Simple anomaly detection using plain SQL

Note that the table has a row for every minute, even if the status code was never returned in that minute. Given a table of statuses, it's very tempting to do something like this:

-- Wrong!
SELECT
   date_trunc('minute', timestamp) AS period,
   status_code,
   count(*) AS entries
FROM
   server_log
GROUP BY
   period,
   status_code;

This is a common mistake and it can leave you with gaps in the data. Zero is a value, and it holds a significant meaning. A better approach is to create an "axis", and join to it:

-- Correct!
WITH axis AS (
   SELECT
       status_code,
       generate_series(
           date_trunc('minute', now()),
           date_trunc('minute', now() - interval '1 hour'),
           interval '1 minute' * -1
       ) AS period
   FROM (
       VALUES (200), (400), (404), (500)
   ) AS t(status_code)
)
SELECT
   a.period,
   a.status_code,
   count(*) AS entries
FROM
   axis a
   LEFT JOIN server_log l ON (
       date_trunc('minute', l.timestamp) = a.period
       AND l.status_code = a.status_code
   )
GROUP BY
   period,
   status_code;

If I understand correctly, the problem the author is highlighting here is that in the "wrong" result set you'll skip over time frames without any data. Wouldn't this just come down to what tool you use to visualize your data? I would expect many visualizers would fill in the gaps with 0s.