Promscale has been discontinued. We strongly recommend that you do not use Promscale in a production environment. Learn more.
You can use Docker Compose to easily run Promscale in an isolated environment built with Docker containers. This quick start guide demonstrates how to use Docker Compose to set up and run Promscale as a unified metric and trace observability backend for Prometheus, Jaeger and OpenTelemetry. It also includes some additional pre-configured tooling for you to get familiar with additional Promscale features, such as application performance monitoring dashboards and visualization tools.
For instructions on installing Promscale in your production or staging environments, see Installing Promscale.
Before you begin, make sure that you have installed Docker Compose.
Clone the repository that defines the services for Promscale, change to the
promscale-demodirectory and use docker compose to run Promscale:git clone [email protected]:timescale/promscale.gitcd promscale/docker-compose/promscale-demodocker compose up -d
The above quick start contains services for these below components:
- Promscale for analytics and long term storage of metrics and traces
- Prometheus with the node exporter to generate and collect metrics
- A microservices application and the OpenTelemetry Collector to generate and collect traces
- Grafana and Jaeger to visualize metrics and traces
When you have Promscale up and running, you can
explore the services installed
by Docker Compose. For example, you can use
Grafana to see the data sources
that are configured for Promscale, and use Grafana
dashboards to visualize trace
data. Access Grafana by navigating to
http://localhost:3000 in your web
and log in as
admin with the password
admin. Access Jaeger by navigating to
The data sources that are configured for Promscale are:
- Promscale-PromQL: PromQL query endpoint
- Promscale-Tracing: Jaeger query endpoint
- Promscale-SQL: SQL query endpoint for both metrics and traces
You can use Promscale for application/service performance monitoring using trace data. The Docker Compose configuration includes some pre-configured dashboards for you to explore.
View all the services with service name, number of requests served per second, average and p90 request duration and error rate. Click the service name to view the service level metrics.
View the top 50 slowest requests across all services with their corresponding traceID. Click the traceID to access the gantt chart view of the corresponding trace.
View the rate, error, and duration (RED) metrics specific to a service to understand the number of requests per second served by a service, the number of failed requests per second, and the amount of time it takes to process requests.
You can view the upstream dependencies for the selected service and operation. This allows you to see a map of all the services and operations called across all requests, before the selected service and operation is called. This helps identify unexpected behaviors, such as calls between services and operations that were not part of the original design. It also makes it faster to investigate the root cause of changes in RED metrics for the selected service and operation. For example, if there is a significant increase in the number of requests a particular operation called from many different services is receiving, you can track down which service and operation is at the origin of that increase.