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5 Commits
test/keyle
...
fix/infra-
| Author | SHA1 | Date | |
|---|---|---|---|
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a846a6c3ee | ||
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999824e8b5 | ||
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a07f1396fe | ||
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9acc7700e4 | ||
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36c37ce603 |
@@ -850,8 +850,10 @@ func (m *module) getPerGroupDistinctCounts(
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valueExpr = fmt.Sprintf("(%s)", strings.Join(parts, ", "))
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}
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// Prefix the alias so it never collides with a groupBy col alias
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// (e.g. clusters grouped by k8s.node.name, which is also counted).
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selectCols = append(selectCols,
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fmt.Sprintf("uniqExactIf(%s, %s != '') AS %s", valueExpr, extract, quoteIdentifier(attr)),
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fmt.Sprintf("uniqExactIf(%s, %s != '') AS %s", valueExpr, extract, quoteIdentifier(fmt.Sprintf("__count_%s", attr))),
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)
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}
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sb.Select(selectCols...)
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19
tests/fixtures/inframonitoring.py
vendored
19
tests/fixtures/inframonitoring.py
vendored
@@ -50,3 +50,22 @@ def expected_status_counts(**nonzero: int) -> dict:
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counts = {bucket: 0 for bucket in STATUS_BUCKETS}
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counts.update(nonzero)
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return counts
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# All buckets of the clusters-API per-group resource counts (camelCase, matches
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# inframonitoringtypes ClusterRecord.Counts / the API response).
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RESOURCE_COUNT_BUCKETS = (
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"nodes",
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"namespaces",
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"deployments",
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"daemonSets",
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"jobs",
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"statefulSets",
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)
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def expected_resource_counts(**nonzero: int) -> dict:
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"""Full resource-counts dict with the given buckets set, rest 0."""
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counts = {bucket: 0 for bucket in RESOURCE_COUNT_BUCKETS}
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counts.update(nonzero)
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return counts
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@@ -1,36 +1,36 @@
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{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.deployment.name":"gb-dep-shared","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.deployment.name":"gb-dep-b3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.deployment.name":"gb-dep-b4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
|
||||
@@ -354,6 +354,32 @@ _GROUPBY_FLOAT_FIELDS = {
|
||||
},
|
||||
id="cluster",
|
||||
),
|
||||
# groupBy on a counted attr: regression guard for the counts-query
|
||||
# alias collision (CH error 179).
|
||||
pytest.param(
|
||||
{
|
||||
"fixture": "namespaces_groupby.jsonl",
|
||||
"group_by": "k8s.deployment.name",
|
||||
"filter": None,
|
||||
"group_meta_keys": ["k8s.deployment.name"],
|
||||
"expected_type": "grouped_list",
|
||||
"groups": {
|
||||
"gb-dep-shared": {
|
||||
"namespaceName": "",
|
||||
"counts": {"deployments": 2, "daemonSets": 0, "jobs": 0, "statefulSets": 0},
|
||||
},
|
||||
"gb-dep-b3": {
|
||||
"namespaceName": "",
|
||||
"counts": {"deployments": 1, "daemonSets": 0, "jobs": 0, "statefulSets": 0},
|
||||
},
|
||||
"gb-dep-b4": {
|
||||
"namespaceName": "",
|
||||
"counts": {"deployments": 1, "daemonSets": 0, "jobs": 0, "statefulSets": 0},
|
||||
},
|
||||
},
|
||||
},
|
||||
id="deployment_name_counted_attr",
|
||||
),
|
||||
# Default groupBy (no groupBy in request) => [k8s.namespace.name,
|
||||
# k8s.cluster.name] (module.go ListNamespaces), response list. Namespaces
|
||||
# are cluster-scoped, so a same-named namespace must NOT collapse across
|
||||
|
||||
@@ -10,7 +10,7 @@ import requests
|
||||
from fixtures import types
|
||||
from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
|
||||
from fixtures.fs import get_testdata_file_path
|
||||
from fixtures.inframonitoring import expected_status_counts
|
||||
from fixtures.inframonitoring import expected_resource_counts, expected_status_counts
