feat(infra-monitoring): v2 jobs integration tests (#11441)

* chore: updated logic and use centralized function in the module

* chore: filter metric groups

* chore: filter metric groups

* chore: formula correction

* chore: added step flooring note

* chore: comment correction

* chore: comment correction

* chore: removed function

* chore: renamed variables

* chore: added happy test

* chore: added test 2 for accuracy and test 3 for missing metrics check

* chore: added filter test 4

* chore: added 5th test for filterByStatus

* chore: added group by tests

* chore: pagination test added

* chore: added validation tests

* chore: added auth test

* chore: added all tests

* chore: fix for surfacing meta for pods custom group by

* chore: added nodes integration test suite

* chore: namespaces integration tests

* ci: register inframonitoring suite + ruff format 01_hosts

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

* chore: added integration tests for clusters

* chore: formatting changed

* chore: formatting changed

* chore: formatting changed

* chore: added volumes integration tests

* chore: added deployments

* chore: added tests

* chore: added integration tests for jobs api

* chore: added order by host.name test

* refactor(infra-monitoring): address review comments on hosts integration tests

- inline _post helper at call sites
- combine filter operator tests into parametrized test_hosts_filter
- combine bad attr/grammar tests into parametrized test_hosts_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_hosts_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align pods integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_pods_filter
- combine bad attr/grammar tests into parametrized test_pods_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_pods_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align nodes integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_nodes_filter
- combine bad attr/grammar tests into parametrized test_nodes_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_nodes_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align namespaces integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_namespaces_filter
- combine bad attr/grammar tests into parametrized test_namespaces_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_namespaces_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align clusters integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_clusters_filter
- combine bad attr/grammar tests into parametrized test_clusters_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_clusters_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align volumes integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_volumes_filter
- combine bad attr/grammar tests into parametrized test_volumes_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_volumes_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align deployments integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_deployments_filter
- combine bad attr/grammar tests into parametrized test_deployments_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_deployments_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align statefulsets integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_statefulsets_filter
- combine bad attr/grammar tests into parametrized test_statefulsets_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_statefulsets_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align jobs integration tests with review feedback

- inline _post helper at call sites
- combine filter operator tests into parametrized test_jobs_filter
- combine bad attr/grammar tests into parametrized test_jobs_filter_invalid
- convert orderby total-invariant nested loop to stacked parametrize
- drop redundant test_jobs_auth (auth covered globally)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine hosts groupby tests into parametrized test

- merge test_hosts_groupby_hostname + test_hosts_groupby_os_type into
  test_hosts_groupby parametrized on (dataset, group key, expected counts/values)
- preserves all assertions incl hostName populated-vs-empty branch coverage

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine hosts orderby tests into parametrized test

- merge total_invariant_across_orderby + orderby_correctness + orderby_by_host_name
  into test_hosts_orderby parametrized on (column, record_field) x direction
- each case asserts both the total/len invariant and sortedness; sortedness now
  covered for all metric columns, not just cpu
- single dataset (hosts_orderby.jsonl) + CONTAINS 'order-' guard on all cases

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine hosts pagination tests

- fold offset-beyond-total case into the test_hosts_pagination page walk
  (offset K+5 expects 0 records via max(0, min(limit, K-offset)); total
  invariant covers the beyond-total page's total == K)
- single seed instead of two

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): merge hosts happy_path into test_hosts_accuracy

- happy_path and value_accuracy datasets were structurally identical
  (same 4 metrics, same sample counts, 2 hosts); one test now asserts
  shape/contract + exact metric values in a single seed/request
- drop unused hosts_happy_path.jsonl

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine pods integration tests logically

- merge happy_path into test_pods_accuracy (datasets structurally identical);
  drop unused pods_happy_path.jsonl
- merge groupby namespace/deployment into parametrized test_pods_groupby
- merge orderby invariant + correctness + by-pod-name into test_pods_orderby
  ((column, record_field) x direction; sortedness now covered for all columns)
- fold offset-beyond-total into test_pods_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine nodes integration tests logically

- merge happy_path into test_nodes_accuracy; drop unused nodes_happy_path.jsonl
- merge orderby invariant + correctness + by-node-name into test_nodes_orderby
  ((column, record_field) x direction; sortedness now covered for all columns)
- fold offset-beyond-total into test_nodes_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine namespaces integration tests logically

- merge happy_path into test_namespaces_accuracy; drop unused namespaces_happy_path.jsonl
- merge orderby invariant + correctness + by-namespace-name into test_namespaces_orderby
- fold offset-beyond-total into test_namespaces_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine clusters integration tests logically

- merge happy_path into test_clusters_accuracy; drop unused clusters_happy_path.jsonl
- merge orderby invariant + correctness + by-cluster-name into test_clusters_orderby
- fold offset-beyond-total into test_clusters_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine volumes integration tests logically

- merge happy_path into test_volumes_accuracy; drop unused volumes_happy_path.jsonl
- merge orderby invariant + correctness + by-pvc-name into test_volumes_orderby
- fold offset-beyond-total into test_volumes_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine deployments integration tests logically

- merge happy_path into test_deployments_accuracy; drop unused deployments_happy_path.jsonl
- merge orderby invariant + correctness + by-deployment-name into test_deployments_orderby
- fold offset-beyond-total into test_deployments_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine statefulsets integration tests logically

- merge happy_path into test_statefulsets_accuracy; drop unused statefulsets_happy_path.jsonl
- merge orderby invariant + correctness + by-statefulset-name into test_statefulsets_orderby
- fold offset-beyond-total into test_statefulsets_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): combine jobs integration tests logically

- merge happy_path into test_jobs_accuracy; drop unused jobs_happy_path.jsonl
- merge orderby invariant + correctness + by-job-name into test_jobs_orderby
- fold offset-beyond-total into test_jobs_pagination page walk

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in hosts filter tests

- regenerate hosts_filter_dataset.jsonl so every host mirrors the acc-h1
  sample pattern from hosts_value_accuracy.jsonl (CI-proven expected values)
- test_hosts_filter now asserts cpu/memory/wait/load15/diskUsage per filtered
  record against FILTER_DATASET_EXPECTED (1e-9), proving filters don't
  distort aggregation

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): scope filter expected values inside test_hosts_filter

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in pods filter tests

- regenerate pods_filter_dataset.jsonl so every pod mirrors the acc-p1
  sample pattern from pods_value_accuracy.jsonl (CI-proven expected values)
- test_pods_filter now asserts the 6 CPU/memory fields per filtered record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in nodes filter tests

- regenerate nodes_filter_dataset.jsonl so every node mirrors the acc-n1
  sample pattern from nodes_value_accuracy.jsonl (CI-proven expected values)
- test_nodes_filter now asserts the 4 CPU/memory fields per filtered record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in namespaces filter tests

- regenerate namespaces_filter_dataset.jsonl so every namespace mirrors the
  acc-ns-1 sample pattern (2 pods) from namespaces_value_accuracy.jsonl
- test_namespaces_filter now asserts namespaceCPU/namespaceMemory per record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in clusters filter tests

- regenerate clusters_filter_dataset.jsonl so every cluster mirrors the
  acc-cluster-1 sample pattern (2 nodes) from clusters_value_accuracy.jsonl
- test_clusters_filter now asserts the 4 CPU/memory fields per filtered record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in volumes filter tests

