一、迭代器模式核心优势

传统数据处理方式的痛点与迭代器解决方案：

• 惰性求值：按需处理大数据集
• 可组合性：管道式数据处理
• 异步友好：支持异步数据源
• 内存高效：避免中间数组分配

二、电商数据分析案例

1. 基础数据管道实现

function* createDataPipeline(source) {
    // 数据清洗阶段
    yield* filterInvalidRecords(source);
    // 数据转换阶段
    yield* transformCurrency(yield*);
    // 数据分析阶段
    yield* calculateStatistics(yield*);
}

function* filterInvalidRecords(source) {
    for (const item of source) {
        if (item.price > 0 && item.quantity > 0) {
            yield item;
        }
    }
}

function* transformCurrency(source) {
    const exchangeRate = 6.5; // USD to CNY
    for (const item of source) {
        yield {
            ...item,
            localPrice: item.price * exchangeRate,
            currency: 'CNY'
        };
    }
}

2. 可中断处理机制

class PipelineController {
    constructor(pipeline) {
        this.iterator = pipeline;
        this.isAborted = false;
    }

    process(limit = Infinity) {
        const results = [];
        let count = 0;
        
        for (const item of this.iterator) {
            if (this.isAborted) break;
            if (++count > limit) break;
            
            results.push(item);
        }
        return results;
    }

    abort() {
        this.isAborted = true;
    }
}

// 使用示例
const controller = new PipelineController(
    createDataPipeline(getSalesData())
);
const result = controller.process(1000); // 只处理1000条

3. 异步数据源支持

async function* asyncDataPipeline(source) {
    for await (const chunk of source) {
        yield* processChunk(chunk);
    }
}

async function processChunk(chunk) {
    // 模拟异步转换
    const processed = await Promise.all(
        chunk.map(async item => ({
            ...item,
            discount: await calculateDiscount(item)
        }))
    );
    yield* processed;
}

三、高级应用技巧

1. 管道组合器

function composePipelines(...pipelines) {
    return function* (source) {
        let current = source;
        for (const pipeline of pipelines) {
            current = pipeline(current);
        }
        yield* current;
    };
}

// 创建组合管道
const fullPipeline = composePipelines(
    filterInvalidRecords,
    transformCurrency,
    calculateStatistics
);

2. 性能监控装饰器

function withMetrics(pipeline) {
    return function* (source) {
        const start = performance.now();
        let count = 0;
        
        for (const item of pipeline(source)) {
            count++;
            yield item;
        }
        
        console.log(`处理 ${count} 条数据，耗时 ${performance.now() - start}ms`);
    };
}

四、实际应用场景

• 大数据分块处理：GB级JSON文件流式处理
• 实时数据分析：WebSocket数据流处理
• 复杂ETL流程：多阶段数据转换
• UI数据渲染：分帧渲染避免界面卡顿