Java并发编程-关于CompletableFuture的实验

1 背景

最近的项目中，有这样的场景：
根据商品id列表，分别调用不同服务查询这批商品的价格、库存、卖点等信息，然后将这批商品信息组合返回。

都是根据商品id查询的不同服务，如果拿着id挨个服务去串行执行（先查询价格服务->再查询库存服务->再查询卖点服务...），由于这些服务都是远程调用，很明显性能很低，而且后面的服务查询不依赖前面的查询结果，明显没有串行执行的必要。

2 场景模拟

2.1 服务准备

简化的商品VO:

@Data
public class ItemVO {
    private Long itemId;
    private Long price;
    private Long quantity;
    private List sellings;
}

模拟价格查询器：

public class PriceQueryService {
    public Map queryPrice(List itemIds) {
        Map result = new HashMap<>(itemIds.size());
        ThreadLocalRandom random = ThreadLocalRandom.current();
        itemIds.forEach(id -> {
            Long price = random.nextLong(30000);
            result.put(id, price);
        });
      // 模拟延迟
        try {
            Thread.sleep(300);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
        return result;
    }
}

模拟库存查询器：

public class QuantityQueryService {
    public Map queryQuantity(List itemIds) {
        Map result = new HashMap<>(itemIds.size());
        ThreadLocalRandom random = ThreadLocalRandom.current();
        itemIds.forEach(id -> {
            Long price = random.nextLong(100);
            result.put(id, price);
        });
      // 模拟延迟
        try {
            Thread.sleep(400);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
        return result;
    }
}

模拟卖点信息查询器：

public class SellingsQueryService {
    public Map> querySellingText(List itemIds) {
        Map> result = new HashMap<>(itemIds.size());
        itemIds.forEach(id -> {
            List text = Arrays.asList("可拆装","刺绣");
            result.put(id, text);
        });
      // 模拟延迟
        try {
            Thread.sleep(500);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
        return result;
    }
}

2.2 CompletableFuture调用各个服务

串行执行，1264毫秒左右

public class QueryItemInfoService {
    private static final Logger log = LoggerFactory.getLogger(QueryItemInfoService.class);

    public static void main(String[] args) {
        log.info("start");
        Long start = System.currentTimeMillis();
        List itemIds = Arrays.asList(31332L, 23123L, 43834L, 34442L, 23213L, 434112L, 45342L);
        PriceQueryService priceQueryService = new PriceQueryService();
        QuantityQueryService quantityQueryService = new QuantityQueryService();
        SellingsQueryService sellingsQueryService = new SellingsQueryService();

        Map priceMap = priceQueryService.queryPrice(itemIds);
        Map quantityMap = quantityQueryService.queryQuantity(itemIds);
        Map> sellingsMap = sellingsQueryService.querySellingText(itemIds);

        List itemVOS = itemIds.stream().map(
                itemId -> {
                    ItemVO itemVO = new ItemVO();
                    itemVO.setItemId(itemId);
                    itemVO.setPrice(priceMap.getOrDefault(itemId, 0L));
                    itemVO.setQuantity(quantityMap.getOrDefault(itemId, 0L));
                    itemVO.setSellings(sellingsMap.getOrDefault(itemId, Collections.emptyList()));
                    return itemVO;
                }
        ).collect(Collectors.toList());
        
        Long end = System.currentTimeMillis();
        log.info("end, duration = {}", end - start);
    }
}

异步执行, 550毫秒左右

public class QueryItemInfoService {
    private static final Logger log = LoggerFactory.getLogger(QueryItemInfoService.class);

    public static void main(String[] args) {
        log.info("start");
        Long start = System.currentTimeMillis();
        List itemIds = Arrays.asList(31332L, 23123L, 43834L, 34442L, 23213L, 434112L, 45342L);
        PriceQueryService priceQueryService = new PriceQueryService();
        QuantityQueryService quantityQueryService = new QuantityQueryService();
        SellingsQueryService sellingsQueryService = new SellingsQueryService();

        CompletableFuture> priceFuture = CompletableFuture.supplyAsync(() -> priceQueryService.queryPrice(itemIds));
        CompletableFuture> quantityFuture = CompletableFuture.supplyAsync(() -> quantityQueryService.queryQuantity(itemIds));
        CompletableFuture>> sellingsFuture = CompletableFuture.supplyAsync(() -> sellingsQueryService.querySellingText(itemIds));

        Map priceMap = priceFuture.join();
        Map quantityMap = quantityFuture.join();
        Map> sellingsMap = sellingsFuture.join();

        List itemVOS = itemIds.stream().map(
                itemId -> {
                    ItemVO itemVO = new ItemVO();
                    itemVO.setItemId(itemId);
                    itemVO.setPrice(priceMap.getOrDefault(itemId, 0L));
                    itemVO.setQuantity(quantityMap.getOrDefault(itemId, 0L));
                    itemVO.setSellings(sellingsMap.getOrDefault(itemId, Collections.emptyList()));
                    return itemVO;
                }
        ).collect(Collectors.toList());


        Long end = System.currentTimeMillis();
        log.info("end, duration = {}", end - start);
    }
}

3 深入分析

CompletableFuture 是Java8引入的，实现了Future和CompletionStage两个接口。弥补了Future类无法手动完成，无法链式调用等问题。

常用的两个方法是runAsync和supplyAsync，两个方法的区别是，runAsync的入参是实现了Runnable接口的方法，也就是说没有返回值，返回的是CompletableFuture；supplyAsync入参是实现了Supplier接口的方法，也就是说有返回值。

4 assemble