To help understand why a complex query with many joins comes back empty, I like to provide helper debug functions that builds the query step by step. Each block checks one prerequisite and tells the user what’s missing:

```sql FUNCTION debug_user(user_id):

  IF NOT (SELECT FROM user WHERE user.user_id = user_id) THEN
    -- user doesn’t exist
  END IF;

  IF NOT (SELECT FROM user
           JOIN user_addr ON user_addr.user_id = user.user_id
           WHERE user.user_id = user_id) THEN
    -- user has no address
  END IF;

  -- keep adding joins until you find the break

```

because when you have a query that involves 6 relations, and you don't get results, it is nice to know why.

Counterpoint: you should not be connecting to your production database, you should not be running non-critical queries on production database servers, and you probably shouldn’t have permission to see all this data about your users.

Obviously your mileage may vary, your scale is your own, your trade offs are your own trade offs.

But be aware that there comes an operational scale where this is not an acceptable way - operationally, legally, privacy-wise - to investigate customer issues, and you’ll need different tricks.

If you build your queries with CTEs, it is easy to pull them out in a console and query each step separately to find the issue.

I implemented something similar for a 3M/day ad tech platform. We created a few materialized views for my boss, myself, and the DevOps team to monitor, instead of querying over 100 tables. I used stored procedures in PostgreSQL, which made it fast, efficient, and non-blocking also not to mention we avoided setting up complex Grafana/UI in admin dashboard, boss: i want to know x, y ,z, okay -> tableplus, -> export csv boom!

The next logical step is to realize that using these views to drive your actual application has great potential to both vastly simplify the logic and make it super high performance. You can even take this so far as to make a view that spits out XML (or I guess json): you basically get SSR for free with easy to inspect/compose queries.

"Obviously querying over project_shorthand = '@unison/cloud' isn't going to be able to use an index, so isn't going to be the most performant query"

If you know you're going to be querying on username and project shorthand a lot you're just a handful more lines away from instantiating that as a database function which can take your shorthand apart with string functions and get back to a high-performance query. Functions can return rowsets which you can then further filter with WHERE clauses.

Although in that case I think I'd just take the first and second parameters separately anyhow, since

    debug_contribution("sophie", "@unison/cloud")

and

    debug_contributions("sophie", "unison", "cloud")

isn't a big enough difference to be worth writing string-parsing code. But do as you like.

I was very confused about the term "Debug" view. We just called them views and used them normally. The main challenge was predicate pushdown which, though usually worked, was somewhat dependent on how smart the optimizer was. Also, things could get tricky when you start building views on top of views. Soon you are not certain why a simple looking query selecting from a simple looking view became slow until you see that it is views all the way down. As with most things, balance is the key.

I do the same but it typically only stays my personal sphere or documented until you get to something like retool/admin ui as a common place.

Or write the query as a function in code, that way, everyone can know it exists, call it easily, and change it along with any other changes to your logic or schema.

I agree. So I did. And over the span of a year I was the only one that used them. ¯\_(ツ)_/¯

This plus some automated assertion checks on whatever invariants you can think of makes for a very cheap and scalable canary.

See an alert? Just query the view and you should have all the client/job/whatever IDs you need to trace the problem.