- regenerate volumes_filter_dataset.jsonl so every PVC mirrors the acc-pvc-1
  sample pattern from volumes_value_accuracy.jsonl (CI-proven expected values)
- test_volumes_filter now asserts all 6 volume fields per filtered record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in deployments filter tests

- regenerate deployments_filter_dataset.jsonl so every deployment mirrors the
  acc-dep-1 sample pattern (2 pods) from deployments_value_accuracy.jsonl
- test_deployments_filter now asserts the 6 CPU/memory fields per filtered record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in statefulsets filter tests

- regenerate statefulsets_filter_dataset.jsonl so every statefulset mirrors
  the acc-ss-1 sample pattern (2 pods) from statefulsets_value_accuracy.jsonl
- test_statefulsets_filter now asserts the 6 CPU/memory fields per filtered record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* feat(infra-monitoring): assert metric value accuracy in jobs filter tests

- regenerate jobs_filter_dataset.jsonl so every job mirrors the acc-job-1
  sample pattern (2 pods) from jobs_value_accuracy.jsonl
- test_jobs_filter now asserts the 6 CPU/memory fields per filtered record

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align nodes groupby + pod-phase tests with structure

- merge pod_phase_counts_list_mode + _no_pods_on_node into parametrized
  test_nodes_pod_phase_counts ((dataset, node, filter, expected) cases)
- rename groupby_cluster -> test_nodes_groupby, parametrize over
  k8s.node.name + k8s.cluster.name; adds the node-name-in-groupBy branch
  (nodeName populated, condition derived) that hosts/pods both cover

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align namespaces groupby with structure

- rename groupby_cluster -> test_namespaces_groupby, parametrize over
  k8s.namespace.name + k8s.cluster.name; adds the namespace-name-in-groupBy
  branch (namespaceName populated) that hosts/pods/nodes all cover

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align clusters groupby with structure

- rename groupby_cloud_provider -> test_clusters_groupby, parametrize over
  k8s.cluster.name + cloud.provider; adds the cluster-name-in-groupBy branch
  (clusterName populated) that hosts/pods/nodes/namespaces all cover

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align volumes groupby with structure

- rename groupby_namespace -> test_volumes_groupby, parametrize over
  k8s.persistentvolumeclaim.name + k8s.namespace.name; adds the
  pvc-name-in-groupBy branch (persistentVolumeClaimName populated) that the
  other endpoints all cover

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align deployments groupby with structure

- rename groupby_namespace -> test_deployments_groupby, parametrize over
  k8s.deployment.name + k8s.namespace.name; adds the deployment-name-in-groupBy
  branch (deploymentName populated) that the other endpoints all cover

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align statefulsets groupby with structure

- rename groupby_namespace -> test_statefulsets_groupby, parametrize over
  k8s.statefulset.name + k8s.namespace.name; adds the statefulset-name-in-groupBy
  branch (statefulSetName populated) that the other endpoints all cover

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

* refactor(infra-monitoring): align jobs groupby with structure

- rename groupby_namespace -> test_jobs_groupby, parametrize over
  k8s.job.name + k8s.namespace.name; adds the job-name-in-groupBy branch
  (jobName populated) that the other endpoints all cover

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>

pkg/modules/inframonitoring/implinframonitoring/helpers_test.go

@@ -269,26 +269,6 @@ func TestCompositeKeyFromLabels(t *testing.T) {
 			groupBy:  []qbtypes.GroupByKey{groupByKey("a"), groupByKey("m"), groupByKey("z")},
 			expected: "first\x00middle\x00last",
 		},
-		{
-			// deployments default group identity: (deployment, namespace).
-			name: "deployment and namespace group-by",
-			labels: map[string]string{
-				"k8s.deployment.name": "web-1",
-				"k8s.namespace.name":  "ns-x",
-			},
-			groupBy:  []qbtypes.GroupByKey{deploymentNameGroupByKey, namespaceNameGroupByKey},
-			expected: "web-1\x00ns-x",
-		},
-		{
-			// statefulsets default group identity: (statefulset, namespace).
-			name: "statefulset and namespace group-by",
-			labels: map[string]string{
-				"k8s.statefulset.name": "web-1",
-				"k8s.namespace.name":   "ns-x",
-			},
-			groupBy:  []qbtypes.GroupByKey{statefulSetNameGroupByKey, namespaceNameGroupByKey},
-			expected: "web-1\x00ns-x",
-		},
 	}
 
 	for _, tt := range tests {

pkg/modules/inframonitoring/implinframonitoring/module.go

@@ -620,7 +620,7 @@ func (m *module) ListDeployments(ctx context.Context, orgID valuer.UUID, req *in
 	}
 
 	if len(req.GroupBy) == 0 {
-		req.GroupBy = []qbtypes.GroupByKey{deploymentNameGroupByKey, namespaceNameGroupByKey}
+		req.GroupBy = []qbtypes.GroupByKey{deploymentNameGroupByKey}
 		resp.Type = inframonitoringtypes.ResponseTypeList
 	} else {
 		resp.Type = inframonitoringtypes.ResponseTypeGroupedList
@@ -710,7 +710,7 @@ func (m *module) ListStatefulSets(ctx context.Context, orgID valuer.UUID, req *i
 	}
 
 	if len(req.GroupBy) == 0 {
-		req.GroupBy = []qbtypes.GroupByKey{statefulSetNameGroupByKey, namespaceNameGroupByKey}
+		req.GroupBy = []qbtypes.GroupByKey{statefulSetNameGroupByKey}
 		resp.Type = inframonitoringtypes.ResponseTypeList
 	} else {
 		resp.Type = inframonitoringtypes.ResponseTypeGroupedList

tests/integration/testdata/inframonitoring/deployments_same_name_across_namespaces.jsonl

@@ -1,54 +0,0 @@
-{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.deployment.desired","labels":{"k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.deployment.available","labels":{"k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.deployment.desired","labels":{"k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.deployment.available","labels":{"k8s.deployment.name":"dup-dep","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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tests/integration/testdata/inframonitoring/jobs_completed.jsonl

@@ -0,0 +1,75 @@
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tests/integration/testdata/inframonitoring/jobs_filter_dataset.jsonl

@@ -0,0 +1,432 @@
+{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "etl-a-prod-acc-1-p1-uid", "k8s.pod.name": "etl-a-prod-acc-1-p1", "k8s.job.name": "etl-a-prod", "k8s.namespace.name": "ns-a", "k8s.cluster.name": "cluster-x", "env": "prod"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 0.4, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
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tests/integration/testdata/inframonitoring/jobs_groupby.jsonl

@@ -0,0 +1,132 @@
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+{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"gb-job-b2-p1-uid","k8s.pod.name":"gb-job-b2-p1","k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"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.job.active_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.successful_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"gb-job-b2","k8s.namespace.name":"gb-ns-b","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}

tests/integration/testdata/inframonitoring/jobs_lifecycle.jsonl

@@ -0,0 +1,33 @@
+{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"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":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"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":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"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":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"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":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"lc-pen-1-uid","k8s.pod.name":"lc-pen-1","k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.successful_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.successful_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.successful_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"lc-job","k8s.namespace.name":"ns-lc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}

tests/integration/testdata/inframonitoring/jobs_missing_metrics.jsonl

@@ -0,0 +1,3 @@
+{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"miss-p1-uid","k8s.pod.name":"miss-p1","k8s.job.name":"miss-job","k8s.namespace.name":"ns-miss","k8s.cluster.name":"cluster-x"},"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":"miss-p1-uid","k8s.pod.name":"miss-p1","k8s.job.name":"miss-job","k8s.namespace.name":"ns-miss","k8s.cluster.name":"cluster-x"},"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":"miss-p1-uid","k8s.pod.name":"miss-p1","k8s.job.name":"miss-job","k8s.namespace.name":"ns-miss","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}

tests/integration/testdata/inframonitoring/jobs_non_job_pods.jsonl

@@ -0,0 +1,96 @@
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tests/integration/testdata/inframonitoring/jobs_orderby.jsonl

