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Find the stage where data disappears before changing scrape intervals or dashboard settings. A gap can begin with target discovery, network reachability, a failed scrape, timestamp or stale-series handling, relabeling, missing instrumentation, storage, or simply a query window that does not include the samples. In Prometheus, start with the target’s up series and target status, then follow the metric through scraping, relabeling, querying, and visualization.

What a “gap” actually tells you

A dashboard showing an empty area does not by itself prove that collection stopped. The raw series may still contain samples while the panel uses a different time range, aggregation, label filter, or evaluation time. Conversely, a target can be reachable and report up=1 while one application metric is absent because the exporter or application no longer emits it.

Classify the scope before investigating:

  • One series: a metric name, label value, or instrumentation path may have changed.
  • Several series from one target: the exporter, endpoint, relabeling, or target process may be involved.
  • An entire job: discovery, credentials, DNS, network access, or a shared configuration problem is more likely.
  • One monitoring region or collector: investigate the forwarding or storage boundary rather than every target individually.
  • Only one panel: first compare the panel query and time range with a direct query.

Record the metric name, complete label set, target and job, first and last observed timestamps, the dashboard’s time range, and whether another query shows the same behavior. This creates a precise incident boundary instead of treating every blank panel as a collection failure.

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Follow the monitoring pipeline in order

Diagnose from the outside in. The first stage that has no evidence of success is where the investigation should concentrate.

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Pipeline stage Evidence to check Typical explanation for missing data Next action
Target discovery Expected target appears with the intended address, job, and labels Service discovery changed, target was removed, or labels no longer match the query Compare discovered targets with the intended inventory and current label selectors
Reachability Collector can resolve and connect to the endpoint with required credentials DNS, routing, firewall, authentication, certificate, or target availability failure Test the path from the collector’s network boundary and inspect target status
Scrape up, scrape duration, and sample counts Endpoint error, timeout, malformed response, or a slow or overloaded target Use scrape diagnostics to separate connection failure from an empty or costly response
Relabeling and ingestion Samples before and after metric relabeling Rules dropped the metric or changed labels so the expected selector no longer matches Review active relabel rules and compare sample counts
Storage and query Direct query at the relevant evaluation time and range Lookback, stale markers, explicit timestamps, or an incorrectly aligned query Widen or shift the query deliberately and inspect the newest sample time
Visualization Panel expression, variables, aggregation, and time range Panel transformation or time window hides valid samples Run the exact expression in the expression browser, then compare results

Use Prometheus’s built-in scrape signals

Prometheus creates its own scrape metrics for each target. Query them directly before looking at an application metric.

Series What it means How to interpret it
up One when the target is healthy and reachable for the scrape; zero when the scrape fails up=0 establishes a scrape failure, but up=1 does not establish that every expected application metric exists
scrape_duration_seconds Time spent performing the scrape A value approaching the configured timeout points toward a slow endpoint, overloaded target, or network problem
scrape_samples_scraped Samples returned by the target A sudden drop or zero can indicate exporter behavior, endpoint changes, or a response that contains fewer metrics
scrape_samples_post_metric_relabeling Samples remaining after metric relabeling A lower value than the pre-relabel count indicates that relabel rules removed samples

For example, begin with expressions such as:

up{job="api"}
scrape_duration_seconds{job="api"}
scrape_samples_scraped{job="api"}
scrape_samples_post_metric_relabeling{job="api"}

Inspect the target status page as well as these series. A target marked down with up=0 requires a reachability or scrape investigation; it is not fixed by changing a panel query.

Check discovery, endpoint, and target labels

Confirm that the target still exists

Open Prometheus’s target status view and verify that the expected target is present, not merely that its old time series remains in a query result. Compare the current address, scheme, job label, and instance label with the labels used by the dashboard. A discovery system can remove a target or replace its address while leaving the dashboard’s selector unchanged.

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Check the complete endpoint path

Confirm the scheme, host, port, metrics path, and any authentication or certificate settings in the effective job configuration. A target process may be healthy on its application port while the metrics endpoint has moved, requires credentials, or is blocked from the collector’s network.

