微服务（三）

mybatis 一对多使用场景

<!--  一对多查询  -->
    <resultMap id="getFilmInfoMap" type="com.stylefeng.guns.api.cinema.vo.FilmInfoVO">
        <result column="film_id" property="filmId" />
        <result column="film_name" property="filmName" />
        <result column="film_length" property="filmLength" />
        <result column="film_language" property="filmType" />
        <result column="film_cats" property="filmCats" />
        <result column="actors" property="actors" />
        <result column="img_address" property="imgAddress" />
        <collection property="filmFields" ofType="com.stylefeng.guns.api.cinema.vo.FilmFieldVO">
            <result column="UUID" property="fieldId"></result>
            <result column="begin_time" property="beginTime"></result>
            <result column="end_time" property="endTime"></result>
            <result column="film_language" property="language"></result>
            <result column="hall_name" property="hallName"></result>
            <result column="price" property="price"></result>
        </collection>
    </resultMap>

    <select id="getFilmInfos" parameterType="java.lang.Integer" resultMap="getFilmInfoMap">
        select
            info.film_id,
            info.film_name,
            info.film_length,
            info.film_language,
            info.film_cats,
            info.actors,
            info.img_address,
            field.UUID,
            field.begin_time,
            field.end_time,
            field.hall_name,
            field.price
        from
            mooc_hall_film_info_t info
                left join
            mooc_field_t field
            on field.film_id = info.film_id
                and field.cinema_id = #{cinemaId}
    </select>

Dubbo缓存
结果缓存
1、可以通过注解对热点数据进行缓存
2、Dubbo本地缓存 Redis分布式缓存
3、cache = “lru”
4、结果缓存

Dubbo并发、连接控制
1、对连接和并发数量进行控制
2、超出部分以错误形式返回
3、并发控制

protocol : accepts
provider serviceImpl : executes
consumers: connection

----订----单----模----块----

1、业务开发
2、限流和熔断、降级
3、分组、聚合、版本控制

mac 搭建本地ftp服务器
python -m SimpleHTTPServer

服务器搭建vsftpd FTP服务

订单模块的横向和纵向拆表 业务复杂度高了
横向：类别拆分
纵向：年份 月份拆分
服务限流

1、系统高可用
2、并发和连接控制进行限流
3、漏桶法 令牌桶

// 因为令牌桶对业务有一定的容忍度
public class TokenBucket {

    private int bucketNums=100;  // 桶的容量
    private int rate=1;          // 流入速度
    private int nowTokens;      //  当前令牌数量
    private long timestamp=getNowTime();     //  时间

    private long getNowTime(){
        return System.currentTimeMillis();
    }

    private int min(int tokens){
        if(bucketNums > tokens){
            return tokens;
        }else{
            return bucketNums;
        }
    }

    public boolean getToken(){
        // 记录来拿令牌的时间
        long nowTime = getNowTime();
        // 添加令牌【判断该有多少个令牌】
        nowTokens = nowTokens + (int)((nowTime - timestamp)*rate);
        // 添加以后的令牌数量与桶的容量那个小
        nowTokens = min(nowTokens);
        System.out.println("当前令牌数量"+nowTokens);
        // 修改拿令牌的时间
        timestamp = nowTime;
        // 判断令牌是否足够
        if(nowTokens < 1){
           return false;
        }else{
            nowTokens -= 1;
            return true;
        }
    }
}

服务熔断降级
某一个服务器挂掉 — 扇延 导致 雪崩
熔断器 Hystrix

// 依赖

<!-- 熔断器依赖 -->
        <dependency>
            <groupId>org.springframework.cloud</groupId>
            <artifactId>spring-cloud-starter-netflix-hystrix</artifactId>
            <version>2.0.0.RELEASE</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-actuator</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.cloud</groupId>
            <artifactId>spring-cloud-starter-netflix-hystrix-dashboard</artifactId>
            <version>2.0.0.RELEASE</version>
        </dependency>

注入

@EnableHystrixDashboard
@EnableCircuitBreaker
@EnableHystrix

实现

public ResponseVO error(Integer fieldId, String soldSeats, String seatsName) {
        return ResponseVO.serviceFail("很抱歉，当前购买人数较多，请稍后再试");
    }

    // 购票
    @HystrixCommand(fallbackMethod = "error", commandProperties = {
            @HystrixProperty(name="execution.isolation.strategy", value = "THREAD"),
            @HystrixProperty(name = "execution.isolation.thread.timeoutInMilliseconds", value
                    = "4000"),
            @HystrixProperty(name = "circuitBreaker.requestVolumeThreshold", value = "10"),
            @HystrixProperty(name = "circuitBreaker.errorThresholdPercentage", value = "50")
                    }, threadPoolProperties = {
                    @HystrixProperty(name = "coreSize", value = "1"),
                    @HystrixProperty(name = "maxQueueSize", value = "10"),
                    @HystrixProperty(name = "keepAliveTimeMinutes", value = "1000"),
                    @HystrixProperty(name = "queueSizeRejectionThreshold", value = "8"),
                    @HystrixProperty(name = "metrics.rollingStats.numBuckets", value = "12"),
                    @HystrixProperty(name = "metrics.rollingStats.timeInMilliseconds", value = "1500")
            })
    @RequestMapping(value = "buyTickets", method = RequestMethod.POST)
    public ResponseVO buyTickets(Integer fieldId, String soldSeats, String seatsName) throws IOException {

        try {
            if (tokenBucket.getToken()) {
                // 验证前端参数
                // 验证售出的票是否为真
                boolean isTrue = orderServiceAPI.isTrueSeats(fieldId + "", soldSeats);
                // 已销售的座位里 有没有这些座位
                boolean isNotSold = orderServiceAPI.isNotSoldSeats(fieldId + "", soldSeats);

                if (isTrue && isNotSold) {
                    // 创建订单信息
                    String userId = CurrentUser.getCurrentUserId();
                    if (userId != null && userId.trim().length()>0) {
                        OrderVO orderVO = orderServiceAPI.saveOrderInfo(fieldId,soldSeats,seatsName,Integer.parseInt(userId));
                        if (orderVO == null) {
                            log.error("购票未成功");
                            return ResponseVO.serviceFail("购票业务异常");
                        } else {
                            return ResponseVO.success(orderVO);
                        }
                    } else {
                        return ResponseVO.serviceFail("用户未登录");
                    }
                } else {
                    return ResponseVO.serviceFail("订单中的座位编号有问题");
                }
            } else {
                return ResponseVO.serviceFail("购买人数较多，请稍后再试");
            }
        } catch (Exception e) {
            log.error("购票未成功");
            return ResponseVO.serviceFail("购票业务异常");
        }
    }

分组
group：
1、分组进行蓝绿发布版本分布
2、进行同级别压力测试

聚合
提供前端 统一的数据 多表查询聚合结果

多版本的蓝绿上线
版本控制
version
通过蓝绿版本 进行访问控制