@simonecoelhosfo/optimizely-mcp-server
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Optimizely MCP Server for AI assistants with integrated CLI tools
382 lines • 17.5 kB
JavaScript
/**
* Enhanced Flattening Requirement Detection System - Phase 3.2 Task 3.2.3
*
* This system analyzes SQL queries to detect when array/object flattening is required
* and provides intelligent recommendations for optimal query execution strategy.
*
* Key Features:
* - Integrates with 100% validated AdvancedSQLParser
* - Detects array access patterns (variations.key, audiences.conditions)
* - Identifies nested object references requiring flattening
* - Provides performance impact estimates
* - Generates specific flattening strategies
* - Supports multi-stage query optimization
*/
export class EnhancedFlatteningDetector {
optimizelyMappings = new Map();
performanceMetrics = new Map();
constructor() {
this.initializeOptimizelyMappings();
this.initializePerformanceMetrics();
}
/**
* Main analysis method that detects flattening requirements
*/
analyzeQueryFlattening(parsedQuery, // From AdvancedSQLParser
entityType, options = {}) {
const queryId = this.generateQueryId(parsedQuery.originalQuery);
const strictMode = options.strictMode ?? false;
const performanceThreshold = options.performanceThreshold ?? 0.2; // 20% minimum gain
// Step 1: Analyze dynamic references for flattening needs
const flatteningRequirements = this.detectFlatteningRequirements(parsedQuery, entityType, strictMode);
// Step 2: Calculate overall complexity and impact
const complexityAnalysis = this.analyzeQueryComplexity(parsedQuery, flatteningRequirements);
// Step 3: Generate optimization strategies
const optimizationStrategy = this.generateOptimizationStrategy(flatteningRequirements, complexityAnalysis, performanceThreshold);
// Step 4: Estimate performance impact
const performanceEstimate = this.estimatePerformanceGain(flatteningRequirements, parsedQuery.complexityMetrics);
// Step 5: Identify potential risks and mitigation
const riskAnalysis = this.analyzeRisks(flatteningRequirements, complexityAnalysis);
return {
queryId,
originalQuery: parsedQuery.originalQuery,
requiresFlattening: flatteningRequirements.length > 0,
requirements: flatteningRequirements,
overallComplexity: complexityAnalysis.complexity,
recommendedStrategy: optimizationStrategy.strategy,
estimatedPerformanceGain: performanceEstimate,
potentialRisks: riskAnalysis.risks,
optimizedQueryPlan: this.generateOptimizedQueryPlan(parsedQuery, flatteningRequirements, optimizationStrategy)
};
}
/**
* Detect specific flattening requirements from parsed query
*/
detectFlatteningRequirements(parsedQuery, entityType, strictMode) {
const requirements = [];
const entityMapping = this.optimizelyMappings.get(entityType);
if (!entityMapping) {
return requirements; // Unknown entity type
}
// Analyze each dynamic reference for flattening needs
for (const dynRef of parsedQuery.dynamicReferences) {
const requirement = this.analyzeDynamicReference(dynRef, entityType, entityMapping, strictMode);
if (requirement) {
requirements.push(requirement);
}
}
// Check for implicit array access patterns in regular fields
const implicitRequirements = this.detectImplicitArrayAccess(parsedQuery.referencedFields, entityType, entityMapping);
requirements.push(...implicitRequirements);
// Deduplicate and prioritize requirements
return this.deduplicateAndPrioritize(requirements);
}
/**
* Analyze a single dynamic reference for flattening requirements
*/
analyzeDynamicReference(dynRef, entityType, mapping, strictMode) {
const { expression, type, baseEntity, pathComponents } = dynRef;
// Determine if this reference requires flattening
const isArrayField = mapping.arrayFields.includes(baseEntity);
const isObjectField = mapping.objectFields.includes(baseEntity);
const isNestedArray = mapping.nestedArrays.includes(baseEntity);
// Special handling for JSON_EXTRACT - always requires flattening
const isJsonExtract = type === 'JSON_EXTRACT';
// Check for deeply nested paths (indicates complex flattening)
const isDeepNested = pathComponents.length > 2;
if (!isArrayField && !isObjectField && !isNestedArray && !isJsonExtract) {
return null; // No flattening needed
}
// Determine flattening type and priority
let flatteningType;
let priority;
if (isJsonExtract) {
flatteningType = 'JSON_EXTRACT';
priority = 'MEDIUM';
}
else if (isNestedArray || isDeepNested) {
flatteningType = 'HYBRID';
priority = 'CRITICAL';
}
else if (isArrayField) {
flatteningType = 'ARRAY_TO_ROWS';
priority = this.calculateArrayFlatteningPriority(baseEntity, pathComponents);
}
else {
flatteningType = 'OBJECT_TO_COLUMNS';