Separate target health from metric availability

up=1 means Prometheus completed a healthy scrape. It does not guarantee that a particular metric name, label, or event-specific series was returned. If only selected series are absent, continue to instrumentation and relabeling rather than repeatedly testing the network.

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Verify scrape interval and timeout as deployed

Inspect the configuration that the running Prometheus process actually loaded. The Prometheus configuration reference documents a current global default scrape interval of one minute and a global scrape timeout of ten seconds, but job-level settings and deployed versions can override those values. The timeout cannot be greater than the scrape interval.

Setting Question Failure pattern
Scrape interval How often should this job be collected? Gaps that repeat at a regular cadence may reflect an interval longer than the dashboard assumes, or a target that is intermittently unavailable between scrapes
Scrape timeout How long may one scrape run before it fails? Long scrape durations followed by up=0 indicate a timeout or endpoint performance issue
Job and target overrides Does this job differ from the global values? Assuming global defaults can make a valid job look misconfigured

A Prometheus tutorial commonly demonstrates a 15-second scrape interval. That value is an example configuration, not a universal default or recommendation. Use the effective value from your deployed configuration and version.

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Do not increase the timeout blindly. A longer timeout can allow a slow endpoint to finish, but it also consumes collector resources and can make failures less obvious. First measure scrape_duration_seconds, then determine whether the endpoint is slow because it performs expensive collection, is CPU-saturated, or is waiting on a dependency.

Investigate timestamps, staleness, and query timing

Understand when Prometheus stops returning a series

When a scrape or rule evaluation no longer returns a previously seen series, Prometheus can mark that series stale. Queries after the stale point return no value until new samples arrive. This behavior can look like erased data even though it is a deliberate indication that the series is no longer being produced.

Check the lookback window

Prometheus instant queries use the newest sample within a five-minute lookback period by default. If the last sample is older than that window, the query returns no value even though the sample may still exist in storage. Check the query’s evaluation time, dashboard range, and any configured lookback override. A panel evaluated just after a target stopped scraping can therefore show a blank result while a wider historical range still shows the earlier samples.

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Check explicit timestamps and clock alignment

Targets can expose their own sample timestamps. Prometheus configuration determines whether those timestamps are honored or ignored. If explicit timestamps are in use, compare the target’s clock with the collector and inspect whether samples are being assigned times far in the past or future. Clock drift can create apparent gaps, out-of-order behavior, or samples that fall outside the panel’s selected range.

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When testing, run a direct query at a known evaluation time and then use a range query that spans the last confirmed sample, the suspected gap, and the first sample after recovery. This distinguishes a query-window problem from a collection interruption.

Check instrumentation and metric relabeling

Confirm that the exporter still emits the metric

If the target is reachable and the scrape returns samples, inspect the raw metrics endpoint or the target’s exporter output for the missing name. Application releases can remove a metric, rename it, alter label keys, or emit it only after a code path has executed. A selector that expects the old label set will then return nothing even though collection is functioning.

Distinguish “not observed yet” from zero

For a known series that should exist before an event occurs, exporting a default zero makes “the event has not happened” distinguishable from “the metric is not being emitted.” This is especially important for counters or state indicators that are created only when a request, error, or job first occurs. Use a zero only when zero is semantically valid; do not manufacture values for an entity that genuinely does not exist.

Review metric relabel rules

Compare scrape_samples_scraped with scrape_samples_post_metric_relabeling. If the latter falls while the former remains stable, inspect drop, keep, label-map, and label-replacement rules for the affected job. A changed label value can also move a time series to a new identity, making the old label selector appear empty.

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Instrument custom collectors

Custom collectors should expose their own collection duration and error count. Those signals reveal an internal collector failure even when the HTTP endpoint remains reachable and Prometheus reports up=1. Without them, an exporter can return a technically valid response that silently omits the metrics you need.

“You should never take a rate() of a gauge.” — Prometheus project, Instrumentation documentation.

