KaiwuDB 数据库故障诊断工具详解

Python  import pandas as pd  import json
# 加载规则  def load\_rules(rule\_file):      with open(rule_file, 'r') as file:          return json.load(file)
# 自定义规则函数，这个函数将检查特定SQL执行时CPU使用率是否有显著增加  def sql\_cpu\_bottleneck(row, threshold):      # 比较当前行的CPU使用率是否超过阈值      return row\['sql\_query'\] == 'SELECT * FROM table\_name' and row\['cpu_usage'\] > threshold
# 应用规则  def apply\_rules(data, rules\_config, custom_rules):      for rule in rules_config:          data\[rule\['name'\]\] = data.eval(rule\['expression'\])
    for rule\_name, custom\_rule in custom_rules.items():          data\[rule\_name\] = data.apply(custom\_rule, axis=1)            return data
# 读取CSV数据  df = pd.read\_csv('sql\_performance_data.csv')
# 加载规则  rules\_config = load\_rules('rules.json')
# 定义自定义规则  custom_rules = {      'sql\_cpu\_bottleneck': lambda row: sql\_cpu\_bottleneck(row, threshold=80)  }
# 应用规则并得到结果  df = apply\_rules(df, rules\_config, custom_rules)
# 输出带有规则检查结果的数据  df.to\_csv('evaluated\_sql_performance.csv', index=False)  

JSON  \[      {          "name": "high\_execution\_time",          "expression": "execution_time > 5"      },      {          "name": "general\_high\_cpu_usage",          "expression": "cpu_usage > 80"      }，      {          "name": "slow_query",          "expression": "query_time > 5"      },      {          "name": "error\_code\_check",          "expression": "error_code not in \[0, 200, 404\]"      }      // 其他规则可以在此添加  \]  

Python  from sklearn.tree import DecisionTreeClassifier  from sklearn.model\_selection import train\_test_split  from sklearn.metrics import accuracy_score
# 读取CSV数据  df = pd.read\_csv('performance\_data.csv')
# 假设我们已经有了一个标记了性能问题的列 'performance_issue'  # 这个列可以通过规则引擎或历史数据分析得到
# 特征和标签  X = df\[\['cpu\_usage', 'disk\_io', 'query_time'\]\]  y = df\['performance_issue'\]
# 分割数据集为训练集和测试集  X\_train, X\_test, y\_train, y\_test = train\_test\_split(X, y, test\_size=0.2, random\_state=42)
# 创建决策树模型  model = DecisionTreeClassifier()
# 训练模型  model.fit(X\_train, y\_train)
# 预测测试集  y\_pred = model.predict(X\_test)
# 打印准确率  print(f'Accuracy: {accuracy\_score(y\_test, y_pred)}')
# 保存模型，以便以后使用  import joblib  joblib.dump(model, 'performance\_predictor\_model.joblib')
# 若要使用模型进行实时预测  def predict\_performance(cpu\_usage, disk\_io, query\_time):      model = joblib.load('performance\_predictor\_model.joblib')      prediction = model.predict(\[\[cpu\_usage, disk\_io, query_time\]\])      return 'Issue' if prediction\[0\] == 1 else 'No issue'
# 示例：使用模型预测一个新的数据点  print(predict_performance(85, 90, 3))  

Plaintext  SELECT avg(temperature) FROM device_readings  WHERE device_id = ? AND time > now() - interval '1 hour'  GROUP BY time_bucket('5 minutes', time);Plaintext  

SQL  ALTER TABLE device\_readings ADD TAG device\_id;