Redis07 Feed流与两种数据类型

4. Feed流

127.0.0.1:6379> ZREVRANGEBYSCORE z1 1000 0 WITHSCORES LIMIT 0 3
根据分数查询z1 最大当前时间 最小时间 显示分数 从0开始查3条
127.0.0.1:6379> ZREVRANGEBYSCORE z1 5 0 WITHSCORES LIMIT 1 3
上次查询最小的是 5 所以最大从5 开始最小值不管 然后 LIMIE 后面找下一个 不包括5 所以为1 查询3条

根据Zset查询 因为第一次查询没有上一次的最小值 所以为当前时间戳 然后LIMIT开始给0

滚动查询 每一次查询上一次查询的最小值(分数)开始 防止Feed新消息导致消息混乱

但是当查询到的元素有score一样的情况 任然会混乱 所以LIMIT跳过的应当为上一次查询的最小值重复的个数

Controller

@GetMapping("of/follow")
public Result getFeedBlog(
    @RequestParam("lastId") Long max, // 上一次查询的最小值的时间戳
    @RequestParam(value = "offset", defaultValue = "0") Integer offset// 偏移量 第一次没有 默认0
) {
    return followService.queryFeedById(max, offset);
}

Service

public Result queryFeedById(Long max, Integer offset) {
    // 获取当前用户id
    Long userId = UserHolder.getUser().getId();
    if (userId == null) {
        return Result.fail("请登录！");
    }
    String key = BlogConst.FAN_INBOX + userId;
    // 查询收件箱
    Set<ZSetOperations.TypedTuple<String>> blogIdByScores = stringRedisTemplate.opsForZSet()
        .reverseRangeByScoreWithScores(key, 0, max, offset, 3);
    if (blogIdByScores == null || blogIdByScores.isEmpty()) {
        return Result.ok();
    }
    // 解析数据
    List<Long> ids = new ArrayList<>(blogIdByScores.size());
    long minTime = 0; // 这个Set底层是TreeSet
    int os = 1; // 初始化为1
    for (ZSetOperations.TypedTuple<String> blogIdByScore : blogIdByScores) {
        String idStr = blogIdByScore.getValue();
        long time = blogIdByScore.getScore().longValue(); // 5 4 4 2 2 不置为1的话offset为 4 就错了
        if (time == minTime) {
            os++;
        } else {
            minTime = time;
            os = 1;
        }
        ids.add(Long.valueOf(idStr));
    }
    // 根据id查询blog
    List<Blog> blogs = query()
        .in("id",ids)
        .last("ORDER BY FIELD( id"+StrUtil.join(",",ids)+")")
        .list();
    blogs.forEach(blog -> {
        queryBlogUser(blog); // 查询有关用户
        isBlogLiked(blog); // 查询是否被点赞
    });
    // 封装返回
    ScoreResult<Blog> result = new ScoreResult<>(blogs,minTime,offset);
    return Result.ok(result);
}

4 附近商铺

4.1 GEO数据类型

$ 1 
GEOADD g1 116.378248 39.865275 bjn 116.42803 39.903738 bj 116.322287 39.893729 bjx
$ 2
127.0.0.1:6379> GEODIST g1 bjx bj
"9091.5648"
127.0.0.1:6379> GEODIST g1 bjx bj KM
"9.0916"
$ 3 
127.0.0.1:6379> GEOSEARCH g1 FROMLONLAT 116.397904 39.909005 BYRADIUS 10 km WITHDIST
1) 1) "bj"
   2) "2.6361"
2) 1) "bjn"
   2) "5.1452"
3) 1) "bjx"
   2) "6.6723"
GEOSEARCH key [FROMMEMBER member] [FROMLONLAT longitude latitude] [BYRADIUS radius M|KM|FT|MI] [BYBOX width height M|KM|FT|MI] [ASC|DESC] [COUNT count [ANY]] [WITHCOORD] [WITHDIST] [WITHHASH]
  summary: Query a sorted set representing a geospatial index to fetch members inside an area of a box or a circle.
  since: 6.2.0