|
||||
from fixtures.metrics import Metrics
|
||||
from fixtures.querier import compare_values, get_all_warnings
|
||||
|
||||
@@ -406,17 +406,18 @@ def test_clusters_pod_status_aggregation(
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"group_key,expected",
|
||||
"group_key,flt,expected",
|
||||
[
|
||||
# groupBy=[k8s.cluster.name]: one record per cluster, clusterName
|
||||
# populated (clusters.go:29-32). Each cluster has 1 ready node, 1 pod.
|
||||
pytest.param(
|
||||
"k8s.cluster.name",
|
||||
None,
|
||||
{
|
||||
"gb-gcp-1": {"readiness": {"ready": 1, "notReady": 0}},
|
||||
"gb-gcp-2": {"readiness": {"ready": 1, "notReady": 0}},
|
||||
"gb-aws-1": {"readiness": {"ready": 1, "notReady": 0}},
|
||||
"gb-aws-2": {"readiness": {"ready": 1, "notReady": 0}},
|
||||
"gb-gcp-1": {"readiness": {"ready": 1, "notReady": 0}, "counts": expected_resource_counts(nodes=1, namespaces=1)},
|
||||
"gb-gcp-2": {"readiness": {"ready": 1, "notReady": 0}, "counts": expected_resource_counts(nodes=1, namespaces=1)},
|
||||
"gb-aws-1": {"readiness": {"ready": 1, "notReady": 0}, "counts": expected_resource_counts(nodes=1, namespaces=1)},
|
||||
"gb-aws-2": {"readiness": {"ready": 1, "notReady": 0}, "counts": expected_resource_counts(nodes=1, namespaces=1)},
|
||||
},
|
||||
id="cluster_name",
|
||||
),
|
||||
@@ -424,25 +425,38 @@ def test_clusters_pod_status_aggregation(
|
||||
# clusterName empty (custom-groupBy branch).
|
||||
pytest.param(
|
||||
"cloud.provider",
|
||||
None,
|
||||
{
|
||||
"gcp": {"readiness": {"ready": 2, "notReady": 0}},
|
||||
"aws": {"readiness": {"ready": 2, "notReady": 0}},
|
||||
"gcp": {"readiness": {"ready": 2, "notReady": 0}, "counts": expected_resource_counts(nodes=2, namespaces=2)},
|
||||
"aws": {"readiness": {"ready": 2, "notReady": 0}, "counts": expected_resource_counts(nodes=2, namespaces=2)},
|
||||
},
|
||||
id="cloud_provider",
|
||||
),
|
||||
# groupBy on a counted attr: regression guard for the counts-query
|
||||
# alias collision (CH error 179).
|
||||
pytest.param(
|
||||
"k8s.namespace.name",
|
||||
"k8s.namespace.name = 'ns-x'",
|
||||
{
|
||||
"ns-x": {"readiness": {"ready": 0, "notReady": 0}, "counts": expected_resource_counts(nodes=4, namespaces=4)},
|
||||
},
|
||||
id="namespace_name_counted_attr",
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_clusters_groupby(
|
||||
def test_clusters_groupby( # pylint: disable=too-many-arguments,too-many-positional-arguments
|
||||
signoz: types.SigNoz,
|
||||
create_user_admin: None, # pylint: disable=unused-argument
|
||||
get_token,
|
||||
insert_metrics,
|
||||
group_key: str,
|
||||
flt,
|
||||
expected: dict,
|
||||
) -> None:
|
||||
"""groupBy returns one record per distinct group with aggregated readiness.
|
||||
clusterName is populated only when grouping by k8s.cluster.name
|
||||
(clusters.go:29-32 list-vs-grouped branch); meta surfaces the groupBy key."""
|
||||
"""groupBy returns one record per distinct group with aggregated readiness
|
||||
and resource counts. clusterName is populated only when grouping by
|
||||
k8s.cluster.name (clusters.go:29-32 list-vs-grouped branch); meta surfaces
|
||||
the groupBy key."""