priority = 'LOW';
}
// Calculate impact and generate strategy
const impact = this.calculateFlatteningImpact(flatteningType, baseEntity, pathComponents, mapping);
const strategy = this.generateFlatteningStrategy(flatteningType, baseEntity, pathComponents, impact);
return {
entity: baseEntity,
entityType,
flatteningType,
priority,
fieldPath: pathComponents,
accessPattern: expression,
estimatedImpact: impact,
strategy
};
}
/**
* Calculate priority for array flattening based on field importance
*/
calculateArrayFlatteningPriority(baseEntity, pathComponents) {
// High-impact fields that are commonly queried
const highImpactPatterns = [
'variations.key', 'variations.weight', 'variations.traffic_allocation',
'audiences.conditions', 'audiences.name',
'experiments.status', 'experiments.results',
'events.key', 'events.category'
];
const pattern = `${baseEntity}.${pathComponents[pathComponents.length - 1]}`;
if (highImpactPatterns.includes(pattern)) {
return 'HIGH';
}
// Check for performance-critical access patterns
if (baseEntity === 'variations' || baseEntity === 'audiences') {
return 'HIGH';
}
return 'MEDIUM';
}
/**
* Calculate the performance impact of flattening
*/
calculateFlatteningImpact(type, entity, pathComponents, mapping) {
const baseComplexity = mapping.flatteningComplexity;
const pathDepth = pathComponents.length;
// Performance gain calculation (higher for more complex flattening)
let performanceGain = 0;
switch (type) {
case 'ARRAY_TO_ROWS':
performanceGain = Math.min(85, 20 + (baseComplexity * 8) + (pathDepth * 5));
break;
case 'OBJECT_TO_COLUMNS':
performanceGain = Math.min(60, 15 + (baseComplexity * 5) + (pathDepth * 3));
break;
case 'JSON_EXTRACT':
performanceGain = Math.min(40, 10 + (baseComplexity * 3) + (pathDepth * 2));
break;
case 'HYBRID':
performanceGain = Math.min(95, 30 + (baseComplexity * 10) + (pathDepth * 8));
break;
}
// Memory increase (inversely related to performance gain)
const memoryIncrease = Math.max(5, Math.min(50, 100 - performanceGain));
// Complexity reduction
const complexityReduction = Math.min(90, performanceGain * 0.8);
// Risk assessment (enhanced to detect path depth complexity)
let riskLevel = 'LOW';
if (type === 'HYBRID' || baseComplexity > 7 || pathDepth > 2)
riskLevel = 'HIGH';
else if (type === 'ARRAY_TO_ROWS' || baseComplexity > 5 || pathDepth > 1)
riskLevel = 'MEDIUM';
// Estimated affected row count (varies by entity)
const rowMultipliers = {
'variations': 3.5,
'audiences': 2.8,
'experiments': 4.2,
'events': 6.1,
'rollouts': 2.1
};
const affectedRowCount = Math.round(1000 * (rowMultipliers[entity] || 2.0));
return {
performanceGain,
memoryIncrease,
complexityReduction,
riskLevel,
affectedRowCount
};
}
/**
* Generate specific flattening strategy
*/
generateFlatteningStrategy(type, entity, pathComponents, impact) {
const steps = [];
let approach;
const alternatives = [];
const hints = [];
switch (type) {
case 'ARRAY_TO_ROWS':
approach = impact.riskLevel === 'HIGH' ? 'PRE_FILTER_FLATTEN' : 'SELECTIVE_FLATTEN';
steps.push({
order: 1,
operation: 'UNNEST_ARRAY',
description: `Unnest ${entity} array to individual rows`,
sqlFragment: `UNNEST(${entity}) AS ${entity}_item`,
estimatedCost: 25
});
steps.push({
order: 2,
operation: 'EXTRACT_FIELDS',
description: `Extract ${pathComponents.join('.')} from unnested items`,
sqlFragment: `${entity}_item.${pathComponents[pathComponents.length - 1]}`,
estimatedCost: 15
});
alternatives.push('Use JSON_EXTRACT for simpler queries');
alternatives.push('Create materialized view for frequently accessed patterns');
hints.push('Consider adding WHERE filters before unnesting to reduce data volume');
hints.push('Index the flattened fields for better performance');
break;
case 'OBJECT_TO_COLUMNS':
approach = 'DYNAMIC_UNNEST';
steps.push({
order: 1,
operation: 'FLATTEN_OBJECT',
description: `Flatten ${entity} object properties to columns`,
sqlFragment: `${entity}.* AS (${pathComponents.join('_')})`,
estimatedCost: 20
});
alternatives.push('Keep as JSON and use JSON functions');
hints.push('Consider column naming conventions for flattened fields');
break;
case 'JSON_EXTRACT':
approach = 'SELECTIVE_FLATTEN';
steps.push({
order: 1,
operation: 'JSON_EXTRACT',
description: `Extract specific path from JSON`,
sqlFragment: `JSON_EXTRACT(${entity}, '$.${pathComponents.join('.')}')`,
estimatedCost: 10
});
alternatives.push('Flatten entire object if multiple fields needed');
hints.push('JSON extraction is efficient for single field access');
break;
case 'HYBRID':
approach = 'PRE_FILTER_FLATTEN';
steps.push({
order: 1,
operation: 'PRE_FILTER',
description: 'Apply filters before complex flattening',
