Prometheus Exposition — Cumulative Histogram (prom)
A classic Prometheus histogram: eleven finite le buckets plus +Inf, with monotonically non-decreasing cumulative counts and matching _sum and _count series. The fixture for bucket ordering, cumulative arithmetic and histogram_quantile interpolation.
# HELP http_request_duration_seconds End-to-end request latency in seconds.
# TYPE http_request_duration_seconds histogram
http_request_duration_seconds_bucket{service="checkout-api",le="0.005"} 12
http_request_duration_seconds_bucket{service="checkout-api",le="0.01"} 341
http_request_duration_seconds_bucket{service="checkout-api",le="0.025"} 2210
http_request_duration_seconds_bucket{service="checkout-api",le="0.05"} 7412
http_request_duration_seconds_bucket{service="checkout-api",le="0.1"} 15908
http_request_duration_seconds_bucket{service="checkout-api",le="0.25"} 21744
http_request_duration_seconds_bucket{service="checkout-api",le="0.5"} 23511
http_request_duration_seconds_bucket{service="checkout-api",le="1.0"} 24102
http_request_duration_seconds_bucket{service="checkout-api",le="2.5"} 24398
http_request_duration_seconds_bucket{service="checkout-api",le="5.0"} 24487
http_request_duration_seconds_bucket{service="checkout-api",le="10.0"} 24512
http_request_duration_seconds_bucket{service="checkout-api",le="+Inf"} 24518
http_request_duration_seconds_sum{service="checkout-api"} 3418.2214
http_request_duration_seconds_count{service="checkout-api"} 24518
Specifications
- Buckets
- 12
- Cumulative
- true
- Has Inf Bucket
- true
- Count
- 24518
- Sum
- 3418.2214
- Lowest Bound
- 0.005
- Highest Finite Bound
- 10
- Implied P99 Bound
- 1
Testing contract
Expected to pass- Scenario
- Parse the family and compute a 99th-percentile estimate from the buckets.
- Expected result
- Bucket counts increase monotonically to 24518, the +Inf bucket equals _count exactly, and the p99 estimate falls inside the 0.5 to 1.0 second bucket.
What is a .prom file?
A .prom file holds metrics in the Prometheus text exposition format, the same body a `/metrics` endpoint returns. Each metric family is introduced by `# HELP` and `# TYPE` comments and followed by one sample per line: a metric name, an optional brace-delimited label set, a value, and an optional millisecond timestamp. Counters, gauges, histograms (with `_bucket`, `_sum`, `_count` series and a `+Inf` bucket), and summaries with quantile labels all use this one grammar.
How to use this file
Use an example .prom file to test exposition-format parsers, scrapers, and the node_exporter textfile collector — checking label escaping, histogram bucket ordering and cumulative counts, and the handling of `NaN` and `+Inf` values.
How to use this file for testing
“Prometheus Exposition — Cumulative Histogram (prom)” is a deterministic Novus Examples fixture for Observability, Time-series data, Time-series data. Structured and plain-text telemetry with known timestamps, levels, request identifiers, and error states for testing log ingestion, correlation, dashboards, and alert pipelines.
Documented properties for this file: PROM · 1,171 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Telemetry fixtures use fixed trace IDs, span IDs, and timestamps so ingestion is reproducible run to run. Point your collector, parser, or query layer at the file and assert the documented span tree, metric families, or severity mix; service and host names are invented.
Related files
- jsonOTLP Metrics — Counter Reset on Process Restart (json)A monotonic counter that drops from 94,880 to 1,120 when its process restarts, with startTimeUnixNano changing at exactly that point to mark the reset. Rate calculations that subtract consecutive values without checking the start timestamp produce a large negative rate here.
- csvMetric Range Export — Long (Tidy) CSV (csv)The same 90 samples as the query_range response, exported one row per observation with both epoch and ISO 8601 timestamps. The long layout every dataframe library prefers, and half of a reshaping pair.
- csvMetric Range Export — Wide (Pivoted) CSV (csv)The same 90 samples pivoted to one column per service and one row per timestamp — the layout a spreadsheet chart expects. Paired with the long-format export so a reshape can be scored in both directions.
- promOpenMetrics — Full Document with UNIT, info and stateset (prom)A complete OpenMetrics document — the _total and _created series a counter really has, UNIT metadata, an info metric carrying build metadata and a stateset with exactly one active state — terminated by the mandatory # EOF. This is what the exposition format became once it was standardised.
- promOpenMetrics — Histogram Exemplars Linking to the Trace Fixtures (prom)Histogram buckets carrying exemplars whose trace_id and span_id resolve against the OTLP trace fixtures in this category — the exact-to-example link that turns a latency spike on a graph into a specific request. Five exemplars, each with its own observed value and timestamp.
- jsonOTLP Metrics — Base-2 Exponential Histogram (json)An exponential histogram at scale 3 — eight buckets per power of two — with a negative offset, a zero bucket and its threshold. Bucket i covers (base^(offset+i), base^(offset+i+1)], and getting that indexing wrong silently shifts every percentile, which is what this fixture is for.
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