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+---
+id: use-case-prometheus
+title: Monitoring Custom Metrics Using OpenTelemetry
+sidebar_label: Use case - Prometheus
+description: Learn how to import and monitor your existing Prometheus-formatted metrics into Sumo Logic.
+---
+
+import useBaseUrl from '@docusaurus/useBaseUrl';
+
+If you're collecting metrics using Prometheus, a popular tool for monitoring app performance, it can be challenging to visualize and analyze that data in a single location.
+
+With the Sumo Logic OpenTelemetry Collector, you can import your Prometheus-formatted metrics into Sumo Logic and create a custom dashboard for them. This diagram shows the flow of metrics data from a custom application which has been instrumented to provide Prometheus-formatted metrics.
+
+})
+
+Metrics are scraped by our OpenTelemetry Collector using the `prometheus` receiver, then sent to Sumo Logic’s OLTP-compatible ingest API via the `sumologic` exporter. The data is stored and indexed by Sumo Logic, where it can then be queried and displayed in a custom dashboard within Sumo Logic.
+
+The OpenTelemetry Collector is a vendor-agnostic agent that can collect, process, and export telemetry data to various backends. It supports various protocols, including Prometheus.
+
+In this use case below, we'll use a simple simulator to provide some custom metrics, however, you can use anything that exports metrics in Prometheus format. You don't have to use Prometheus; receivers are available for all popular metrics formats and should work for whatever your device is producing.
+
+
+## Step 1: Set up the Prometheus Metrics Server Simulator
+
+To get started, we need to have some Prometheus-formatted metrics to work with. We will use the [IBM Prometheus Metrics Server Simulator](https://github.com/IBM/prometheus-metrics-server-simulator) to generate sample metrics.
+
+1. Install the simulator by following the instructions in the [official repository](https://github.com/IBM/prometheus-metrics-server-simulator).
+1. Once you have installed the simulator, start it up with the following command:
+ ```bash
+ docker run -p 8080:8080 \
+ -v $(pwd)/example:/etc/conf \
+ -it ibmcom/prometheus-metrics-server-simulator
+ ```
+1. Confirm that the simulator starts with the default configuration. You can modify that configuration to produce whatever custom metrics you want to mock.
+ ```bash title="cat examples/config.yaml"
+ counters:
+ - prefix: mock_metric_counter
+ number: 3
+ labels:
+ - name: app
+ valueset:
+ - "prometheus-mock-metrics-server"
+ - name: client_addr
+ valueset:
+ - "client1"
+ - "client2"
+ - "client3"
+ - prefix: mock_metric_request_total
+ labels:
+ - name: app
+ valueset:
+ - "prometheus-mock-metrics-server"
+ valuemode: http
+
+ gauges:
+ - prefix: mock_metric_gauges
+ number: 4
+ range:
+ upper: 1000
+ lower: 2
+ labels:
+ - name: app
+ valueset:
+ - "prometheus-mock-metrics-server"
+ - name: client_addr
+ valueset:
+ - "client1"
+ - "client2"
+ - "client3"%
+ ```
+1. To verify that it's up and running, curl the metrics endpoint to see the Prometheus-formatted metrics it is generating. You should see something like this:
+ ```bash
+ curl localhost:8080/metrics
+ # HELP mock_metric_counter_0
+ # TYPE mock_metric_counter_0 counter
+ mock_metric_counter_0{app="prometheus-mock-metrics-server",client_addr="client3"} 5448
+ # HELP mock_metric_counter_1
+ # TYPE mock_metric_counter_1 counter
+ mock_metric_counter_1{app="prometheus-mock-metrics-server",client_addr="client2"} 5424
+ # HELP mock_metric_counter_2
+ # TYPE mock_metric_counter_2 counter
+ mock_metric_counter_2{app="prometheus-mock-metrics-server",client_addr="client2"} 5362
+ # HELP mock_metric_gauges_0
+ # TYPE mock_metric_gauges_0 gauge
+ mock_metric_gauges_0{app="prometheus-mock-metrics-server",client_addr="client1"} 71
+ # HELP mock_metric_gauges_1
+ # TYPE mock_metric_gauges_1 gauge
+ mock_metric_gauges_1{app="prometheus-mock-metrics-server",client_addr="client2"} 44
+ # HELP mock_metric_gauges_2
+ # TYPE mock_metric_gauges_2 gauge
+ mock_metric_gauges_2{app="prometheus-mock-metrics-server",client_addr="client3"} 4
+ # HELP mock_metric_gauges_3
+ # TYPE mock_metric_gauges_3 gauge
+ mock_metric_gauges_3{app="prometheus-mock-metrics-server",client_addr="client3"} 41
+ # HELP mock_metric_request_total
+ # TYPE mock_metric_request_total counter
+ mock_metric_request_total{app="prometheus-mock-metrics-server"} 0
+ ```
+
+## Step 2: Add Prometheus receiver to your OpenTelemetry Collector configuration
+
+Next, we'll configure the [Sumo Logic OpenTelemetry Collector](https://github.com/SumoLogic/sumologic-otel-collector) to receive metrics from the Prometheus Metrics Server Simulator, and then forward them to Sumo Logic.
