"""Performance Optimizer for Automation Intelligence Analysis.
This module provides comprehensive performance analysis, optimization recommendations,
and efficiency tracking for automation workflows with intelligent insights and
actionable improvement suggestions based on behavioral pattern analysis.
Security: Secure performance analysis with privacy-protected data processing.
Performance: Optimized algorithms with intelligent caching and efficient analysis.
Type Safety: Complete branded type system with contract-driven validation.
"""
from __future__ import annotations
import statistics
from dataclasses import dataclass
from datetime import UTC, datetime
from enum import Enum
from typing import TYPE_CHECKING, Any
from src.core.contracts import require
from src.core.either import Either
from src.core.errors import IntelligenceError
from src.core.logging import get_logger
if TYPE_CHECKING:
from src.core.suggestion_system import UserBehaviorPattern
logger = get_logger(__name__)
class OptimizationTarget(Enum):
"""Performance optimization target metrics."""
EFFICIENCY = "efficiency" # Overall workflow efficiency
SPEED = "speed" # Execution speed optimization
ACCURACY = "accuracy" # Success rate improvement
RESOURCE_USAGE = "resource_usage" # Resource consumption optimization
USER_SATISFACTION = "user_satisfaction" # User experience optimization
ERROR_REDUCTION = "error_reduction" # Error rate minimization
class PerformanceCategory(Enum):
"""Performance analysis categories."""
EXECUTION_TIME = "execution_time" # Time-based performance metrics
SUCCESS_RATE = "success_rate" # Success/failure rate analysis
RESOURCE_CONSUMPTION = "resource_consumption" # Resource usage analysis
USER_EXPERIENCE = "user_experience" # User interaction metrics
SYSTEM_EFFICIENCY = "system_efficiency" # Overall system performance
@dataclass(frozen=True)
class PerformanceInsight:
"""Comprehensive performance insight with actionable recommendations."""
insight_id: str
category: PerformanceCategory
title: str
description: str
impact_level: str # low|medium|high|critical
confidence: float
affected_patterns: list[str]
metrics: dict[str, float]
recommendations: list[str]
potential_improvement: float
implementation_effort: str # low|medium|high
@require(lambda self: 0.0 <= self.confidence <= 1.0)
@require(lambda self: self.potential_improvement >= 0.0)
@require(lambda self: self.impact_level in ["low", "medium", "high", "critical"])
@require(lambda self: self.implementation_effort in ["low", "medium", "high"])
def __post_init__(self):
pass
@dataclass(frozen=True)
class OptimizationRecommendation:
"""Detailed optimization recommendation with implementation guidance."""
recommendation_id: str
target: OptimizationTarget
title: str
description: str
priority: str # low|medium|high|critical
confidence: float
expected_improvement: float
implementation_steps: list[str]
affected_workflows: list[str]
success_criteria: list[str]
estimated_timeline: str
resource_requirements: list[str]
@require(lambda self: 0.0 <= self.confidence <= 1.0)
@require(lambda self: self.expected_improvement >= 0.0)
@require(lambda self: self.priority in ["low", "medium", "high", "critical"])
def __post_init__(self):
pass
class PerformanceOptimizer:
"""Comprehensive performance optimization with intelligent analysis."""
def __init__(self):
self.optimization_algorithms: dict[OptimizationTarget, Any] = {}
self.performance_cache: dict[str, Any] = {}
self.optimization_history: list[dict[str, Any]] = []
# Performance analysis configuration
self.min_patterns_for_analysis = 3
self.confidence_threshold = 0.6
self.improvement_threshold = 0.1 # 10% improvement minimum
# Performance thresholds
self.performance_thresholds = {
"execution_time_slow": 30.0, # Seconds
"success_rate_low": 0.8, # 80%
"efficiency_poor": 0.6, # 60%
"error_rate_high": 0.15, # 15%
}
self._initialize_optimization_algorithms()
async def initialize(self) -> Either[IntelligenceError, None]:
"""Initialize performance optimizer with analysis algorithms."""
try:
# Configure optimization algorithms
self._configure_performance_analysis()
logger.info("Performance optimizer initialized successfully")
return Either.right(None)
except Exception as e:
logger.error(f"Performance optimizer initialization failed: {e!s}")
return Either.left(IntelligenceError.initialization_failed(str(e)))
@require(lambda __self, patterns: len(patterns) >= 1)
async def analyze_performance(
self,
patterns: list[UserBehaviorPattern],
target: OptimizationTarget = OptimizationTarget.EFFICIENCY,
) -> Either[IntelligenceError, list[PerformanceInsight]]:
"""Analyze performance patterns and generate comprehensive insights.
