千万级别的数据导出Redis、php

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要处理千万级别数据涵盖了多表联查、条件筛选、Redis缓存优化、yield生成器优化内存，以及将数据导出到Excel文件。

方案概述

多表分步查询：避免直接的多表联查，通过逐表查询并在应用层进行数据组合，以减少数据库的锁定风险
Redis 缓存：在多表联查过程中使用 Redis 进行中间结果的缓存，减少数据库查询次数。
yield生成器：通过 PHP 的 yield 生成器逐条处理数据，减少内存占用。
分批导出：使用 PhpSpreadsheet 进行数据的分批写入，控制内存占用。

MySQL 表结构示例

假设有三张表 orders, customers, 和 products，分别存储订单信息、客户信息和商品信息。

CREATE TABLE customers (id BIGINT PRIMARY KEY,name VARCHAR(255),email VARCHAR(255)
);CREATE TABLE products (id BIGINT PRIMARY KEY,name VARCHAR(255),price DECIMAL(10, 2)
);CREATE TABLE orders (id BIGINT PRIMARY KEY,customer_id BIGINT,product_id BIGINT,quantity INT,order_date DATETIME,FOREIGN KEY (customer_id) REFERENCES customers(id),FOREIGN KEY (product_id) REFERENCES products(id)
);

1. 分步查询与数据组合

首先分步查询每个表的数据，然后在应用层面合并这些数据，避免复杂的 SQL JOIN 操作

function fetchOrders($pdo, $batchSize, $offset, $conditions) {$sql = "SELECT * FROM orders WHERE $conditions LIMIT :limit OFFSET :offset";$stmt = $pdo->prepare($sql);$stmt->bindValue(':limit', $batchSize, PDO::PARAM_INT);$stmt->bindValue(':offset', $offset, PDO::PARAM_INT);$stmt->execute();return $stmt->fetchAll(PDO::FETCH_ASSOC);
}function fetchCustomers($pdo, $customerIds) {$inQuery = implode(',', array_fill(0, count($customerIds), '?'));$sql = "SELECT * FROM customers WHERE id IN ($inQuery)";$stmt = $pdo->prepare($sql);$stmt->execute($customerIds);return $stmt->fetchAll(PDO::FETCH_ASSOC);
}function fetchProducts($pdo, $productIds) {$inQuery = implode(',', array_fill(0, count($productIds), '?'));$sql = "SELECT * FROM products WHERE id IN ($inQuery)";$stmt = $pdo->prepare($sql);$stmt->execute($productIds);return $stmt->fetchAll(PDO::FETCH_ASSOC);
}function combineData($orders, $customers, $products) {$customerMap = array_column($customers, null, 'id');$productMap = array_column($products, null, 'id');foreach ($orders as &$order) {$order['customer_name'] = $customerMap[$order['customer_id']]['name'] ?? '';$order['product_name'] = $productMap[$order['product_id']]['name'] ?? '';$order['price'] = $productMap[$order['product_id']]['price'] ?? 0.0;}return $orders;
}

2. 使用 Redis 缓存中间数据

在进行下一步的查询和数据处理之前，缓存中间结果，减少重复查询。

function cacheOrdersInRedis($redis, $orders, $batchSize) {foreach ($orders as $order) {$redis->rpush('orders_cache', json_encode($order));}
}function getOrdersFromRedis($redis, $batchSize) {return array_map('json_decode', $redis->lrange('orders_cache', 0, $batchSize - 1));
}

3. 使用 `yield` 优化内存使用

通过 yield 逐条处理数据，避免内存占用过多。

function orderGenerator($redis, $batchSize) {while (true) {$orders = getOrdersFromRedis($redis, $batchSize);if (empty($orders)) {break;}foreach ($orders as $order) {yield $order;}// 删除已经处理的数据$redis->ltrim('orders_cache', $batchSize, -1);}
}

4. 分批导出到 Excel

使用 PhpSpreadsheet 分批写入数据到 Excel 文件中。

use PhpOffice\PhpSpreadsheet\Spreadsheet;
use PhpOffice\PhpSpreadsheet\Writer\Xlsx;function exportToExcel($filename, $dataGenerator) {$spreadsheet = new Spreadsheet();$sheet = $spreadsheet->getActiveSheet();$row = 1;foreach ($dataGenerator as $dataRow) {$col = 1;foreach ($dataRow as $value) {$sheet->setCellValueByColumnAndRow($col, $row, $value);$col++;}$row++;}$writer = new Xlsx($spreadsheet);$writer->save($filename);
}

5. 综合实现

$pdo = new PDO("mysql:host=localhost;dbname=test", "user", "pass");
$redis = new Redis();
$redis->connect('127.0.0.1', 6379);$batchSize = 10000;
$cacheKey = "orders_cache";
$filename = "export.xlsx";// 假设要筛选最近一个月的订单
$conditions = "order_date >= DATE_SUB(NOW(), INTERVAL 1 MONTH)";// 清空 Redis 缓存
$redis->del($cacheKey);$offset = 0;
while ($orders = fetchOrders($pdo, $batchSize, $offset, $conditions)) {// 获取关联数据$customerIds = array_column($orders, 'customer_id');$productIds = array_column($orders, 'product_id');$customers = fetchCustomers($pdo, $customerIds);$products = fetchProducts($pdo, $productIds);// 合并数据$combinedData = combineData($orders, $customers, $products);// 缓存数据到 RediscacheOrdersInRedis($redis, $combinedData, $batchSize);$offset += $batchSize;
}// 使用 `yield` 生成器逐条获取数据并导出
$dataGenerator = orderGenerator($redis, $batchSize);
exportToExcel($filename, $dataGenerator);echo "Data export completed.";