[ROB-2447] Oomkill job fix

robusta_krr/core/integrations/kubernetes/__init__.py

@@ -23,6 +23,14 @@
 from robusta_krr.core.models.result import ResourceAllocations
 from robusta_krr.utils.object_like_dict import ObjectLikeDict
 
+
+class LightweightJobInfo:
+    """Lightweight job object containing only the fields needed for GroupedJob processing."""
+    def __init__(self, name: str, namespace: str):
+        self.name = name
+        self.namespace = namespace
+
+
 from . import config_patch as _
 
 logger = logging.getLogger("krr")
@@ -261,6 +269,88 @@ def _should_list_resource(self, resource: str) -> bool:
             return True
         return resource in settings.resources
 
+    def _is_job_owned_by_cronjob(self, job: V1Job) -> bool:
+        """Check if a job is owned by a CronJob."""
+        return any(owner.kind == "CronJob" for owner in job.metadata.owner_references or [])
+
+    def _is_job_grouped(self, job: V1Job) -> bool:
+        """Check if a job has any of the grouping labels."""
+        if not settings.job_grouping_labels or not job.metadata.labels:
+            return False
+        return any(label in job.metadata.labels for label in settings.job_grouping_labels)
+
+    async def _list_namespaced_or_global_objects_batched(
+        self,
+        kind: KindLiteral,
+        all_namespaces_request: Callable,
+        namespaced_request: Callable,
+        limit: Optional[int] = None,
+        continue_ref: Optional[str] = None,
+    ) -> tuple[list[Any], Optional[str]]:
+        logger.debug("Listing %s in %s with batching (limit=%d)", kind, self.cluster, limit)
+        loop = asyncio.get_running_loop()
+
+        try:
+            if self.namespaces == "*":
+                requests = [
+                    loop.run_in_executor(
+                        self.executor,
+                        lambda: all_namespaces_request(
+                            watch=False,
+                            label_selector=settings.selector,
+                            limit=limit,
+                            _continue=continue_ref,
+                        ),
+                    )
+                ]
+            else:
+                requests = [
+                    loop.run_in_executor(
+                        self.executor,
+                        lambda ns=namespace: namespaced_request(
+                            namespace=ns,
+                            watch=False,
+                            label_selector=settings.selector,
+                            limit=limit,
+                            _continue=continue_ref,
+                        ),
+                    )
+                    for namespace in self.namespaces
+                ]
+
+            result = [
+                item
+                for request_result in await asyncio.gather(*requests)
+                for item in request_result.items
+            ]
+
+            next_continue_ref = None
+            if requests:
+                first_result = await requests[0]
+                next_continue_ref = getattr(first_result.metadata, '_continue', None)
+
+            return result, next_continue_ref
+
+        except ApiException as e:
+            if e.status == 410 and e.body:
+                # Continue token expired
+                import json
+                try:
+                    error_body = json.loads(e.body)
+                    new_continue_token = error_body.get("metadata", {}).get("continue")
+                    if new_continue_token:
+                        logger.info("Continue token expired for jobs listing. Continuing")
+                        return await self._list_namespaced_or_global_objects_batched(
+                            kind=kind,
+                            all_namespaces_request=all_namespaces_request,
+                            namespaced_request=namespaced_request,
+                            limit=limit,
+                            continue_ref=new_continue_token,
+                        )
+                except (json.JSONDecodeError, KeyError):
+                    pass
+            raise
+
     async def _list_namespaced_or_global_objects(
         self,
         kind: KindLiteral,
@@ -458,26 +548,55 @@ def _list_all_daemon_set(self) -> list[K8sObjectData]:
             extract_containers=lambda item: item.spec.template.spec.containers,
         )
 
-    def _list_all_jobs(self) -> list[K8sObjectData]:
-        def filter_jobs(item):
-            # Skip jobs owned by CronJobs
-            if any(owner.kind == "CronJob" for owner in item.metadata.owner_references or []):
-                return False
+
+    async def _list_all_jobs(self) -> list[K8sObjectData]:
+        """List all jobs using batched loading with 500 batch size."""
+        if not self._should_list_resource("Job"):
+            return []
+
+        all_jobs = []
+        try:
+            continue_ref: Optional[str] = None
+            batch_count = 0
 
