graph TD
A[SQL解析] --> B[代价估算]
B --> C[执行计划生成]
C --> D[运行时优化]
D --> E[反馈学习]subgraph 优化层
B1[基数估计] --> B2[代价模型]
C1[物理算子选择] --> C2[分布式调度]
D1[自适应重优化] --> D2[资源调控]
E1[执行反馈] --> E2[模型更新]
endsubgraph 存储层
F1[分区管理] --> F2[索引推荐]
F3[数据冷热分离] --> F4[压缩优化]
end

import torch
import torch.nn as nnclass CardinalityEstimator(nn.Module):def __init__(self, input_dim, hidden_dim=128):super().__init__()self.embedding = nn.Embedding(1000, 8)  # 列值嵌入self.lstm = nn.LSTM(8, hidden_dim, batch_first=True)self.attention = nn.MultiheadAttention(hidden_dim, num_heads=4)self.regressor = nn.Sequential(nn.Linear(hidden_dim, 64),nn.ReLU(),nn.Linear(64, 1)def forward(self, query_tokens):# 嵌入层embedded = self.embedding(query_tokens)# LSTM编码lstm_out, _ = self.lstm(embedded)# 注意力聚合attn_out, _ = self.attention(lstm_out, lstm_out, lstm_out)# 池化+回归pooled = torch.mean(attn_out, dim=1)return self.regressor(pooled).exp()  # 返回基数估计值# 实时训练循环
def online_train(estimator, plan, actual_rows):optimizer = torch.optim.Adam(estimator.parameters(), lr=1e-4)loss_fn = nn.MSELoss()# 转换查询为token序列tokens = tokenize_query(plan['query'])# 前向传播pred = estimator(tokens)# 计算损失并反向传播loss = loss_fn(pred, torch.tensor([actual_rows]).float())loss.backward()optimizer.step()

public class AdaptiveExecutor {private PlanNode currentPlan;private RuntimeStats stats;private Reoptimizer reoptimizer;public ResultSet execute(Query query) {currentPlan = Optimizer.choosePlan(query);while (currentPlan != null) {try {// 执行当前算子ResultSet partial = currentPlan.execute();// 收集运行时统计信息stats.collect(currentPlan, partial);// 检查是否需要重优化if (reoptimizer.needReoptimize(currentPlan, stats)) {currentPlan = reoptimizer.reoptimize(currentPlan, stats);continue;}return partial;} catch (PlanAbortException e) {currentPlan = reoptimizer.handleFailure(currentPlan, e);}}throw new ExecutionException("Failed to execute query");}
}class RuntimeReoptimizer {public boolean needReoptimize(PlanNode plan, RuntimeStats stats) {// 实际行数/估计行数 > 阈值double ratio = (double)stats.actualRows / plan.estimatedRows;return ratio > 10.0 || ratio < 0.1;  // 严重偏离时重优化}
}

type Scheduler struct {nodeMap     map[string]NodeStatsdataLocator *DataLocationService
}func (s *Scheduler) Schedule(task Task) Node {// 获取任务所需数据位置dataNodes := s.dataLocator.Locate(task.DataShards)// 选择最优节点bestNode := ""minCost := math.MaxFloat64for node, stats := range s.nodeMap {// 计算节点成本：网络开销 + 负载cost := 0.0// 数据局部性加分if contains(dataNodes, node) {cost -= 50.0  // 数据本地化奖励}// 节点负载成本cost += stats.CPUUsage * 10cost += stats.MemUsage * 5cost += stats.NetworkLatency * 2if cost < minCost {minCost = costbestNode = node}}return bestNode
}

class ResourceGovernor:def __init__(self, cluster):self.cluster = clusterself.query_profiles = {}def allocate(self, query_id, query):# 预测资源需求if query in self.query_profiles:profile = self.query_profiles[query]else:profile = self.predict_resource(query)# 动态分配资源required_cores = min(profile['cores'], self.cluster.max_cores_per_query)required_mem = min(profile['mem'], self.cluster.max_mem_per_query)# 申请资源槽slot = self.cluster.allocate_slot(cores=required_cores, mem=required_mem,duration=profile['duration'])return slotdef predict_resource(self, query):# 使用历史执行数据训练的资源预测模型return self.resource_model.predict(query)

class IndexAdvisor {def recommendIndexes(workload: Workload): Set[Index] = {// 1. 提取查询模式val patterns = extractAccessPatterns(workload)// 2. 计算索引收益val candidateIndexes = generateCandidates(patterns)val benefitMap = costModel.evaluate(candidateIndexes, workload)// 3. 背包算法选择最优组合val selected = knapSack(items = candidateIndexes,weight = _.storageCost,value = benefitMap(_),maxWeight = storageBudget)selected}private def extractAccessPatterns(workload: Workload): Set[AccessPattern] = {workload.queries.flatMap { query =>val whereClauses = parseWhereClauses(query.sql)val joinClauses = parseJoinClauses(query.sql)whereClauses.map(p => AccessPattern(p.columns, p.predicateType)) ++joinClauses.map(j => AccessPattern(j.columns, "JOIN"))}}
}

class AutoPartitioner:def __init__(self, table, access_log):self.table = tableself.access_log = access_logdef repartition(self):# 分析访问模式hot_keys = self.analyze_hot_keys()correlated_cols = self.find_correlated_columns()# 生成新分区方案if len(hot_keys) > 0:# 热点数据单独分区return RangePartition(column=self.primary_key,ranges=[(min_key, max_key) for key_range in hot_keys])elif correlated_cols:# 关联列哈希分区return HashPartition(columns=correlated_cols,buckets=32)else:# 默认时间分区return TimePartition(column="create_time",interval="1 DAY")def analyze_hot_keys(self):# 使用Count-Min Sketch检测热点sketch = CountMinSketch(width=1000, depth=5)for access in self.access_log:sketch.update(access.row_key, access.count)return [key for key in self.table.keys if sketch.estimate(key) > HOT_THRESHOLD]

public class CircuitBreaker {private int failureCount = 0;private long lastFailureTime = 0;private PlanNode faultyPlan;public void reportFailure(PlanNode plan) {if (plan == faultyPlan) {failureCount++;} else {faultyPlan = plan;failureCount = 1;}lastFailureTime = System.currentTimeMillis();}public boolean isTriggered() {return failureCount > THRESHOLD && (System.currentTimeMillis() - lastFailureTime) < TIME_WINDOW;}public PlanNode getAlternativePlan(Query query) {// 返回降级执行计划return DegradePlanner.getSafePlan(query);}
}

func MonitorResourceLeaks() {ticker := time.NewTicker(5 * time.Minute)for range ticker.C {// 检测连接泄漏conns := db.MonitorOpenConnections()for app, count := range conns {if count > 100 {alert := fmt.Sprintf("%s has %d open connections", app, count)sendAlert(alert)// 自动回收连接if count > 500 {killAppConnections(app)}}}// 检测内存泄漏if mem := sys.GetProcessMemory(); mem.RSS > 10*1024*1024*1024 {dumpMemoryProfile()restartProcess()}}
}