OTLP Metrics — Summary with Client-Side Quantiles (json)
The OTLP summary type — quantiles computed on the client, kept only for translating legacy Prometheus summaries. It carries the same four quantiles as the Prometheus summary fixture, and like that one it cannot be re-aggregated across instances without producing a wrong number.
{
"resourceMetrics": [
{
"resource": {
"attributes": [
{
"key": "service.name",
"value": {
"stringValue": "payments-api"
}
},
{
"key": "service.namespace",
"value": {
"stringValue": "shop"
}
},
{
"key": "service.version",
"value": {
"stringValue": "3.1.4"
}
},
{
"key": "service.instance.id",
"value": {
"stringValue": "payments-api-31ef60-7wz"
}
},
{
"key": "deployment.environment.name",
"value": {
"stringValue": "staging"
}
},
{
"key": "host.name",
"value": {
"stringValue": "node-b2"
}
},
{
"key": "host.ip",
"value": {
"stringValue": "192.0.2.22"
}
},
{
"key": "cloud.region",
"value": {Specifications
- Quantiles
- 4
- Count
- 14022
- Sum
- 918.4471
- Legacy Type
- true
- Reaggregatable
- false
- Matches Prometheus Fixture
- prometheus-exposition-summary
Testing contract
Expected to pass- Scenario
- Translate this summary to Prometheus exposition and attempt a cross-instance aggregation.
- Expected result
- It converts one-for-one to the quantile-labelled series in the Prometheus summary fixture, and an attempt to average the quantiles across instances is refused rather than silently performed.
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 — Summary with Client-Side Quantiles (json)” is a deterministic Novus Examples fixture for Observability, JSON parsing, Conversion testing. 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 · 3,628 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-summary.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonJaeger JSON Trace — Same 13-Span Checkout (json)The checkout trace in the Jaeger query-API shape — a data array holding one trace, spans carrying microsecond startTime and duration, CHILD_OF references, typed tags and a per-service processes map. The same span tree as the OTLP and Zipkin twins, so a converter can be scored exactly.
- jsonOTLP Trace — Stock proto3 JSON Encoding (base64 IDs) (json)The same 13-span trace serialised the way a stock proto3 JSON marshaller writes it: trace and span IDs base64-encoded and span kind and status as enum names rather than numbers. Paired with the hex/numeric OTLP/JSON twin so a receiver can be tested against both encodings of one payload.
- jsonZipkin v2 JSON Trace — Same 13-Span Checkout (json)The checkout trace as a flat Zipkin v2 span list with localEndpoint, string-only tags and annotations. Two conversions are deliberately visible: Zipkin has no INTERNAL kind, so those two spans omit kind, and every typed attribute is stringified.
- 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 — Cumulative Temporality (json)Cumulative sums whose start timestamp stays fixed across exports, so every point is the running total since process start. Paired with a delta-temporality twin covering the same window, because converting between the two is a stateful operation that a bridge must get right in both directions.
- jsonOTLP Metrics — Delta Temporality (json)The same 24 hours of cart updates as six delta points, each with its own start and end timestamp covering a four-hour window. The delta twin of the cumulative fixture: summing these must reproduce the cumulative total exactly, and a gap or overlap in the windows is the failure to catch.
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