|
||||
now = datetime.now(tz=UTC).replace(microsecond=0)
|
||||
insert_metrics(
|
||||
Metrics.load_from_file(
|
||||
@@ -452,21 +466,24 @@ def test_clusters_groupby(
|
||||
)
|
||||
|
||||
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
|
||||
body: dict = {
|
||||
"start": int((now - timedelta(minutes=5)).timestamp() * 1000),
|
||||
"end": int(now.timestamp() * 1000),
|
||||
"limit": 50,
|
||||
"groupBy": [
|
||||
{
|
||||
"name": group_key,
|
||||
"fieldDataType": "string",
|
||||
"fieldContext": "resource",
|
||||
}
|
||||
],
|
||||
}
|
||||
if flt is not None:
|
||||
body["filter"] = {"expression": flt}
|
||||
response = requests.post(
|
||||
signoz.self.host_configs["8080"].get(ENDPOINT),
|
||||
headers={"authorization": f"Bearer {token}"},
|
||||
json={
|
||||
"start": int((now - timedelta(minutes=5)).timestamp() * 1000),
|
||||
"end": int(now.timestamp() * 1000),
|
||||
"limit": 50,
|
||||
"groupBy": [
|
||||
{
|
||||
"name": group_key,
|
||||
"fieldDataType": "string",
|
||||
"fieldContext": "resource",
|
||||
}
|
||||
],
|
||||
},
|
||||
json=body,
|
||||
timeout=5,
|
||||
)
|
||||
assert response.status_code == HTTPStatus.OK, response.text
|
||||
@@ -483,6 +500,7 @@ def test_clusters_groupby(
|
||||
# empty otherwise.
|
||||
assert rec["clusterName"] == (group if group_key == "k8s.cluster.name" else "")
|
||||
assert rec["nodeCountsByReadiness"] == exp["readiness"]
|
||||
assert rec["counts"] == exp["counts"], f"{group}: got {rec['counts']}, expected {exp['counts']}"
|
||||
assert group_key in rec["meta"], rec["meta"]
|
||||
|
||||
|
||||
|
||||
@@ -1,314 +0,0 @@
|
||||
"""Pins the keyless-row contract for filter operators, per signal.
|
||||
|
||||
The contract (deliberate product semantics, enforced by
|
||||
`FilterOperator.AddDefaultExistsFilter` in
|
||||
pkg/types/querybuildertypes/querybuildertypesv5/builder_elements.go):
|
||||
|
||||
- Negative operators (!=, NOT IN, NOT LIKE, NOT CONTAINS, ...) are a set
|
||||
complement over ALL rows: a row that does not carry the key at all MUST
|
||||
match. Users opt into presence explicitly with `AND key EXISTS`.
|
||||
- Positive operators carry an implicit existence guard: a keyless row must
|
||||
NOT match `key = ''`-style comparisons against sentinel defaults.
|
||||
- EXISTS / NOT EXISTS partition rows exactly by key presence.
|
||||
- Numeric attributes inherit the map-default sentinel: a missing key reads
|
||||
as 0, so `num != 0` excludes keyless rows while `num != 5` includes them.
|
||||
This conflation is deliberate and pinned here as the reference for any
|
||||
value-expression change (for example coalesce tails in semconv families).
|
||||
|
||||
Any implementation change that makes these assertions fail is a behavior
|
||||
break, not a cleanup. Family-field behavior must mirror this matrix; see
|
||||
queriertraces/13_semconv_evolution.py.
|
||||
|
||||
The attribute names used here are deliberately outside every semantic
|
||||
convention family so this file pins the base contract regardless of the
|
||||
semconv overlay state.
|
||||
"""
|
||||
|
||||
from collections.abc import Callable, Generator
|
||||
from datetime import UTC, datetime, timedelta
|
||||
from http import HTTPStatus
|
||||
|
||||
import pytest
|
||||
|
||||
from fixtures import types
|
||||
from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
|
||||
from fixtures.logs import Logs
|
||||
from fixtures.metrics import Metrics
|
||||
from fixtures.querier import (
|
||||
BuilderQuery,
|
||||
OrderBy,
|
||||
RequestType,
|
||||
TelemetryFieldKey,
|
||||
aligned_epoch,
|
||||
build_builder_query,
|
||||
get_all_series,
|
||||
get_column_data_from_response,
|
||||
make_query_request,
|
||||
)
|
||||
from fixtures.traces import TraceIdGenerator, Traces, TracesKind, TracesStatusCode
|
||||
|
||||
PREFIX = "keyless-sem"
|
||||
STRING_KEY = "tenant.tier"
|
||||
NUMBER_KEY = "retry.count"
|
||||
METRIC_NAME = "keyless_semantics_gauge"
|
||||
METRIC_LABEL = "tenant_tier"
