📖 系统架构设计
1.1 整体架构概述
该系统是采用微服务架构，通过 AI 技术自动发现和追踪数据在异构ETL环境中的血缘关系。
📖 数据血缘发现流程
2.1 自动化血缘发现流程
2.2 SQL 解析与血缘提取
基于 AI 的SQL解析引擎：
class AISQLLineageExtractor: def __init__(self): self.nlp = spacy.load("en_core_web_sm") self.classifier = pipeline("text-classification", model="distilbert-base-uncased") def extract_lineage_from_sql(self, sql_query, context=None): """ 从SQL查询中提取数据血缘关系 """ # 解析SQL结构 parsed = sqlparse.parse(sql_query)[0] # 使用sql_metadata进行基础解析 parser = Parser(sql_query) # AI增强的语义分析 ai_insights = self.analyze_sql_semantics(sql_query, context) # 构建血缘关系 lineage_data = { 'input_tables': parser.tables, 'input_columns': parser.columns, 'output_columns': self.extract_output_columns(parsed), 'transformations': self.extract_transformations(parsed, ai_insights), 'confidence_score': ai_insights.get('confidence', 0.8), 'parsing_metadata': { 'query_type': self.classify_query_type(sql_query), 'complexity_score': self.calculate_complexity(sql_query), 'ai_analysis': ai_insights } } return lineage_data def analyze_sql_semantics(self, sql_query, context): """使用AI分析SQL语义""" # 构建分析提示 analysis_prompt = f""" Analyze the following SQL query and extract data lineage information: SQL: {sql_query} Context: {context} Please identify: 1. Source tables and columns 2. Transformation logic 3. Target tables and columns 4. Any data quality operations """ # 使用NLP模型进行分析 analysis_result = self.nlp(analysis_prompt) return { 'entities': [(ent.text, ent.label_) for ent in analysis_result.ents], 'dependencies': self.extract_semantic_dependencies(analysis_result), 'confidence': self.calculate_semantic_confidence(analysis_result) } # 使用示例 extractor = AISQLLineageExtractor() sql = """ SELECT customer_id, SUM(order_amount) as total_spent, COUNT(DISTINCT order_id) as order_count FROM orders WHERE order_date >= '2024-01-01' GROUP BY customer_id """ lineage = extractor.extract_lineage_from_sql(sql) print(f"发现的输入表: {lineage['input_tables']}") print(f"输出列: {[col['name'] for col in lineage['output_columns']]}")📖 血缘关系存储设计
3.1 图数据库 Schema 设计
class DataLineageGraph: def __init__(self, neptune_client): self.client = neptune_client def create_lineage_graph(self, lineage_data): """创建血缘关系图""" # 创建表节点 table_nodes = {} for table in lineage_data['input_tables'] + [lineage_data.get('output_table')]: if table: table_id = self.create_table_node(table) table_nodes[table] = table_id # 创建列节点和关系 for col_info in lineage_data.get('columns', []): col_id = self.create_column_node(col_info) # 创建列到表的关系 self.client.add_edge( col_id, table_nodes[col_info['table']], 'BELONGS_TO' ) # 创建血缘关系 for source_col in col_info.get('source_columns', []): source_col_id = self.get_column_id(source_col) if source_col_id: self.client.add_edge( source_col_id, col_id, 'LINEAGE', properties={ 'transformation': col_info.get('transformation'), 'confidence': lineage_data.get('confidence_score', 0.8) } )3.2 血缘查询 API 设计
class LineageAPI(Resource): @api.expect(lineage_model) def post(self): """查询数据血缘关系""" data = request.json source_type = data.get('source_type') source_name = data.get('source_name') depth = data.get('depth', 3) try: # 执行血缘查询 if source_type == 'table': result = self.get_table_lineage(source_name, depth) elif source_type == 'column': result = self.get_column_lineage(source_name, depth) else: return {'error': '不支持的源类型'}, 400 return jsonify({ 'status': 'success', 'data': result, 'query': { 'source': source_name, 'depth': depth, 'timestamp': datetime.utcnow().isoformat() } }) except Exception as e: return {'error': str(e)}, 500 def get_table_lineage(self, table_name, depth): """获取表级别血缘关系""" graph = DataLineageGraph(neptune_client) # 获取下游影响 downstream = graph.query_impact_analysis(table_name) # 获取上游依赖 upstream = graph.query_root_cause(table_name, None) return { 'table': table_name, 'downstream_impact': self.format_paths(downstream), 'upstream_dependencies': self.format_paths(upstream), 'summary': { 'total_downstream': len(downstream), 'total_upstream': len(upstream), 'max_depth': depth } }📖 血缘质量评估流程
4.1 质量评估流程图
4.2 质量评估算法实现
