Architecture

This document provides an overview of the K8s Tools architecture, including system design, component interactions, and implementation details.

System Overview

K8s Tools follows a modular architecture with clear separation of concerns:

graph TB
    CLI[CLI Interface] --> Core[Core Engine]
    Core --> Analyzer[K8s Analyzer]
    Core --> Reporter[K8s Reporter]

    Analyzer --> K8sAPI[Kubernetes API]
    Analyzer --> Metrics[Metrics Store]

    Reporter --> Templates[Template Engine]
    Reporter --> Exporters[Export Formats]

    Metrics --> Database[(Database)]
    Templates --> Output[Output Files]
    Exporters --> Output

Core Components

1. CLI Interface (src/cli/)

The command-line interface built with Click provides the main entry point:

• Command Structure: Hierarchical commands (analyze, report, configure)
• Configuration Management: YAML-based configuration with CLI overrides
• Output Formatting: Multiple output formats (JSON, YAML, HTML, PDF)

# CLI structure example
@click.group()
def cli():
    """K8s Tools - Kubernetes Cluster Analysis and Reporting."""
    pass

@cli.command()
@click.option('--view', type=click.Choice(['cluster-overview', 'security']))
def analyze(view):
    """Analyze Kubernetes cluster."""
    pass

2. Core Engine (src/core/)

The core engine orchestrates analysis and reporting:

• Configuration: Centralized configuration management
• Plugin System: Extensible analysis views and exporters
• Error Handling: Comprehensive error handling and recovery
• Logging: Structured logging with configurable levels

3. K8s Analyzer (src/analyzer/)

The analyzer component handles Kubernetes cluster analysis:

Key Classes

class ClusterAnalyzer:
    """Main analyzer for Kubernetes clusters."""

    def __init__(self, client: K8sClient, config: Config):
        self.client = client
        self.config = config

    def analyze(self, view: str) -> AnalysisResult:
        """Perform cluster analysis for specified view."""
        pass

class AnalysisView(ABC):
    """Abstract base class for analysis views."""

    @abstractmethod
    def analyze(self, cluster_data: ClusterData) -> ViewResult:
        """Analyze cluster data and return view-specific results."""
        pass

Analysis Views

Each view implements specific analysis logic:

• Cluster Overview: High-level cluster metrics and status
• Security Analysis: Security posture and vulnerability assessment
• Resource Efficiency: Resource utilization and optimization
• Storage Analysis: Persistent volume and storage metrics
• Temporal Analysis: Time-series analysis and trends

4. K8s Reporter (src/reporter/)

The reporter generates various output formats from analysis results:

Reporter Architecture

class Reporter:
    """Main reporting engine."""

    def __init__(self, template_engine: TemplateEngine):
        self.template_engine = template_engine
        self.exporters = {}

    def generate_report(self, data: AnalysisResult, format: str) -> str:
        """Generate report in specified format."""
        pass

class TemplateEngine:
    """Jinja2-based template engine for reports."""

    def render_template(self, template: str, data: dict) -> str:
        """Render template with provided data."""
        pass

Export Formats

• HTML: Interactive dashboards with charts and tables
• PDF: Static reports for documentation
• JSON/YAML: Machine-readable data formats
• CSV: Tabular data for spreadsheet analysis

Data Models

Core Data Structures

@dataclass
class ClusterData:
    """Complete cluster data snapshot."""
    nodes: List[NodeData]
    pods: List[PodData]
    services: List[ServiceData]
    deployments: List[DeploymentData]
    namespaces: List[NamespaceData]
    persistent_volumes: List[PVData]

    @property
    def total_nodes(self) -> int:
        return len(self.nodes)

@dataclass
class AnalysisResult:
    """Result of cluster analysis."""
    view_name: str
    timestamp: datetime
    cluster_info: ClusterInfo
    metrics: Dict[str, Any]
    recommendations: List[Recommendation]
    alerts: List[Alert]

Kubernetes Resource Models

@dataclass
class NodeData:
    """Kubernetes node information."""
    name: str
    status: NodeStatus
    capacity: ResourceCapacity
    allocatable: ResourceCapacity
    usage: ResourceUsage
    conditions: List[NodeCondition]
    labels: Dict[str, str]
    taints: List[Taint]

@dataclass
class PodData:
    """Kubernetes pod information."""
    name: str
    namespace: str
    status: PodStatus
    containers: List[ContainerData]
    resource_requests: ResourceRequests
    resource_limits: ResourceLimits
    node_name: str

Plugin Architecture

Analysis Views Plugin System

class AnalysisViewRegistry:
    """Registry for analysis view plugins."""

    def __init__(self):
        self._views = {}

    def register(self, name: str, view_class: Type[AnalysisView]):
        """Register an analysis view plugin."""
        self._views[name] = view_class

    def get_view(self, name: str) -> AnalysisView:
        """Get analysis view instance by name."""
        if name not in self._views:
            raise ValueError(f"Unknown view: {name}")
        return self._views[name]()

