Developer
df80c68f89
Route all prometheus widget queries through Grafana /api/ds/query instead of
direct Prom HTTP. PrometheusConfig: drop base_url, add grafana_url +
datasource_uid; secret grafana_api_key (required). PrometheusWidgetSource →
MetricSource with _gateway_query POST method. normalize_grafana_frames
recovered from 65bae95 + shared _dedup_label helper. Gateway-path status
check. Startup old-config validation. CHANGELOG migration note. All adapter
tests rewritten for POST /api/ds/query + Grafana frames mock. Backend: 331
pytest pass, ruff clean. Frontend: build green (unchanged in S1).
258 lines
8.6 KiB
Python
258 lines
8.6 KiB
Python
"""Unit tests for the shared Prometheus range-query helpers (SC-101..SC-104)."""
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from __future__ import annotations
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import pytest
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from media_library_viewer_api.widgets.prometheus_range import (
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WINDOW_PRESETS,
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_dedup_label,
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normalize_grafana_frames,
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normalize_prometheus_matrix,
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step_for_window,
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)
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class TestStepForWindow:
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"""SC-104: every preset must yield 100–300 points."""
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@pytest.mark.parametrize("preset", sorted(WINDOW_PRESETS))
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def test_presets_yield_in_band_point_counts(self, preset: str) -> None:
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window = WINDOW_PRESETS[preset]
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step = step_for_window(window)
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# Clamped minimum.
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assert step >= 15
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point_count = window // step
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assert 100 <= point_count <= 300, f"{preset}: {point_count} points (step={step})"
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def test_floor_of_fifteen_seconds(self) -> None:
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# A tiny window that would otherwise produce a sub-15s step is clamped.
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assert step_for_window(60) == 15
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def test_custom_target_points(self) -> None:
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# Targeting 100 points for 1h yields step 36 (3600/100).
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assert step_for_window(3_600, target_points=100) == 36
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class TestNormalizePrometheusMatrix:
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"""SC-102: label rule + null handling + dedup."""
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def test_empty_matrix(self) -> None:
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assert normalize_prometheus_matrix([]) == []
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def test_drops_dunder_labels_and_joins(self) -> None:
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result = [
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{
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"metric": {"__name__": "node_cpu_seconds_total", "instance": "host:9100", "mode": "idle"},
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"values": [[1_700_000_000, "12.5"], [1_700_000_030, "13.0"]],
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}
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]
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out = normalize_prometheus_matrix(result)
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assert len(out) == 1
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assert out[0]["label"] == "instance=host:9100 mode=idle"
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assert out[0]["points"] == [
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{"t": 1_700_000_000, "v": 12.5},
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{"t": 1_700_000_030, "v": 13.0},
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]
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def test_falls_back_to_value_when_no_labels(self) -> None:
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result = [{"metric": {}, "values": [[100, "1"]]}]
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out = normalize_prometheus_matrix(result)
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assert out[0]["label"] == "value"
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def test_dedup_collisions_with_suffix(self) -> None:
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# Two series with identical visible labels get a "(1)" suffix on the 2nd.
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result = [
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{"metric": {"job": "x"}, "values": [[1, "1"]]},
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{"metric": {"job": "x"}, "values": [[1, "2"]]},
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]
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out = normalize_prometheus_matrix(result)
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labels = [s["label"] for s in out]
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assert labels == ["job=x", "job=x (1)"]
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def test_non_numeric_sentinels_become_none(self) -> None:
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result = [
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{
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"metric": {"job": "x"},
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"values": [
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[1, "NaN"],
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[2, "+Inf"],
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[3, "-Inf"],
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[4, "3.5"],
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],
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}
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]
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out = normalize_prometheus_matrix(result)
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assert out[0]["points"] == [
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{"t": 1, "v": None},
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{"t": 2, "v": None},
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{"t": 3, "v": None},
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{"t": 4, "v": 3.5},
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]
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def test_malformed_values_are_ignored_not_raised(self) -> None:
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result = [
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{
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"metric": {"job": "x"},
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"values": [
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[1, "3.5"],
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["not-a-ts", "9"], # unusable timestamp → dropped
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[3, "junk-value"], # unparseable value → v: None
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],
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}
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]
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out = normalize_prometheus_matrix(result)
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assert out[0]["points"] == [
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{"t": 1, "v": 3.5},
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{"t": 3, "v": None},
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]
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class TestDedupLabel:
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"""The shared label-dedup helper used by both normalizers (GM-104)."""
