Mysql – Updating a table with a single UPDATE statement vs several UPDATE statements

Another way to get the result is this. It finds first all groups that the teacher has surely taught (or is going to) by checking that she has started within the month and then in another subquery it finds - for every group - the last teacher that started at the first day of the month or earlier.

With the unique index you have on the table, the second subquery should be quite efficient. The first subquery would benefit from an index on (teacherid, startdate, groupid):

SELECT groupid
FROM pupilgroupteacher
WHERE teacherid = @teacher 
  AND startdate >= @month + INTERVAL 1 DAY
  AND startdate < @month + INTERVAL 1 MONTH

UNION DISTINCT

SELECT gg.groupid
FROM 
    ( SELECT DISTINCT groupid
      FROM pupilgroupteacher
    ) AS gd
  JOIN pupilgroupteacher AS gg
    ON  gg.groupid = gd.groupid
    AND gg.startdate =
        ( SELECT MAX(gi.startdate)
          FROM pupilgroupteacher AS gi 
          WHERE gi.groupid = gd.groupid
            AND gi.startdate < @month + INTERVAL 1 DAY
        )
WHERE gg.teacherid = @teacher ;

The activity of altering big tables are done in phases:

1. Create a new table with required fields and indexes say in test DB (just structure)
2. Dump the data from the existing table and load the same to the newly created table in test DB
3. Now announce your downtime :)
4. Swap the tables by renaming - RENAME table ur_db.table_name to test.temp, test.table_name to ur_db.table_name, test.temp to test.table_name; This is an atomic operation taking fraction of a second.
5. Load the extra records to the newly created table ( the records that came after dumping and then loading). This step can be done before Step: 3 also once to reduce your downtime.
6. And your system is back

Few notes:

1. You need not hit information schema directly like this, try using SHOW TABLE STATUS from db like 'table_name'
2. The speed of alter table is more or less linked with the I/O speed. Last time when we ran a direct alter table(without the above steps), we had 40GB+ table size it took around 4hours. If your 20GB data is taking years, you are working on some outdated machine.
3. Also drop of the unwanted indexes like index_bslt_ondate, index_base_schedule_line_items_loan_base_schedule as some other indexes has the left most column as the indexed column

Please let me know if you need any clarification on any of these steps.

Edit : A simple python script to automate the process https://github.com/georgecj11/hotswap_mysql_table.git