+
+
+
## Usage
@@ -369,7 +406,7 @@ Robusta KRR uses the following Prometheus queries to gather usage data:
[_Need to customize the metrics? Tell us and we'll add support._](https://github.com/robusta-dev/krr/issues/new)
-Get a free breakdown of KRR recommendations in the [Robusta SaaS](#optional-free-saas-platform).
+Get a free breakdown of KRR recommendations in the [Robusta SaaS](#free-krr-ui-on-robusta-saas).
### Algorithm
@@ -385,21 +422,6 @@ Find about how KRR tries to find the default Prometheus to connect (back to top)
-## Difference with Kubernetes VPA
-
-| Feature 🛠️ | Robusta KRR 🚀 | Kubernetes VPA 🌐 |
-| --------------------------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- |
-| Resource Recommendations 💡 | ✅ CPU/Memory requests and limits | ✅ CPU/Memory requests and limits |
-| Installation Location 🌍 | ✅ Not required to be installed inside the cluster, can be used on your own device, connected to a cluster | ❌ Must be installed inside the cluster |
-| Workload Configuration 🔧 | ✅ No need to configure a VPA object for each workload | ❌ Requires VPA object configuration for each workload |
-| Immediate Results ⚡ | ✅ Gets results immediately (given Prometheus is running) | ❌ Requires time to gather data and provide recommendations |
-| Reporting 📊 | ✅ Detailed CLI Report, web UI in [Robusta.dev](https://home.robusta.dev/) | ❌ Not supported |
-| Extensibility 🔧 | ✅ Add your own strategies with few lines of Python | :warning: Limited extensibility |
-| Explainability 📖 | ✅ See graphs explaining the recommendations | ❌ Not supported |
-| Custom Metrics 📏 | 🔄 Support in future versions | ❌ Not supported |
-| Custom Resources 🎛️ | 🔄 Support in future versions (e.g., GPU) | ❌ Not supported |
-| Autoscaling 🔀 | 🔄 Support in future versions | ✅ Automatic application of recommendations |
-| Default History 🕒 | 14 days | 8 days |
@@ -567,13 +589,16 @@ For discovering Prometheus it scans services for those labels:
+
diff --git a/robusta_krr/core/abstract/strategies.py b/robusta_krr/core/abstract/strategies.py
index 0005a227..b63b2cc7 100644
--- a/robusta_krr/core/abstract/strategies.py
+++ b/robusta_krr/core/abstract/strategies.py
@@ -111,24 +111,15 @@ def __init__(self, settings: _StrategySettings):
self.settings = settings
def __str__(self) -> str:
- return self._display_name.title()
-
- @property
- def _display_name(self) -> str:
- return getattr(self, "display_name", self.__class__.__name__.lower().removeprefix("strategy"))
+ return self.display_name.title()
@property
def description(self) -> Optional[str]:
"""
Generate a description for the strategy.
- You can use the settings in the description by using the format syntax.
- Also you can use Rich's markdown syntax to format the description.
+ You can use Rich's markdown syntax to format the description.
"""
-
- if self.__doc__ is None:
- return None
-
- return f"[b]{self} Strategy[/b]\n\n" + dedent(self.__doc__.format_map(self.settings.dict())).strip()
+ raise NotImplementedError()
