> ## Documentation Index
> Fetch the complete documentation index at: https://opendata.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Timeseries Quickstart

> Install Timeseries locally and query metrics in minutes

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.

## Install OpenData Timeseries

Download and install the Timeseries binary:

```bash theme={null}
curl -fsSL https://www.opendata.dev/install.sh | sh -s -- timeseries
```

This places the `opendata-timeseries` binary in the current directory.

## Configure Timeseries

Create a file called `prometheus.yaml` with the following contents:

```yaml prometheus.yaml theme={null}
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](https://slatedb.io) as the storage
  backend. Metrics are flushed to disk every 30 seconds under `./data`.

## Start the server

Run the binary with the configuration file you just created:

```bash theme={null}
./opendata-timeseries --config prometheus.yaml --port 9090
```

Timeseries starts, begins scraping its own `/metrics` endpoint, and stores
the results locally.

## Query metrics in the UI

Open [http://localhost:9090](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.

### 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.

<Frame>
  <img src="https://mintcdn.com/responsive-99a36759/NvkbHgdsFNXwDQ7c/timeseries/images/quickstart-query-up.png?fit=max&auto=format&n=NvkbHgdsFNXwDQ7c&q=85&s=74d7678a0a3a98d62ac3fbc60c302cd9" alt="Query UI showing up metric graphed over time" width="2680" height="2176" data-path="timeseries/images/quickstart-query-up.png" />
</Frame>

### Explore more metrics

Try these expressions in the query bar to explore the metrics Timeseries
collects about itself:

* `http_requests` — 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.

## Scrape a target — Node Exporter

Scraping its own metrics is useful for verification, but the real value comes
from scraping external targets. [Node Exporter](https://prometheus.io/docs/guides/node-exporter/)
exposes hardware and OS metrics and is a good first target.

### Download and run Node Exporter

Download the latest release for your platform from the
[Prometheus downloads page](https://prometheus.io/download/#node_exporter),
extract it, and start it:

```bash theme={null}
tar xvfz node_exporter-*.tar.gz && rm node_exporter-*.tar.gz
cd node_exporter-*

# on macOS you might need the next line to avoid macOS blocking the execution of the binary
xattr -d com.apple.quarantine ./node_exporter

./node_exporter
```

Verify that metrics are being exposed:

```bash theme={null}
curl localhost:9100/metrics
```

You should see a large set of metrics prefixed with `node_`.

### Add Node Exporter as a scrape target

Update `prometheus.yaml` to include a new job under `scrape_configs`:

```yaml prometheus.yaml theme={null}
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:

```bash theme={null}
./opendata-timeseries --config prometheus.yaml --port 9090
```

### Query Node Exporter metrics

After a few scrape intervals, head back to [http://localhost:9090](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

## Next steps

* Learn about all available settings in [Configuration](/timeseries/configuration).
* Explore the full query and write API in the API Reference section in the sidebar.
* Connect [Grafana](https://grafana.com) as a visualization layer — Timeseries
  is compatible with the Prometheus data source out of the box.