# Calculate usage patterns and data volume in Prometheus

> Sample PromQL queries to understand ingestion rate, read query rate and total time series

Source: https://last9.io/docs/how-to-calculate-usage-patterns-and-data-volume-in-prometheus/

## Calculating Ingestion Rate

This query will calculate the per-minute ingestion rate by averaging the
per-second ingestion rate over the past minute, as measured by the
`prometheus_tsdb_head_samples_appended_total` metric. The result will be a
single value representing the average number of samples ingested per minute over
the past minute.

```sql
 rate(prometheus_tsdb_head_samples_appended_total[1m]) * 60
```

## Calculating Read Query Rate

This query will calculate the per-minute query rate by averaging the per-second
query rate over the past minute, as measured by the
prometheus_http_requests_total metric for GET requests to the /api/v1/query
endpoint. This endpoint is used for executing queries against the Prometheus
database, so this metric represents the number of read queries executed by the
server.

```sql
sum by (handler) (rate(prometheus_http_requests_total{handler="/api/v1/query"}[1m]) * 60)
```

## Calculating Total Time Series

This query will count the number of distinct time series in the database,
regardless of the metric or label values. The regular expression ".+" matches
all series names, so this query effectively counts all series.

```sql
 count({__name__=~".+"})
```

:::tip
Note that counting the total number of time series can be resource-intensive for large databases, and may take some time to complete. Additionally, this query may return inaccurate results if the database is actively ingesting or deleting time series during the query.
:::