OTLP Metrics — Monotonic Sum and Gauge Data Points (json)
An OTLP metrics export with a monotonic cumulative sum and two gauges, showing the parts of the wire format that trip parsers: int data points serialised as JSON strings, UCUM unit annotations like {request} and By, and the start timestamp that makes a counter reset detectable.
{
"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
- Metrics
- 3
- Data Points
- 6
- Temporality
- cumulative (2)
- Monotonic
- true
- Int Points As String
- true
- Units
- {request}, By, 1
Testing contract
Expected to pass- Scenario
- Decode the export and convert it to your internal metric model.
- Expected result
- Six data points decode with asInt values read as 64-bit integers rather than strings, and the sum keeps aggregationTemporality 2 with isMonotonic true so rate() is computed correctly.
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 — Monotonic Sum and Gauge Data Points (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 · 6,772 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-sum-gauge.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.
- jsonPrometheus HTTP API — query_range Matrix Response (json)A Prometheus /api/v1/query_range response: three labelled series of 30 points each at a 60-second step, with sample values as JSON strings and timestamps as float seconds. The exact envelope a dashboard client has to unpack, including the string-typed values that break naive charting code.
- 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.
- 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.
Generated by generation/observability.py. Free for any use, no attribution required — license.