SELECT SUM(users) FROM (
    SELECT
        DATE,
        COUNT(DISTINCT user_id) AS users,
      FROM
        `mytable`
      WHERE
        DATE BETWEEN ('2022-05-01') AND ('2022-05-31')        
      GROUP BY
        DATE
)
users 的值约为：140,000
SELECT
   COUNT(DISTINCT user_id) AS users,
    FROM
      `mytable`
    WHERE
      DATE BETWEEN ('2022-05-01') AND ('2022-05-31')
users 的值约为：120,000
<details>
<summary>英文:</summary>
I believe I am missing something (probably quite simple) in the use of **GROUP BY** in BigQuery, and I am hoping someone can set me straight.  
Comparing these two queries I get different numbers of users
    SELECT SUM(users) FROM (
        SELECT
            DATE,
            COUNT(DISTINCT user_id) AS users,
          FROM
            `mytable`
          WHERE
            DATE BETWEEN (&#39;2022-05-01&#39;) AND (&#39;2022-05-31&#39;)        
          GROUP BY
            DATE
    )
value for users approx: 140000
    SELECT
       COUNT(DISTINCT user_id) AS users,
        FROM
          `mytable`
        WHERE
          DATE BETWEEN (&#39;2022-05-01&#39;) AND (&#39;2022-05-31&#39;)
value for users approx: 120000
</details>
# 答案1
**得分**: 2
在第二个查询中，您正在计算整个日期范围内不同的 user_id 值的数量。在第一个查询中，您正在计算范围内 *每一天* 的不同 user_id 值，然后将它们相加。在第一个查询中，可能会统计不同日期上重复的用户。
<details>
<summary>英文:</summary>
In the second query you&#39;re counting the distinct user_id values in the entire date range. In the first query you&#39;re counting the distinct user_id values *for each day* in the range, then summing those. There are probably duplicate users being counted on different days in the first query.
</details>