AI-Native 数据工程：从数据流水线到智能数据网格

# 智能数据管道示例
class SelfHealingPipeline:
    def process(self, data_source):
        try:
            # 1. Schema 漂移检测
            schema_drift = self.detect_schema_drift(data_source)
            if schema_drift:
                # 自动适应新 Schema
                self.adapt_schema(schema_drift)
            
            # 2. 数据质量监控
            quality_report = self.assess_quality(data_source)
            if quality_report.anomalies:
                # 自动修复或隔离异常数据
                self.handle_anomalies(quality_report)
            
            # 3. 智能路由
            route = self.optimize_route(data_source)
            return self.process_with_route(data_source, route)
            
        except Exception as e:
            # 自动故障恢复
            return self.recover_or_escalate(e)

# 自优化示例：查询优化
class SelfOptimizingQuery:
    def execute(self, query):
        # 1. 查询模式学习
        query_pattern = self.analyze_query_pattern(query)
        
        # 2. 自动索引建议
        if self.should_create_index(query_pattern):
            self.suggest_index_creation()
        
        # 3. 查询重写优化
        optimized_query = self.rewrite_for_performance(query)
        
        # 4. 缓存策略优化
        if self.is_cache_friendly(query_pattern):
            return self.execute_with_caching(optimized_query)
        
        return self.execute_direct(optimized_query)

# AI 驱动的异常检测
class IntelligentQualityMonitor:
    def __init__(self):
        self.baseline_models = {}
    
    def assess_quality(self, dataset):
        anomalies = []
        
        # 1. 统计异常检测
        for column in dataset.numeric_columns:
            z_scores = self.calculate_z_scores(column)
            outliers = self.detect_outliers(z_scores, threshold=3)
            if outliers:
                anomalies.append({
                    'type': 'statistical_outlier',
                    'column': column,
                    'severity': 'warning',
                    'suggestion': 'Review outliers: ' + str(outliers)
                })
        
        # 2. 分布漂移检测
        drift_score = self.detect_distribution_drift(column)
        if drift_score > 0.5:
            anomalies.append({
                'type': 'distribution_drift',
                'column': column,
                'severity': 'error',
                'suggestion': 'Data distribution changed significantly'
            })
        
        # 3. 模式异常检测 (AI)
        pattern_anomalies = self.ai_pattern_detection(dataset)
        anomalies.extend(pattern_anomalies)
        
        return QualityReport(anomalies=anomalies)

# 自动特征工程示例
class AutoFeatureEngineer:
    def generate_features(self, dataset, target):
        features = []
        
        # 1. 自动特征生成
        generated = self.generate_candidate_features(dataset)
        # 包括：统计特征、时序特征、交叉特征、文本特征等
        
        # 2. 特征重要性评估
        importance_scores = self.assess_importance(generated, target)
        
        # 3. 特征选择
        selected = self.select_features(generated, importance_scores, top_k=50)
        
        # 4. 特征验证
        validated = self.validate_features(selected, target)
        
        return FeatureSet(features=validated, pipeline=self.build_pipeline())

# 使用特征服务
from feature_store import FeatureClient

client = FeatureClient()

# 获取用户特征 (自动处理在线/离线特征融合)
user_features = client.get_features(
    entity_type='user',
    entity_id='user_123',
    feature_names=['avg_order_value', 'recent_viewed_categories', 'lifetime_value'],
    point_in_time='2026-03-15T10:00:00Z'  # 时间旅行查询
)

# 特征自动发现
recommended_features = client.discover_features(
    target='purchase_conversion',
    context={'user_segments': ['high_value', 'frequent_buyer']}
)

# 向量服务使用示例
from vector_mesh import VectorClient

client = VectorClient()

# 1. Embedding 生成
embedding = client.embed(
    text="AI-Native 数据工程",
    model="text-embedding-3-large"
)

# 2. 向量存储
client.store(
    collection="product_catalog",
    vectors=[{
        'id': 'product_001',
        'vector': embedding,
        'metadata': {'category': 'tech', 'price': 99.99}
    }]
)

# 3. 语义检索
results = client.search(
    collection="product_catalog",
    query="智能数据管道",
    top_k=10,
    filters={'price': {'$lt': 100}},
    rerank=True
)

# 跨模型向量对齐
class VectorAlignment:
    def __init__(self, source_model, target_model):
        self.translator = self.train_translator(source_model, target_model)
    
    def translate(self, vector, from_model, to_model):
        """将向量从一个模型空间映射到另一个"""
        return self.translator.transform(vector)
    
    def train_translator(self, source, target):
        # 使用对比学习训练映射模型
        # 使语义相似的文本在两种向量空间中距离相近
        pass