-            # Skip jobs that have any of the grouping labels (they will be handled by GroupedJob)
-            if settings.job_grouping_labels and item.metadata.labels:
-                if any(label in item.metadata.labels for label in settings.job_grouping_labels):
-                    return False
+            while batch_count < settings.discovery_job_max_batches:
+                jobs_batch, next_continue_ref = await self._list_namespaced_or_global_objects_batched(
+                    kind="Job",
+                    all_namespaces_request=self.batch.list_job_for_all_namespaces,
+                    namespaced_request=self.batch.list_namespaced_job,
+                    limit=settings.discovery_job_batch_size,
+                    continue_ref=continue_ref,
+                )
+
+                batch_count += 1
+                continue_ref = next_continue_ref
+
+                # refreshed continue token
+                if not jobs_batch and continue_ref:
+                    continue
+
+                for job in jobs_batch:
+                    if self._is_job_owned_by_cronjob(job):
+                        continue
+
+                    if self._is_job_grouped(job):
+                        continue
+
+                    for container in job.spec.template.spec.containers:
+                        all_jobs.append(self.__build_scannable_object(job, container, "Job"))
+
+                if not continue_ref:
+                    break
 
-            return True
-
-        return self._list_scannable_objects(
-            kind="Job",
-            all_namespaces_request=self.batch.list_job_for_all_namespaces,
-            namespaced_request=self.batch.list_namespaced_job,
-            extract_containers=lambda item: item.spec.template.spec.containers,
-            filter_workflows=filter_jobs,
-        )
+            logger.debug("Found %d regular jobs", len(all_jobs))
+            return all_jobs
+            
+        except Exception as e:
+            logger.error(
+                "Failed to run jobs discovery",
+                exc_info=True,
+            )
+            return all_jobs
 
     def _list_all_cronjobs(self) -> list[K8sObjectData]:
         return self._list_scannable_objects(
@@ -497,42 +616,77 @@ async def _list_all_groupedjobs(self) -> list[K8sObjectData]:
             logger.debug("Skipping GroupedJob in cluster")
             return []
 
-        logger.debug(f"Listing GroupedJobs with grouping labels: {settings.job_grouping_labels}")
-
-        # Get all jobs that have any of the grouping labels
-        all_jobs = await self._list_namespaced_or_global_objects(
-            kind="Job",
-            all_namespaces_request=self.batch.list_job_for_all_namespaces,
-            namespaced_request=self.batch.list_namespaced_job,
-        )
+        logger.debug("Listing GroupedJobs with grouping labels: %s", settings.job_grouping_labels)
 
         grouped_jobs = defaultdict(list)
-        for job in all_jobs:
-            if (job.metadata.labels and 
-                not any(owner.kind == "CronJob" for owner in job.metadata.owner_references or [])):
+        grouped_jobs_template = {}  # Store only ONE full job as template per group - needed for class K8sObjectData
+        continue_ref: Optional[str] = None
+        batch_count = 0
+
+        try:
+            while batch_count < settings.discovery_job_max_batches:
+                jobs_batch, next_continue_ref = await self._list_namespaced_or_global_objects_batched(
+                    kind="Job",
+                    all_namespaces_request=self.batch.list_job_for_all_namespaces,
+                    namespaced_request=self.batch.list_namespaced_job,
+                    limit=settings.discovery_job_batch_size,
+                    continue_ref=continue_ref,
+                )
+
+                batch_count += 1
+                continue_ref = next_continue_ref
+
+                # refreshed continue token
+                if not jobs_batch and continue_ref:
+                    continue
 