This rule prevents a query mistake from being diagnosed as a collection gap. A gauge can rise, fall, or remain constant; use a gauge directly or apply an operation appropriate to its semantics rather than treating it as a monotonically increasing counter.

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Trace network and forwarding boundaries

Every network boundary adds failure modes that are independent of the target process. For remote scrapes, agents, or forwarding paths, check the following from the component that performs the connection:

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  • Service discovery: the collector received the current target address and labels.
  • Credentials and certificates: authentication material is present, valid, and accepted by the endpoint.
  • DNS: the collector resolves the intended name and is not receiving stale or split-horizon results.
  • Connectivity: routing, firewall policy, proxy behavior, and security groups permit the metrics request.
  • Target availability: the process and metrics handler are running on the address actually being scraped.
  • Scrape cost: CPU pressure, dependency latency, or an oversized response causes the endpoint to miss its timeout.

The Prometheus Agent Mode documentation notes that crossing network boundaries can introduce failures unrelated to an individual target and can obscure where a metric disappeared. Test each hop separately: discovery to collector, collector to target, target response to ingestion, and ingestion to the querying component.

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Use this end-to-end diagnostic runbook

  1. Define the gap. Record the exact metric and labels, target, job, first and last observed timestamps, affected scope, and dashboard range. Run the raw expression outside the panel.
  2. Check target presence. In the Prometheus target view, verify the target address, scheme, job, instance, and current state. If it is absent, fix discovery or configuration before examining application instrumentation.
  3. Query up. Use the same job and instance labels. A zero sends you to endpoint, credentials, DNS, network, or timeout checks; a one lets you proceed to metric-specific checks.
  4. Measure the scrape. Query scrape_duration_seconds, scrape_samples_scraped, and scrape_samples_post_metric_relabeling over the gap. Look for slow scrapes, zero samples, or a drop caused by relabeling.
  5. Confirm effective configuration. Check the loaded job settings, endpoint, interval, timeout, timestamp policy, and target-discovery rules. Remember that documented global defaults may not match a job override or an older deployment.
  6. Check query timing. Compare the dashboard’s evaluation time and range with the newest sample. Account for the five-minute default lookback and for stale-series behavior when a scrape stops returning a series.
  7. Inspect the metric source. Verify the exporter or application output, metric name, labels, event gating, and custom collector error and duration metrics. Check relabel rules if raw samples exist but the queried series does not.
  8. Trace forwarding. For an agent or remote path, test discovery, authentication, DNS, connectivity, target availability, and each handoff independently.
  9. Apply the smallest repair. Correct the failed stage—such as restoring discovery, fixing credentials, repairing instrumentation, or adjusting a justified timeout—rather than changing unrelated dashboard expressions.
  10. Verify recovery. Confirm the target is healthy, query the expected metric and labels directly, and watch for new samples at the configured cadence. Then reload the dashboard and confirm that its panel expression produces the same result.

Common misleading fixes

Changing the dashboard time range first

A wider range can reveal historical samples, but it does not restore collection. Use it as a test for lookback, staleness, or alignment; do not treat a visible old sample as proof that current scraping works.

Raising the scrape timeout without measuring

This can hide an overloaded endpoint and increase resource use. Check duration and target performance first, and keep the timeout no greater than the interval.

Assuming up=1 proves application correctness

It proves target scrape health, not that every metric is emitted, labeled as expected, or retained after relabeling.

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Using a rate query to test a gauge

A query can be semantically wrong even when the underlying samples are present. Validate metric type and intended behavior before declaring a data gap.

What to record for a durable fix

Close the incident with the evidence that identifies the failed stage: target address and labels, effective interval and timeout, representative up and scrape-diagnostic values, the metric’s raw output, relabeling results, query evaluation time and lookback, and any network boundary involved. Also record whether the series disappeared because of a deployment, discovery change, instrumentation condition, or target outage. This makes the next gap comparable instead of restarting the investigation from an empty dashboard.

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