Apache Ignite SQL和机器学习

前置知识

在学习本文之前，请确保您已经：

• 了解SQL基础知识
• 掌握基本的机器学习概念
• 熟悉Ignite的数据网格功能

SQL查询

1. 表定义

@Data
@QuerySqlField.Group(
    indexes = {
        @QuerySqlField.Index(name = "idx_salary", fields = {"salary"}),
        @QuerySqlField.Index(name = "idx_dept_salary", fields = {"department", "salary"})
    }
)
public class Employee {
    @QuerySqlField(index = true)
    private long id;
    
    @QuerySqlField(index = true)
    private String name;
    
    @QuerySqlField
    private String department;
    
    @QuerySqlField
    private double salary;
}

2. 创建表

// 配置缓存
CacheConfiguration<Long, Employee> cacheCfg = new CacheConfiguration<>();
cacheCfg.setName("employees");
cacheCfg.setIndexedTypes(Long.class, Employee.class);

// 创建缓存
IgniteCache<Long, Employee> cache = ignite.getOrCreateCache(cacheCfg);

3. SQL查询操作

// 执行SQL查询
SqlFieldsQuery query = new SqlFieldsQuery(
    "SELECT name, salary FROM Employee " +
    "WHERE department = ? AND salary > ?"
);

// 设置参数
query.setArgs("IT", 5000);

// 执行查询
List<List<?>> results = cache.query(query).getAll();

// 处理结果
for (List<?> row : results) {
    System.out.println("Name: " + row.get(0) + 
                     ", Salary: " + row.get(1));
}

4. DML操作

// 插入数据
SqlFieldsQuery insert = new SqlFieldsQuery(
    "INSERT INTO Employee(id, name, department, salary) " +
    "VALUES (?, ?, ?, ?)"
);

insert.setArgs(1L, "John Doe", "IT", 6000);
cache.query(insert).getAll();

// 更新数据
SqlFieldsQuery update = new SqlFieldsQuery(
    "UPDATE Employee SET salary = salary * 1.1 " +
    "WHERE department = ?"
);

update.setArgs("IT");
cache.query(update).getAll();

机器学习

1. 配置ML模块

IgniteConfiguration cfg = new IgniteConfiguration();

// 添加ML插件
PluginConfiguration[] plugins = new PluginConfiguration[] {
    new IgniteMLPluginConfiguration()
};
cfg.setPluginConfigurations(plugins);

// 启动节点
Ignite ignite = Ignition.start(cfg);

2. 线性回归示例

// 准备训练数据
LabeledDatasetBuilder<Double> builder = new LabeledDatasetBuilder<>(ignite);

builder.addData(
    new double[] {1.0, 2.0}, 3.0,
    new double[] {2.0, 4.0}, 6.0,
    new double[] {3.0, 6.0}, 9.0
);

LabeledDataset<Double> dataset = builder.build();

// 创建和训练模型
LinearRegressionModel mdl = new LinearRegressionLSQRTrainer()
    .fit(ignite, dataset);

// 预测
double prediction = mdl.predict(new double[] {4.0, 8.0});
System.out.println("Predicted value: " + prediction);

3. 聚类分析

// 准备数据
DatasetBuilder builder = new DatasetBuilder(ignite);

builder.addData(
    new double[] {1.0, 1.0},
    new double[] {1.0, 2.0},
    new double[] {5.0, 5.0},
    new double[] {5.0, 6.0}
);

Dataset dataset = builder.build();

// 创建和训练K-means模型
KMeansModel mdl = new KMeansTrainer()
    .withK(2)
    .fit(ignite, dataset);

// 预测聚类
double[] point = {2.0, 2.0};
int cluster = mdl.predict(point);
System.out.println("Point belongs to cluster: " + cluster);

性能优化

1. SQL查询优化

// 使用查询提示
SqlFieldsQuery query = new SqlFieldsQuery(
    "SELECT /*+ USE_INDEX(Employee, idx_dept_salary) */ " +
    "name, salary FROM Employee " +
    "WHERE department = ? AND salary > ?"
);

// 启用分布式连接
query.setDistributedJoins(true);

// 设置每页大小
query.setPageSize(100);

2. ML性能调优

// 配置训练参数
LinearRegressionLSQRTrainer trainer = new LinearRegressionLSQRTrainer()
    .withMaxIterations(100)
    .withBatchSize(32)
    .withSeed(123L);

// 并行处理
trainer.fit(ignite.compute(), dataset);

最佳实践

SQL优化建议

1. 合理使用索引
2. 优化查询语句
3. 使用参数化查询
4. 控制结果集大小

ML优化建议

1. 数据预处理
2. 选择合适的算法
3. 调整模型参数
4. 验证模型效果

总结

本文详细介绍了Ignite的：

1. ✅ SQL查询功能
2. ✅ 机器学习能力
3. ✅ 性能优化技巧
4. ✅ 最佳实践建议

下一步学习

• 探索更多ML算法
• 了解高级SQL特性
• 实践生产环境优化

希望这篇文章对您有所帮助！如果您有任何问题，欢迎在评论区讨论。