-                for label_name in settings.job_grouping_labels:
-                    if label_name in job.metadata.labels:
+                for job in jobs_batch:
+                    if not job.metadata.labels or self._is_job_owned_by_cronjob(job) or not self._is_job_grouped(job):
+                        continue
+
+                    for label_name in settings.job_grouping_labels:
+                        if label_name not in job.metadata.labels:
+                            continue
+
+                        # label_name is value of grouped job label
                         label_value = job.metadata.labels[label_name]
                         group_key = f"{label_name}={label_value}"
-                        grouped_jobs[group_key].append(job)
+                        lightweight_job = LightweightJobInfo(
+                            name=job.metadata.name,
+                            namespace=job.metadata.namespace
+                        )
+                        # Store lightweight job info only for grouped jobs
+                        grouped_jobs[group_key].append(lightweight_job)
+                        # Keep only ONE full job as template per group
+                        if group_key not in grouped_jobs_template:
+                            grouped_jobs_template[group_key] = job
+
+                if not continue_ref:
+                    break
+
+        except Exception as e:
+            logger.error(
+                "Failed to run grouped jobs discovery",
+                exc_info=True,
+            )
+            raise e
 
         result = []
         for group_name, jobs in grouped_jobs.items():
+            template_job = grouped_jobs_template[group_name]
+
             jobs_by_namespace = defaultdict(list)
             for job in jobs:
-                jobs_by_namespace[job.metadata.namespace].append(job)
+                jobs_by_namespace[job.namespace].append(job)
 
             for namespace, namespace_jobs in jobs_by_namespace.items():
                 limited_jobs = namespace_jobs[:settings.job_grouping_limit]
 
                 container_names = set()
-                for job in limited_jobs:
-                    for container in job.spec.template.spec.containers:
-                        container_names.add(container.name)
+                for container in template_job.spec.template.spec.containers:
+                    container_names.add(container.name)
 
                 for container_name in container_names:
-                    template_job = limited_jobs[0]
                     template_container = None
                     for container in template_job.spec.template.spec.containers:
                         if container.name == container_name:

robusta_krr/core/integrations/prometheus/metrics_service/prometheus_metrics_service.py

@@ -351,7 +351,7 @@ async def load_pods(self, object: K8sObjectData, period: timedelta) -> list[PodD
             del jobs
         elif object.kind == "GroupedJob":
             if hasattr(object._api_resource, '_grouped_jobs'):
-                pod_owners = [job.metadata.name for job in object._api_resource._grouped_jobs]
+                pod_owners = [job.name for job in object._api_resource._grouped_jobs]
                 pod_owner_kind = "Job"
             else:
                 pod_owners = [object.name]

robusta_krr/core/models/config.py

@@ -53,6 +53,10 @@ class Config(pd.BaseSettings):
     # Threading settings
     max_workers: int = pd.Field(6, ge=1)
 
+    # Discovery settings
+    discovery_job_batch_size: int = pd.Field(1000, ge=1, description="Batch size for Kubernetes job API calls")
+    discovery_job_max_batches: int = pd.Field(50, ge=1, description="Maximum number of job batches to process to prevent infinite loops")
+
     # Job grouping settings
     job_grouping_labels: Union[list[str], str, None] = pd.Field(None, description="Label name(s) to use for grouping jobs into GroupedJob workload type")
     job_grouping_limit: int = pd.Field(500, ge=1, description="Maximum number of jobs/pods to query per GroupedJob group")

robusta_krr/main.py

@@ -232,6 +232,18 @@ def run_strategy(
                     help="Maximum number of jobs/pods to query per GroupedJob group (default: 500).",
                     rich_help_panel="Job Grouping Settings",
                 ),
+                discovery_job_batch_size: int = typer.Option(
+                    250,
+                    "--discovery-job-batch-size",
+                    help="Batch size for Kubernetes job API calls (default: 1000).",
+                    rich_help_panel="Job Discovery Settings",
+                ),
+                discovery_job_max_batches: int = typer.Option(
+                    100,
+                    "--discovery-job-max-batches",
+                    help="Maximum number of job batches to process to prevent infinite loops (default: 50).",
+                    rich_help_panel="Job Discovery Settings",
+                ),
                 format: str = typer.Option(
                     "table",
                     "--formatter",
@@ -371,6 +383,8 @@ def run_strategy(
                         max_workers=max_workers,
                         job_grouping_labels=job_grouping_labels,
                         job_grouping_limit=job_grouping_limit,
+                        discovery_job_batch_size=discovery_job_batch_size,
+                        discovery_job_max_batches=discovery_job_max_batches,
                         format=format,
                         show_cluster_name=show_cluster_name,
                         verbose=verbose,