|
||||
|
||||
# Row identities, keyed by which value of the string key they carry.
|
||||
GOLD = f"{PREFIX}-gold"
|
||||
SILVER = f"{PREFIX}-silver"
|
||||
NONE = f"{PREFIX}-none" # carries neither the string nor the number key
|
||||
|
||||
# One matrix, three signals. Each case: (filter over the string key, expected
|
||||
# row identities). The keyless row's membership is the point of every case.
|
||||
STRING_MATRIX = [
|
||||
pytest.param("{key} = 'gold'", {GOLD}, id="eq_excludes_keyless"),
|
||||
pytest.param("{key} != 'gold'", {SILVER, NONE}, id="neq_includes_keyless"),
|
||||
pytest.param("{key} NOT IN ['gold', 'silver']", {NONE}, id="not_in_includes_keyless"),
|
||||
pytest.param("NOT {key} LIKE '%gold%'", {SILVER, NONE}, id="not_like_includes_keyless"),
|
||||
pytest.param("{key} NOT CONTAINS 'gol'", {SILVER, NONE}, id="not_contains_includes_keyless"),
|
||||
pytest.param("{key} EXISTS", {GOLD, SILVER}, id="exists_partitions"),
|
||||
pytest.param("{key} NOT EXISTS", {NONE}, id="not_exists_partitions"),
|
||||
# The documented idiom for "present and not X": composition, not a new
|
||||
# operator semantic.
|
||||
pytest.param("{key} != 'gold' AND {key} EXISTS", {SILVER}, id="neq_composed_with_exists"),
|
||||
]
|
||||
|
||||
# Numeric attributes read the map default (0) for missing keys. `!= 0` is the
|
||||
# deliberate blind spot: a keyless row is indistinguishable from a stored 0.
|
||||
NUMBER_MATRIX = [
|
||||
pytest.param("{key} = 0", {GOLD}, id="numeric_eq_zero_excludes_keyless"),
|
||||
pytest.param("{key} != 5", {GOLD, NONE}, id="numeric_neq_includes_keyless"),
|
||||
pytest.param("{key} != 0", {SILVER}, id="numeric_neq_zero_sentinel_conflation"),
|
||||
]
|
||||
|
||||
|
||||
@pytest.fixture(name="keyless_rows")
|
||||
def keyless_rows(
|
||||
insert_logs: Callable[[list[Logs]], None],
|
||||
insert_traces: Callable[[list[Traces]], None],
|
||||
) -> Generator[datetime]:
|
||||
now = datetime.now(tz=UTC).replace(microsecond=0) - timedelta(minutes=1)
|
||||
|
||||
def resources(identity: str, tier: str | None) -> dict:
|
||||
base = {"service.name": identity}
|
||||
if tier is not None:
|
||||
base[STRING_KEY] = tier
|
||||
return base
|
||||
|
||||
def attributes(tier: str | None, retries: int | None) -> dict:
|
||||
attrs: dict = {}
|
||||
if tier is not None:
|
||||
attrs[STRING_KEY] = tier
|
||||
if retries is not None:
|
||||
attrs[NUMBER_KEY] = retries
|
||||
return attrs
|
||||
|
||||
rows = [
|
||||
(GOLD, "gold", 0, timedelta(seconds=3)),
|
||||
(SILVER, "silver", 5, timedelta(seconds=2)),
|
||||
(NONE, None, None, timedelta(seconds=1)),
|
||||
]
|
||||
|
||||
insert_traces(
|
||||
[
|
||||
Traces(
|
||||
timestamp=now - offset,
|
||||
duration=timedelta(milliseconds=10),
|
||||
trace_id=TraceIdGenerator.trace_id(),
|
||||
span_id=TraceIdGenerator.span_id(),
|
||||
name=identity,
|
||||
kind=TracesKind.SPAN_KIND_SERVER,
|
||||
status_code=TracesStatusCode.STATUS_CODE_OK,
|
||||
resources=resources(identity, tier),
|
||||
attributes=attributes(tier, retries),
|
||||
)
|
||||
for identity, tier, retries, offset in rows
|
||||
]
|
||||
)
|
||||
insert_logs(
|
||||
[
|
||||
Logs(
|
||||
timestamp=now - offset,
|
||||
body=identity,
|
||||