@@ -0,0 +1,165 @@
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tests/integration/testdata/inframonitoring/jobs_pagination.jsonl

@@ -0,0 +1,231 @@
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tests/integration/testdata/inframonitoring/jobs_pod_phases.jsonl

@@ -0,0 +1,138 @@
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tests/integration/testdata/inframonitoring/jobs_value_accuracy.jsonl

@@ -0,0 +1,129 @@
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+{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"acc-2-p3-uid","k8s.pod.name":"acc-2-p3","k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"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.memory.working_set","labels":{"k8s.pod.uid":"acc-2-p3-uid","k8s.pod.name":"acc-2-p3","k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":300000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-2-p3-uid","k8s.pod.name":"acc-2-p3","k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":300000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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+{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"acc-2-p3-uid","k8s.pod.name":"acc-2-p3","k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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+{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"acc-2-p3-uid","k8s.pod.name":"acc-2-p3","k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"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.phase","labels":{"k8s.pod.uid":"acc-2-p3-uid","k8s.pod.name":"acc-2-p3","k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"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":"acc-2-p3-uid","k8s.pod.name":"acc-2-p3","k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.successful_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.successful_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.active_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.failed_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.successful_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
+{"metric_name":"k8s.job.desired_successful_pods","labels":{"k8s.job.name":"acc-job-2","k8s.namespace.name":"ns-acc","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}

tests/integration/testdata/inframonitoring/jobs_value_accuracy_expected.json

@@ -0,0 +1,44 @@
+{
+  "records": [
+    {
+      "jobName": "acc-job-1",
+      "jobCPU": 1.0,
+      "jobCPURequest": 0.5,
+      "jobCPULimit": 0.4,
+      "jobMemory": 300000000.0,
+      "jobMemoryRequest": 0.5,
+      "jobMemoryLimit": 0.4,
+      "desiredSuccessfulPods": 2,
+      "activePods": 1,
+      "failedPods": 0,
+      "successfulPods": 1,
+      "podCountsByPhase": {
+        "pending": 0,
+        "running": 2,
+        "succeeded": 0,
+        "failed": 0,
+        "unknown": 0
+      }
+    },
+    {
+      "jobName": "acc-job-2",
+      "jobCPU": 1.2,
+      "jobCPURequest": 0.4,
+      "jobCPULimit": 0.3,
+      "jobMemory": 600000000.0,
+      "jobMemoryRequest": 0.4,
+      "jobMemoryLimit": 0.3,
+      "desiredSuccessfulPods": 5,
+      "activePods": 3,
+      "failedPods": 1,
+      "successfulPods": 2,
+      "podCountsByPhase": {
+        "pending": 0,
+        "running": 3,
+        "succeeded": 0,
+        "failed": 0,
+        "unknown": 0
+      }
+    }
+  ]
+}

tests/integration/testdata/inframonitoring/statefulsets_same_name_across_namespaces.jsonl

@@ -1,54 +0,0 @@
-{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.desired_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.current_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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-{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"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.memory_request_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.7,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-x-p1-uid","k8s.pod.name":"dup-x-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.desired_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.current_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-x","k8s.cluster.name":"cluster-x"},"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":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.9,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":500000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.desired_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.current_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.9,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":500000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.desired_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.current_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.9,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_request_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.cpu_limit_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":500000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_request_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.memory_limit_utilization","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"dup-y-p1-uid","k8s.pod.name":"dup-y-p1","k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.desired_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":3,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
-{"metric_name":"k8s.statefulset.current_pods","labels":{"k8s.statefulset.name":"dup-ss","k8s.namespace.name":"ns-y","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}

tests/integration/tests/inframonitoring/07_deployments.py

@@ -424,102 +424,23 @@ def test_deployments_base_filter_drops_non_deployment_pods(
     assert all(r["deploymentName"] != "" for r in data["records"])
 
