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180 lines
6 KiB
Markdown
180 lines
6 KiB
Markdown
# Getting Started with Prometheus and Grafana
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- [Export metrics from the application](#export-metrics-from-the-application)
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- [Check results in the browser](#check-results-in-the-browser)
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- [Collect metrics using Prometheus](#collect-metrics-using-prometheus)
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- [Configuration](#configuration)
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- [Start Prometheus](#start-prometheus)
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- [View results in Prometheus](#view-results-in-prometheus)
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- [Explore metrics using Grafana](#explore-metrics-using-grafana)
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- [Learn more](#learn-more)
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## Export metrics from the application
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Run the application with:
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```sh
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bazel run //examples/prometheus:prometheus_example
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```
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OpenTelemetry `PrometheusExporter` will export
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data via the endpoint defined by
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`metrics_exporter::PrometheusExporterOptions::url`,
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which is `http://localhost:9464/` by default.
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```mermaid
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graph LR
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subgraph SDK
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MeterProvider
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MetricReader["PrometheusExporter<br/>(http://localhost:9464/)"]
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end
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subgraph API
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Instrument["Meter(#quot;prometheus_metric_example#quot;, #quot;1.0#quot;)<br/>Histogram(#quot;prometheus_metric_example_histogram#quot;)"]
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end
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Instrument --> | Measurements | MeterProvider
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MeterProvider --> | Metrics | MetricReader
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```
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Also, for our learning purpose, we use a while-loop to keep recoring random
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values until the program stops.
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```cpp
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while (true)
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{
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double val = (rand() % 700) + 1.1;
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std::map<std::string, std::string> labels = get_random_attr();
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auto labelkv = opentelemetry::common::KeyValueIterableView<decltype(labels)>{labels};
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histogram_counter->Record(val, labelkv, context);
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std::this_thread::sleep_for(std::chrono::milliseconds(50));
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}
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```
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### Check results in the browser
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Start the application and keep it running. Now we should be able to see the
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metrics at [http://localhost:9464/metrics](http://localhost:9464/metrics) from a
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web browser:
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Now, we understand how we can configure `PrometheusExporter` to export metrics.
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Next, we are going to learn about how to use Prometheus to collect the metrics.
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## Collect metrics using Prometheus
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Follow the [first steps](https://prometheus.io/docs/introduction/first_steps/)
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to download the [latest release](https://prometheus.io/download/) of Prometheus.
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It is also possible to use `prom/prometheus` docker image.
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### Configuration
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After downloading, extract it to a local location that's easy to
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access. We will find the default Prometheus configuration YAML file in the
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folder, named `prometheus.yml`.
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```yaml
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global:
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scrape_interval: 5s
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scrape_timeout: 2s
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evaluation_interval: 5s
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scrape_configs:
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- job_name: otel
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static_configs:
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- targets: ['localhost:9464']
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```
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### Start Prometheus
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Follow the instructions from
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[starting-prometheus](https://prometheus.io/docs/introduction/first_steps/#starting-prometheus)
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to start the Prometheus server and verify it has been started successfully.
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Please note that we will need pass in `prometheus.yml` file as the argument
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or mount as volume:
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```console
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./prometheus --config.file=prometheus.yml
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# OR:
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docker run -p 9090:9090 -v $(pwd):/etc/prometheus --network="host" prom/prometheus
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```
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### View results in Prometheus
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To use the graphical interface for viewing our metrics with Prometheus, navigate
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to [http://localhost:9090/graph](http://localhost:9090/graph),
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and type `prometheus_metric_example_histogram_bucket` in the expression bar of
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the UI; finally, click the execute button.
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We should be able to see the following chart from the browser:
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From the legend, we can see that the `instance` name and the `job` name are the
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values we have set in `prometheus.yml`.
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Congratulations!
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Now we know how to configure Prometheus server and deploy OpenTelemetry
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`PrometheusExporter` to export our metrics. Next, we are going to explore a tool
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called Grafana, which has powerful visualizations for the metrics.
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## Explore metrics using Grafana
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[Install Grafana](https://grafana.com/docs/grafana/latest/installation/).
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Start the standalone Grafana server (`grafana-server.exe` or
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`./bin/grafana-server`, depending on the operating system). Then, use the
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browser to navigate to [http://localhost:3000/](http://localhost:3000/).
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It is also possible to run `grafana/grafana` container:
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```sh
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docker run -d -p 3000:3000 --network="host" grafana/grafana
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```
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Follow the instructions in the Grafana getting started
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[doc](https://grafana.com/docs/grafana/latest/getting-started/getting-started/#step-2-log-in)
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to log in.
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After successfully logging in, click on the Configuration icon
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on the panel at the left hand side, and click on Prometheus.
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Type in the default endpoint of Prometheus as suggested by the UI
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as the value for the URI.
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```console
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http://localhost:9090
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```
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Then, click on the Explore icon on the left panel of
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the website - we should be able to write some queries to explore our metrics
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now!
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Feel free to find some handy PromQL
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[here](https://promlabs.com/promql-cheat-sheet/).
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```mermaid
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graph TD
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subgraph Prometheus
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PrometheusScraper
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PrometheusDatabase
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end
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PrometheusExporter["PrometheusExporter<br/>(listening at #quot;http://localhost:9464/#quot;)"] -->|HTTP GET| PrometheusScraper{{"Prometheus scraper<br/>(polling #quot;http://localhost:9464/metrics#quot; every 5 seconds)"}}
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PrometheusScraper --> PrometheusDatabase[("Prometheus TSDB (time series database)")]
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PrometheusDatabase -->|http://localhost:9090/graph| PrometheusUI["Browser<br/>(Prometheus Dashboard)"]
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PrometheusDatabase -->|http://localhost:9090/api/| Grafana[Grafana Server]
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Grafana -->|http://localhost:3000/dashboard| GrafanaUI["Browser<br/>(Grafana Dashboard)"]
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```
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## Learn more
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- [What is Prometheus?](https://prometheus.io/docs/introduction/overview/)
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- [Grafana support for
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Prometheus](https://prometheus.io/docs/visualization/grafana/#creating-a-prometheus-graph)
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