resources=resources(identity, tier),
|
||||
attributes=attributes(tier, retries),
|
||||
)
|
||||
for identity, tier, retries, offset in rows
|
||||
]
|
||||
)
|
||||
yield now
|
||||
|
||||
|
||||
@pytest.fixture(name="keyless_series")
|
||||
def keyless_series(insert_metrics: Callable[[list[Metrics]], None]) -> Generator[tuple[int, int]]:
|
||||
start = aligned_epoch(timedelta(minutes=30))
|
||||
points = 5
|
||||
|
||||
def labels(identity: str, tier: str | None) -> dict:
|
||||
base = {"service": identity}
|
||||
if tier is not None:
|
||||
base[METRIC_LABEL] = tier
|
||||
return base
|
||||
|
||||
insert_metrics(
|
||||
[
|
||||
Metrics(
|
||||
metric_name=METRIC_NAME,
|
||||
labels=labels(identity, tier),
|
||||
timestamp=datetime.fromtimestamp(start + minute * 60, tz=UTC),
|
||||
value=10.0,
|
||||
type_="Gauge",
|
||||
is_monotonic=False,
|
||||
)
|
||||
for identity, tier in ((GOLD, "gold"), (SILVER, "silver"), (NONE, None))
|
||||
for minute in range(points)
|
||||
]
|
||||
)
|
||||
yield start, start + points * 60
|
||||
|
||||
|
||||
def _matching_rows(
|
||||
signoz: types.SigNoz,
|
||||
token: str,
|
||||
now: datetime,
|
||||
signal: str,
|
||||
identity_field: str,
|
||||
identity_column: str,
|
||||
expression: str,
|
||||
) -> set[str]:
|
||||
response = make_query_request(
|
||||
signoz,
|
||||
token,
|
||||
start_ms=int((now - timedelta(minutes=2)).timestamp() * 1000),
|
||||
end_ms=int((now + timedelta(minutes=1)).timestamp() * 1000),
|
||||
request_type=RequestType.RAW,
|
||||
queries=[
|
||||
BuilderQuery(
|
||||
signal=signal,
|
||||
name="A",
|
||||
limit=100,
|
||||
filter_expression=expression,
|
||||
select_fields=[TelemetryFieldKey(identity_field)],
|
||||
order=[OrderBy(TelemetryFieldKey("timestamp"), "asc")],
|
||||
).to_dict()
|
||||
],
|
||||
)
|
||||
assert response.status_code == HTTPStatus.OK, response.text
|
||||
# Set semantics keep the assertions stable when the shared stack is reused
|
||||
# across runs and older rows with the same identities are still present.
|
||||
return {
|
||||
value
|
||||
for value in get_column_data_from_response(response.json(), identity_column)
|
||||
if isinstance(value, str) and value.startswith(PREFIX)
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.parametrize("expression_template,expected", STRING_MATRIX)
|
||||
@pytest.mark.parametrize("context", ["resource", "attribute"])
|
||||
@pytest.mark.parametrize(
|
||||
"signal,identity_field,identity_column",
|
||||
[
|
||||
pytest.param("traces", "span.name", "name", id="traces"),
|
||||
pytest.param("logs", "body", "body", id="logs"),
|
||||
],
|
||||
)
|
||||
def test_negative_operators_include_keyless_rows(
|
||||
signoz: types.SigNoz,
|
||||
create_user_admin: None, # pylint: disable=unused-argument
|
||||
get_token: Callable[[str, str], str],
|
||||
keyless_rows: datetime,
|
||||
signal: str,
|
||||
identity_field: str,
|
||||
identity_column: str,
|
||||
context: str,
|
||||
expression_template: str,
|
||||
expected: set[str],
|
||||
) -> None:
|
||||
"""Negative operators are a set complement over all rows; presence is an
|
||||
explicit EXISTS opt-in. Holds identically for resource and attribute
|
||||
contexts on traces and logs."""
