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Redis 笔记 03:高级结构和实战

Redis 笔记 03:高级结构和实战

这是本人根据黑马视频学习 Redis 的相关笔记,系列文章导航:《Redis设计与实现》笔记与汇总

点赞功能:Set

基本功能实现

需求:

  1. 同一个用户只能点赞一次,再次点击则取消点赞
  2. 如果当前用户已经点赞,则点赞按钮高亮显示

实体类 Blog :添加一个字段, 注解是 MyBatisPlus 的注解,表示不在表中

/**
* 是否点赞过了
*/
@TableField(exist = false)
private Boolean isLike;

Controller

@PutMapping("/like/{id}")
public Result likeBlog(@PathVariable("id") Long id) {
    // 修改点赞数量
    return blogService.likeBlog(id);
}

Service

@Override
public Result likeBlog(Long id) {
    // 1. 获取登录用户
    Long userId = UserHolder.getUser().getId();
    // 2. 判断是否点赞
    String key = RedisConstants.BLOG_LIKED_KEY + id;
    Boolean isMember = stringRedisTemplate.opsForSet().isMember(key, userId.toString());

    // 3. 未点赞,点赞,修改数据库,保存用户到 Redis 中
    if (BooleanUtil.isFalse(isMember)) {
        boolean isSuccess = update().setSql("liked = liked + 1").eq("id", id).update();
        if (isSuccess) {
            stringRedisTemplate.opsForSet().add(key, userId.toString());
        }
    } else {
        // 4. 已经点赞,取消,修改数据库,删除用户
        boolean isSuccess = update().setSql("liked = liked - 1").eq("id", id).update();
        stringRedisTemplate.opsForSet().remove(key, userId.toString());
    }

    return Result.ok();
}

这里用的是 Redis 中的 Set 来解决这个问题的

点赞排行榜

按点赞时间先后排序,返回 Top5 的用户

ZADD ZSCORE ZRANGE

Service

@Override
public Result queryBlogLikes(Long id) {
    // 1. 查询 top 5
    // ZRANGE KEY 0 4
    String key = RedisConstants.BLOG_LIKED_KEY + id;

    Set<String> top5 = stringRedisTemplate.opsForZSet().range(key, 0, 4);

    if (top5 == null || top5.isEmpty()) {
        return Result.ok();
    }

    // 2. 解析其中的用户id
    List<Long> ids = top5.stream().map(Long::valueOf).collect(Collectors.toList());

    // 3. 根据id查询用户
    String join = StrUtil.join(",", ids);
    List<UserDTO> userDTOS = userService.query().in("id", ids).last("ORDER BY FIELD (id," + join + ")").list()
        .stream()
        .map(user -> BeanUtil.copyProperties(user, UserDTO.class))
        .collect(Collectors.toList());
    // 4. 反回
    return Result.ok(userDTOS);
}

注意这里的查询数据库的代码

好友关注:SortedSet

关注和取关

这个和 Redis 没有关系,直接在数据库中查询就可以了

这里是用了一个 follow 表用来记录关注和被关注者的 id 信息

Service

@Override
public Result follow(Long followUserId, Boolean isFollow) {
    // 判断是关注还是取关
    Long userId = UserHolder.getUser().getId();
    // 关注,新增
    if (isFollow) {
        Follow follow = new Follow();
        follow.setUserId(userId);
        follow.setFollowUserId(followUserId);
        save(follow);
    } else {
        remove(new QueryWrapper<Follow>()
               .eq("user_id", userId).eq("follow_user_id", followUserId));
    }

    // 取关,删除
    return Result.ok();
}

@Override
public Result isFollow(Long followUserId) {
    Long userId = UserHolder.getUser().getId();
    Integer count = query().eq("user_id", userId).eq("follow_user_id", followUserId).count();
    return Result.ok(count > 0);
}

共同关注

可以用 Set 结构进行判断

关注的逻辑 :增加:关注时保存在 Redis 中

@Override
public Result follow(Long followUserId, Boolean isFollow) {
    // 判断是关注还是取关
    Long userId = UserHolder.getUser().getId();
    // 关注,新增
    String key = RedisConstants.FOLLOW_KEY + userId;
    if (isFollow) {
        Follow follow = new Follow();
        follow.setUserId(userId);
        follow.setFollowUserId(followUserId);
        boolean isSuccess = save(follow);
        if (isSuccess) {
            stringRedisTemplate.opsForSet().add(key, followUserId.toString());
        }
    } else {
        remove(new QueryWrapper<Follow>()
               .eq("user_id", userId).eq("follow_user_id", followUserId));
        stringRedisTemplate.opsForSet().remove(key, followUserId.toString());
    }

