OTLP 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.
{
"resourceMetrics": [
{
"resource": {
"attributes": [
{
"key": "service.name",
"value": {
"stringValue": "cart-api"
}
},
{
"key": "service.namespace",
"value": {
"stringValue": "shop"
}
},
{
"key": "service.version",
"value": {
"stringValue": "0.9.7"
}
},
{
"key": "service.instance.id",
"value": {
"stringValue": "cart-api-58bd21-9qh"
}
},
{
"key": "deployment.environment.name",
"value": {
"stringValue": "staging"
}
},
{
"key": "host.name",
"value": {
"stringValue": "node-a2"
}
},
{
"key": "host.ip",
"value": {
"stringValue": "192.0.2.12"
}
},
{
"key": "cloud.region",
"value": {Specifications
- Temporality
- cumulative (2)
- Data Points
- 2
- Window Seconds
- 86400
- Add Operation Total
- 118422
- Start Time Stable
- true
Testing contract
Reference control- Scenario
- Convert this export to delta temporality and compare with the paired delta fixture.
- Expected result
- The 24-hour cumulative total of 118422 add operations decomposes to the delta twin's per-interval counts, and startTimeUnixNano stays constant to mark that no counter reset occurred.
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 — Cumulative Temporality (json)” is a deterministic Novus Examples fixture for Observability, Time-series data, 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 · 3,568 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-cumulative-temporality.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- 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 — 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.
- jsonOTLP Metrics — Exemplars with Trace and Span IDs (json)An OTLP histogram carrying two exemplars, each with the trace and span ID of a request that landed in that bucket — including the 2.0031-second outlier that corresponds to the failed payment in the error-trace fixture. The metrics-to-traces jump, with a resolvable target.
Generated by generation/observability.py. Free for any use, no attribution required — license.