 
-# Float record fields compared with tolerance; everything else compared with ==.
-_GROUPBY_FLOAT_FIELDS = {
-    "deploymentCPU",
-    "deploymentCPURequest",
-    "deploymentCPULimit",
-    "deploymentMemory",
-    "deploymentMemoryRequest",
-    "deploymentMemoryLimit",
-}
-
-
-def _phase(pending=0, running=0, succeeded=0, failed=0, unknown=0) -> dict:
-    return {"pending": pending, "running": running, "succeeded": succeeded, "failed": failed, "unknown": unknown}
-
-
 @pytest.mark.parametrize(
-    "scenario",
+    "group_key,expected_running",
     [
-        # Explicit groupBy=[k8s.deployment.name]: one record per deployment,
-        # deploymentName populated (deployments.go:28-31), response grouped_list.
-        # 1 running pod each.
+        # groupBy=[k8s.deployment.name]: one record per deployment,
+        # deploymentName populated (deployments.go:28-31). 1 running pod each.
         pytest.param(
-            {
-                "fixture": "deployments_groupby.jsonl",
-                "group_by": "k8s.deployment.name",
-                "filter": None,
-                "group_meta_key": "k8s.deployment.name",
-                "expected_type": "grouped_list",
-                "groups": {
-                    "gb-dep-a1": {"deploymentName": "gb-dep-a1", "podCountsByPhase": _phase(running=1)},
-                    "gb-dep-a2": {"deploymentName": "gb-dep-a2", "podCountsByPhase": _phase(running=1)},
-                    "gb-dep-b1": {"deploymentName": "gb-dep-b1", "podCountsByPhase": _phase(running=1)},
-                    "gb-dep-b2": {"deploymentName": "gb-dep-b2", "podCountsByPhase": _phase(running=1)},
-                },
-            },
+            "k8s.deployment.name",
+            {"gb-dep-a1": 1, "gb-dep-a2": 1, "gb-dep-b1": 1, "gb-dep-b2": 1},
             id="deployment_name",
         ),
-        # Explicit groupBy=[k8s.namespace.name]: aggregated across each namespace's
-        # 2 deployments, deploymentName cleared, response grouped_list. 2 running each.
+        # groupBy=[k8s.namespace.name]: aggregated across each namespace's 2
+        # deployments, deploymentName cleared. 2 x 1 = 2 running pods each.
         pytest.param(
-            {
-                "fixture": "deployments_groupby.jsonl",
-                "group_by": "k8s.namespace.name",
-                "filter": None,
-                "group_meta_key": "k8s.namespace.name",
-                "expected_type": "grouped_list",
-                "groups": {
-                    "gb-ns-a": {"deploymentName": "", "podCountsByPhase": _phase(running=2)},
-                    "gb-ns-b": {"deploymentName": "", "podCountsByPhase": _phase(running=2)},
-                },
-            },
+            "k8s.namespace.name",
+            {"gb-ns-a": 2, "gb-ns-b": 2},
             id="namespace",
         ),
-        # Default groupBy (no groupBy in request) => [k8s.deployment.name,
-        # k8s.namespace.name] (module.go ListDeployments), response list. Two
-        # deployments sharing a name across namespaces must NOT collapse: each row
-        # keeps its per-namespace metrics/replicas/phase. Single pod per (dep, ns)
-        # => SpaceAggregationSum == Avg == seeded value, so exact expectations.
-        # Regression guard for namespace-scoped k8s name uniqueness; fails on the
-        # pre-change name-only default (would yield 1 summed row).
-        pytest.param(
-            {
-                "fixture": "deployments_same_name_across_namespaces.jsonl",
-                "group_by": None,
-                "filter": "k8s.deployment.name = 'dup-dep'",
-                "group_meta_key": "k8s.namespace.name",
-                "expected_type": "list",
-                "groups": {
-                    "ns-x": {
-                        "deploymentName": "dup-dep",
-                        "deploymentCPU": 0.3,
-                        "deploymentCPURequest": 0.6,
-                        "deploymentCPULimit": 0.7,
-                        "deploymentMemory": 100000000.0,
-                        "deploymentMemoryRequest": 0.6,
-                        "deploymentMemoryLimit": 0.7,
-                        "desiredPods": 2,
-                        "availablePods": 2,
-                        "podCountsByPhase": _phase(running=1),
-                    },
-                    "ns-y": {
-                        "deploymentName": "dup-dep",
-                        "deploymentCPU": 0.9,
-                        "deploymentCPURequest": 0.2,
-                        "deploymentCPULimit": 0.3,
-                        "deploymentMemory": 500000000.0,
-                        "deploymentMemoryRequest": 0.2,
-                        "deploymentMemoryLimit": 0.3,
-                        "desiredPods": 3,
-                        "availablePods": 1,
-                        "podCountsByPhase": _phase(failed=1),
-                    },
-                },
-            },
-            id="default_disambiguates_ns",
-        ),
     ],
 )
 def test_deployments_groupby(
@@ -527,60 +448,55 @@ def test_deployments_groupby(
     create_user_admin: None,  # pylint: disable=unused-argument
     get_token,
     insert_metrics,
-    scenario: dict,
+    group_key: str,
+    expected_running: dict,
 ) -> None:
-    """groupBy determines row identity. Explicit groupBy returns one grouped_list
-    record per distinct group (deploymentName populated only when grouping by
-    k8s.deployment.name; deployments.go:28-31). With no groupBy the default is
-    [k8s.deployment.name, k8s.namespace.name] (module.go ListDeployments), so
-    same-named deployments across namespaces stay as separate, un-collapsed list
-    rows. meta always surfaces the grouping key(s)."""
+    """groupBy returns one record per distinct group with aggregated pod-phase
+    counts. deploymentName is populated only when grouping by k8s.deployment.name
+    (deployments.go:28-31 list-vs-grouped branch); meta surfaces the groupBy key."""
     now = datetime.now(tz=UTC).replace(microsecond=0)
     insert_metrics(
         Metrics.load_from_file(
-            get_testdata_file_path(f"inframonitoring/{scenario['fixture']}"),
+            get_testdata_file_path("inframonitoring/deployments_groupby.jsonl"),
             base_time=now - timedelta(minutes=4),
         )
     )
 
-    body: dict = {
-        "start": int((now - timedelta(minutes=5)).timestamp() * 1000),
-        "end": int(now.timestamp() * 1000),
-        "limit": 50,
-    }
-    if scenario["group_by"] is not None:
-        body["groupBy"] = [
-            {"name": scenario["group_by"], "fieldDataType": "string", "fieldContext": "resource"}
-        ]
-    if scenario["filter"] is not None:
-        body["filter"] = {"expression": scenario["filter"]}
-
     token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
     response = requests.post(
         signoz.self.host_configs["8080"].get(ENDPOINT),
         headers={"authorization": f"Bearer {token}"},
-        json=body,
+        json={
+            "start": int((now - timedelta(minutes=5)).timestamp() * 1000),
+            "end": int(now.timestamp() * 1000),
+            "limit": 50,
+            "groupBy": [
+                {
+                    "name": group_key,
+                    "fieldDataType": "string",
+                    "fieldContext": "resource",
+                }
+            ],
+        },
         timeout=5,
     )
     assert response.status_code == HTTPStatus.OK, response.text
     data = response.json()["data"]
+    assert data["total"] == len(expected_running)
 
-    groups = scenario["groups"]
-    meta_key = scenario["group_meta_key"]
-    assert data["type"] == scenario["expected_type"]
-    assert data["total"] == len(groups)
+    is_dep_group = group_key == "k8s.deployment.name"
+    group_of = lambda r: r["deploymentName"] if is_dep_group else r["meta"][group_key]  # noqa: E731  # pylint: disable=unnecessary-lambda-assignment
+    by_group = {group_of(r): r for r in data["records"]}
+    assert set(by_group.keys()) == set(expected_running.keys())
 
-    by_group = {r["meta"][meta_key]: r for r in data["records"]}
-    assert set(by_group.keys()) == set(groups.keys())
-
-    for gid, exp in groups.items():
-        rec = by_group[gid]
-        assert meta_key in rec["meta"], rec["meta"]
-        for field, val in exp.items():
-            if field in _GROUPBY_FLOAT_FIELDS:
-                assert compare_values(rec[field], val, 1e-6), f"{gid}.{field}: got {rec[field]}, expected {val}"
-            else:
-                assert rec[field] == val, f"{gid}.{field}: got {rec[field]}, expected {val}"
+    for group, running in expected_running.items():
+        rec = by_group[group]
+        # deploymentName populated per deployment when grouping by it, empty otherwise.
+        assert rec["deploymentName"] == (group if is_dep_group else "")
+        assert rec["podCountsByPhase"]["running"] == running
+        for other in ("pending", "succeeded", "failed", "unknown"):
+            assert rec["podCountsByPhase"][other] == 0
+        assert group_key in rec["meta"], rec["meta"]
 
 
 def test_deployments_pagination(

tests/integration/tests/inframonitoring/08_statefulsets.py

@@ -424,102 +424,23 @@ def test_statefulsets_base_filter_drops_non_statefulset_pods(
     assert all(r["statefulSetName"] != "" for r in data["records"])
 