Performs sophisticated performance analysis on behavioral patterns to
identify optimization opportunities, performance bottlenecks, and
efficiency improvements with actionable recommendations.
Args:
patterns: Behavioral patterns for performance analysis
target: Optimization target focus for analysis
Returns:
Either error or list of performance insights with recommendations
Security:
- Privacy-protected performance analysis
- Secure pattern processing with no sensitive data exposure
- Validated insights with confidence scoring
"""
try:
if len(patterns) < self.min_patterns_for_analysis:
return Either.left(
IntelligenceError.optimization_failed(
f"Insufficient patterns for analysis: {len(patterns)} < {self.min_patterns_for_analysis}",
),
)
# Check cache for recent analysis
cache_key = self._generate_performance_cache_key(patterns, target)
if cache_key in self.performance_cache:
cached_insights = self.performance_cache[cache_key]["insights"]
logger.debug(
f"Retrieved {len(cached_insights)} performance insights from cache",
)
return Either.right(cached_insights)
# Perform comprehensive performance analysis
insights = []
# Execution time analysis
execution_insights = await self._analyze_execution_performance(
patterns,
target,
)
insights.extend(execution_insights)
# Success rate analysis
success_insights = await self._analyze_success_rate_performance(
patterns,
target,
)
insights.extend(success_insights)
# Efficiency analysis
efficiency_insights = await self._analyze_efficiency_performance(
patterns,
target,
)
insights.extend(efficiency_insights)
# Resource usage analysis
resource_insights = await self._analyze_resource_performance(
patterns,
target,
)
insights.extend(resource_insights)
# Filter insights by confidence and impact
qualified_insights = [
insight
for insight in insights
if insight.confidence >= self.confidence_threshold
and insight.potential_improvement >= self.improvement_threshold
]
# Sort by impact and confidence
sorted_insights = sorted(
qualified_insights,
key=lambda i: (self._get_impact_score(i.impact_level), i.confidence),
reverse=True,
)
# Cache results
self.performance_cache[cache_key] = {
"insights": sorted_insights,
"timestamp": datetime.now(UTC),
"target": target.value,
}
# Track analysis
self._track_performance_analysis(patterns, sorted_insights, target)
logger.info(
f"Generated {len(sorted_insights)} performance insights for {len(patterns)} patterns",
)
return Either.right(sorted_insights)
except Exception as e:
logger.error(f"Performance analysis failed: {e!s}")
return Either.left(IntelligenceError.optimization_failed(str(e)))
@require(lambda __self, insights: len(insights) >= 1)
async def generate_optimization_recommendations(
self,
insights: list[PerformanceInsight],
target: OptimizationTarget = OptimizationTarget.EFFICIENCY,
) -> Either[IntelligenceError, list[OptimizationRecommendation]]:
"""Generate detailed optimization recommendations from performance insights.
Converts performance insights into actionable optimization recommendations
with implementation guidance, priority scoring, and success criteria
for systematic performance improvement.
Args:
insights: Performance insights for recommendation generation
target: Optimization target for focused recommendations
Returns:
Either error or list of optimization recommendations
"""
try:
recommendations = []
# Group insights by category for comprehensive recommendations
categorized_insights = self._categorize_insights_by_performance_area(
insights,
)
# Generate recommendations for each category
for category, category_insights in categorized_insights.items():
category_recommendations = (
await self._generate_category_recommendations(
category_insights,
category,
target,
)
)
recommendations.extend(category_recommendations)
# Generate cross-cutting optimization recommendations
cross_cutting_recommendations = (
await self._generate_cross_cutting_recommendations(insights, target)
)
recommendations.extend(cross_cutting_recommendations)
# Prioritize recommendations
prioritized_recommendations = self._prioritize_recommendations(
recommendations,
)
logger.info(
f"Generated {len(prioritized_recommendations)} optimization recommendations",
)
return Either.right(prioritized_recommendations)
except Exception as e:
logger.error(f"Optimization recommendation generation failed: {e!s}")
return Either.left(IntelligenceError.optimization_failed(str(e)))
async def _analyze_execution_performance(
self,
patterns: list[UserBehaviorPattern],
_target: OptimizationTarget,
) -> list[PerformanceInsight]:
"""Analyze execution time performance patterns."""