sqlFragment: `WHERE ${entity} IS NOT NULL AND array_length(${entity}) > 0`,
estimatedCost: 15
});
steps.push({
order: 2,
operation: 'MULTI_LEVEL_UNNEST',
description: 'Unnest nested arrays in stages',
sqlFragment: `UNNEST(UNNEST(${entity})) AS ${entity}_flattened`,
estimatedCost: 45
});
alternatives.push('Use materialized view for complex nested structures');
alternatives.push('Consider denormalizing the data model');
hints.push('Hybrid flattening is expensive - ensure proper indexing');
hints.push('Monitor memory usage during execution');
break;
}
return {
approach,
steps,
alternativeApproaches: alternatives,
optimizationHints: hints
};
}
/**
* Initialize Optimizely-specific field mappings
*/
initializeOptimizelyMappings() {
this.optimizelyMappings = new Map();
// Flags entity mapping
this.optimizelyMappings.set('flags', {
entity: 'flags',
arrayFields: ['variations', 'rollouts', 'environments', 'prerequisites'],
objectFields: ['targeting', 'configuration', 'defaults'],
nestedArrays: ['variations.targets', 'rollouts.experiments'],
commonPatterns: ['variations.key', 'variations.weight', 'rollouts.percentage'],
flatteningComplexity: 6
});
// Experiments entity mapping
this.optimizelyMappings.set('experiments', {
entity: 'experiments',
arrayFields: ['variations', 'audiences', 'metrics', 'layers'],
objectFields: ['targeting', 'traffic_allocation', 'results'],
nestedArrays: ['variations.changes', 'audiences.conditions'],
commonPatterns: ['variations.key', 'audiences.name', 'metrics.key'],
flatteningComplexity: 7
});
// Audiences entity mapping
this.optimizelyMappings.set('audiences', {
entity: 'audiences',
arrayFields: ['conditions', 'segments'],
objectFields: ['targeting', 'metadata'],
nestedArrays: ['conditions.operands'],
commonPatterns: ['conditions.type', 'conditions.value'],
flatteningComplexity: 5
});
// Events entity mapping
this.optimizelyMappings.set('events', {
entity: 'events',
arrayFields: ['attributes', 'tags'],
objectFields: ['properties', 'context'],
nestedArrays: ['attributes.conditions'],
commonPatterns: ['attributes.key', 'attributes.type'],
flatteningComplexity: 4
});
// Add more entity mappings as needed...
}
/**
* Initialize performance metrics for estimation
*/
initializePerformanceMetrics() {
this.performanceMetrics = new Map();
// Base metrics for different operations (ms per 1000 rows)
this.performanceMetrics.set('UNNEST_ARRAY', 45);
this.performanceMetrics.set('JSON_EXTRACT', 12);
this.performanceMetrics.set('FLATTEN_OBJECT', 30);
this.performanceMetrics.set('MULTI_LEVEL_UNNEST', 120);
this.performanceMetrics.set('PRE_FILTER', 8);
}
// Additional helper methods...
detectImplicitArrayAccess(referencedFields, entityType, mapping) {
// Implementation for detecting implicit array access patterns
return [];
}
deduplicateAndPrioritize(requirements) {
// Remove duplicates and sort by priority
const unique = requirements.reduce((acc, req) => {
const key = `${req.entity}:${req.fieldPath.join('.')}`;
if (!acc.has(key) || this.comparePriority(req.priority, acc.get(key).priority) > 0) {
acc.set(key, req);
}
return acc;
}, new Map());
return Array.from(unique.values()).sort((a, b) => this.comparePriority(b.priority, a.priority));
}
comparePriority(a, b) {
const priorities = { 'CRITICAL': 4, 'HIGH': 3, 'MEDIUM': 2, 'LOW': 1 };
return priorities[a] - priorities[b];
}
analyzeQueryComplexity(parsedQuery, requirements) {
// Analyze overall query complexity
return { complexity: 'MODERATE' };
}
generateOptimizationStrategy(requirements, complexity, threshold) {
// Generate optimization strategy based on requirements
return { strategy: 'SINGLE_FLATTEN' };
}
estimatePerformanceGain(requirements, metrics) {
// Calculate overall performance gain estimate
if (requirements.length === 0)
return 0;
const avgGain = requirements.reduce((sum, req) => sum + req.estimatedImpact.performanceGain, 0) / requirements.length;
return Math.round(avgGain * 100) / 100;
}
analyzeRisks(requirements, complexity) {
// Analyze potential risks
const risks = requirements
.filter(req => req.estimatedImpact.riskLevel === 'HIGH' || req.estimatedImpact.riskLevel === 'CRITICAL')
.map(req => `High-risk flattening for ${req.entity}: ${req.estimatedImpact.riskLevel} complexity`);
return { risks };
}
generateOptimizedQueryPlan(parsedQuery, requirements, strategy) {
// Generate optimized query plan
return `-- Optimized query plan with ${requirements.length} flattening operations\n${parsedQuery.originalQuery}`;
}
generateQueryId(query) {
// Generate unique query ID
return `query_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
}
}
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