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Start by defining exactly what is missing
“The dashboard is blank” is not yet a diagnosis. Record the smallest reproducible description of the gap before changing configuration:
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- The metric name and every relevant label.
- The dashboard panel, raw query, target, job, and instance involved.
- The first timestamp at which samples disappear and the last timestamp at which they were present.
- Whether the problem affects one series, all series from one target, an entire job, or a whole monitoring region.
- Whether the blank area appears in a direct query as well as in the dashboard visualization.
A panel can look empty because its time range, step, aggregation, or evaluation time differs from the query you tested manually. Compare the raw result with the panel’s configured range before treating a visualization artifact as a collection failure. Other monitoring products may use different query and gap semantics, so apply this check using the platform’s own query interface.
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Monitoring data normally passes through several boundaries. A useful fault model is:
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- Discovery: the monitoring system learns which targets should exist and which endpoint, job, and instance labels identify them.
- Scrape or collection: the system reaches the endpoint and obtains an exposition response within the configured limits.
- Ingestion and relabeling: samples are accepted, transformed, or discarded before storage.
- Storage and query evaluation: the query engine selects samples for a time and lookback window and applies functions or aggregation.
- Instrumentation: the exporter or application actually emits the expected metric and labels.
- Forwarding and network boundaries: service discovery, credentials, DNS, routing, proxies, agents, or remote destinations can fail between any two components.
Check these stages in order. A target that was removed from discovery cannot be repaired by increasing a scrape timeout, and a metric that the application no longer emits cannot be recovered by changing a dashboard panel.
Prometheus signals that separate scrape failure from missing metrics
Prometheus exposes diagnostic series for each scrape. Query them directly for the affected job and instance, then inspect the target status page as well.
| Signal | What it tells you | How to interpret it |
|---|---|---|
up |
Whether the last scrape of a target succeeded | 1 means the target was reachable and the scrape was healthy; 0 means the scrape failed. A value of 1 does not prove that every application metric is present. |
scrape_duration_seconds |
How long the scrape took | Values approaching the timeout point to a slow exporter, overloaded target, or network path. A sudden increase can precede intermittent failures. |
scrape_samples_scraped |
How many samples the endpoint returned | A zero or sharply reduced count can indicate an exporter or application problem even when the target itself is reachable. |
scrape_samples_post_metric_relabeling |
How many samples remained after metric relabeling | If this is lower than the pre-relabel count, relabeling rules may be dropping the series you expect. |
For a quick comparison, run the equivalent direct expressions for the affected labels:
up{job="your-job", instance="your-instance"}
scrape_duration_seconds{job="your-job", instance="your-instance"}
scrape_samples_scraped{job="your-job", instance="your-instance"}
scrape_samples_post_metric_relabeling{job="your-job", instance="your-instance"}Use the labels actually deployed; a changed job or instance label can make a healthy target appear to have vanished when you are querying its old identity.
Step-by-step troubleshooting workflow
1. Confirm that the target is still discovered
Open the Prometheus targets view and find the job and endpoint that should produce the missing series. Confirm that the target is present, its address and scheme are correct, and its job and instance labels match the labels used by your query.
- If the target is absent entirely, inspect service-discovery responses and the configuration that filters or relabels discovered targets.
- If a target reappears under a new address or label set, update the query or the discovery configuration deliberately; do not assume the old series will continue under the old identity.
- If only one monitoring region lacks the target, compare discovery and network configuration in that region with a healthy region.
Discovery changes are a common explanation for a gap affecting every metric from one endpoint at once. They are not fixed by modifying application instrumentation.
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2. Check up and target status
For a discovered target, inspect up over the entire gap. A sustained up=0 establishes a failed scrape during that period. Look at the target’s reported error and endpoint status to distinguish DNS, connection, authentication, protocol, and response failures where the Prometheus deployment exposes those details.
An up=1 result narrows the search but does not close it. Prometheus can successfully scrape an endpoint that omits a particular metric, exports it under changed labels, or loses it to metric relabeling. Continue to sample counts and instrumentation checks whenever the target is healthy but one family of metrics is missing.