 
-# Float record fields compared with tolerance; everything else compared with ==.
-_GROUPBY_FLOAT_FIELDS = {
-    "statefulSetCPU",
-    "statefulSetCPURequest",
-    "statefulSetCPULimit",
-    "statefulSetMemory",
-    "statefulSetMemoryRequest",
-    "statefulSetMemoryLimit",
-}
-
-
-def _phase(pending=0, running=0, succeeded=0, failed=0, unknown=0) -> dict:
-    return {"pending": pending, "running": running, "succeeded": succeeded, "failed": failed, "unknown": unknown}
-
-
 @pytest.mark.parametrize(
-    "scenario",
+    "group_key,expected_running",
     [
-        # Explicit groupBy=[k8s.statefulset.name]: one record per statefulset,
-        # statefulSetName populated (statefulsets.go:28-31), response grouped_list.
-        # 1 running pod each.
+        # groupBy=[k8s.statefulset.name]: one record per statefulset,
+        # statefulSetName populated (statefulsets.go:28-31). 1 running pod each.
         pytest.param(
-            {
-                "fixture": "statefulsets_groupby.jsonl",
-                "group_by": "k8s.statefulset.name",
-                "filter": None,
-                "group_meta_key": "k8s.statefulset.name",
-                "expected_type": "grouped_list",
-                "groups": {
-                    "gb-ss-a1": {"statefulSetName": "gb-ss-a1", "podCountsByPhase": _phase(running=1)},
-                    "gb-ss-a2": {"statefulSetName": "gb-ss-a2", "podCountsByPhase": _phase(running=1)},
-                    "gb-ss-b1": {"statefulSetName": "gb-ss-b1", "podCountsByPhase": _phase(running=1)},
-                    "gb-ss-b2": {"statefulSetName": "gb-ss-b2", "podCountsByPhase": _phase(running=1)},
-                },
-            },
+            "k8s.statefulset.name",
+            {"gb-ss-a1": 1, "gb-ss-a2": 1, "gb-ss-b1": 1, "gb-ss-b2": 1},
             id="statefulset_name",
         ),
-        # Explicit groupBy=[k8s.namespace.name]: aggregated across each namespace's
-        # 2 statefulsets, statefulSetName cleared, response grouped_list. 2 running each.
+        # groupBy=[k8s.namespace.name]: aggregated across each namespace's 2
+        # statefulsets, statefulSetName cleared. 2 x 1 = 2 running pods each.
         pytest.param(
-            {
-                "fixture": "statefulsets_groupby.jsonl",
-                "group_by": "k8s.namespace.name",
-                "filter": None,
-                "group_meta_key": "k8s.namespace.name",
-                "expected_type": "grouped_list",
-                "groups": {
-                    "gb-ns-a": {"statefulSetName": "", "podCountsByPhase": _phase(running=2)},
-                    "gb-ns-b": {"statefulSetName": "", "podCountsByPhase": _phase(running=2)},
-                },
-            },
+            "k8s.namespace.name",
+            {"gb-ns-a": 2, "gb-ns-b": 2},
             id="namespace",
         ),
-        # Default groupBy (no groupBy in request) => [k8s.statefulset.name,
-        # k8s.namespace.name] (module.go ListStatefulSets), response list. Two
-        # statefulsets sharing a name across namespaces must NOT collapse: each row
-        # keeps its per-namespace metrics/replicas/phase. Single pod per (ss, ns)
-        # => SpaceAggregationSum == Avg == seeded value, so exact expectations.
-        # Regression guard for namespace-scoped k8s name uniqueness; fails on the
-        # pre-change name-only default (would yield 1 summed row).
-        pytest.param(
-            {
-                "fixture": "statefulsets_same_name_across_namespaces.jsonl",
-                "group_by": None,
-                "filter": "k8s.statefulset.name = 'dup-ss'",
-                "group_meta_key": "k8s.namespace.name",
-                "expected_type": "list",
-                "groups": {
-                    "ns-x": {
-                        "statefulSetName": "dup-ss",
-                        "statefulSetCPU": 0.3,
-                        "statefulSetCPURequest": 0.6,
-                        "statefulSetCPULimit": 0.7,
-                        "statefulSetMemory": 100000000.0,
-                        "statefulSetMemoryRequest": 0.6,
-                        "statefulSetMemoryLimit": 0.7,
-                        "desiredPods": 2,
-                        "currentPods": 2,
-                        "podCountsByPhase": _phase(running=1),
-                    },
-                    "ns-y": {
-                        "statefulSetName": "dup-ss",
-                        "statefulSetCPU": 0.9,
-                        "statefulSetCPURequest": 0.2,
-                        "statefulSetCPULimit": 0.3,
-                        "statefulSetMemory": 500000000.0,
-                        "statefulSetMemoryRequest": 0.2,
-                        "statefulSetMemoryLimit": 0.3,
-                        "desiredPods": 3,
-                        "currentPods": 1,
-                        "podCountsByPhase": _phase(failed=1),
-                    },
-                },
-            },
-            id="default_disambiguates_ns",
-        ),
     ],
 )
 def test_statefulsets_groupby(
@@ -527,60 +448,55 @@ def test_statefulsets_groupby(
     create_user_admin: None,  # pylint: disable=unused-argument
     get_token,
     insert_metrics,
-    scenario: dict,
+    group_key: str,
+    expected_running: dict,
 ) -> None:
-    """groupBy determines row identity. Explicit groupBy returns one grouped_list
-    record per distinct group (statefulSetName populated only when grouping by
-    k8s.statefulset.name; statefulsets.go:28-31). With no groupBy the default is
-    [k8s.statefulset.name, k8s.namespace.name] (module.go ListStatefulSets), so
-    same-named statefulsets across namespaces stay as separate, un-collapsed list
-    rows. meta always surfaces the grouping key(s)."""
+    """groupBy returns one record per distinct group with aggregated pod-phase
+    counts. statefulSetName is populated only when grouping by k8s.statefulset.name
+    (statefulsets.go:28-31 list-vs-grouped branch); meta surfaces the groupBy key."""
     now = datetime.now(tz=UTC).replace(microsecond=0)
     insert_metrics(
         Metrics.load_from_file(
-            get_testdata_file_path(f"inframonitoring/{scenario['fixture']}"),
+            get_testdata_file_path("inframonitoring/statefulsets_groupby.jsonl"),
             base_time=now - timedelta(minutes=4),
         )
     )
 
-    body: dict = {
-        "start": int((now - timedelta(minutes=5)).timestamp() * 1000),
-        "end": int(now.timestamp() * 1000),
-        "limit": 50,
-    }
-    if scenario["group_by"] is not None:
-        body["groupBy"] = [
-            {"name": scenario["group_by"], "fieldDataType": "string", "fieldContext": "resource"}
-        ]
-    if scenario["filter"] is not None:
-        body["filter"] = {"expression": scenario["filter"]}
-
     token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
     response = requests.post(
         signoz.self.host_configs["8080"].get(ENDPOINT),
         headers={"authorization": f"Bearer {token}"},
-        json=body,
+        json={
+            "start": int((now - timedelta(minutes=5)).timestamp() * 1000),
+            "end": int(now.timestamp() * 1000),
+            "limit": 50,
+            "groupBy": [
+                {
+                    "name": group_key,
+                    "fieldDataType": "string",
+                    "fieldContext": "resource",
+                }
+            ],
+        },
         timeout=5,
     )
     assert response.status_code == HTTPStatus.OK, response.text
     data = response.json()["data"]
+    assert data["total"] == len(expected_running)
 
-    groups = scenario["groups"]
-    meta_key = scenario["group_meta_key"]
-    assert data["type"] == scenario["expected_type"]
-    assert data["total"] == len(groups)
+    is_ss_group = group_key == "k8s.statefulset.name"
+    group_of = lambda r: r["statefulSetName"] if is_ss_group else r["meta"][group_key]  # noqa: E731  # pylint: disable=unnecessary-lambda-assignment
+    by_group = {group_of(r): r for r in data["records"]}
+    assert set(by_group.keys()) == set(expected_running.keys())
 
