Large Analytics SQL Queries are Bad

A single large query in SQL can be hard to understand, test, debug, or change in the same way that an over-large function in code can be. A large query is also much harder to write! Feedback loops while writing large queries are slow and you'll often find yourself needing to guess at where the problem in your query is.

When I started writing analytics queries, I wrote some pretty rough ones that are now hard to debug and maintain! (Apologies to everyone who has had to deal with any of my old SQL queries.) Over time, I think I've gotten better at them, and I wanted to write down some of the things I'm doing differently now that I hope will be useful to other people in similar situations.

Finally, I'm a product engineer who rarely writes these sorts of queries! This advice is much less applicable to someone who does this daily & is using better & more focused tools to do analyses.

Temporary tables, views, and with

Large queries are hard, but it's pretty simple to break a large query into smaller pieces. For analytics queries, I normally create small temporary tables (often with the temporary keyword) that normalize data, filter out deleted rows and rows I'm not interested in, and organize my data into a format that makes querying easy. Views or with clauses can accomplish similar things, but I like using temporary tables for this because they cache results and make subsequent queries faster.

I also try to put constants into a temporary table or with clause. When working on a query, it can be easy to forget to update a constant in one spot and then get completely meaningless results. (Shoutout to Ben Haley for showing me this trick!)

All of this might sound a little abstract: let's take a somewhat contrived query and try to refactor it. We want to bucket and count US-based teachers who were active in 2021 by how many classes they created during that time period. Here's what that might look like as a single query:

select 
  case when class_count < 5 then class_count::varchar else 'many' end as bucket,
  count(*)
  from (
    select count(distinct class.classId) as class_count
    from teacher
    join user_teacher ON teacher.teacherId = user_teacher.teacherid
    -- left join class_teacher to make sure we're counting teachers who haven't created classes
    left join class_teacher on class_teacher.teacherId = user_teacher.teacherId and class_teacher.creator
    left join user USING(userId)
    join class using(classId)
    join (
      select distinct teacherId
      from teacher_active
      where active_date between '2021-01-01' and '2022-01-01'
    ) as ats on teacher.teacherId = ats.teacherId
    and class.createdat between '2021-01-01' and '2022-01-01'
    and not class.autocreated_demo
    and lower(user.country) in ('usa', 'us')
    group by teacherId
  )
group by 1
 

This query isn't particularly complex, but it's still enough logic that I'd be a little worried about changing it or verifying that it's correct. I'd be tempted to try to pull out constants and then separate out the filtering logic from the calculation logic.

drop table if exists _constant;
create temporary table _constant as (
 select '2021-01-01' as start, '2022-01-01' as end
);
 
drop table if exists _teacher;
create temporary table _teacher as (
 -- us_user is probably overkill: this might be better in the `where` clause!
 with us_user as (
   select userId
   from user
   where lower(country) in ('usa', 'us')
 )
 select distinct teacherId
 from teacher_active
 join user_teacher USING(teacherId)
 join us_user using(userid)
 where active_date between (select start from _constant)
   and (select end from _constant)
);
drop table if exists _class;
create temporary table _class (
 select classId
 from class
 where class.created between (select start from _constant)
   and (select end from _constant)
   and not class.autocreated_demo
);
 
drop table if exists _classes_created_by_teacher;
create temporary table _classes_created_by_teacher (
 with class_creator as (
   select class_teacher.*
   from class_teacher
   join _class USING(classId)
   where class_teacher.creator
 )
 select teacherId, count(distinct classId) as classes_created
 from _teacher
 left join class_creator using(teacherId)
 group by teacherId
);
 
select
 case when class_count < 5 then class_count::varchar else 'many' end as bucket,
 count(*)
from _classes_created_by_teacher
group by bucket;
 

It's arguable whether this is actually better! The initial query is short enough that it's not that much logic to understand: it might be the right size for the team that you're working with. There are also certainly better ways of factoring this same query that could make the logic even more clear. Overall though, I'd much rather work with the updated query:

I think many data-focused engineers use jupyter notebooks and pandas to break down large queries. I think how you're breaking down a large query into smaller pieces is much less important than doing that breakdown!

Make feedback loops FAST!

One of the most frustrating parts of working on a large query is that feedback loops can be slow. Making a change and waiting tens of minutes can completely kill any programming flow or focus that you have.

In general, putting effort into how quickly you get feedback while working makes it much easier to find flow and be effective. A little bit of effort put into setting up nice tables, improving data layout, and optimizing sortkeys can pay large dividends.