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Get a Prometheus-compatible metrics database running on your machine in under five minutes. You will install Timeseries, configure it to scrape its own metrics, query those metrics in the built-in UI, and then add an external target.

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Install OpenData Timeseries

Download and install the Timeseries binary: 
curl https://www.opendata.dev/install.sh | sh
This places the opendata-timeseries binary in the current directory.

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Configure Timeseries

Create a file called prometheus.yaml with the following contents:
prometheus.yaml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: "timeseries-server"
    scrape_interval: 5s
    scrape_timeout: 4s
    metrics_path: /metrics
    scheme: http
    static_configs:
      - targets: ["localhost:9090"]

storage:
  type: SlateDb
  path: data
  object_store:
    type: Local
    path: ./data

flush_interval_secs: 30
This configuration has three sections:
  • global sets the default scrape and evaluation intervals for all jobs.
  • scrape_configs tells Timeseries what to scrape. For now, it scrapes itself at localhost:9090 every 5 seconds.
  • storage configures local SlateDB as the storage backend. Metrics are flushed to disk every 30 seconds under ./data.

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Start the server

Run the binary with the configuration file you just created: 
./opendata-timeseries --config prometheus.yaml --port 9090
Timeseries starts, begins scraping its own /metrics endpoint, and stores the results locally.

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Query metrics in the UI

Open http://localhost:9090 in your browser. Timeseries ships with a built-in query UI where you can run PromQL expressions and visualize results as tables or graphs.

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Check that the target is up

Type up into the expression bar and click Execute. Switch to the Graph tab to see the result over time. A steady line at 1 means the target is healthy and being scraped successfully.
Query UI showing up metric graphed over time

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Explore more metrics

Try these expressions in the query bar to explore the metrics Timeseries collects about itself:
  • http_requests_total — total HTTP requests broken down by method, endpoint, and status
  • http_request_duration_seconds — request latency histogram by endpoint
  • scrape_samples_scraped — how many samples were collected per scrape
Use the Table tab for instant values or the Graph tab to see how metrics change over time. The Duration and Step controls above the graph let you adjust the time window and resolution.

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Scrape a target — Node Exporter

Scraping its own metrics is useful for verification, but the real value comes from scraping external targets. Node Exporter exposes hardware and OS metrics and is a good first target.

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Download and run Node Exporter

Download the latest release for your platform from the Prometheus downloads page, extract it, and start it: 
tar xvfz node_exporter-*.tar.gz
cd node_exporter-*
./node_exporter
Verify that metrics are being exposed: 
curl localhost:9100/metrics
You should see a large set of metrics prefixed with node_.

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Add Node Exporter as a scrape target

Update prometheus.yaml to include a new job under scrape_configs:
prometheus.yaml
scrape_configs:
  - job_name: "timeseries-server"
    scrape_interval: 5s
    scrape_timeout: 4s
    metrics_path: /metrics
    scheme: http
    static_configs:
      - targets: ["localhost:9090"]

  - job_name: "node-exporter"
    static_configs:
      - targets: ["localhost:9100"]
Restart the Timeseries server to pick up the new configuration: 
./opendata-timeseries --config prometheus.yaml --port 9090

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Query Node Exporter metrics

After a few scrape intervals, head back to http://localhost:9090 and try these expressions:
  • node_memory_free_bytes — free system memory in bytes
  • node_memory_total_bytes — total system memory in bytes
  • up — now returns two results, one for each scrape target
  • sum(up) — total number of healthy targets

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Next steps

  • Learn about all available settings in Configuration.
  • Explore the full query and write API in the API Reference section in the sidebar.
  • Connect Grafana as a visualization layer — Timeseries is compatible with the Prometheus data source out of the box.