在不同表上使用SUM和COUNT进行SQL聚合选择

Question

I have a table emails

id date sent_to
1 2013-01-01 345
2 2013-01-05 990
3 2013-02-05 1000

table2 is responses

email_id email  response
1   xyz@email.com   xxxx
1   xyzw@email.com  yyyy
.
.
.

I want a result with the following format:

Month total_number_of_subscribers_sent total_responded
2013-01 1335 2
.
.

this is my query:

SELECT
DATE_FORMAT(e.date, '%Y-%m')AS `Month`,
    count(*) AS total_responded,
SUM(e.sent_to) AS total_sent
FROM
    responses r
LEFT JOIN emails e ON e.id = r.email_id 
WHERE
e.date > '2012-12-31' AND e.date < '2013-10-01'
GROUP BY
    DATE_FORMAT(e.date, '%Y %m')

it works ok with total_responded, but the total_sent goes crazy in millions, obviously because the resultant join table has the redundant values.

So basically can I do a SUM and COUNT in the same query on separate tables ?

Answer 1

If you want to count duplicates in each table, then the query is a little complicated.

You need to aggregate the sends and responses separately, before joining them together. The join is on the date, which necessarily comes from the "sent" information:

select r.`Month`, coalesce(total_sent, 0) as total_sent, coalesce(total_emails, 0) as total_emails,
       coalesce(total_responses, 0) as total_responses,
       coalesce(total_email_responses, 0) as total_email_responses
from (select DATE_FORMAT(e.date, '%Y-%m') as `Month`,
             count(*) as total_sent, count(distinct email) as total_emails
      from emails e
      where e.date > '2012-12-31' AND e.date < '2013-10-01'
      group by DATE_FORMAT(r.date, '%Y-%m') 
     ) e left outer join
     (select DATE_FORMAT(e.date, '%Y-%m') as `Month`,
             count(*) as total_responses, count(distinct r.email) as total_email_responses
      from emails e join
           responses r
           on e.email = r.email
      where e.date > '2012-12-31' AND e.date < '2013-10-01'
     ) r
     on e.`Month` = r.`Month`;

The apparent fact that your responses have no link to the "sent" information -- not even the date -- suggests a real problem with your operations and data.