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def test_first_use_returns_label_unchanged(self) -> None:
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seen: dict[str, int] = {}
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assert _dedup_label("value", seen) == "value"
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assert seen == {"value": 0}
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def test_collision_appends_suffix(self) -> None:
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seen: dict[str, int] = {}
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assert _dedup_label("job=x", seen) == "job=x"
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assert _dedup_label("job=x", seen) == "job=x (1)"
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assert _dedup_label("job=x", seen) == "job=x (2)"
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def test_different_labels_dont_collide(self) -> None:
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seen: dict[str, int] = {}
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assert _dedup_label("a", seen) == "a"
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assert _dedup_label("b", seen) == "b"
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class TestNormalizeGrafanaFrames:
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"""GM-104: frames normalizer recovered from 65bae95 + shared dedup."""
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def test_empty_response(self) -> None:
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assert normalize_grafana_frames({"results": {}}) == []
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assert normalize_grafana_frames({}) == []
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def test_single_frame_with_values(self) -> None:
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raw = {
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"results": {
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"A": {
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"frames": [
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{
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"data": {"values": [[1000, 2000], [1.5, 2.5]]},
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"schema": {"fields": [{"name": "Time"}, {"name": "Value"}]},
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}
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]
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}
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}
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}
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out = normalize_grafana_frames(raw)
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assert len(out) == 1
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assert out[0]["label"] == "Value"
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assert out[0]["points"] == [
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{"t": 1000, "v": 1.5},
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{"t": 2000, "v": 2.5},
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]
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def test_display_name_takes_priority(self) -> None:
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raw = {
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"results": {
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"A": {
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"frames": [
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{
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"data": {"values": [[100, 200], [0.75, 0.80]]},
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"schema": {
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"fields": [
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{"name": "Time"},
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{
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"name": "Value",
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"labels": {"instance": "host:9100"},
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"config": {"displayName": "CPU Usage"},
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},
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]
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},
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}
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]
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}
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}
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}
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out = normalize_grafana_frames(raw)
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assert out[0]["label"] == "CPU Usage"
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def test_labels_fallback_when_no_display_name(self) -> None:
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raw = {
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"results": {
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"A": {
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"frames": [
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{
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"data": {"values": [[100], [1.0]]},
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"schema": {
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"fields": [
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{"name": "Time"},
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{
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"name": "Value",
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"labels": {"__name__": "up", "instance": "h:9100"},
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},
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]
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},
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}
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]
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}
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}
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}
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out = normalize_grafana_frames(raw)
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assert out[0]["label"] == "instance=h:9100"
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def test_falls_back_to_value_when_no_metadata(self) -> None:
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raw = {
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"results": {
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"A": {
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"frames": [
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{
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"data": {"values": [[100], [1.0]]},
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"schema": {"fields": [{"name": "Time"}, {}]},
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}
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]
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}
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}
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}
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out = normalize_grafana_frames(raw)
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assert out[0]["label"] == "value"
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def test_dedup_collisions(self) -> None:
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raw = {
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"results": {
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"A": {
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"frames": [
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{
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"data": {"values": [[100], [1.0]]},
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"schema": {"fields": [{}, {"name": "Value"}]},
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},
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{
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"data": {"values": [[100], [2.0]]},
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"schema": {"fields": [{}, {"name": "Value"}]},
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},
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]
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}
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}
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}
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out = normalize_grafana_frames(raw)
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labels = [s["label"] for s in out]
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assert labels == ["Value", "Value (1)"]
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def test_skips_frames_with_insufficient_values(self) -> None:
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raw = {
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"results": {
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"A": {
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"frames": [
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{"data": {"values": [[100]]}, "schema": {"fields": []}},
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{"data": {"values": [[100], [1.0]]}, "schema": {"fields": [{}, {}]}},
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]
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}
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}
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}
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out = normalize_grafana_frames(raw)
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assert len(out) == 1
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