-    by_group = {r["meta"][meta_key]: r for r in data["records"]}
-    assert set(by_group.keys()) == set(groups.keys())
-
-    for gid, exp in groups.items():
-        rec = by_group[gid]
-        assert meta_key in rec["meta"], rec["meta"]
-        for field, val in exp.items():
-            if field in _GROUPBY_FLOAT_FIELDS:
-                assert compare_values(rec[field], val, 1e-6), f"{gid}.{field}: got {rec[field]}, expected {val}"
-            else:
-                assert rec[field] == val, f"{gid}.{field}: got {rec[field]}, expected {val}"
+    for group, running in expected_running.items():
+        rec = by_group[group]
+        # statefulSetName populated per statefulset when grouping by it, empty otherwise.
+        assert rec["statefulSetName"] == (group if is_ss_group else "")
+        assert rec["podCountsByPhase"]["running"] == running
+        for other in ("pending", "succeeded", "failed", "unknown"):
+            assert rec["podCountsByPhase"][other] == 0
+        assert group_key in rec["meta"], rec["meta"]
 
 
 def test_statefulsets_pagination(

tests/integration/tests/inframonitoring/09_jobs.py

@@ -0,0 +1,740 @@
+"""Integration tests for v2 infra-monitoring jobs endpoint."""
+
+import json
+from datetime import UTC, datetime, timedelta
+from http import HTTPStatus
+
+import pytest
+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.metrics import Metrics
+from fixtures.querier import compare_values
+
+ENDPOINT = "/api/v2/infra_monitoring/jobs"
+
+# Required metrics for the v2 jobs endpoint
+# (pkg/modules/inframonitoring/implinframonitoring/jobs_constants.go:24-36).
+REQUIRED_METRICS = {
+    "k8s.pod.phase",
+    "k8s.pod.cpu.usage",
+    "k8s.pod.cpu_request_utilization",
+    "k8s.pod.cpu_limit_utilization",
+    "k8s.pod.memory.working_set",
+    "k8s.pod.memory_request_utilization",
+    "k8s.pod.memory_limit_utilization",
+    "k8s.job.active_pods",
+    "k8s.job.failed_pods",
+    "k8s.job.successful_pods",
+    "k8s.job.desired_successful_pods",
+}
+
+
+def test_jobs_accuracy(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+) -> None:
+    """Assert response shape/contract + exact per-job metric values, all 4
+    lifecycle counts, and phase counts against precomputed expected output.
+
+    Locks in Sum vs Avg split across pod-level metrics
+    (jobs_constants.go:81-198): A/D = SpaceAggregationSum across pods;
+    B/C/E/F = SpaceAggregationAvg. All 4 job-level counts (H/I/J/K) use
+    TimeAggregationLatest + SpaceAggregationSum + ReduceToLast.
+    """
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_value_accuracy.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    with open(
+        get_testdata_file_path("inframonitoring/jobs_value_accuracy_expected.json"),
+        encoding="utf-8",
+    ) as f:
+        expected = json.load(f)
+    exp_by_name = {r["jobName"]: r for r in expected["records"]}
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+
+    # Shape/contract.
+    assert data["total"] == len(expected["records"])
+    assert len(data["records"]) == len(expected["records"])
+    assert data["requiredMetricsCheck"]["missingMetrics"] == []
+    assert data["endTimeBeforeRetention"] is False
+    assert {r["jobName"] for r in data["records"]} == set(exp_by_name.keys())
+
+    for record in data["records"]:
+        for field in (
+            "jobName",
+            "jobCPU",
+            "jobCPURequest",
+            "jobCPULimit",
+            "jobMemory",
+            "jobMemoryRequest",
+            "jobMemoryLimit",
+            "desiredSuccessfulPods",
+            "activePods",
+            "failedPods",
+            "successfulPods",
+            "podCountsByPhase",
+            "meta",
+        ):
+            assert field in record, f"missing {field} in {record!r}"
+
+        # All 4 lifecycle counts must be ints (not floats).
+        for int_field in ("desiredSuccessfulPods", "activePods", "failedPods", "successfulPods"):
+            assert isinstance(record[int_field], int), f"{int_field} should be int, got {type(record[int_field]).__name__}"
+
+        for bucket in ("pending", "running", "succeeded", "failed", "unknown"):
+            assert bucket in record["podCountsByPhase"]
+            assert isinstance(record["podCountsByPhase"][bucket], int)
+
+        assert record["meta"].get("k8s.job.name") == record["jobName"]
+        assert "k8s.namespace.name" in record["meta"]
+        assert "k8s.cluster.name" in record["meta"]
+
+        # Exact values.
+        exp = exp_by_name[record["jobName"]]
+        for field in (
+            "jobCPU",
+            "jobCPURequest",
+            "jobCPULimit",
+            "jobMemory",
+            "jobMemoryRequest",
+            "jobMemoryLimit",
+        ):
+            assert compare_values(record[field], exp[field], 1e-6), f"{record['jobName']}.{field}: got {record[field]}, expected {exp[field]}"
+        for int_field in ("desiredSuccessfulPods", "activePods", "failedPods", "successfulPods"):
+            assert record[int_field] == exp[int_field], f"{record['jobName']}.{int_field}: got {record[int_field]}, expected {exp[int_field]}"
+        assert record["podCountsByPhase"] == exp["podCountsByPhase"]
+
+
+def test_jobs_missing_metrics(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+) -> None:
+    """Seed only k8s.pod.cpu.usage; assert other 10 required metrics flagged missing."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_missing_metrics.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+
+    assert set(data["requiredMetricsCheck"]["missingMetrics"]) == (REQUIRED_METRICS - {"k8s.pod.cpu.usage"})
+    assert data["records"] == []
+    assert data["total"] == 0
+
+
+@pytest.mark.parametrize(
+    "expression,expected",
+    [
+        pytest.param(
+            "k8s.namespace.name = 'ns-a' AND env = 'prod'",
+            {"etl-a-prod", "cron-a-prod"},
+            id="and",
+        ),
+        pytest.param(
+            "k8s.job.name IN ('etl-a-prod', 'cron-b-dev')",
+            {"etl-a-prod", "cron-b-dev"},
+            id="in",
+        ),
+        # NOT IN on the partition key (k8s.job.name) returns the rest.
+        # NOT IN on non-partition labels is unreliable in QB v5; covered indirectly
+        # via the and_not_in combo. Same workaround as clusters/volumes/deployments/SS.
+        pytest.param(
+            "k8s.job.name NOT IN ('etl-a-prod', 'etl-a-dev', 'cron-a-prod', 'cron-a-dev')",
+            {"etl-b-prod", "etl-b-dev", "cron-b-prod", "cron-b-dev"},
+            id="not_in",
+        ),
+        pytest.param(
+            "k8s.job.name CONTAINS 'etl'",
+            {"etl-a-prod", "etl-a-dev", "etl-b-prod", "etl-b-dev"},
+            id="contains",
+        ),
+        pytest.param(
+            "k8s.namespace.name = 'ns-a' AND k8s.job.name IN ('etl-a-prod', 'cron-a-prod')",
+            {"etl-a-prod", "cron-a-prod"},
+            id="and_in",
+        ),
+        pytest.param(
+            "k8s.namespace.name = 'ns-a' AND k8s.job.name NOT IN ('etl-a-prod', 'etl-a-dev')",
+            {"cron-a-prod", "cron-a-dev"},
+            id="and_not_in",
+        ),
+        pytest.param(
+            "env = 'prod' AND k8s.job.name CONTAINS 'etl'",
+            {"etl-a-prod", "etl-b-prod"},
+            id="and_contains",
+        ),
+        pytest.param(
+            "k8s.job.name IN ('etl-a-prod', 'etl-b-prod', 'cron-a-prod') AND k8s.job.name CONTAINS 'etl'",
+            {"etl-a-prod", "etl-b-prod"},
+            id="in_contains",
+        ),
+    ],
+)
+def test_jobs_filter(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+    expression: str,
+    expected: set,
+) -> None:
+    """Filter operators (=, IN, NOT IN, CONTAINS) and their AND-combinations