insights = []
# Calculate execution time statistics
execution_times = [p.average_completion_time for p in patterns]
avg_execution_time = statistics.mean(execution_times)
# Identify slow patterns
slow_patterns = [
p
for p in patterns
if p.average_completion_time
> self.performance_thresholds["execution_time_slow"]
]
if slow_patterns:
total_slow_time = sum(
p.average_completion_time * p.frequency for p in slow_patterns
)
potential_savings = total_slow_time * 0.3 # Assume 30% improvement possible
insight = PerformanceInsight(
insight_id=f"execution_time_{datetime.now(UTC).timestamp()}",
category=PerformanceCategory.EXECUTION_TIME,
title="Slow Execution Performance Detected",
description=f"Found {len(slow_patterns)} patterns with execution times > {self.performance_thresholds['execution_time_slow']}s",
impact_level=self._calculate_impact_level(
len(slow_patterns),
len(patterns),
),
confidence=0.8,
affected_patterns=[p.pattern_id for p in slow_patterns],
metrics={
"average_execution_time": avg_execution_time,
"slow_patterns_count": len(slow_patterns),
"potential_time_savings": potential_savings,
},
recommendations=[
"Optimize slow automation workflows",
"Consider parallel processing for independent tasks",
"Review and streamline complex action sequences",
],
potential_improvement=potential_savings / max(1, total_slow_time),
implementation_effort="medium",
)
insights.append(insight)
# Analyze execution time consistency
if len(execution_times) > 1:
time_variance = statistics.stdev(execution_times)
consistency_score = 1.0 - min(1.0, time_variance / avg_execution_time)
if consistency_score < 0.7: # Poor consistency
insight = PerformanceInsight(
insight_id=f"execution_consistency_{datetime.now(UTC).timestamp()}",
category=PerformanceCategory.EXECUTION_TIME,
title="Inconsistent Execution Times",
description=f"Execution times vary significantly (consistency: {consistency_score:.1%})",
impact_level="medium",
confidence=0.7,
affected_patterns=[p.pattern_id for p in patterns],
metrics={
"consistency_score": consistency_score,
"time_variance": time_variance,
"average_time": avg_execution_time,
},
recommendations=[
"Investigate causes of timing variability",
"Standardize workflow execution environments",
"Add performance monitoring to workflows",
],
potential_improvement=0.2,
implementation_effort="medium",
)
insights.append(insight)
return insights
async def _analyze_success_rate_performance(
self,
patterns: list[UserBehaviorPattern],
_target: OptimizationTarget,
) -> list[PerformanceInsight]:
"""Analyze success rate performance patterns."""
insights = []
# Calculate success rate statistics
success_rates = [p.success_rate for p in patterns]
avg_success_rate = statistics.mean(success_rates)
# Identify low success rate patterns
low_success_patterns = [
p
for p in patterns
if p.success_rate < self.performance_thresholds["success_rate_low"]
]
if low_success_patterns:
total_failures = sum(
(1 - p.success_rate) * p.frequency for p in low_success_patterns
)
insight = PerformanceInsight(
insight_id=f"success_rate_{datetime.now(UTC).timestamp()}",
category=PerformanceCategory.SUCCESS_RATE,
title="Low Success Rate Patterns Detected",
description=f"Found {len(low_success_patterns)} patterns with success rates < {self.performance_thresholds['success_rate_low']:.0%}",
impact_level=self._calculate_impact_level(
len(low_success_patterns),
len(patterns),
),
confidence=0.85,
affected_patterns=[p.pattern_id for p in low_success_patterns],
metrics={
"average_success_rate": avg_success_rate,
"low_success_patterns_count": len(low_success_patterns),
"total_failure_instances": total_failures,
},
recommendations=[
"Add error handling and validation to workflows",
"Review and fix common failure points",
"Implement retry mechanisms for transient failures",
],
potential_improvement=0.15, # 15% success rate improvement
implementation_effort="medium",
)
insights.append(insight)
return insights
async def _analyze_efficiency_performance(
self,
patterns: list[UserBehaviorPattern],
_target: OptimizationTarget,
) -> list[PerformanceInsight]:
"""Analyze workflow efficiency patterns."""