3. Compare scrape duration and sample counts
Plot or query scrape_duration_seconds alongside up. A duration that rises toward the timeout suggests that the endpoint or path is too slow. Next compare scrape_samples_scraped with scrape_samples_post_metric_relabeling:
- Low pre-relabel count: the exporter or application returned fewer metrics than expected, or collection stopped partway through.
- Normal pre-relabel count but low post-relabel count: metric relabeling is filtering samples.
- Normal counts and
up=1but a blank panel: investigate labels, timestamps, query range, lookback, aggregation, and panel settings.
These signals describe the Prometheus scrape path; another monitoring system may expose different health indicators.
4. Verify the effective endpoint, interval, and timeout
Inspect the configuration that is actually loaded by the running Prometheus process, not only the file in source control. Confirm the endpoint URL, scheme, job, instance labels, authentication, and any per-job overrides.
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|---|---|---|
| Global scrape interval | 1 minute | Documented current global default; a job-level setting or deployed version can change the effective value. |
| Global scrape timeout | 10 seconds | Documented current global default; verify the running configuration. |
| Timeout relationship | Cannot exceed the scrape interval | Prometheus configuration validation enforces this relationship. |
| Tutorial example | 15-second scrape interval | The getting-started tutorial uses this as an example configuration, not as a universal default or recommendation. |
A timeout shorter than the endpoint’s normal response time creates intermittent up=0 results. Raising the timeout without checking target latency can simply make each failed scrape consume more resources. Conversely, a long interval can make a healthy target look quiet if the dashboard expects more frequent samples. Always record the Prometheus version and effective job settings when comparing behavior across environments.
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5. Inspect ingestion and metric relabeling
Once the target is reachable, determine whether the missing samples entered the server. Compare the two scrape sample counters and review metric-relabeling rules attached to the job.
- Check for
drop,keep, label replacement, or regular expressions that now match the affected metric. - Look for a label rename or removal that changes the series identity. A query using the former label set will return nothing even if the metric is still present.
- Check whether a configuration rollout changed the job or instance labels used by recording rules and dashboards.
Do not infer that storage deleted data merely because a query returns no current value. First establish whether the sample was scraped and retained after relabeling.
6. Check timestamps, clocks, and stale-series behavior
Prometheus can honor or ignore explicit timestamps supplied by a target. If an exporter emits timestamps, verify that its timestamp handling matches the Prometheus configuration and that the exporter’s clock is aligned. A clock that is far ahead or behind can place samples outside the dashboard’s selected window.
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Prometheus marks a series stale when a scrape or rule evaluation no longer returns it. After that point, an instant query does not continue to show the last value indefinitely. Prometheus selects the newest sample within its lookback window, which is five minutes by default; after the series becomes stale or falls outside the applicable lookback, the query returns no value until new samples arrive.
- Set the query evaluation time inside the period where samples should exist.
- Test a range query that spans before, during, and after the gap.
- Check whether the dashboard or query uses a custom lookback or step.
- Distinguish a stale series from a genuinely erased historical sample; they produce different recovery actions.
A brief collection interruption can therefore appear as a blank instant-query result even though older samples remain in storage. The exact stale-data behavior of other platforms must be checked in their documentation rather than assumed to match Prometheus.
7. Verify the exporter and application instrumentation
If up=1, sample counts are plausible, and relabeling is not dropping the metric, inspect the code or exporter that should emit it.
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- Confirm that the metric name is still registered and exposed on the endpoint.
- Check whether a deployment changed label names, label values, or metric type.
- Determine whether the metric is event-driven and therefore legitimately absent until an event occurs.
- Review custom collector logs and errors for the period of the gap.
For a known series that should exist before its first event, export a default zero. This makes “the event has not happened” distinguishable from “the collector disappeared.” A zero is not interchangeable with absence: queries, alerts, and arithmetic treat them differently.
Custom collectors should expose their own collection duration and error count. Without those signals, a target can remain scrapeable while one internal collector repeatedly fails.
“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. Use rate() for counters; use a gauge directly or with a gauge-appropriate transformation. A misleading rate result can look like missing data even when the underlying gauge is present.
