大数据学习（125）-hive数据分析-EW帮帮网

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1. 连续登录问题变种

题目：
找出恰好连续登录 3 天的用户（不允许更长的连续区间）。
表结构：user_logs(user_id, login_date)。

参考答案：

WITH ranked_logs AS (
    SELECT 
        user_id,
        login_date,
        ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY login_date) AS rn
    FROM user_logs
),
consecutive_groups AS (
    SELECT 
        user_id,
        DATE_SUB(login_date, INTERVAL rn DAY) AS grp,
        MIN(login_date) AS start_date,
        MAX(login_date) AS end_date,
        COUNT(*) AS days
    FROM ranked_logs
    GROUP BY user_id, grp
)
SELECT user_id, start_date, end_date
FROM consecutive_groups
WHERE days = 3;

2. 连续未登录问题

题目：
找出用户最长连续未登录天数（假设表中仅记录登录日期）。
表结构：user_logs(user_id, login_date)。

参考答案：

WITH next_logs AS (
    SELECT 
        user_id,
        login_date,
        LEAD(login_date) OVER (PARTITION BY user_id ORDER BY login_date) AS next_login
    FROM user_logs
)
SELECT 
    user_id,
    MAX(DATEDIFF(next_login, login_date) - 1) AS max_consecutive_missing
FROM next_logs
WHERE next_login IS NOT NULL
GROUP BY user_id;

二、窗口函数高级应用

3. 移动平均值计算

题目：
计算用户最近 7 天的平均消费金额（滑动窗口）。
表结构：orders(user_id, order_date, amount)。

参考答案：

SELECT 
    user_id,
    order_date,
    AVG(amount) OVER (
        PARTITION BY user_id 
        ORDER BY order_date 
        RANGE BETWEEN INTERVAL '6 DAY' PRECEDING AND CURRENT ROW
    ) AS rolling_7day_avg
FROM orders;

4. 增长率计算

题目：
计算每个用户月消费金额的环比增长率。
表结构：orders(user_id, order_date, amount)。

参考答案：

WITH monthly_sales AS (
    SELECT 
        user_id,
        DATE_FORMAT(order_date, '%Y-%m') AS month,
        SUM(amount) AS total_amount
    FROM orders
    GROUP BY user_id, month
)
SELECT 
    user_id,
    month,
    total_amount,
    (total_amount / LAG(total_amount) OVER (PARTITION BY user_id ORDER BY month) - 1) * 100 AS growth_rate
FROM monthly_sales;

三、时间序列分析

5. 缺失日期填充

题目：
生成用户每日登录状态（0 = 未登录，1 = 登录），包括缺失的日期。
表结构：user_logs(user_id, login_date)。

参考答案：

WITH date_range AS (
    SELECT 
        user_id,
        MIN(login_date) AS start_date,
        MAX(login_date) AS end_date
    FROM user_logs
    GROUP BY user_id
),
all_dates AS (
    SELECT 
        dr.user_id,
        d.calendar_date
    FROM date_range dr
    CROSS JOIN (
        SELECT CURDATE() - INTERVAL n DAY AS calendar_date
        FROM (SELECT @row := @row + 1 AS n FROM (SELECT 0 UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3) t1,
                    (SELECT 0 UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3) t2,
                    (SELECT @row := -1) t3) t
    ) d
    WHERE d.calendar_date BETWEEN dr.start_date AND dr.end_date
)
SELECT 
    ad.user_id,
    ad.calendar_date,
    IF(ul.login_date IS NULL, 0, 1) AS is_logged_in
FROM all_dates ad
LEFT JOIN user_logs ul 
ON ad.user_id = ul.user_id AND ad.calendar_date = ul.login_date;

6. 周期性检测

题目：
找出用户每周固定某天登录的行为模式（如每周一登录）。
表结构：user_logs(user_id, login_date)。

参考答案：

WITH day_of_week AS (
    SELECT 
        user_id,
        login_date,
        DAYOFWEEK(login_date) AS dow
    FROM user_logs
)
SELECT 
    user_id,
    dow,
    COUNT(DISTINCT WEEK(login_date)) AS weeks_count,
    COUNT(*) AS login_count
FROM day_of_week
GROUP BY user_id, dow
HAVING login_count = weeks_count; -- 每周该天均登录