+
+1. Install the [Sumo Logic OpenTelemetry Collector](/docs/send-data/opentelemetry-collector/).
+2. Create a new configuration file for the OpenTelemetry Collector called **/etc/otelcol-sumo/conf.d/prom.yaml** and paste in the following snippet:
+ ```yml
+ receivers:
+ prometheus:
+ config:
+ scrape_configs:
+ - job_name: 'mock-metrics'
+ scrape_interval: 10s
+ static_configs:
+ - targets: ['localhost:8080']
+ service:
+ pipelines:
+ metrics:
+ receivers: [prometheus]
+ exporters: [sumologic]
+ ```
+ In this configuration, we:
+ - Define a `prometheus` receiver that scrapes metrics from the Prometheus Metrics Server Simulator running on `localhost:8080`.
+ - Define a metrics pipeline that uses the `prometheus` receiver to scrape the metrics.
+ - Send the metrics to Sumo Logic via the `sumologic` exporter.
+3. Save the configuration file as `/etc/otelcol-sumo/conf.d/prom.yaml`.
+4. Start the OpenTelemetry Collector with the following command:
+ ```bash
+ sudo otelcol-sumo \
+ --config=/etc/otelcol-sumo/sumologic.yaml \
+ --config "glob:/etc/otelcol-sumo/conf.d/*.yaml"
+ ```
+
+This will start the collector with the configuration you defined, and you'll begin receiving and exporting metrics to Sumo Logic.
+
+
+## Step 3: Create a New Dashboard in Sumo Logic
+
+Finally, we will create a new dashboard in Sumo Logic to visualize the custom Prometheus-formatted metrics we are receiving.
+
+To create a new dashboard:
+
+1. [**Classic UI**](/docs/get-started/sumo-logic-ui/). Go to the Sumo Logic **Home** tab and select **Dashboard**.
[**New UI**](/docs/get-started/sumo-logic-ui/). In the main Sumo Logic menu, select **Dashboards > New Dashboard**. You can also click the **Go To...** menu at the top of the screen and select **New Dashboard**.
+1. Click the name of the dashboard and write in a unique title like **Custom Metrics Dashboard**.
})
+1. Click the **Time Series** button to create a time series chart panel.
})
+1. Select the icon next to the query box to choose a **Metrics** query.
})
+1. Select **mock_metric_counter_0** from the drop down (of course, this could be any metric. This is just an example).
})
+1. From filters, choose the **client_addr** dimension. This will let us only show the metrics for specific client(s).
})
+1. Choose the `=() In` operator. This will let us specify a list of clients we want to include on the chart.
})
+1. Select the desired clients. In this case, there’s only a single client, **client3**.
})
+1. Click the search button (magnifying glass) to execute the search and see example output.
})
+1. You should see something like this below.
})
If it looks right, click **Add to Dashboard**.
+1. Now we have a new dashboard in our Library called **Custom Metrics Dashboard** with the time-series chart showing just the clients we want to observe.
})
+
+
+## Step 4: Set up alerts
+
+Next, [set up a monitor](/docs/alerts/monitors/create-monitor) so that you get alert emails when a particular metric (for example, room temperature) exceeds a threshold and stays above that value for (2 cycles or 10 minutes, not sure which is doable).
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