The most widely usedopen-source visualization and dashboarding platformfor Prometheus data isGrafana. Grafana provides native integration with Prometheus as a data source, allowing users to createreal-time, interactive dashboardsusing PromQL queries.

Grafana supports advanced visualization panels (graphs, heatmaps, gauges, tables, etc.) and enables users to design custom dashboards to monitor infrastructure, application performance, and service-level objectives (SLOs). It also provides alerting capabilities that can complement or extend Prometheus’s own alerting system.

WhileKibanais part of the Elastic Stack and focuses on log analytics,Thanosextends Prometheus for long-term storage and high availability, andLokiis a log aggregation system. None of these tools serve as the primary dashboarding solution for Prometheus metrics the way Grafana does.

Grafana’s seamless Prometheus integration and templating support make it thede facto standard visualization toolin the Prometheus ecosystem.

The correct way to expose a timestamp from an application in Prometheus is to use agauge metricwhere thetimestamp value(in Unix time, seconds since epoch) is stored as the metric’svalue. This approach aligns with the Prometheus data model, which discourages embedding timestamps as labels or metadata.

Example:

app_last_successful_backup_timestamp_seconds 1.696358e+09

In this example, the gauge represents the timestamp of the last successful backup. The _seconds suffix indicates the unit of measurement, making the metric self-descriptive. Prometheus automatically assigns timestamps to scraped samples, so the metric’s value is treated purely as data, not as a Prometheus sample time.

Options B and D are incorrect because Prometheus does not allow arbitrary timestamps or labels for time values. Option C is incorrect since counters are monotonically increasing and not suited for discrete timestamp values.

Theincrease()function computes thetotal increase in a counter metricover a specified range vector. It accounts for counter resets and only measures the net change in the counter’s value during the time window.

Example:

increase(http_requests_total[5m])

This query returns how many HTTP requests occurred in the last five minutes. Unlike rate(), which provides a per-second average rate, increase() gives theabsolute number of increments.

Thederiv()function in PromQL calculates theper-second derivativeof a time series using linear regression over the provided time range. It estimates theinstantaneous rate of changefor metrics that can both increase and decrease — which are typicallygauges.

Because counters can only increase (except when reset), rate() or increase() functions are more appropriate for them. deriv() is used to identify trends in fluctuating metrics like CPU temperature, memory utilization, or queue depth, where values rise and fall continuously.

In contrast,summariesandhistogramsconsist of multiple sub-metrics (e.g., _count, _sum, _bucket) and are not directly suited for derivative calculation without decomposition.

In PromQL, theoffset modifierallows you to query metricsas they were at a past timerelative to the current evaluation. To retrieve the value of node_memory_MemAvailable_bytes as it was10 minutes ago, you place the offset keywordinside the aggregation function’s argument, not after it.

The correct query is:

sum(node_memory_MemAvailable_bytes offset 10m)

This computes the total available memory across all instances, based on data from exactly 10 minutes in the past.

Placing offset after the aggregation (as in option B) is syntactically invalid because modifiers apply toinstant and range vector selectors, not to complete expressions.

Theavg_over_time()function calculates theaverage value of a time series over a specified range vector. It is used to measure how a gauge metric (like available filesystem bytes) behaves over time rather than at a single instant.

For example:

avg_over_time(node_filesystem_avail_bytes[1h])

This query returns theaverage amount of available filesystem spaceobserved across all samples within the last hour for each time series.

By contrast:

avg() performs aggregation acrossdifferent series at a single point, not over time.

sum() and sum_over_time() compute totals rather than averages.

Thus, only avg_over_time() provides the correct temporal average.

Prometheus counters with labels can causemissing time seriesin queries if some label combinations have not yet been observed. To ensure visibility and continuity, therecommended best practiceis toinstantiate counters with all expected label values at application startup, even if their initial value is zero.

This ensures that every possible labeled time series is exported consistently, which helps when dashboards or alerting rules expect the presence of those series. For example, if a counter like http_requests_total{method="POST",status="200"} has not yet received a POST request, initializing it with a zero ensures it is still exposed.

Option A is incorrect — label values should never be encoded into metric names.

Option B adds redundancy and does not solve the initialization issue.

Option D is discouraged; counters should reset naturally upon restart, reflecting Prometheus’sephemeral metric model.