"""
Agentic Spark Job Optimization System
A production-ready system for analyzing Spark applications using LLM-powered agents
to provide configuration and code optimization recommendations.
Main Components:
- SparkHistoryClient: Fetches metrics from Spark History Server
- OptimizationEngine: Orchestrates specialized analysis agents
- Agents: Specialized analyzers for different performance aspects
- LLMClient: Interface to Gemini API for intelligent analysis
Usage:
from src.optimizer.engine import OptimizationEngine
from src.client import SparkHistoryClient
from src.llm_client import LLMClient
client = SparkHistoryClient("http://localhost:18080")
llm = LLMClient(api_key="your-api-key")
engine = OptimizationEngine(client, llm)
report = engine.analyze_application("application_123")
"""
__version__ = "1.0.0"
__author__ = "Spark Optimization Team"
from .client import SparkHistoryClient
from .llm_client import LLMClient
from .models import OptimizationReport
__all__ = [
"SparkHistoryClient",
"LLMClient",
"OptimizationReport",
]