insights = []
# Calculate efficiency scores
efficiency_scores = [p.get_efficiency_score() for p in patterns]
avg_efficiency = statistics.mean(efficiency_scores)
# Identify low efficiency patterns
low_efficiency_patterns = [
p
for p in patterns
if p.get_efficiency_score() < self.performance_thresholds["efficiency_poor"]
]
if low_efficiency_patterns:
insight = PerformanceInsight(
insight_id=f"efficiency_{datetime.now(UTC).timestamp()}",
category=PerformanceCategory.SYSTEM_EFFICIENCY,
title="Low Efficiency Workflows Identified",
description=f"Found {len(low_efficiency_patterns)} workflows with efficiency < {self.performance_thresholds['efficiency_poor']:.0%}",
impact_level=self._calculate_impact_level(
len(low_efficiency_patterns),
len(patterns),
),
confidence=0.75,
affected_patterns=[p.pattern_id for p in low_efficiency_patterns],
metrics={
"average_efficiency": avg_efficiency,
"low_efficiency_count": len(low_efficiency_patterns),
"efficiency_gap": self.performance_thresholds["efficiency_poor"]
- avg_efficiency,
},
recommendations=[
"Streamline workflow steps and remove redundancies",
"Optimize tool usage and action sequences",
"Consider automation for repetitive manual steps",
],
potential_improvement=0.25,
implementation_effort="high",
)
insights.append(insight)
return insights
async def _analyze_resource_performance(
self,
patterns: list[UserBehaviorPattern],
_target: OptimizationTarget,
) -> list[PerformanceInsight]:
"""Analyze resource usage performance patterns."""
insights = []
# Analyze frequency and resource consumption
high_frequency_patterns = [p for p in patterns if p.frequency >= 10]
if high_frequency_patterns:
total_resource_consumption = sum(
p.frequency * p.average_completion_time for p in high_frequency_patterns
)
insight = PerformanceInsight(
insight_id=f"resource_usage_{datetime.now(UTC).timestamp()}",
category=PerformanceCategory.RESOURCE_CONSUMPTION,
title="High Resource Consumption Patterns",
description=f"Found {len(high_frequency_patterns)} high-frequency patterns consuming significant resources",
impact_level="medium",
confidence=0.7,
affected_patterns=[p.pattern_id for p in high_frequency_patterns],
metrics={
"high_frequency_patterns": len(high_frequency_patterns),
"total_resource_consumption": total_resource_consumption,
"average_frequency": statistics.mean(
[p.frequency for p in high_frequency_patterns],
),
},
recommendations=[
"Optimize high-frequency workflows for resource efficiency",
"Consider caching for repeated operations",
"Implement batch processing where applicable",
],
potential_improvement=0.2,
implementation_effort="medium",
)
insights.append(insight)
return insights
def _calculate_impact_level(self, affected_count: int, total_count: int) -> str:
"""Calculate impact level based on affected patterns ratio."""
ratio = affected_count / max(1, total_count)
if ratio >= 0.5:
return "critical"
if ratio >= 0.3:
return "high"
if ratio >= 0.1:
return "medium"
return "low"
def _get_impact_score(self, impact_level: str) -> float:
"""Convert impact level to numerical score for sorting."""
impact_scores = {"critical": 4.0, "high": 3.0, "medium": 2.0, "low": 1.0}
return impact_scores.get(impact_level, 1.0)
def _categorize_insights_by_performance_area(
self,
insights: list[PerformanceInsight],
) -> dict[PerformanceCategory, list[PerformanceInsight]]:
"""Categorize insights by performance area."""
categorized = {}
for insight in insights:
if insight.category not in categorized:
categorized[insight.category] = []
categorized[insight.category].append(insight)
return categorized
async def _generate_category_recommendations(
self,
insights: list[PerformanceInsight],
category: PerformanceCategory,
target: OptimizationTarget,
) -> list[OptimizationRecommendation]:
"""Generate recommendations for specific performance category."""
recommendations = []
if category == PerformanceCategory.EXECUTION_TIME:
recommendation = OptimizationRecommendation(
recommendation_id=f"optimize_execution_{datetime.now(UTC).timestamp()}",
target=target,
title="Optimize Execution Time Performance",
description="Systematic approach to reducing workflow execution times",
priority="high",
confidence=0.8,
expected_improvement=0.25,
implementation_steps=[
"Profile slow workflows to identify bottlenecks",
"Optimize critical path operations",
"Implement parallel processing where possible",
"Cache frequently accessed data",
],
affected_workflows=[insight.insight_id for insight in insights],
success_criteria=[
"Average execution time reduced by 20%+",
"No workflows exceeding 30-second threshold",
"Improved execution time consistency",
],
estimated_timeline="2-4 weeks",
resource_requirements=[
"Development time",
"Performance testing",
"Monitoring tools",
],
)
recommendations.append(recommendation)
return recommendations
async def _generate_cross_cutting_recommendations(
self,
_insights: list[PerformanceInsight],
target: OptimizationTarget,
) -> list[OptimizationRecommendation]:
"""Generate cross-cutting optimization recommendations."""