# Usage
registry = AnalysisViewRegistry()
registry.register('security', SecurityAnalysisView)
registry.register('efficiency', ResourceEfficiencyView)

Exporter Plugin System

class ExporterRegistry:
    """Registry for export format plugins."""

    def register_exporter(self, format: str, exporter: Exporter):
        """Register export format handler."""
        self.exporters[format] = exporter

    def export(self, data: AnalysisResult, format: str) -> bytes:
        """Export data in specified format."""
        return self.exporters[format].export(data)

Configuration Management

Configuration Structure

# config.yaml
cluster:
  name: "production"
  kubeconfig: "~/.kube/config"
  context: "production-cluster"

analysis:
  views:
    - cluster-overview
    - security-analysis
  metrics:
    collection_interval: "5m"
    retention_period: "7d"

output:
  format: "html"
  destination: "./reports/"
  template: "default"

database:
  url: "sqlite:///k8s-tools.db"
  pool_size: 10

Configuration Loading

class ConfigManager:
    """Centralized configuration management."""

    def __init__(self, config_file: Optional[str] = None):
        self.config_file = config_file or "config.yaml"
        self._config = None

    def load_config(self) -> Config:
        """Load configuration from file and environment."""
        if not self._config:
            self._config = self._load_from_file()
            self._apply_env_overrides()
        return self._config

    def _load_from_file(self) -> Dict:
        """Load configuration from YAML file."""
        with open(self.config_file, 'r') as f:
            return yaml.safe_load(f)

Database Schema

Metrics Storage

-- Node metrics table
CREATE TABLE node_metrics (
    id SERIAL PRIMARY KEY,
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    node_name VARCHAR(255) NOT NULL,
    cpu_capacity DECIMAL(10,2),
    memory_capacity BIGINT,
    cpu_usage DECIMAL(10,2),
    memory_usage BIGINT,
    pod_count INTEGER
);

-- Pod metrics table
CREATE TABLE pod_metrics (
    id SERIAL PRIMARY KEY,
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    pod_name VARCHAR(255) NOT NULL,
    namespace VARCHAR(255) NOT NULL,
    cpu_request DECIMAL(10,2),
    memory_request BIGINT,
    cpu_limit DECIMAL(10,2),
    memory_limit BIGINT,
    status VARCHAR(50)
);

Security Considerations

Authentication and Authorization

• Kubernetes RBAC: Respects cluster RBAC policies
• Service Account: Runs with minimal required permissions
• Secret Management: Secure handling of sensitive data

Data Privacy

• Local Processing: Analysis performed locally when possible
• Data Anonymization: Option to anonymize cluster data
• Secure Storage: Encrypted storage for sensitive metrics

Performance Optimization

Caching Strategy

class MetricsCache:
    """Cache for expensive Kubernetes API calls."""

    def __init__(self, ttl: int = 300):
        self.ttl = ttl
        self._cache = {}

    @lru_cache(maxsize=128)
    def get_nodes(self) -> List[NodeData]:
        """Get cached node data."""
        return self._fetch_nodes()

Async Processing

async def analyze_cluster_async(analyzer: ClusterAnalyzer) -> AnalysisResult:
    """Asynchronous cluster analysis."""
    tasks = []

    # Parallel data collection
    tasks.append(analyzer.collect_node_data())
    tasks.append(analyzer.collect_pod_data())
    tasks.append(analyzer.collect_service_data())

    results = await asyncio.gather(*tasks)
    return analyzer.process_results(results)

Testing Architecture

Test Structure

tests/
├── unit/           # Fast, isolated tests
├── integration/    # Component interaction tests
├── e2e/           # End-to-end workflow tests
└── fixtures/      # Test data and mocks

Mock Strategy

• Kubernetes API: Mock using unittest.mock
• External Services: Use responses library
• File System: tempfile for temporary files
• Time: freezegun for time-based testing

Deployment Patterns

Standalone Tool

• PyPI Package: pip install k8s-analyzer
• pipx Installation: Isolated installation
• Docker Container: Containerized execution

CI/CD Integration

• GitHub Actions: Automated analysis in workflows
• Jenkins: Pipeline integration
• GitLab CI: Scheduled cluster analysis

Monitoring and Observability

Logging

import structlog

logger = structlog.get_logger(__name__)

def analyze_cluster(self, view: str):
    logger.info(
        "Starting cluster analysis",
        view=view,
        cluster=self.cluster_name
    )

Metrics Collection

• Internal Metrics: Tool performance and usage
• Cluster Metrics: Kubernetes resource metrics
• Error Tracking: Exception monitoring and alerting

Future Architecture Considerations

Scalability

• Multi-cluster Support: Analyze multiple clusters
• Distributed Processing: Scale analysis across nodes
• Real-time Updates: Live cluster monitoring

Extensibility

• Custom Views: User-defined analysis views
• Plugin Marketplace: Community-contributed plugins
• API Endpoints: REST API for programmatic access

Related Documentation

• Development Setup
• Contributing Guidelines
• Testing Guide

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