How does the GROUP BY Sql work?

AI generated answer (using internal Slack threads):

The GROUP BY statement in SQL is used to arrange identical data into groups with the help of some functions. It's often used with aggregate functions (COUNT, MAX, MIN, SUM, AVG) to group the result-set by one or more columns. Here's a simple example:

sql
SELECT column_name(s)
FROM table_name
WHERE condition
GROUP BY column_name(s)
ORDER BY column_name(s);

In this example,

column_name(s)

is the column or columns that you want to group. The

WHERE

clause is optional and specifies a condition for the grouped records. The

ORDER BY

clause is also optional and orders the groups. In the context of NetSuite and SuiteQL, you can use the GROUP BY clause in a similar way. For example, you can group by a built-in function:

sql
select 
    BUILTIN.DF(currency),
    count(*)
from account
where accttype = 'Bank'
group by BUILTIN.DF(currency)

In this example, the query groups the results by the currency of the account. You can also group by multiple columns. The order of the columns in the GROUP BY clause determines the hierarchy of grouping. The first column listed in the GROUP BY clause groups the results at the highest level, the second column groups the results at the next level, and so on. For example, if you want to group first by account and then by project, you can do:

sql
SELECT ...
FROM ...
GROUP BY account, project

In this example, the results are first grouped by account, and then within each account group, the results are grouped by project. When grouping by a date or timestamp, you might want to convert the timestamp to a simple date field to avoid grouping by the millisecond. You can use the

TO_CHAR

function to do this:

sql
SELECT ... ,to_char(createdDate,'YYYY-MM-DD') as day, ...
...
GROUP BY day

In this example, the

createdDate

is converted to a simple date format ('YYYY-MM-DD') and the results are grouped by this date. ****** Disclaimer: This response was generated by an artificial intelligence assistant based on previous conversations from multiple Slack channels, and may contain inaccuracies or mistakes. ******