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# Hot R.O.D. - Rides on Demand - Grafana integration
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This is the Hot R.O.D. demo application that consists of the same components as the `examples/hotrod/`, only Grafana, Loki and Prometheus integration is added to this setup, so you can correlate logs, metrics and traces in one application.
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This example combines the Hot R.O.D. demo application ([examples/hotrod/](../hotrod/)) with Grafana, Loki and Prometheus integration, to demonstrate logs, metrics and traces correlation.
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## Running via `docker-compose`
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### Prerequisites
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* Clone the Jaeger repository `git clone https://github.com/jaegertracing/jaeger.git`, then `cd examples/grafana-integration`
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* All services will log to Loki via the loki docker logging plugin.
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To install run this on the host:
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* All services will log to Loki via the [official Docker driver plugin](https://grafana.com/docs/loki/latest/clients/docker-driver/).
Currently the most powerful way to correlate application logs with traces can be performed via Grafana's Explore interface.
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After setting the datasource to Loki, all the log labels become available, and can be easily filtered using [Loki's LogQL query language](https://grafana.com/docs/loki/latest/logql/).
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For example after selecting the compose project, errors can be filtered with the following expression:
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For example, after selecting the compose project/service under Log labels , errors can be filtered with the following expression:
@@ -44,13 +44,12 @@ which will list the redis timeout events.
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Since the HotROD application can expose its metrics in Prometheus' format, these can be also used during investigation.
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This example includes a dashboard, which contains a log panel, which displays the logs for the selected services in real time. These can be also filtered by a search field.
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This example includes a dashboard that contains a log panelfor the selected services in real time. These can be also filtered by a search field, that provides `grep`-like features.
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There are also panels to display the ratio/percentage of errors in the current timeframe.
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Additionally, there are graphs for each service, visualing the rate of the requests and showing latency percentiles.
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### Clean up
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To stop the containers use `docker-compose -f path-to-yml-file down`
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