+    return exactly the matching jobs, with undistorted per-job metric values."""
+    # Every job in jobs_filter_dataset.jsonl carries the same sample pattern
+    # as acc-job-1 in jobs_value_accuracy.jsonl (2 pods), so all filtered
+    # records must resolve to these exact values (mirrors
+    # jobs_value_accuracy_expected.json acc-job-1).
+    expected_values = {
+        "jobCPU": 1.0,
+        "jobCPURequest": 0.5,
+        "jobCPULimit": 0.4,
+        "jobMemory": 300000000.0,
+        "jobMemoryRequest": 0.5,
+        "jobMemoryLimit": 0.4,
+    }
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_filter_dataset.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+            "filter": {"expression": expression},
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+    assert {r["jobName"] for r in data["records"]} == expected
+    assert data["total"] == len(expected)
+
+    # Filtering must not distort per-job aggregation values.
+    for record in data["records"]:
+        for field in expected_values:
+            assert compare_values(record[field], expected_values[field], 1e-6), f"{record['jobName']}.{field}: got {record[field]}, expected {expected_values[field]}"
+
+
+@pytest.mark.parametrize(
+    "expression,err_substr",
+    [
+        pytest.param("k8s.job.namee = 'etl-a-prod'", "k8s.job.namee", id="bad_attr_name"),
+        pytest.param("k8s.job.name =", None, id="trailing_op"),
+        pytest.param("(k8s.job.name = 'etl-a-prod'", None, id="unclosed_paren"),
+    ],
+)
+def test_jobs_filter_invalid(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+    expression: str,
+    err_substr,
+) -> None:
+    """Invalid filter expressions (typo'd attribute key, malformed grammar) return
+    400 invalid_input with structured errors; bad attribute keys are named in them."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_filter_dataset.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+            "filter": {"expression": expression},
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.BAD_REQUEST, f"expected 400, got {response.status_code}: {response.text}"
+    body = response.json()
+    assert body["status"] == "error"
+    assert body["error"]["code"] == "invalid_input"
+    assert len(body["error"]["errors"]) > 0
+    if err_substr is not None:
+        assert any(err_substr in e["message"] for e in body["error"]["errors"]), f"{err_substr!r} not surfaced: {body['error']['errors']!r}"
+
+
+def test_jobs_pod_phase_aggregation(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+) -> None:
+    """Job with mixed pod phases: 2 Running + 3 Succeeded + 1 Failed (in-progress)."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_pod_phases.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+            "filter": {"expression": "k8s.job.name = 'pp-job'"},
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+    assert data["total"] == 1
+    rec = data["records"][0]
+    assert rec["jobName"] == "pp-job"
+    assert rec["podCountsByPhase"] == {
+        "pending": 0,
+        "running": 2,
+        "succeeded": 3,
+        "failed": 1,
+        "unknown": 0,
+    }
+
+
+def test_jobs_lifecycle_counts(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+) -> None:
+    """Lifecycle counters (active=2, failed=1, successful=3, desired_successful=4)
+    are independent of pod phase counts. Seed deliberately mismatches: 1 Pending pod
+    only. Validates the 4 int counters come straight from k8s.job.* metrics, not
+    derived from observable pod phases (cumulative counters vs latest phase)."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_lifecycle.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+            "filter": {"expression": "k8s.job.name = 'lc-job'"},
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+    assert data["total"] == 1
+    rec = data["records"][0]
+    assert rec["jobName"] == "lc-job"
+    for int_field in ("desiredSuccessfulPods", "activePods", "failedPods", "successfulPods"):
+        assert isinstance(rec[int_field], int), f"{int_field} should be int"
+    assert rec["desiredSuccessfulPods"] == 4
+    assert rec["activePods"] == 2
+    assert rec["failedPods"] == 1
+    assert rec["successfulPods"] == 3
+    # Pod phase counts deliberately disagree: only 1 Pending pod seeded.
+    assert rec["podCountsByPhase"] == {
+        "pending": 1,
+        "running": 0,
+        "succeeded": 0,
+        "failed": 0,
+        "unknown": 0,
+    }
+
+
+def test_jobs_completed_job(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+) -> None:
+    """Terminal-state job (active=0, failed=0, successful=3, desired_successful=3).
+    Asserts activePods==0 and failedPods==0 are real zeros, not the -1 'no data'
+    sentinel that buildJobRecords (jobs.go:30-43) initializes."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_completed.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+            "filter": {"expression": "k8s.job.name = 'done-job'"},
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+    assert data["total"] == 1
+    rec = data["records"][0]
+    assert rec["jobName"] == "done-job"
+    assert rec["activePods"] == 0, f"activePods=0 leaked sentinel: {rec['activePods']}"
+    assert rec["failedPods"] == 0, f"failedPods=0 leaked sentinel: {rec['failedPods']}"
+    assert rec["successfulPods"] == 3
+    assert rec["desiredSuccessfulPods"] == 3
+    assert rec["podCountsByPhase"]["succeeded"] == 3
+
+
+def test_jobs_base_filter_drops_non_job_pods(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+) -> None:
+    """Locks in jobsBaseFilterExpr (jobs_constants.go:10, :67-72):
+    standalone pods (no k8s.job.name), Deployment pods (k8s.deployment.name only),
+    and StatefulSet pods (k8s.statefulset.name only) are all dropped.
+    Only the real job row appears, total=1, no empty-name group."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_non_job_pods.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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,
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+    assert data["total"] == 1, f"expected only the real job row; got {[r['jobName'] for r in data['records']]}"
+    rec = data["records"][0]
+    assert rec["jobName"] == "nj-job"
+    assert all(r["jobName"] != "" for r in data["records"])
+
+
+@pytest.mark.parametrize(
+    "group_key,expected_running",
+    [
+        # groupBy=[k8s.job.name]: one record per job, jobName populated
+        # (jobs.go:28-31). 1 running pod each.
+        pytest.param(
+            "k8s.job.name",
+            {"gb-job-a1": 1, "gb-job-a2": 1, "gb-job-b1": 1, "gb-job-b2": 1},
+            id="job_name",
+        ),
+        # groupBy=[k8s.namespace.name]: aggregated across each namespace's 2
+        # jobs, jobName cleared. 2 x 1 = 2 running pods each.
+        pytest.param(
+            "k8s.namespace.name",
+            {"gb-ns-a": 2, "gb-ns-b": 2},
+            id="namespace",
+        ),
+    ],
+)
+def test_jobs_groupby(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+    group_key: str,
+    expected_running: dict,
+) -> None:
+    """groupBy returns one record per distinct group with aggregated pod-phase
+    counts. jobName is populated only when grouping by k8s.job.name
+    (jobs.go:28-31 list-vs-grouped branch); meta surfaces the groupBy key."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_groupby.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    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",
+                }
+            ],
+        },
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.OK, response.text
+    data = response.json()["data"]
+    assert data["total"] == len(expected_running)
+
+    is_job_group = group_key == "k8s.job.name"
