Implement prometheus request batching

robusta_krr/core/integrations/prometheus/metrics/base.py

@@ -4,8 +4,10 @@
 import asyncio
 import datetime
 import enum
+import copy
 from concurrent.futures import ThreadPoolExecutor
 from typing import Any, TYPE_CHECKING, Optional
+from functools import reduce
 
 import numpy as np
 import pydantic as pd
@@ -156,7 +158,6 @@ async def load_data(
         )
 
         if result == []:
-            self.warning(f"{self.service_name} returned no {self.__class__.__name__} metrics for {object}")
             return {}
 
         return {pod_result["metric"]["pod"]: np.array(pod_result["values"], dtype=np.float64) for pod_result in result}
@@ -177,7 +178,7 @@ class QueryMetric(PrometheusMetric):
 PrometheusSeries = Any
 
 
-class FilterMetric(PrometheusMetric):
+class FilterJobsMixin(PrometheusMetric):
     """
     This is the version of the BasicMetricLoader, that filters out data,
     if multiple metrics with the same name were found.
@@ -206,16 +207,16 @@ def filter_prom_jobs_results(
             return series_list_result
 
         target_names = {
-            FilterMetric.get_target_name(series)
+            FilterJobsMixin.get_target_name(series)
             for series in series_list_result
-            if FilterMetric.get_target_name(series)
+            if FilterJobsMixin.get_target_name(series)
         }
         return_list: list[PrometheusSeries] = []
 
         # takes kubelet job if exists, return first job alphabetically if it doesn't
         for target_name in target_names:
             relevant_series = [
-                series for series in series_list_result if FilterMetric.get_target_name(series) == target_name
+                series for series in series_list_result if FilterJobsMixin.get_target_name(series) == target_name
             ]
             relevant_kubelet_metric = [series for series in relevant_series if series["metric"].get("job") == "kubelet"]
             if len(relevant_kubelet_metric) == 1:
@@ -228,3 +229,68 @@ def filter_prom_jobs_results(
     async def query_prometheus(self, data: PrometheusMetricData) -> list[PrometheusSeries]:
         result = await super().query_prometheus(data)
         return self.filter_prom_jobs_results(result)
+
+
+class BatchedRequestMixin(PrometheusMetric):
+    """
+    This type of PrometheusMetric is used to split the query into multiple queries,
+    each querying a subset of the pods of the object.
+
+    The results of the queries are then combined into a single result.
+
+    This is useful when the number of pods is too large for a single query.
+    """
+
+    pods_batch_size = 50
+
+    def combine_batches(self, results: list[PodsTimeData]) -> PodsTimeData:
+        """
+        Combines the results of multiple queries into a single result.
+
+        Args:
+        results (list[MetricPodData]): A list of query results.
+
+        Returns:
+        MetricPodData: A combined result.
+        """
+
+        return reduce(lambda x, y: x | y, results, {})
+
+    @staticmethod
+    def _slice_object(object: K8sObjectData, s: slice) -> K8sObjectData:
+        obj_copy = copy.deepcopy(object)
+        obj_copy.pods = object.pods[s]
+        return obj_copy
+
+    @staticmethod
+    def _split_objects(object: K8sObjectData, max_pods: int) -> list[K8sObjectData]:
+        """
+        Splits the object into multiple objects, each containing at most max_pods pods.
+
+        Args:
+        object (K8sObjectData): The object to split.
+
+        Returns:
+        list[K8sObjectData]: A list of objects.
+        """
+        return [
+            BatchedRequestMixin._slice_object(object, slice(i, i + max_pods))
+            for i in range(0, len(object.pods), max_pods)
+        ]
+
+    async def load_data(
+        self, object: K8sObjectData, period: datetime.timedelta, step: datetime.timedelta
+    ) -> PodsTimeData:
+        splitted_objects = self._split_objects(object, self.pods_batch_size)
+
+        # If we do not exceed the batch size, we can use the regular load_data method.
+        if len(splitted_objects) <= 1:
+            return await super().load_data(object, period, step)
+
+        results = await asyncio.gather(
+            *[
+                super(BatchedRequestMixin, self).load_data(splitted_object, period, step)
+                for splitted_object in splitted_objects
+            ]
+        )
+        return self.combine_batches(results)

robusta_krr/core/integrations/prometheus/metrics/cpu.py

@@ -1,9 +1,10 @@
 from robusta_krr.core.models.objects import K8sObjectData
+from robusta_krr.core.abstract.strategies import PodsTimeData
 
-from .base import QueryMetric, QueryRangeMetric, FilterMetric
+from .base import QueryMetric, QueryRangeMetric, FilterJobsMixin, BatchedRequestMixin
 
 
-class CPULoader(QueryRangeMetric, FilterMetric):
+class CPULoader(QueryRangeMetric, FilterJobsMixin, BatchedRequestMixin):
     def get_query(self, object: K8sObjectData, resolution: str) -> str:
         pods_selector = "|".join(pod.name for pod in object.pods)
         cluster_label = self.get_prometheus_cluster_label()
@@ -21,7 +22,7 @@ def get_query(self, object: K8sObjectData, resolution: str) -> str:
         """
 
