add required layer option to conditionally run runtimes queries

[khansaad]

Description

This PR adds a new required_layer attribute in the aggregation_functions alongside the query to conditionally run the runtime queries based on the corresponding layer detection.

Fixes # (issue)

Type of change

• Bug fix
• New feature
• Docs update
• Breaking change (What changes might users need to make in their application due to this PR?)
• Requires DB changes

How has this been tested?

Please describe the tests that were run to verify your changes and steps to reproduce. Please specify any test configuration required.

• New Test X
• Functional testsuite

Test Configuration

• Kubernetes clusters tested on:

Checklist 🎯

• Followed coding guidelines
• Comments added
• Dependent changes merged
• Documentation updated
• Tests added or updated

Additional information

Include any additional information such as links, test results, screenshots here

Summary by Sourcery

Add support for conditionally executing runtime aggregation queries based on detected container layers via a new required layer attribute on aggregation functions.

New Features:

• Introduce a requiredLayer attribute on aggregation functions to gate metric queries on specific detected runtime layers.

Enhancements:

• Update recommendation engine logic to skip runtime metrics whose required layers are not present on the target container.
• Annotate JVM-related aggregation functions in performance profile manifests with required runtime layers to align with conditional query execution.

[sourcery-ai]

Reviewer's Guide

Adds a new optional required_layer attribute to aggregation functions and uses it in the recommendation engine to conditionally execute runtime metrics queries only when matching container layers are detected, updating JVM-related performance profiles to specify applicable runtimes.

Sequence diagram for conditional metric query execution based on required_layer

sequenceDiagram
    participant RecommendationEngine
    participant KruizeObject
    participant ContainerData
    participant AggregationFunctions

    RecommendationEngine->>KruizeObject: getContainerData(containerName)
    KruizeObject-->>RecommendationEngine: ContainerData
    RecommendationEngine->>ContainerData: getLayerMap()
    ContainerData-->>RecommendationEngine: layerMap

    loop for each metricEntry
        RecommendationEngine->>metricEntry: getAggregationFunctionsMap()
        metricEntry-->>RecommendationEngine: aggregationFunctions
        loop for each aggregationFunctionsEntry
            RecommendationEngine->>AggregationFunctions: getRequiredLayer()
            AggregationFunctions-->>RecommendationEngine: requiredLayer
            alt requiredLayer is null or empty
                RecommendationEngine->>AggregationFunctions: getQuery()
                AggregationFunctions-->>RecommendationEngine: promQL
                RecommendationEngine->>RecommendationEngine: executePromQuery(promQL)
            else requiredLayer is set
                RecommendationEngine->>RecommendationEngine: split requiredLayer into requiredLayers
                RecommendationEngine->>ContainerData: getLayerMap()
                ContainerData-->>RecommendationEngine: layerMap
                alt any requiredLayers in layerMap
                    RecommendationEngine->>AggregationFunctions: getQuery()
                    AggregationFunctions-->>RecommendationEngine: promQL
                    RecommendationEngine->>RecommendationEngine: executePromQuery(promQL)
                else no requiredLayers detected
                    RecommendationEngine->>RecommendationEngine: skip metric (no query executed)
                end
            end
        end
    end

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Class diagram for updated AggregationFunctions with required_layer

classDiagram
    class AggregationFunctions {
        - String function
        - String query
        - String version
        - String requiredLayer
        + AggregationFunctions(String function, String query, String version)
        + String getFunction()
        + void setFunction(String function)
        + String getQuery()
        + void setQuery(String query)
        + String getVersion()
        + void setVersion(String version)
        + String getRequiredLayer()
        + void setRequiredLayer(String requiredLayer)
        + String toString()
    }

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File-Level Changes

Change	Details	Files
Introduce requiredLayer field on aggregation functions and expose accessors for it.	Add requiredLayer property to the AggregationFunctions model alongside function, query, and version. Implement getter and setter for requiredLayer to support configuration-driven usage. Extend toString to include requiredLayer for easier debugging and logging.	src/main/java/com/autotune/common/data/metrics/AggregationFunctions.java
Conditionally execute runtime metric queries based on detected container layers.	Before building the PromQL query, read requiredLayer from the aggregation function entry. Parse comma-separated requiredLayer values into individual layers and check presence in containerData.layerMap. Skip executing the metric (with debug log) when none of the required layers are detected for the container, otherwise proceed as before.	src/main/java/com/autotune/analyzer/recommendations/engine/RecommendationEngine.java
Wire required_layer configuration into JVM-related performance profile definitions.	Annotate jvmInfo metric aggregation_functions with required_layer set to "hotspot,semeru" in local monitoring profile. Annotate jvmInfoTotal metric aggregation_functions with required_layer set to "hotspot,semeru" in local monitoring profile. Mirror the same required_layer annotations in the no-recording-rules local monitoring profile and associated JSON profile, ensuring runtime metrics only run for supported JVM layers.	manifests/autotune/performance-profiles/resource_optimization_local_monitoring.yaml manifests/autotune/performance-profiles/resource_optimization_local_monitoring_norecordingrules.yaml manifests/autotune/performance-profiles/resource_optimization_local_monitoring.json

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[sourcery-ai]

Hey - I've left some high level feedback:

• In fetchContainerMetricsBasedOnDataSourceAndProfile, you repeatedly call containerData.getLayerMap() and null-check it inside the inner loop; consider pulling the map into a local variable and short-circuiting when it's null to simplify the logic and avoid repeated lookups.
• The AggregationFunctions class now has a requiredLayer field but no constructor path to set it explicitly; if this type is ever instantiated programmatically (not just via deserialization), you may want to add an overloaded constructor or builder method that accepts requiredLayer to avoid partially initialized instances.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- In `fetchContainerMetricsBasedOnDataSourceAndProfile`, you repeatedly call `containerData.getLayerMap()` and null-check it inside the inner loop; consider pulling the map into a local variable and short-circuiting when it's null to simplify the logic and avoid repeated lookups.
- The `AggregationFunctions` class now has a `requiredLayer` field but no constructor path to set it explicitly; if this type is ever instantiated programmatically (not just via deserialization), you may want to add an overloaded constructor or builder method that accepts `requiredLayer` to avoid partially initialized instances.

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