OTLP Metrics — Explicit-Bucket Histogram (json)
An OTLP explicit-bucket histogram over the same latency distribution as the Prometheus fixture — but with per-bucket counts rather than cumulative ones, and one more bucket count than bounds. Converting between the two is where most OTLP-to-Prometheus bridges get the arithmetic wrong.
{
"resourceMetrics": [
{
"resource": {
"attributes": [
{
"key": "service.name",
"value": {
"stringValue": "checkout-api"
}
},
{
"key": "service.namespace",
"value": {
"stringValue": "shop"
}
},
{
"key": "service.version",
"value": {
"stringValue": "1.14.2"
}
},
{
"key": "service.instance.id",
"value": {
"stringValue": "checkout-api-7d9f4c-2xk"
}
},
{
"key": "deployment.environment.name",
"value": {
"stringValue": "staging"
}
},
{
"key": "host.name",
"value": {
"stringValue": "node-a1"
}
},
{
"key": "host.ip",
"value": {
"stringValue": "192.0.2.11"
}
},
{
"key": "cloud.region",
"value": {Specifications
- Buckets
- 12
- Bounds
- 11
- Cumulative Buckets
- false
- Count
- 24518
- Sum
- 3418.2214
- Min
- 0.0012
- Max
- 2.0031
- Same Bounds As
- prometheus-exposition-histogram
Testing contract
Expected to pass- Scenario
- Convert this histogram to Prometheus exposition and diff against the Prometheus histogram fixture.
- Expected result
- The 12 delta bucket counts accumulate to the cumulative le series exactly, the +Inf bucket equals count 24518, and no bucket is lost to the off-by-one between 11 bounds and 12 counts.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“OTLP Metrics — Explicit-Bucket Histogram (json)” is a deterministic Novus Examples fixture for Observability, JSON parsing, 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: JSON · 3,945 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.
Code examples
import json
with open("otlp-metrics-histogram.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- 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.
- promPrometheus 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.
- promPrometheus Exposition — Explicit and Skewed Timestamps (prom)Samples with explicit millisecond timestamps alongside samples without any, including one an hour stale and one an hour in the future. Optional timestamps are the part of the exposition format most parsers get wrong, and out-of-window samples are what a scraper must reject rather than backfill.
- promPrometheus Exposition — Summary with and without Quantiles (prom)Two summary families — one exposing four client-side quantiles including quantile="1" for the observed maximum, and one exposing only _sum and _count, which is legal and common. Summaries cannot be re-aggregated across instances, and this fixture is where that gets tested.
- jsonPrometheus HTTP API — Instant Query Vector with Warnings (json)An instant-query response: five up series at a single evaluation timestamp, one of them zero, plus a warnings array that clients routinely ignore. A successful response can carry warnings, and dropping them hides truncated results.
- jsonPrometheus HTTP API — Metric Metadata with a HELP Conflict (json)The metadata endpoint's response, where each metric name maps to a list because different targets can disagree. http_requests_total here has two conflicting HELP strings — the real-world state that a metric catalogue or documentation generator has to resolve rather than assume away.
Generated by generation/observability.py. Free for any use, no attribution required — license.