? You can easily reset it, see how [here](https://github.com/AndersonQ/steamdeck-sensors-api). Now we have an agent and a policy, we can add and configure the [Custom API integration](https://www.elastic.co/docs/current/integrations/httpjson) Go to Management \> Integrations and search for "custom api": ![integration-add](https://us1.discourse-cdn.com/elastic/original/3X/6/8/68eca52a054a36572dfcb5ce1bad20157393a35a.png) Add it to your Steam Deck policy and let's configure it: - "Dataset name" to `system.gpu` - "Request URL": `http://localhost:4242/gpu` - "Request Interval": `30s` - you can choose another interval if you prefer. To start playing with it and testing visualisations I used `1s` to see new data in real time. - "Request HTTP Method": `GET` - "Processors": ```yaml - decode_json_fields: fields: ["message"] target: "system.gpu" overwrite_keys: true add_error_key: true expand_keys: true ``` ![integration-configure-1](https://us1.discourse-cdn.com/elastic/original/3X/9/7/97038eef0f3b9c0b4fd6fd198b3d74b2022648a3.png) ![integration-configure-2](https://us1.discourse-cdn.com/elastic/original/3X/d/5/d5865c2a50ff9505ec6e2829d3c3d733878e17ed.png) The processor is crucial for formatting the final event correctly. Without it, the `steamdeck-sensors-api` response would be a string in the `message` field. Now save and add the integration to your Steam Deck policy. If you already installed the agent, it'll automatically deploy the newly updated policy, if you haven't installed it yet, go and install the agent. ## Checking the data Go to Discover, select the `metrics-*` data view. Filter for `event.dataset :"system.process"`. Then, add `process.name`, `system.process.cpu.total.pct` and `system.process.memory.size`. Open some game on your Steam Deck and try to find it among all the process metrics. 🙂 ![discover-metrics](https://us1.discourse-cdn.com/elastic/original/3X/6/f/6f4818f7bbc5b7d5f7bba1bbfe3114a72999438e.png) For the GPU data, go to the `logs-*` data view and filter for `event.dataset :"system.gpu"` ![discover-gpu-data](https://us1.discourse-cdn.com/elastic/original/3X/1/8/188eb455e6c70644850329eca458463ecfee1a28.png) Now we just need to create a dashboard. ## Steam Deck dashboard Here is the dashboard I created: ![dashboard](https://us1.discourse-cdn.com/elastic/original/3X/3/f/3f785866349642217d5d75fefa9968bc376f131d.png) `Astro-Win64-Shi` is [Astroneer](https://store.steampowered.com/app/361420/ASTRONEER/), great game, I totally recommend it. You can import this dashboard, it's on my [github repo](https://github.com/AndersonQ/steamdeck-sensors-api/blob/main/steam-deck-dashboard.ndjson). I'll explain how to import it in a bit. But first let's see how to create a dashboard and add it a visualisation to it. Go to `Dashboards \> Create dashboard - In the query bar, add a filter to display data only from your Steam Deck. Filter by `host.hostname : "steamdeck"`, adjusting the `hostname` if you've changed it on your device." - Click on `Create visialization` - Select the `metrics-*` dataview - search for `process.cpu` - drag and drop `system.process.cpu.total.pct` to the drop area it'll create a chart like this: ![lens-1](https://us1.discourse-cdn.com/elastic/original/3X/e/b/eb0a63ea6e167dae0185553716ec771817532520.png) - go to "Breakdown" and choose `process.name`, and set "Number of values" to 15, then close the panel - instead of "Bar", choose "Treemap" for the visualization it should be like that: ![lens-2](https://us1.discourse-cdn.com/elastic/original/3X/0/d/0d3d162bd0013815b463f272de981ad894c60b3a.png) - go ahead and click "Save and Return" Now lets create one for the most played games. - click on Create Visualization" - on the search bar add the filter `process.working_directory.text :"/home/deck/.local/share/Steam/steamapps/common/*"`. This query on `process.working_directory` will limit the process metrics to only the process which working directory is `/home/deck/.local/share/Steam/steamapps/common/*`. This should be the path for all your installed games. Depending on your setup, if you have an SD card, it might change. So if this does not work for you, try checking the process metrics, find the one for your games and see which `working_directory` they're using. - drag and drop `process.name` to the drop area - select "Tag cloud" for the visualization" type - click on "Top 5 values of process.name" and set the "Number of Values" to 15 - close the panel - click on "Save and Return" It should be like that before you click on "Save and Return" ![lens-3](https://us1.discourse-cdn.com/elastic/original/3X/f/3/f3905aa00f28a1a8d42d59ca56dd49d772861a4e.png) The dashboard will look like: ![dashboard-2](https://us1.discourse-cdn.com/elastic/original/3X/e/e/eecd9754d77e8d203b387aa709ec2f6f12f84afb.png) To build visualisations with the GPU data, select the `logs-*` data view and filter for `event.dataset : "system.gpu"`, like that: ![lens-4](https://us1.discourse-cdn.com/elastic/original/3X/a/e/ae42d6be9402bbf79ceb936fa1417e143e4e1670.png) I'll leave the creation of the other visualizations as an exercise for you. 😉 ## Importing and exporting dashboards You can import the dashboards with the [Kibana Saved Objects API](https://www.elastic.co/docs/api/doc/kibana/v8/operation/operation-importsavedobjectsdefault). ```auto curl -u USER:PASSWORD -X POST -H 'Content-Type: multipart/form-data' -H 'kbn-xsrf: true' YOUR-KIBANA-HOST/api/saved_objects/_import\?createNewCopies\=true --form file=@steam-deck-dashboard.ndjson ``` Get the dashboard to import [here](https://github.com/AndersonQ/steamdeck-sensors-api/blob/main/steam-deck-dashboard.ndjson). To export a dashboard, use ```auto curl -u USER:PASSWORD -X POST -H 'Content-Type: application/json' -H 'kbn-xsrf: true' YOUR-KIBANA-HOST/api/saved_objects/_export \ -H "Content-Type: application/json; Elastic-Api-Version=2023-10-31" \ -H "kbn-xsrf: string" \ -d '{"objects":[{"id":"YOUR-DASHBOARD-ID","type":"dashboard"}],"excludeExportDetails":true,"includeReferencesDeep":true}' > steam-deck-dashboard.ndjson ``` To find the dashboard ID, open the dashboard, check the URL, it'll be something like that: ```auto https://KIBANA-HOST/app/dashboards#/view/? https://KIBANA-HOST/app/dashboards#/view/bfcd09b3-effe-4a65-b58b-b6c3d528cc3e? ``` ## Conclusion It's great fun to use the Elastic Stack to monitor my Steam Deck, gaining insights into its performance, how games utilize resources, and identifying running programs and game binaries. Most importantly, it's a fun way to get started with the Elastic Agent, ingesting monitoring data, and creating visualizations and dashboards. It also provides a glimpse into the vast amount of data we can collect, inspiring different ways to use it. 🙂 You can grab a [free trial on Elastic Cloud](https://www.elastic.co/cloud/elasticsearch-service/signup) or easily run your own [Elastic Stack on Docker](https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html) or [download](https://www.elastic.co/downloads) and run it manually. Now you already know how to monitor your Steam Deck and how to run a Elastic Stack, why don't you try it yourself?