8. Trace network and forwarding boundaries
When scraping or forwarding crosses a network boundary, investigate the path independently of the target process. Check:
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- Credentials, certificates, and authorization at the endpoint or intermediary.
- DNS resolution from the Prometheus or agent host.
- Routing, firewall rules, proxies, and connectivity from the actual scraper location.
- Whether the target is down, CPU-saturated, or responding too slowly to meet the timeout.
- Agent, remote-write, or forwarding queues when data is collected in one location and stored in another.
Crossing a network boundary introduces failure causes that are unrelated to the exporter’s metric code. A target can be healthy from its own host while unreachable from the scraper, and a forwarding path can fail after collection has succeeded.
9. Verify the repair at every stage
After changing discovery, configuration, instrumentation, or network access, verify the complete path rather than trusting a green deployment:
- Confirm that the intended target is present with the expected endpoint and labels.
- Check that
upis1across several expected scrape cycles. - Confirm that scrape duration is below the timeout and that sample counts include the expected family.
- Query the metric directly with its expected labels, without dashboard aggregation.
- Check that new samples arrive at the configured cadence and that timestamps fall in the expected range.
- Reopen the dashboard with a time range that includes the repair and verify the panel query separately from its visualization.
The Prometheus getting-started workflow similarly verifies a newly configured time series in the expression browser after reloading or restarting with updated configuration.
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| Observed symptom | Most useful first checks | Likely next action |
|---|---|---|
| Every metric from one target disappears | Target list, discovery filters, endpoint address, DNS, connectivity, up |
Restore discovery or reachability before changing application code. |
up=0 during the gap |
Target error, scrape duration, timeout, credentials, protocol, network path | Fix the failed scrape cause and verify several subsequent cycles. |
up=1, but one metric family is absent |
Exporter output, label changes, pre/post-relabel sample counts | Repair instrumentation or relabeling; the target itself is reachable. |
| Pre-relabel samples are present, post-relabel samples are missing | Metric relabeling rules and regular expressions | Correct the rule and verify the resulting label set. |
| Only an instant query is blank near the end of a series | Staleness, five-minute default lookback, evaluation time, custom lookback | Test a range query and align the dashboard time window with sample timestamps. |
| Samples arrive, but the graph is shifted in time | Explicit timestamps, exporter clock, Prometheus timestamp handling | Correct clock alignment or timestamp configuration. |
| Data exists in the scraper but not in a remote destination | Forwarding path, credentials, queues, network boundaries | Trace the post-scrape path separately from target collection. |
How to compare two monitoring paths or configurations
When a working and failing environment are available, compare the same six axes rather than comparing dashboard screenshots:
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- Discovery and reachability: identical target membership, endpoint, DNS result, and network route.
- Collection cadence: effective scrape interval, timeout, and observed duration.
- Timestamp behavior: explicit timestamps, clock alignment, and query evaluation time.
- Series lifecycle: whether the metric exists before and after relabeling, with the same labels.
- Query semantics: range, step, lookback, staleness, aggregation, and function choice.
- Forwarding path: agent, proxy, remote destination, authentication, and queue health.
This is a diagnostic comparison framework, not a claim that every monitoring vendor exposes the same indicators or applies the same defaults. For a non-Prometheus system, map each axis to the equivalent target-health, collection, ingestion, storage, and query controls documented for that product.
Prevent recurring gaps
- Alert on failed scrapes and abnormal scrape duration, not only on application metrics.
- Track scrape sample counts so a reachable endpoint that suddenly emits almost nothing is visible.
- Review discovery and relabeling changes together with dashboard and recording-rule changes.
- Document the effective interval, timeout, timestamp policy, and deployed version for each critical job.
- Give custom collectors observable duration and error metrics.
- Use explicit zero values for known pre-event series when absence would otherwise be ambiguous.
- Test dashboards with direct queries during deployment so label or metric-name changes are caught before an incident.
The central rule is simple: establish where the series stopped—discovery, scrape, ingestion, query evaluation, instrumentation, or forwarding—then repair that boundary and verify new samples end to end.
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