四、复杂业务场景

7. 购买间隔分析

题目：
计算用户平均购买间隔，并找出间隔超过 30 天的用户。
表结构：orders(user_id, order_date)。

参考答案：

WITH order_intervals AS (
    SELECT 
        user_id,
        order_date,
        DATEDIFF(order_date, LAG(order_date) OVER (PARTITION BY user_id ORDER BY order_date)) AS days_since_last
    FROM orders
)
SELECT 
    user_id,
    AVG(days_since_last) AS avg_interval
FROM order_intervals
WHERE days_since_last IS NOT NULL
GROUP BY user_id
HAVING avg_interval > 30;

8. 活跃 / 流失用户分析

题目：
标记用户每月状态（活跃 = 当月有登录，流失 = 连续 3 个月未登录）。
表结构：user_logs(user_id, login_date)。

参考答案：

WITH months AS (
    SELECT 
        user_id,
        DATE_FORMAT(login_date, '%Y-%m') AS month,
        MAX(login_date) AS last_login
    FROM user_logs
    GROUP BY user_id, month
),
status AS (
    SELECT 
        m.user_id,
        m.month,
        m.last_login,
        LEAD(m.last_login, 3) OVER (PARTITION BY m.user_id ORDER BY m.month) AS next_3rd_month_login
    FROM months m
)
SELECT 
    user_id,
    month,
    CASE 
        WHEN next_3rd_month_login IS NULL THEN '流失'
        ELSE '活跃'
    END AS status
FROM status;

五、进阶挑战

9. 最长连续事件链

题目：
找出用户最长的连续事件链（如连续点赞、评论等，事件类型相同）。
表结构：events(user_id, event_time, event_type)。

参考答案：

WITH ranked_events AS (
    SELECT 
        user_id,
        event_time,
        event_type,
        ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_time) AS rn
    FROM events
),
event_groups AS (
    SELECT 
        user_id,
        event_type,
        DATE_SUB(event_time, INTERVAL rn SECOND) AS grp,
        COUNT(*) AS chain_length
    FROM ranked_events
    GROUP BY user_id, event_type, grp
)
SELECT 
    user_id,
    event_type,
    MAX(chain_length) AS max_chain
FROM event_groups
GROUP BY user_id, event_type;

10. 会话识别

题目：
将用户行为按会话分组（假设会话间隔为 30 分钟）。
表结构：actions(user_id, action_time, action_type)。

参考答案：

WITH time_diff AS (
    SELECT 
        user_id,
        action_time,
        action_type,
        TIMESTAMPDIFF(MINUTE, 
                      LAG(action_time) OVER (PARTITION BY user_id ORDER BY action_time), 
                      action_time) AS minutes_since_last
    FROM actions
),
session_markers AS (
    SELECT 
        user_id,
        action_time,
        action_type,
        IF(minutes_since_last > 30 OR minutes_since_last IS NULL, 1, 0) AS new_session
    FROM time_diff
),
sessions AS (
    SELECT 
        user_id,
        action_time,
        action_type,
        SUM(new_session) OVER (PARTITION BY user_id ORDER BY action_time) AS session_id
    FROM session_markers
)
SELECT * FROM sessions;

先手动模拟数据：创建测试表并插入少量数据，验证逻辑正确性。
对比不同方法：例如连续值问题，尝试用 LEAD()、DATE_SUB + ROW_NUMBER 等多种方法实现。
注意边界条件：处理空值、同一天多次记录、跨年 / 跨月等场景。

大数据学习（125）-hive数据分析

1. 连续登录问题变种

2. 连续未登录问题

二、窗口函数高级应用

3. 移动平均值计算

4. 增长率计算

三、时间序列分析

5. 缺失日期填充

6. 周期性检测

四、复杂业务场景

7. 购买间隔分析

8. 活跃 / 流失用户分析

五、进阶挑战

9. 最长连续事件链

10. 会话识别

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