4.2 java

@Test
void shopImportGeo() {
    Map<Long, List<Shop>> listMap = shopService.list().stream().collect(Collectors.groupingBy(Shop::getTypeId));
    for (Map.Entry<Long, List<Shop>> entry : listMap.entrySet()) {
        Long typeId = entry.getKey();
        List<Shop> shopList = entry.getValue();
        List<RedisGeoCommands.GeoLocation<String>> loctions = shopList
                .stream()
                .map(shop ->
                        new RedisGeoCommands.GeoLocation<>(shop.getId().toString(), new Point(shop.getX(), shop.getY())))
                .collect(Collectors.toList());
        redisTemplate.opsForGeo().add(CacheConst.SHOP_GEO_PREFIX + typeId, loctions);
    }
}

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-redis</artifactId>
    <exclusions>
        <exclusion>
            <groupId>org.springframework.data</groupId>
            <artifactId>spring-data-redis</artifactId>
        </exclusion>
        <exclusion>
            <groupId>io.lettuce</groupId>
            <artifactId>lettuce-core</artifactId>
        </exclusion>
    </exclusions>
</dependency>
<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>6.1.8.RELEASE</version>
</dependency>

@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) {
        // 不需要坐标查询，按数据库查询
        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。结果：shopId、distance
    String key = SHOP_GEO_KEY + typeId;
    GeoResults<RedisGeoCommands.GeoLocation<String>> results = stringRedisTemplate.opsForGeo() // GEOSEARCH key BYLONLAT x y BYRADIUS 10 WITHDISTANCE
            .search(
                    key,
                    GeoReference.fromCoordinate(x, y),
                    new Distance(5000),
// RedisGeoCommands.GeoSearchCommandArgs.newGeoSearchArgs() e
        RedisGeoCommands.GeoSearchCommandArgs.newGeoSearchArgs().includeDistance().limit(end)
            );
    // 4.解析出id
    if (results == null) {
        return Result.ok(Collections.emptyList());
    }
    List<GeoResult<RedisGeoCommands.GeoLocation<String>>> list = results.getContent();
    if (list.size() <= from) {
        // 没有下一页了，结束
        return Result.ok(Collections.emptyList());
    }
    // 4.1.截取 from ~ end的部分
    List<Long> ids = new ArrayList<>(list.size());
    Map<String, Distance> distanceMap = new HashMap<>(list.size());
    list.stream().skip(from).forEach(result -> {
        // 4.2.获取店铺id
        String shopIdStr = result.getContent().getName();
        ids.add(Long.valueOf(shopIdStr));
        // 4.3.获取距离
        Distance distance = result.getDistance();
        distanceMap.put(shopIdStr, distance);
    });
    // 5.根据id查询Shop
    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());
    }
    // 6.返回
    return Result.ok(shops);
}

5 BitMap

11110000
BITFIELD b1 GET u2 0
# BITFIELD key GET u(无符号)2(获取几位) 0(从0开始)
# 获取到 11 转换10进制就是3

5.1 Java

5.2 统计签到

从当前时间判断之前的时间里是否为0 为0表示断签

运算符 https://cloud.tencent.com/developer/article/1338265

Long userId = user.getId();
LocalDate now = LocalDate.now();
String key = CacheConst.getCurrentUserSign(now, userId);
// 返回签到记录 为十进制数字
List<Long> result = redisTemplate.opsForValue().bitField(
        key,
        BitFieldSubCommands
                .create()
                .get(BitFieldSubCommands.BitFieldType.unsigned(now.getDayOfMonth()))
                .valueAt(0)
);
if (result == null || result.isEmpty()) {
    return Result.ok(0);
}
// 循环遍历
Long num = result.get(0);
int count = 0;
if (num == null || num == 0) {
    return Result.ok(0);
}
// 与1做与运算 得到数字最后一个Bit位
while (true) {
    if ((num & 1) == 0) {
        // 为0 直接结束
        break;
    }else {
        // 不为0 计数加一
        count++;
    }
    // 数字右移动
    num >>>= 1;
}
// 判断bit位是否为0 不为0 加1
return Result.ok(count);

& 按位与运算 将两个数字的二进制位做对比 都为1 才是一

110111 55

111000 56

=

110000 48 55&56=48

\n

1100011 99

10100 20 [不足左边补0]

=

0000000 0 99&20=0

| 按位或 有一边为1就为1

1010 10

001 1

=

1011 11

5 101

9 1000

=

13 1101

6 HyperLogLog