|
||||
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
|
||||
expression = expression_template.format(key=f"{context}.{STRING_KEY}")
|
||||
assert (
|
||||
_matching_rows(signoz, token, keyless_rows, signal, identity_field, identity_column, expression) == expected
|
||||
), expression
|
||||
|
||||
|
||||
@pytest.mark.parametrize("expression_template,expected", NUMBER_MATRIX)
|
||||
@pytest.mark.parametrize(
|
||||
"signal,identity_field,identity_column",
|
||||
[
|
||||
pytest.param("traces", "span.name", "name", id="traces"),
|
||||
pytest.param("logs", "body", "body", id="logs"),
|
||||
],
|
||||
)
|
||||
def test_numeric_sentinel_semantics(
|
||||
signoz: types.SigNoz,
|
||||
create_user_admin: None, # pylint: disable=unused-argument
|
||||
get_token: Callable[[str, str], str],
|
||||
keyless_rows: datetime,
|
||||
signal: str,
|
||||
identity_field: str,
|
||||
identity_column: str,
|
||||
expression_template: str,
|
||||
expected: set[str],
|
||||
) -> None:
|
||||
"""A missing numeric key reads as the map default 0. `!= 0` therefore
|
||||
excludes keyless rows while every other negative comparison includes
|
||||
them. Inherited sentinel behavior, pinned on purpose."""
|
||||
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
|
||||
expression = expression_template.format(key=f"attribute.{NUMBER_KEY}")
|
||||
assert (
|
||||
_matching_rows(signoz, token, keyless_rows, signal, identity_field, identity_column, expression) == expected
|
||||
), expression
|
||||
|
||||
|
||||
def _matching_series(
|
||||
signoz: types.SigNoz,
|
||||
token: str,
|
||||
window: tuple[int, int],
|
||||
expression: str,
|
||||
) -> set[str]:
|
||||
start, end = window
|
||||
response = make_query_request(
|
||||
signoz,
|
||||
token,
|
||||
start_ms=start * 1000,
|
||||
end_ms=end * 1000,
|
||||
queries=[
|
||||
build_builder_query(
|
||||
"A",
|
||||
METRIC_NAME,
|
||||
"avg",
|
||||
"sum",
|
||||
group_by=["service"],
|
||||
filter_expression=expression,
|
||||
)
|
||||
],
|
||||
)
|
||||
assert response.status_code == HTTPStatus.OK, response.text
|
||||
matched = set()
|
||||
for series in get_all_series(response.json(), "A"):
|
||||
for label in series.get("labels") or []:
|
||||
key = label.get("key")
|
||||
name = key.get("name") if isinstance(key, dict) else key
|
||||
value = label.get("value")
|
||||
if name == "service" and isinstance(value, str) and value.startswith(PREFIX):
|
||||
matched.add(value)
|
||||
return matched
|
||||
|
||||
|
||||
@pytest.mark.parametrize("expression_template,expected", STRING_MATRIX)
|
||||
def test_metrics_negative_operators_include_keyless_series(
|
||||
signoz: types.SigNoz,
|
||||
create_user_admin: None, # pylint: disable=unused-argument
|
||||
get_token: Callable[[str, str], str],
|
||||
keyless_series: tuple[int, int],
|
||||
expression_template: str,
|
||||
expected: set[str],
|
||||
) -> None:
|
||||
"""The same contract holds for metric labels: a series without the label
|
||||
matches every negative filter on it, and EXISTS opts into presence."""
|
||||
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
|
||||
expression = expression_template.format(key=METRIC_LABEL)
|
||||
assert _matching_series(signoz, token, keyless_series, expression) == expected, expression
|
||||
Reference in New Issue
Block a user