Since I’m very familiar with SQL, if I have to do something a bit more complicated with Ecto I start with the SQL and then work towards figuring out how to translate it to Ecto. It’s good to keep in mind that if the going gets too rough with the translation you are always left with the “out” of just executing raw SQL. You can map the result set into a schema and from the perspective of a programmer using the API it looks just the same.

The last query I had that was somewhat more complicated involved a part of our AWS Detective app that monitors limits on our AWS account. Users can set thresholds and be sent emails when the limits for a particular service/resource is a high enough percent of the maximum allowed. This allows them to open up a case with Amazon to increase the maximum. We wanted to define one percentage limit as a warning and another as a critical.

The thing is, we don’t want to bombard somebody with emails. Once we’ve sent them an email indicating that their threshold is exceeded (for either warning or critical) we do not want to send them another one for at least a day - unless the percentage moves between states. That is, once a service/resource is warning we send an email and if the service/resource goes into critical we’d send an email. Likewise we’d send an “all-clear” email if a service was previously in a warning or critical state and is now below the defined percentages.

As a final requirement, if 24 hours go by and the resource is still in warning/critical we’d like to send another email to remind the receiver that there is still an issue and they should take a look at it.

For storage of emails sent we defined a limit_check_threshold_emails table. The structure of this table is:

  
          Column          |            Type             |
--------------------------+-----------------------------
 id                       | bigint                      |
 email                    | character varying(255)      |
 limit_check_threshold_id | bigint                      |
 previous_state           | character varying(10)       |
 current_state            | character varying(10)       |
 percent_reported         | integer                     |
 inserted_at              | timestamp without time zone |
 updated_at               | timestamp without time zone |
  

So we’re storing the email address, naturally, and the association to the limit_check_threshold the user has defined. We also store the current_state, the previous state and what percent the resource was at when we emailed. We have the following states defined: “ok”, “warning”, “critical”. Storing these as states in db along with the percent_reported allows us to show somewhat interesting email history.

Since we send emails on transitions (warning to critical) we could have multiple emails for the same LimitCheckThreshold for a single day. In order to make decisions on whether to send an email or not we really need the last email we sent within the previous 24 hours. In SQL we could get this with:

  
select max(inserted_at) as max_inserted_at,
email,
limit_check_threshold_id
FROM limit_check_threshold_emails
where inserted_at > now() - interval '24 hour'
GROUP BY email, limit_check_threshold_id;
  

and then select rows using these values via a join. So how do we translate this to Ecto? Its best to take a piece of a time and build it up. So, lets express the query above in Ecto.

  
def recent_email_group do
    from(
      e in LimitCheckThresholdEmail,
      select: %{
        max_inserted_at: max(e.inserted_at),
        limit_check_threshold_id: e.limit_check_threshold_id,
        email: e.email
      },
      group_by: [e.limit_check_threshold_id, e.email],
      where: e.inserted_at > fragment("now() - interval '24 hours'")
    )
end
  

You can see we used a fragment to express the time frame that we want to examine. We could have calculated this time in code and used it directly as a substitution parameter in the code but we chose to use a fragment. We used a map style select so we have “named” values we can refer to when we get to having to somehow join this in order to get our LimitCheckThresholdEmail rows.

Ok, so this gives us the same set as the SQL above. Now, how do we get the actual LimitCheckThresholdEmail rows in our table? Well, to do that we’ll need to join.

  
  def list_recent_threshold_emails do
    from(
      e in LimitCheckThresholdEmail,
      join: s in subquery(recent_email_group()),
      on:
        s.max_inserted_at == e.inserted_at and s.email == e.email and
          s.limit_check_threshold_id == e.limit_check_threshold_id,
      join: t in assoc(e, :limit_check_threshold),
      preload: [limit_check_threshold: t],
      where: e.inserted_at > fragment("now() - interval '1 day'")
    )
    |> Repo.all()
  end
  

This generates the following SQL:

  
SELECT l0."id", l0."previous_state", l0."current_state",
l0."email", l0."percent_reported", l0."inserted_at",
l0."updated_at", l0."limit_check_threshold_id", l2."id",
l2."warning_percent_threshold", l2."critical_percent_threshold",
l2."emails", l2."service", l2."resource",
l2."inserted_at", l2."updated_at", l2."account_id"
FROM "limit_check_threshold_emails" AS l0
INNER JOIN (SELECT max(l0."inserted_at") AS "max_inserted_at",
 l0."limit_check_threshold_id" AS "limit_check_threshold_id",
 l0."email" AS "email"
 FROM "limit_check_threshold_emails" AS l0
 WHERE (l0."inserted_at" > now() - interval '24 hours')
 GROUP BY l0."limit_check_threshold_id", l0."email") AS s1
 ON ((s1."max_inserted_at" = l0."inserted_at")
 AND (s1."email" = l0."email"))
 AND (s1."limit_check_threshold_id" = l0."limit_check_threshold_id")
 INNER JOIN "limit_check_thresholds" AS l2
 ON l2."id" = l0."limit_check_threshold_id"
 WHERE (l0."inserted_at" > now() - interval '1 day')
  

You might wonder, couldn’t we have just selected all the data that we need in the recent_email_group function and done away with the need for the subquery? Its a good thought but not in this case. Since we need the state information and we want the percent_reported for the emails we’ll craft we needed to use the subquery so we can get the whole row. Using GROUP BY with these additional fields would pull in more than one row for the service/resource/email combination.