    // 取关,删除
    return Result.ok();
}

判断的逻辑

@Override
public Result followCommons(Long id) {
    Long userId = UserHolder.getUser().getId();
    String key = RedisConstants.FOLLOW_KEY + userId;
    String key2 = RedisConstants.FOLLOW_KEY + id;
    Set<String> intersect = stringRedisTemplate.opsForSet().intersect(key, key2);
    if (intersect == null || intersect.isEmpty()) {
        System.out.println("111");
        return Result.ok(Collections.emptyList());
    }
    List<Long> ids = intersect.stream().map(Long::valueOf).collect(Collectors.toList());
    Stream<UserDTO> users = userService.listByIds(ids)
        .stream()
        .map(user -> BeanUtil.copyProperties(user, UserDTO.class));

    return Result.ok(users);

}

关注推送

需求

pximage

pximage

分析

用什么结构?

SortedSet 实现滚动分页

如果没有新数据的插入,那么正常的分页查询即可以,即利用 List 也可以完成这个工作

但是由于可能会有新数据的插入,数据角标也在不断变化中,所以需要使用滚动分页

pximage

pximage

在 Redis 中进行滚动查询需要四个参数,如下图所示:

pximage

  • MAX :如果是第一次查询,则返回当前时间戳,否则应为上一次查询的最小值
  • MIN :可以设为 0,无需变动
  • OFFSET :相对于 MAX 的偏移量,第一次查询时设为 0 即可,之后需要设为上一次查询的最小值的数量
  • SIZE :大小,一般是固定的,比如 10 条数据/页

实现

用户保存博客时

@Override
public Result saveBlog(Blog blog) {
    // 获取登录用户
    UserDTO user = UserHolder.getUser();
    blog.setUserId(user.getId());
    // 保存探店博文
    save(blog);
    // 查询笔记作者的所有粉丝
    List<Follow> follows = followService.query().eq("follow_user_id", user.getId()).list();

    for (Follow f : follows) {
        Long userId = f.getUserId();
        // 推送
        String key = RedisConstants.FEED_KEY + userId;
        stringRedisTemplate.opsForZSet().add(key, blog.getId().toString(), System.currentTimeMillis());

    }
    return Result.ok(blog.getId());
}

查询博客时

@Override
public Result queryBlogOfFollow(Long max, Integer offset) {
    // 获取当前用户
    Long userId = UserHolder.getUser().getId();
    String key = RedisConstants.FEED_KEY + userId;
    // 找到收件箱
    Set<ZSetOperations.TypedTuple<String>> typedTuples = stringRedisTemplate.opsForZSet()
        .reverseRangeByScoreWithScores(key, 0, max, offset, 2);
    // 判断
    if (typedTuples == null || typedTuples.isEmpty()) {
        return Result.ok();
    }
    // 解析数据

    ArrayList<Long> ids = new ArrayList<>();
    long minTime = 0;
    int os = 1;
    for (ZSetOperations.TypedTuple<String> tuple : typedTuples) {
        ids.add(Long.valueOf(tuple.getValue()));
        if (tuple.getScore().longValue() == minTime) {
            os++;
        } else {
            minTime = tuple.getScore().longValue();
        }
    }
    // 根据 id 查询 blog
    String idStr = StrUtil.join(",", ids);
    List<Blog> blogs = query().in("id", ids).last("ORDER BY FIELD(id," + idStr + ")").list();
    for (Blog blog : blogs) {
        queryBlogUser(blog);
        isBlogLiked(blog);
    }
    ScrollResult scrollResult = new ScrollResult();
    scrollResult.setList(blogs);
    scrollResult.setOffset(os);
    scrollResult.setMinTime(minTime);
    return Result.ok(scrollResult);
}

附近用户:GEO

基本使用

pximage

pximage

预先准备

先写一个测试类,将数据保存到 Redis 中:

@Test
public void loadShopData() {
    List<Shop> list = shopService.list();

    Map<Long, List<Shop>> map = list.stream().collect(Collectors.groupingBy(Shop::getTypeId));

    for (Map.Entry<Long, List<Shop>> longListEntry : map.entrySet()) {

        Long typeId = longListEntry.getKey();
        String key = RedisConstants.SHOP_GEO_KEY + typeId;

        List<Shop> shops = longListEntry.getValue();

        List<RedisGeoCommands.GeoLocation<String>> locations = new ArrayList<>(shops.size());

        for (Shop shop : shops) {
            locations.add(new RedisGeoCommands.GeoLocation<>(
                shop.getId().toString(),
                new Point(shop.getX(), shop.getY())
            ));
        }
        stringRedisTemplate.opsForGeo().add(key, locations);
    }
}

pximage

依赖问题

SpringDataRedis2.3.9 版本并不支持 Redis 6.2 提供的 GEOSEARCH 命令,我们要修改其版本

下载 IDEA 的插件 Maven Helper

移除默认的版本的包:

pximage

再手动添加新版本:

<dependency>
    <groupId>org.springframework.data</groupId>
    <artifactId>spring-data-redis</artifactId>
    <version>2.6.2</version>
</dependency>
<dependency>
    <groupId>io.lettuce</groupId>
    <artifactId>lettuce-core</artifactId>
    <version>6.1.6.RELEASE</version>
</dependency>

代码实现

思路:

  • 如果不需要坐标,则直接从数据库查询

  • 如果需要坐标查询,则

    1. 先从 Redis 查询距离排序
    2. 分页
    3. 根据 id 查询店铺信息
    4. 填充距离
@Override
public Result queryShopByType(Integer typeId, Integer current, Double x, Double y) {
    // 1. 判断是否需要根据坐标查询
    if (x == null || y == null) {
        // 根据类型分页查询
        Page<Shop> page = query()
            .eq("type_id", typeId)
            .page(new Page<>(current, SystemConstants.DEFAULT_PAGE_SIZE));
        // 返回数据
        return Result.ok(page.getRecords());
    }

    // 2. 计算分页参数
    int from = (current - 1) * SystemConstants.DEFAULT_PAGE_SIZE;
    int end = (current) * SystemConstants.DEFAULT_PAGE_SIZE;

    // 3. 查询 Redis、按照距离排序、分页
    String key = RedisConstants.SHOP_GEO_KEY + typeId;
    GeoResults<RedisGeoCommands.GeoLocation<String>> results = stringRedisTemplate.opsForGeo().search(key, GeoReference.fromCoordinate(x, y),
                                                                                                      new Distance(5000),
                                                                                                      RedisGeoCommands.GeoSearchCommandArgs
                                                                                                      .newGeoSearchArgs().includeDistance().limit(end));
    if (results == null) {
        return Result.ok(Collections.emptyList());
    }

    List<GeoResult<RedisGeoCommands.GeoLocation<String>>> list = results.getContent();

    if (list.size() <= from) {
        return Result.ok(Collections.emptyList());
    }

    // 截取
    List<Long> ids = new ArrayList<>(list.size());
    HashMap<String, Distance> distanceMap = new HashMap<>(list.size());

    list.stream().skip(from).forEach(result -> {
        String shopIdStr = result.getContent().getName();
        ids.add(Long.valueOf(shopIdStr));
        Distance distance = result.getDistance();
        distanceMap.put(shopIdStr, distance);
    });

    String idStr = StrUtil.join(",", ids);
    List<Shop> shops = query().in("id", ids).last("ORDER BY FIELD (id, " + idStr + ")").list();
    for (Shop shop : shops) {
        shop.setDistance(distanceMap.get(shop.getId().toString()).getValue());
    }

    return Result.ok(shops);
}

用户签到:BitMap

基本使用

pximage

pximage

签到功能

直接上服务层代码:

@Override
public Result sign() {
    Long userId = UserHolder.getUser().getId();
    LocalDateTime now = LocalDateTime.now();
    String keySuffix = now.format(DateTimeFormatter.ofPattern(":yyyyMM"));
    String key = USER_SIGN_KEY + userId + keySuffix;
    int dayOfMonth = now.getDayOfMonth();
    stringRedisTemplate.opsForValue().setBit(key, dayOfMonth - 1, true);
    return Result.ok();
}

签到统计

获取到今天为止的连续签到次数

@Override
public Result signCount() {
    // 获取记录
    Long userId = UserHolder.getUser().getId();
    LocalDateTime now = LocalDateTime.now();
    String keySuffix = now.format(DateTimeFormatter.ofPattern(":yyyyMM"));
    String key = USER_SIGN_KEY + userId + keySuffix;
    int dayOfMonth = now.getDayOfMonth();

    List<Long> result = stringRedisTemplate.opsForValue().bitField(
        key,
        BitFieldSubCommands.create().get(BitFieldSubCommands.BitFieldType.unsigned(dayOfMonth))
        .valueAt(0)
    );
    if (result == null || result.isEmpty()) {
        return Result.ok(0);
    }

    Long num = result.get(0);
    if (num == null || num == 0) {
        return Result.ok(0);
    }
    int count = 0;
    while (true) {
        if ((num & 1) == 0) {
            break;
        } else {
            count++;
        }
        num >>>= 1;
    }

    return Result.ok(count);
}

UV统计:HLL

pximage

一个测试:

插入 100 万条数据,模仿 1000 人访问,看最后结果如何

pximage

posted @ 2022-07-03 10:43  樵仙  阅读(30)  评论(0编辑  收藏  举报