recommendations = []
# General performance monitoring recommendation
recommendation = OptimizationRecommendation(
recommendation_id=f"performance_monitoring_{datetime.now(UTC).timestamp()}",
target=target,
title="Implement Comprehensive Performance Monitoring",
description="Establish continuous performance monitoring and alerting",
priority="medium",
confidence=0.9,
expected_improvement=0.15,
implementation_steps=[
"Set up performance metrics collection",
"Create performance dashboards",
"Implement automated alerting for degradation",
"Establish performance baseline measurements",
],
affected_workflows=["all_workflows"],
success_criteria=[
"Real-time performance visibility",
"Automated performance alerts",
"Historical performance trends",
"Performance regression detection",
],
estimated_timeline="1-2 weeks",
resource_requirements=[
"Monitoring infrastructure",
"Dashboard tools",
"Alert configuration",
],
)
recommendations.append(recommendation)
return recommendations
def _prioritize_recommendations(
self,
recommendations: list[OptimizationRecommendation],
) -> list[OptimizationRecommendation]:
"""Prioritize recommendations by impact and feasibility."""
priority_scores = {"critical": 4.0, "high": 3.0, "medium": 2.0, "low": 1.0}
def recommendation_score(rec: OptimizationRecommendation) -> float:
priority_score = priority_scores.get(rec.priority, 1.0)
return priority_score * rec.confidence * rec.expected_improvement
return sorted(recommendations, key=recommendation_score, reverse=True)
def _generate_performance_cache_key(
self,
patterns: list[UserBehaviorPattern],
target: OptimizationTarget,
) -> str:
"""Generate cache key for performance analysis."""
pattern_ids = sorted([p.pattern_id for p in patterns])
return f"{target.value}_{hash(tuple(pattern_ids[:10]))}"
def _track_performance_analysis(
self,
patterns: list[UserBehaviorPattern],
insights: list[PerformanceInsight],
target: OptimizationTarget,
) -> None:
"""Track performance analysis for historical trending."""
analysis_record = {
"timestamp": datetime.now(UTC),
"patterns_analyzed": len(patterns),
"insights_generated": len(insights),
"target": target.value,
"high_impact_insights": len(
[i for i in insights if i.impact_level in ["high", "critical"]],
),
"average_confidence": statistics.mean([i.confidence for i in insights])
if insights
else 0.0,
}
self.optimization_history.append(analysis_record)
# Limit history size
if len(self.optimization_history) > 100:
self.optimization_history = self.optimization_history[-50:]
def _initialize_optimization_algorithms(self) -> None:
"""Initialize optimization algorithms for different targets."""
self.optimization_algorithms = {
OptimizationTarget.EFFICIENCY: self._optimize_for_efficiency,
OptimizationTarget.SPEED: self._optimize_for_speed,
OptimizationTarget.ACCURACY: self._optimize_for_accuracy,
OptimizationTarget.RESOURCE_USAGE: self._optimize_for_resource_usage,
OptimizationTarget.USER_SATISFACTION: self._optimize_for_user_satisfaction,
OptimizationTarget.ERROR_REDUCTION: self._optimize_for_error_reduction,
}
def _configure_performance_analysis(self) -> None:
"""Configure performance analysis parameters."""
# Configuration for different analysis modes
# Placeholder optimization algorithms
def _optimize_for_efficiency(
self,
_patterns: list[UserBehaviorPattern],
) -> dict[str, Any]:
"""Optimize patterns for efficiency."""
return {}
def _optimize_for_speed(
self,
_patterns: list[UserBehaviorPattern],
) -> dict[str, Any]:
"""Optimize patterns for speed."""
return {}
def _optimize_for_accuracy(
self,
_patterns: list[UserBehaviorPattern],
) -> dict[str, Any]:
"""Optimize patterns for accuracy."""
return {}
def _optimize_for_resource_usage(
self,
_patterns: list[UserBehaviorPattern],
) -> dict[str, Any]:
"""Optimize patterns for resource usage."""
return {}
def _optimize_for_user_satisfaction(
self,
_patterns: list[UserBehaviorPattern],
) -> dict[str, Any]:
"""Optimize patterns for user satisfaction."""
return {}
def _optimize_for_error_reduction(
self,
_patterns: list[UserBehaviorPattern],
) -> dict[str, Any]:
"""Optimize patterns for error reduction."""
return {}
