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The Art of PostgreSQL

Modeling Example

Learn about schema and MVP through an example.

We'll cover the following...

Now that we have some data to play with, we can test some application queries for known user stories in the MVP, such as listing the most recent articles per category with the first three comments on each article.

Playing with schema

That’s when we realize our previous schema design misses publication timestamps for articles and comments. We need to add this information to our draft model. As it’s all a draft with random data, the easiest way around this—you already committed the data previously (by editing the script)—is to simply drop schema cascade as shown here:

PostgreSQL
drop schema sandbox cascade;

The next version of our schema then looks like this:

PostgreSQL
begin;
create schema if not exists sandbox;
create table sandbox.category
 (
   id    serial primary key,
   name  text not null
 );
insert into sandbox.category(name)
     values ('sport'),('news'),('box office'),('music');
create table sandbox.article
 (
   id         bigserial primary key,
   category   integer references sandbox.category(id),
   pubdate    timestamptz,
   title      text not null,
   content    text
 );
create table sandbox.comment
 (
   id         bigserial primary key,
   article    integer references sandbox.article(id),
   pubdate    timestamptz,
   content    text
 );
insert into sandbox.article(category, title, pubdate, content)
     select random(1, 4) as category,
            initcap(sandbox.lorem(5)) as title,
            random( now() - interval '3 months',
                    now() + interval '1 months') as pubdate,
            sandbox.lorem(100) as content
       from generate_series(1, 1000) as t(x);
insert into sandbox.comment(article, pubdate, content)
     select random(1, 1000) as article,
            random( now() - interval '3 months',
                    now() + interval '1 months') as pubdate,
            sandbox.lorem(150) as content
       from generate_series(1, 50000) as t(x);
            
select article.id, category.name, title
  from      sandbox.article
       join sandbox.category
         on category.id = article.category
 limit 3;
select count(*),
       avg(length(title))::int as avg_title_length,
       avg(length(content))::int as avg_content_length
  from sandbox.article;
   select article.id, article.title, count(*)
     from      sandbox.article
          join sandbox.comment
            on article.id = comment.article
group by article.id
order by count desc
   limit 5;
select category.name,
       count(distinct article.id) as articles,
       count(*) as comments
  from      sandbox.category
       left join sandbox.article on article.category = category.id
       left join sandbox.comment on comment.article = article.id
group by category.name
order by category.name;
commit;

Random timestamp entries

To be able to generate random timestamp entries, the script uses another function that’s not provided by default in PostgreSQL, and here’s its definition:

PostgreSQL
create or replace function random
 (
  a timestamptz,
  b timestamptz
 )
 returns timestamptz
 volatile
 language sql
as $$
  select a
         + random(0, extract(epoch from (b-a))::int)
           * interval '1 sec';
$$;

Now we can have a go at solving the first query of the product’s MVP, as specified before, on this schema draft version. That should provide a taste of the schema and how well it implements the business rules.

Enlist the data

The following query lists the most recent articles per category with the first three comments on ...