 
-class MaxCPULoader(QueryMetric, FilterMetric):
+class MaxCPULoader(QueryMetric, FilterJobsMixin, BatchedRequestMixin):
     def get_query(self, object: K8sObjectData, resolution: str) -> str:
         pods_selector = "|".join(pod.name for pod in object.pods)
         cluster_label = self.get_prometheus_cluster_label()
@@ -40,7 +41,7 @@ def get_query(self, object: K8sObjectData, resolution: str) -> str:
 
 
 def PercentileCPULoader(percentile: float) -> type[QueryMetric]:
-    class PercentileCPULoader(QueryMetric, FilterMetric):
+    class PercentileCPULoader(QueryMetric, FilterJobsMixin, BatchedRequestMixin):
         def get_query(self, object: K8sObjectData, resolution: str) -> str:
             pods_selector = "|".join(pod.name for pod in object.pods)
             cluster_label = self.get_prometheus_cluster_label()

robusta_krr/core/integrations/prometheus/metrics/memory.py

@@ -1,9 +1,11 @@
+from robusta_krr.core.abstract.strategies import PodsTimeData
+
 from robusta_krr.core.models.objects import K8sObjectData
 
-from .base import QueryMetric, QueryRangeMetric, FilterMetric
+from .base import QueryMetric, QueryRangeMetric, FilterJobsMixin, BatchedRequestMixin
 
 
-class MemoryLoader(QueryRangeMetric, FilterMetric):
+class MemoryLoader(QueryRangeMetric, FilterJobsMixin, BatchedRequestMixin):
     def get_query(self, object: K8sObjectData, resolution: str) -> str:
         pods_selector = "|".join(pod.name for pod in object.pods)
         cluster_label = self.get_prometheus_cluster_label()
@@ -19,7 +21,7 @@ def get_query(self, object: K8sObjectData, resolution: str) -> str:
         """
 
 
-class MaxMemoryLoader(QueryMetric, FilterMetric):
+class MaxMemoryLoader(QueryMetric, FilterJobsMixin, BatchedRequestMixin):
     def get_query(self, object: K8sObjectData, resolution: str) -> str:
         pods_selector = "|".join(pod.name for pod in object.pods)
         cluster_label = self.get_prometheus_cluster_label()
@@ -36,7 +38,7 @@ def get_query(self, object: K8sObjectData, resolution: str) -> str:
 
 
 def PercentileMemoryLoader(percentile: float) -> type[QueryMetric]:
-    class PercentileMemoryLoader(QueryMetric, FilterMetric):
+    class PercentileMemoryLoader(QueryMetric, FilterJobsMixin, BatchedRequestMixin):
         def get_query(self, object: K8sObjectData, resolution: str) -> str:
             pods_selector = "|".join(pod.name for pod in object.pods)
             cluster_label = self.get_prometheus_cluster_label()

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

@@ -157,7 +157,14 @@ async def gather_data(
         self.debug(f"Gathering {LoaderClass.__name__} metric for {object}")
 
         metric_loader = LoaderClass(self.config, self.prometheus, self.name, self.executor)
-        return await metric_loader.load_data(object, period, step)
+        data = await metric_loader.load_data(object, period, step)
+
+        if len(data) == 0:
+            self.warning(
+                f"{metric_loader.service_name} returned no {metric_loader.__class__.__name__} metrics for {object}"
+            )
+
+        return data
 
     async def load_pods(self, object: K8sObjectData, period: datetime.timedelta) -> None:
         """
@@ -176,13 +183,14 @@ async def load_pods(self, object: K8sObjectData, period: datetime.timedelta) ->
         cluster_label = self.get_prometheus_cluster_label()
         if object.kind == "Deployment":
             replicasets = await self.query(
-                "kube_replicaset_owner{"
-                f'owner_name="{object.name}", '
-                f'owner_kind="Deployment", '
-                f'namespace="{object.namespace}"'
-                f"{cluster_label}"
-                "}"
-                f"[{period_literal}]"
+                f"""
+                kube_replicaset_owner{{
+                    owner_name="{object.name}",
+                    owner_kind="Deployment",
+                    namespace="{object.namespace}"
+                    {cluster_label}
+                }}[{period_literal}]
+                """
             )
             pod_owners = [replicaset["metric"]["replicaset"] for replicaset in replicasets]
             pod_owner_kind = "ReplicaSet"

robusta_krr/strategies/simple.py

@@ -1,4 +1,3 @@
-from typing import Sequence
 import numpy as np
 import pydantic as pd
 
@@ -12,7 +11,6 @@
     RunResult,
     StrategySettings,
 )
-from robusta_krr.core.abstract.metrics import BaseMetric
 from robusta_krr.core.integrations.prometheus.metrics import PercentileCPULoader, MaxMemoryLoader, PrometheusMetric
 
 
@@ -27,7 +25,7 @@ def calculate_memory_proposal(self, data: PodsTimeData) -> float:
         if len(data_) == 0:
             return float("NaN")
 
-        return max(data_) * (1 + self.memory_buffer_percentage / 100)
+        return np.max(data_) * (1 + self.memory_buffer_percentage / 100)
 
     def calculate_cpu_proposal(self, data: PodsTimeData) -> float:
         if len(data) == 0:
@@ -38,7 +36,7 @@ def calculate_cpu_proposal(self, data: PodsTimeData) -> float:
         else:
             data_ = list(data.values())[0][:, 1]
 
-        return np.percentile(data_, self.cpu_percentile)
+        return np.max(data_)
 
 
 class SimpleStrategy(BaseStrategy[SimpleStrategySettings]):