+    group_of = lambda r: r["jobName"] if is_job_group else r["meta"][group_key]  # noqa: E731  # pylint: disable=unnecessary-lambda-assignment
+    by_group = {group_of(r): r for r in data["records"]}
+    assert set(by_group.keys()) == set(expected_running.keys())
+
+    for group, running in expected_running.items():
+        rec = by_group[group]
+        # jobName populated per job when grouping by it, empty otherwise.
+        assert rec["jobName"] == (group if is_job_group else "")
+        assert rec["podCountsByPhase"]["running"] == running
+        for other in ("pending", "succeeded", "failed", "unknown"):
+            assert rec["podCountsByPhase"][other] == 0
+        assert group_key in rec["meta"], rec["meta"]
+
+
+def test_jobs_pagination(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+) -> None:
+    """Pagination: per-page len matches min(limit, total-offset), total invariant,
+    pages cover the full set with no overlap. The final offset is beyond total:
+    it returns empty records while total still reflects dataset size."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_pagination.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    K, limit = 7, 3
+    seen_names: list[str] = []
+    seen_totals: set[int] = set()
+
+    for offset in (0, 3, 6, K + 5):
+        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": limit,
+                "offset": offset,
+                "filter": {"expression": "k8s.job.name CONTAINS 'page-'"},
+            },
+            timeout=5,
+        )
+        assert response.status_code == HTTPStatus.OK, response.text
+        data = response.json()["data"]
+        seen_totals.add(data["total"])
+        expected_len = max(0, min(limit, K - offset))
+        assert len(data["records"]) == expected_len, f"offset={offset}: expected {expected_len}, got {len(data['records'])}"
+        seen_names.extend(r["jobName"] for r in data["records"])
+
+    assert seen_totals == {K}
+    assert len(seen_names) == K
+    assert set(seen_names) == {f"page-job-{i}" for i in range(1, K + 1)}
+
+
+# orderBy keys per jobs_constants.go (snake_case request keys, camelCase
+# response fields). k8s.job.name sorts via the metadata-name branch
+# (PaginateMetadataByName) and is only allowed when groupBy is empty.
+@pytest.mark.parametrize(
+    "column,record_field",
+    [
+        pytest.param("cpu", "jobCPU", id="cpu"),
+        pytest.param("cpu_request", "jobCPURequest", id="cpu_request"),
+        pytest.param("cpu_limit", "jobCPULimit", id="cpu_limit"),
+        pytest.param("memory", "jobMemory", id="memory"),
+        pytest.param("memory_request", "jobMemoryRequest", id="memory_request"),
+        pytest.param("memory_limit", "jobMemoryLimit", id="memory_limit"),
+        pytest.param("desired_successful_pods", "desiredSuccessfulPods", id="desired_successful_pods"),
+        pytest.param("active_pods", "activePods", id="active_pods"),
+        pytest.param("failed_pods", "failedPods", id="failed_pods"),
+        pytest.param("successful_pods", "successfulPods", id="successful_pods"),
+        pytest.param("k8s.job.name", "jobName", id="job_name"),
+    ],
+)
+@pytest.mark.parametrize("direction", ["asc", "desc"])
+def test_jobs_orderby(  # pylint: disable=too-many-arguments,too-many-positional-arguments
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    insert_metrics,
+    column: str,
+    record_field: str,
+    direction: str,
+) -> None:
+    """Every orderBy column x direction: total/len stay K (invariant under
+    sort) and records come back sorted by the requested column."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    insert_metrics(
+        Metrics.load_from_file(
+            get_testdata_file_path("inframonitoring/jobs_orderby.jsonl"),
+            base_time=now - timedelta(minutes=4),
+        )
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    K = 5
+
+    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,
+            "orderBy": {"key": {"name": column}, "direction": direction},
+            # Guards against jobs seeded by other tests in the shared backend.
+            "filter": {"expression": "k8s.job.name CONTAINS 'order-'"},
+        },
+        timeout=5,
+    )
+    ctx = f"orderBy={column} {direction}"
+    assert response.status_code == HTTPStatus.OK, f"{ctx}: {response.text}"
+    data = response.json()["data"]
+    assert data["total"] == K, f"{ctx}: total={data['total']}"
+    assert len(data["records"]) == K, f"{ctx}: len(records)={len(data['records'])}"
+
+    values = [r[record_field] for r in data["records"]]
+    expected = sorted(values, reverse=(direction == "desc"))
+    assert values == expected, f"{ctx} not sorted; got {values}"
+
+
+@pytest.mark.parametrize(
+    "payload_override,err_substr",
+    [
+        pytest.param({"start": 0}, "start must be greater than 0", id="start_zero"),
+        pytest.param({"start": -1}, "start must be greater than 0", id="start_negative"),
+        pytest.param({"end": 0}, "end must be greater than 0", id="end_zero"),
+        pytest.param({"end": -1}, "end must be greater than 0", id="end_negative"),
+        pytest.param({"_use_end_eq_start": True}, "must be less than end", id="start_equals_end"),
+        pytest.param({"_use_start_gt_end": True}, "must be less than end", id="start_greater_than_end"),
+        pytest.param({"limit": 0}, "limit must be between", id="limit_zero"),
+        pytest.param({"limit": 5001}, "limit must be between", id="limit_too_large"),
+        pytest.param({"offset": -1}, "offset cannot be negative", id="offset_negative"),
+        pytest.param(
+            {"orderBy": {"key": {"name": "bogus_col"}, "direction": "desc"}},
+            "invalid order by key",
+            id="orderby_invalid_key",
+        ),
+        pytest.param(
+            {"orderBy": {"key": {"name": "cpu"}, "direction": "up"}},
+            "invalid order by direction",
+            id="orderby_invalid_direction",
+        ),
+        pytest.param(
+            {
+                "orderBy": {"key": {"name": "k8s.job.name"}, "direction": "desc"},
+                "groupBy": [
+                    {
+                        "name": "k8s.namespace.name",
+                        "fieldDataType": "string",
+                        "fieldContext": "resource",
+                    }
+                ],
+            },
+            "is only allowed when groupBy is empty",
+            id="orderby_jobname_with_groupby",
+        ),
+    ],
+)
+def test_jobs_validation_errors(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token,
+    payload_override: dict,
+    err_substr: str,
+) -> None:
+    """All PostableJobs.Validate() rules reject with 400 + descriptive error.
+    See pkg/types/inframonitoringtypes/jobs.go:48-99."""
+    now = datetime.now(tz=UTC).replace(microsecond=0)
+    body: dict = {
+        "start": int((now - timedelta(minutes=5)).timestamp() * 1000),
+        "end": int(now.timestamp() * 1000),
+        "limit": 50,
+    }
+    if payload_override.pop("_use_end_eq_start", False):
+        body["end"] = body["start"]
+    if payload_override.pop("_use_start_gt_end", False):
+        body["start"] = body["end"] + 1
+    body.update(payload_override)
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    response = requests.post(
+        signoz.self.host_configs["8080"].get(ENDPOINT),
+        headers={"authorization": f"Bearer {token}"},
+        json=body,
+        timeout=5,
+    )
+    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
+    error = response.json()["error"]
+    assert error["code"] == "invalid_input"
+    assert err_substr.lower() in error["message"].lower(), f"expected substring {err_substr!r} not found in: {error['message']!r}"