Add minimal points required for recommendation

1 files changed, 28 insertions, 3 deletions

diff --git a/robusta_krr/strategies/simple.py b/robusta_krr/strategies/simple.py
index d7efae8..8cb8bfc 100644
--- a/robusta_krr/strategies/simple.py
+++ b/robusta_krr/strategies/simple.py
@@ -11,7 +11,13 @@ from robusta_krr.core.abstract.strategies import (
     RunResult,
     StrategySettings,
 )
-from robusta_krr.core.integrations.prometheus.metrics import MaxMemoryLoader, PercentileCPULoader, PrometheusMetric
+from robusta_krr.core.integrations.prometheus.metrics import (
+    MaxMemoryLoader,
+    PercentileCPULoader,
+    PrometheusMetric,
+    CPUAmountLoader,
+    MemoryAmountLoader,
+)
 
 
 class SimpleStrategySettings(StrategySettings):
@@ -19,6 +25,9 @@ class SimpleStrategySettings(StrategySettings):
     memory_buffer_percentage: float = pd.Field(
         15, gt=0, description="The percentage of added buffer to the peak memory usage for memory recommendation."
     )
+    points_required: int = pd.Field(
+        100, ge=1, description="The number of data points required to make a recommendation for a resource."
+    )
 
     def calculate_memory_proposal(self, data: PodsTimeData) -> float:
         data_ = [np.max(values[:, 1]) for values in data.values()]
@@ -57,16 +66,24 @@ class SimpleStrategy(BaseStrategy[SimpleStrategySettings]):
 
     @property
     def metrics(self) -> list[type[PrometheusMetric]]:
-        return [PercentileCPULoader(self.settings.cpu_percentile), MaxMemoryLoader]
+        return [PercentileCPULoader(self.settings.cpu_percentile), MaxMemoryLoader, CPUAmountLoader, MemoryAmountLoader]
 
     def __calculate_cpu_proposal(
         self, history_data: MetricsPodData, object_data: K8sObjectData
     ) -> ResourceRecommendation:
         data = history_data["PercentileCPULoader"]
+        data_count = {pod: values[0, 1] for pod, values in history_data["CPUAmountLoader"].items()}
 
         if len(data) == 0:
             return ResourceRecommendation.undefined(info="No data")
 
+        filtered_data = {
+            pod: values for pod, values in data.items() if data_count.get(pod, 0) >= self.settings.points_required
+        }
+
+        if len(filtered_data) == 0:
+            return ResourceRecommendation.undefined(info="Not enough data")
+
         if object_data.hpa is not None and object_data.hpa.target_cpu_utilization_percentage is not None:
             return ResourceRecommendation.undefined(info="HPA detected")
 
@@ -77,14 +94,22 @@ class SimpleStrategy(BaseStrategy[SimpleStrategySettings]):
         self, history_data: MetricsPodData, object_data: K8sObjectData
     ) -> ResourceRecommendation:
         data = history_data["MaxMemoryLoader"]
+        data_count = {pod: values[0, 1] for pod, values in history_data["MemoryAmountLoader"].items()}
 
         if len(data) == 0:
             return ResourceRecommendation.undefined(info="No data")
 
+        filtered_data = {
+            pod: value for pod, value in data.items() if data_count.get(pod, 0) >= self.settings.points_required
+        }
+
+        if len(filtered_data) == 0:
+            return ResourceRecommendation.undefined(info="Not enough data")
+
         if object_data.hpa is not None and object_data.hpa.target_memory_utilization_percentage is not None:
             return ResourceRecommendation.undefined(info="HPA detected")
 
-        memory_usage = self.settings.calculate_memory_proposal(data)
+        memory_usage = self.settings.calculate_memory_proposal(filtered_data)
         return ResourceRecommendation(request=memory_usage, limit=memory_usage)
 
     def run(self, history_data: MetricsPodData, object_data: K8sObjectData) -> RunResult: