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Grafana Cloud Enterprise Open source
Last reviewed: June 8, 2026

Amazon Managed Service for Prometheus query editor

This document explains how to use the Amazon Managed Service for Prometheus query editor. The query editor is the same as the core Grafana Prometheus query editor and uses PromQL to query your workspace.

Before you begin

Before you write queries, ensure you have:

Key concepts

If you’re new to Prometheus, these terms are used throughout this document:

TermDescription
PromQLThe Prometheus Query Language, used to select and aggregate time series data.
Instant queryReturns a single value per series at the end of the time range.
Range queryReturns a series of values over the dashboard time range.
Metrics browserA tool in the query editor that helps you search metrics, select labels, and build a selector.

Query editor modes

The query editor has two modes that you switch between with the toggle in the upper-right of the editor.

Builder mode

Builder mode is a visual, guided way to build queries without writing PromQL by hand. Use builder mode to:

  • Select a metric from the Metric drop-down or open the metrics browser.
  • Add label filters to narrow the result set.
  • Add operations such as rate, sum, or histogram_quantile.

Click Explain to display a step-by-step, plain-language description of what the query does.

Code mode

Code mode lets you write raw PromQL with autocomplete, syntax highlighting, and the metrics browser. Use code mode for complex queries or when you already know PromQL.

To open the metrics browser in code mode, focus the query field and click Metrics browser. From there you can search metrics, select labels and label values, validate a selector, and insert it into your query.

Kickstart your query

Click Kickstart your query to choose from a list of query patterns, such as rate or histogram patterns. Grafana inserts the pattern into the editor so you can adapt it to your metrics.

Query options

Expand Options in the query editor to configure how Grafana runs and displays the query.

OptionDescription
LegendControls the time series name in the legend. Use Auto, Verbose, or a Custom template such as {{label_name}}.
FormatSets the result format: Time series, Table, or Heatmap.
TypeSets the query type: Range, Instant, or Both.
Min stepThe lower bound for the query step and $__interval. Match this to your scrape interval.
ExemplarsToggles whether to show exemplars alongside the query results.

Macros

Use macros in your queries to reference the dashboard time range and interval. Grafana replaces the macro with the computed value at query time.

MacroDescription
$__intervalThe interval Grafana calculates from the time range and panel width.
$__interval_msThe interval in milliseconds.
$__rangeThe full dashboard time range, for example 1h.
$__range_sThe dashboard time range in seconds.
$__range_msThe dashboard time range in milliseconds.
$__rate_intervalAn interval tuned for rate functions that’s always at least four times the scrape interval.
$__rate_interval_msThe rate interval in milliseconds.

Query examples

The following examples show common PromQL queries you can run against your workspace. Replace the metric and label names with the ones in your workspace.

Rates and throughput

Use the rate function with $__rate_interval to chart per-second rates from counters.

Calculate the per-second rate of HTTP requests:

promql 
rate(http_requests_total[$__rate_interval])

Calculate total requests per second across all instances of a service:

promql 
sum(rate(http_requests_total{job="api"}[$__rate_interval]))

Aggregations

Use aggregation operators such as sum, avg, and max with by to group results.

Aggregate CPU usage by instance:

promql 
sum by (instance) (rate(node_cpu_seconds_total{mode!="idle"}[$__rate_interval]))

Find the top five pods by memory usage:

promql 
topk(5, sum by (pod) (container_memory_working_set_bytes))

Error rates and ratios

Divide a filtered rate by a total rate to compute an error percentage.

Calculate the percentage of HTTP 5xx responses:

promql 
sum(rate(http_requests_total{status=~"5.."}[$__rate_interval]))
/
sum(rate(http_requests_total[$__rate_interval]))
* 100

Latency percentiles

Use histogram_quantile with a _bucket metric to chart latency percentiles.

Calculate the 95th percentile request latency:

promql 
histogram_quantile(0.95, sum by (le) (rate(http_request_duration_seconds_bucket[$__rate_interval])))

Resource utilization

Combine metrics to express utilization as a percentage.

Calculate memory utilization per node as a percentage:

promql 
100 * (1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)

Show which targets are currently down:

promql 
up == 0

Use a template variable in a query

Reference a template variable to make a query interactive. For example, filter by a selected instance value:

promql 
rate(node_cpu_seconds_total{instance=~"$instance"}[$__rate_interval])

Use cases

Use cases help you understand what’s possible and provide starting points for your own dashboards:

  • Monitor container workloads: Track CPU, memory, and restart counts for Kubernetes and Amazon ECS workloads that send metrics to your workspace.
  • Track service-level indicators: Build error-rate and latency panels from request metrics to power service-level objective dashboards.
  • Capacity planning: Aggregate resource usage over long time ranges to spot trends and plan scaling.

Next steps