class LineageQualityAssessor: def __init__(self): self.scaler = StandardScaler() self.classifier = RandomForestClassifier(n_estimators=100) self.quality_metrics = [] def assess_lineage_quality(self, lineage_data): """评估血缘关系质量""" # 计算质量指标 metrics = self.calculate_quality_metrics(lineage_data) # AI质量分类 quality_score = self.ai_quality_classification(metrics) # 问题检测 issues = self.detect_quality_issues(lineage_data, metrics) assessment_result = { 'overall_score': quality_score, 'quality_level': self.get_quality_level(quality_score), 'metrics': metrics, 'issues': issues, 'recommendations': self.generate_recommendations(issues, metrics), 'confidence': self.calculate_confidence(metrics, issues) } return assessment_result def calculate_quality_metrics(self, lineage_data): """计算血缘质量指标""" metrics = {} # 完整性指标 metrics['completeness'] = self.calculate_completeness(lineage_data) # 准确性指标 metrics['accuracy'] = self.calculate_accuracy(lineage_data) # 一致性指标 metrics['consistency'] = self.calculate_consistency(lineage_data) # 时效性指标 metrics['freshness'] = self.calculate_freshness(lineage_data) # 复杂性指标 metrics['complexity'] = self.calculate_complexity(lineage_data) return metrics def ai_quality_classification(self, metrics): """使用AI进行质量分类""" # 准备特征数据 features = np.array([[ metrics['completeness'], metrics['accuracy'], metrics['consistency'], metrics['freshness'], metrics['complexity'] ]]) # 标准化特征 features_scaled = self.scaler.transform(features) # 预测质量分数 quality_score = self.classifier.predict_proba(features_scaled)[0][1] return quality_score # 使用示例 assessor = LineageQualityAssessor() quality_result = assessor.assess_lineage_quality(lineage_data) print(f"质量评分: {quality_result['overall_score']:.2f}") print(f"质量等级: {quality_result['quality_level']}") print(f"发现的问题: {len(quality_result['issues'])}个")📖 实时监控与告警
5.1 监控告警流程图
5.2 实时监控实现
class RealTimeLineageMonitor: def __init__(self, kafka_config, lineage_graph, alert_service): self.consumer = KafkaConsumer( kafka_config['topic'], bootstrap_servers=kafka_config['bootstrap_servers'], value_deserializer=lambda m: json.loads(m.decode('utf-8')) ) self.graph = lineage_graph self.alert_service = alert_service self.monitoring_rules = self.load_monitoring_rules() async def start_monitoring(self): """启动实时监控""" print("启动血缘关系实时监控...") for message in self.consumer: try: # 处理变更事件 await self.process_change_event(message.value) except Exception as e: print(f"处理监控事件失败: {e}") # 发送监控错误告警 await self.send_monitoring_alert( "MONITORING_ERROR", f"处理变更事件时发生错误: {str(e)}", "HIGH" ) async def process_change_event(self, event): """处理数据变更事件""" event_type = event.get('event_type') resource_type = event.get('resource_type') resource_name = event.get('resource_name') # 分析变更影响 impact_analysis = await self.analyze_change_impact( event_type, resource_type, resource_name ) # 评估风险 risk_assessment = self.assess_change_risk(impact_analysis, event) # 触发相应动作 await self.trigger_actions(risk_assessment, event) async def analyze_change_impact(self, event_type, resource_type, resource_name): """分析变更影响范围""" if resource_type == 'TABLE': # 表级别影响分析 impact_paths = self.graph.query_impact_analysis(resource_name) # 计算影响范围 impact_scope = { 'direct_impact': self.get_direct_dependencies(resource_name), 'indirect_impact': self.get_indirect_dependencies(resource_name, depth=3), 'critical_assets': self.identify_critical_assets(impact_paths), 'business_impact': self.assess_business_impact(impact_paths) } return impact_scope elif resource_type == 'COLUMN': # 列级别影响分析 impact_paths = self.graph.query_impact_analysis(None, resource_name) return { 'column_impact': impact_paths, 'affected_reports': self.find_affected_reports(resource_name), 'data_quality_impact': self.assess_data_quality_impact(resource_name) } # 启动监控服务 async def main(): monitor = RealTimeLineageMonitor( kafka_config={'bootstrap_servers': 'localhost:9092', 'topic': 'data-changes'}, lineage_graph=lineage_graph, alert_service=alert_service ) await monitor.start_monitoring()📖 影响分析与根因追踪
6.1 影响分析流程图
6.2 根因分析流程图
最后
这个增强版方案的核心在于利用 AI 技术（如AmazonBedrock 的LLM模型）提升血缘分析的自动化与智能化水平，准确解析复杂的数据逻辑，同时借助图数据库（Neptune）实现高效的血缘关系管理和分析。数据血缘系统通过构建透明、可信的数据链路，有效提升数据驱动决策的信心。
☞☞【传送门】
以上就是本文的全部内容啦。最后提醒一下各位工友，如果后续不再使用相关服务，别忘了在控制台关闭，避免超出免费额度产生费用～
- GitHub 代码仓库：sample-agentic-data-lineage
- 样例数据模型与模式结构，来源于 dbt-labs/jaffle-shop-classic
- Dbt-colibri: A lightweight, developer-friendly CLI tool and self-hostable dashboard for extracting and visualizing column-level lineage from your dbt projects.
- 仅供概念验证（POC）使用，不适用于生产环境