# Abstract method that needs to be implemented by subclass.
# This method is intended to calculate resource recommendation based on history data and kubernetes object data.
diff --git a/robusta_krr/core/integrations/prometheus/metrics_service/prometheus_metrics_service.py b/robusta_krr/core/integrations/prometheus/metrics_service/prometheus_metrics_service.py
index 8331249f..90efdc16 100644
--- a/robusta_krr/core/integrations/prometheus/metrics_service/prometheus_metrics_service.py
+++ b/robusta_krr/core/integrations/prometheus/metrics_service/prometheus_metrics_service.py
@@ -41,6 +41,7 @@ def find_metrics_url(self, *, api_client: Optional[ApiClient] = None) -> Optiona
"app=rancher-monitoring-prometheus",
"app=prometheus-prometheus",
"app.kubernetes.io/name=prometheus,app.kubernetes.io/component=server",
+ "app=stack-prometheus",
]
)
diff --git a/robusta_krr/core/runner.py b/robusta_krr/core/runner.py
index 8e08521c..7ae4a40d 100644
--- a/robusta_krr/core/runner.py
+++ b/robusta_krr/core/runner.py
@@ -293,7 +293,7 @@ async def _collect_result(self) -> Result:
return Result(
scans=scans,
- description=self._strategy.description,
+ description=f"[b]{self._strategy.display_name.title()} Strategy[/b]\n\n{self._strategy.description}",
strategy=StrategyData(
name=str(self._strategy).lower(),
settings=self._strategy.settings.dict(),
diff --git a/robusta_krr/strategies/simple.py b/robusta_krr/strategies/simple.py
index 3cecd18e..c56e6ecb 100644
--- a/robusta_krr/strategies/simple.py
+++ b/robusta_krr/strategies/simple.py
@@ -1,3 +1,4 @@
+import textwrap
from datetime import timedelta
import numpy as np
@@ -24,7 +25,7 @@
class SimpleStrategySettings(StrategySettings):
- cpu_percentile: float = pd.Field(99, gt=0, le=100, description="The percentile to use for the CPU recommendation.")
+ cpu_percentile: float = pd.Field(95, gt=0, le=100, description="The percentile to use for the CPU recommendation.")
memory_buffer_percentage: float = pd.Field(
15, gt=0, description="The percentage of added buffer to the peak memory usage for memory recommendation."
)
@@ -70,21 +71,7 @@ def history_range_enough(self, history_range: tuple[timedelta, timedelta]) -> bo
class SimpleStrategy(BaseStrategy[SimpleStrategySettings]):
- """
- CPU request: {cpu_percentile}% percentile, limit: unset
- Memory request: max + {memory_buffer_percentage}%, limit: max + {memory_buffer_percentage}%
- History: {history_duration} hours
- Step: {timeframe_duration} minutes
-
- All parameters can be customized. For example: `krr simple --cpu_percentile=90 --memory_buffer_percentage=15 --history_duration=24 --timeframe_duration=0.5`
-
- This strategy does not work with objects with HPA defined (Horizontal Pod Autoscaler).
- If HPA is defined for CPU or Memory, the strategy will return "?" for that resource.
- You can override this behaviour by passing the --allow-hpa flag
-
- Learn more: [underline]https://github.com/robusta-dev/krr#algorithm[/underline]
- """
-
+
display_name = "simple"
rich_console = True
@@ -102,6 +89,27 @@ def metrics(self) -> list[type[PrometheusMetric]]:
return metrics
+ @property
+ def description(self):
+ s = textwrap.dedent(f"""\
+ CPU request: {self.settings.cpu_percentile}% percentile, limit: unset
+ Memory request: max + {self.settings.memory_buffer_percentage}%, limit: max + {self.settings.memory_buffer_percentage}%
+ History: {self.settings.history_duration} hours
+ Step: {self.settings.timeframe_duration} minutes
+
+ All parameters can be customized. For example: `krr simple --cpu_percentile=90 --memory_buffer_percentage=15 --history_duration=24 --timeframe_duration=0.5`
+ """)
+
+ if not self.settings.allow_hpa:
+ s += "\n" + textwrap.dedent(f"""\
+ This strategy does not work with objects with HPA defined (Horizontal Pod Autoscaler).
+ If HPA is defined for CPU or Memory, the strategy will return "?" for that resource.
+ You can override this behaviour by passing the --allow-hpa flag
+ """)
+
+ s += "\nLearn more: [underline]https://github.com/robusta-dev/krr#algorithm[/underline]"
+ return s
+
def __calculate_cpu_proposal(
self, history_data: MetricsPodData, object_data: K8sObjectData
) -> ResourceRecommendation:
Free UI for KRR recommendations
-With the [free Robusta SaaS platform](https://home.robusta.dev/) you can: +We highly recommend using the [free Robusta SaaS platform](https://platform.robusta.dev/signup/?utm_source=github&utm_medium=krr-readme). You can: -- See why KRR recommends what it does +- Understand individual app recommendations with app usage history - Sort and filter recommendations by namespace, priority, and more -- Copy a YAML snippet to fix the problems KRR finds +- Give dev's a YAML snippet to fix the problems KRR finds +- Analyze impact using KRR scan history -![Robusta UI Screen Shot][ui-screenshot] + +
+