Postgresql - sum case with left join

brcebn
September 25, 2024
174 views
0 votes
2 Answers

I would like to count the waves available (LEFT OUT JOIN). My problem is that I also have another LEFT OUTER JOIN on invitations.

The idea is to have all the stats from the same SQL request. Stats from invitations and stats from waves.

An event has many waves
An event has many invitations

CREATE TABLE public.events (
    id bigint NOT NULL,
    uuid uuid DEFAULT public.gen_random_uuid() NOT NULL
    name character varying NOT NULL
);
CREATE TABLE public.invitations (
    id bigint NOT NULL,
    uuid uuid DEFAULT public.gen_random_uuid() NOT NULL,
    event_id bigint NOT NULL
);
CREATE TABLE public.invitation_waves (
    id bigint NOT NULL,
    uuid uuid DEFAULT public.gen_random_uuid() NOT NULL,
    name character varying NOT NULL,
    wavable_type character varying,
    wavable_id bigint,
    scheduled_at timestamp(6) without time zone
);

I have tested 2 options

Option 1

SELECT CAST(sum((case when (waves.id IS NOT NULL) then 1 else 0 end)) AS INTEGER) as total_waves
FROM "events"
         LEFT OUTER JOIN "waves" ON "waves"."wavable_type" = 'Event' AND
                                               "waves"."wavable_id" = "events"."id"
         LEFT OUTER JOIN "invitations" ON "invitations"."event_id" = "events"."id"
WHERE "events"."uuid" = 'XXX'
GROUP BY "events"."id"

-> It gives me the number of invitations created if above the number of waves

Option 2 (with `DISTINCT`)

SELECT CAST(sum(DISTINCT(case when (waves.id IS NOT NULL) then 1 else 0 end)) AS INTEGER) as total_waves
FROM "events"
         LEFT OUTER JOIN "waves" ON "waves"."wavable_type" = 'Event' AND
                                               "waves"."wavable_id" = "events"."id"
         LEFT OUTER JOIN "invitations" ON "invitations"."event_id" = "events"."id"
WHERE "events"."uuid" = 'XXX'
GROUP BY "events"."id"

-> Always returns me 1 which is, I suppose, the number of events matching the request (I’ve scoped it with the uuid).

Not having the DISTINCT is actually multiplying the correct sum by the number of waves.

Having the DISTINCT restrict the entire sum to the number of waves.

Option 3 (sub select) ✅

I’m currently trying this option based on this answer.
It is working but I’m concerned about the dependency between GROUP BY and the selected columns from the LEFT OUTER JOIN.

SELECT CAST(sum((case
                     when (invitations.status = 4 or invitations.status = 5 or invitations.status = 6) then 1
                     else 0 end)) AS INTEGER) as total_validated,
       total_waves,
       waves_scheduled
FROM "events"
         LEFT OUTER JOIN "invitations" ON "invitations"."event_id" = "events"."id"
         LEFT OUTER JOIN (SELECT distinct on (wavable_id)
                                 wavable_id                                                                                      as event_id,
                                 CAST(sum((case when (invitation_waves.id IS NOT NULL) then 1 else 0 end)) AS INTEGER)           as total_waves,
                                 CAST(sum((case when (invitation_waves.scheduled_at IS NOT NULL) then 1 else 0 end)) AS INTEGER) as waves_scheduled
                          FROM invitation_waves
                          WHERE wavable_type = 'Event'
                          GROUP BY 1 
                          ORDER BY wavable_id) as "wave_stats" on "wave_stats"."event_id" = "events"."id"
WHERE "events"."uuid" = '1ee5ec72-6f3c-404c-871c-5c5724f6a1ed'
GROUP BY "events"."id", total_waves, waves_scheduled

Answers

It is not quite clear what you realy want (without sample data and expected result), but it looks like you want to get number of invitations and/or number of waves and/or total number of both – per event. If that is so, using your table strutures and my blind guessed data, the code below could be something that might help you get what you need:

Select     e.id as event_id, e.name as event_name,
           Count(Distinct i.id) as total_event_invitations,
           Count(Distinct iw.id) as total_event_waves, 
           Count(Distinct i.id) + Count(Distinct iw.id) as total_event_invitations_and_waves
From       events e
Left Join  invitations i ON(i.event_id = e.id)
Left Join  invitation_waves iw ON(iw.wavable_id = e.id)
Where      wavable_type = 'Event'
Group By   e.id, e.name
Order By   e.id

/*      R e s u l t :
event_id  event_name  total_event_invitations  total_event_waves  total_event_invitations_and_waves
--------  ----------  -----------------------  -----------------  ---------------------------------
       1  event 1                           2                  2                                  4
       2  event 2                           3                  4                                 7       */

… and if you want just grand totals for all events then just exclude e.id and e.name from selection list and don’t use Group By and Order By:

--    TOTAL FOR ALL EVENTS 
Select     Count(Distinct i.id) as total_invitations,
           Count(Distinct iw.id) as total_waves, 
           Count(Distinct i.id) + Count(Distinct iw.id) as total_invitations_and_waves
From       events e
Left Join  invitations i ON(i.event_id = e.id)
Left Join  invitation_waves iw ON(iw.wavable_id = e.id)
Where      wavable_type = 'Event'

/*    R e s u l t : 
total_invitations   total_waves   total_invitations_and_waves
-----------------   -----------   ---------------------------
                5             6                            11      */

See the fiddle here.

NOTES:

With my data in the fiddle the result would be the same if Inner Join was used instead of Left
Most probably – you will need to adjust this code to your actual context and data

The following SQL creates the tables and populates them with test values to verify that the query is producing the intended results. The event names are the expected counts for invitations, validated invitations, waves, and scheduled waves.

CREATE TABLE events (
  id BIGINT NOT NULL PRIMARY KEY,
  UUID UUID DEFAULT gen_random_uuid () NOT NULL,
  name CHARACTER VARYING NOT NULL
);

CREATE TABLE invitations (
  id BIGINT NOT NULL PRIMARY KEY,
  UUID UUID DEFAULT gen_random_uuid () NOT NULL,
  event_id BIGINT NOT NULL REFERENCES events (id),
  status INTEGER
);

CREATE TABLE invitation_waves (
  id BIGINT NOT NULL PRIMARY KEY,
  UUID UUID DEFAULT gen_random_uuid () NOT NULL,
  name CHARACTER VARYING NOT NULL,
  wavable_type CHARACTER VARYING,
  wavable_id BIGINT,
  scheduled_at TIMESTAMP(6) WITHOUT TIME ZONE
);

INSERT INTO
  events (id, name)
VALUES
  (1, '0 0 0 0'),
  (2, '1 0 0 0'),
  (3, '2 1 0 0'),
  (4, '0 0 1 0'),
  (5, '0 0 2 1'),
  (6, '1 0 1 0'),
  (7, '2 1 1 0'),
  (8, '1 0 2 1'),
  (9, '2 1 2 1');

INSERT INTO
  invitations (id, event_id, status)
VALUES
  (1, 2, 0),
  (2, 3, 0),
  (3, 3, 4),
  (4, 6, 0),
  (5, 7, 0),
  (6, 7, 5),
  (7, 8, 0),
  (8, 9, 0),
  (9, 9, 6);

INSERT INTO
  invitation_waves (id, name, wavable_type, wavable_id, scheduled_at)
VALUES
  (1, 'wave1', 'Event', 4, NULL),
  (2, 'wave2', 'Event', 5, NULL),
  (3, 'wave3', 'Event', 5, '2024-01-01 00:00:00'::TIMESTAMP),
  (4, 'wave4', 'Event', 6, NULL),
  (5, 'wave5', 'Event', 7, NULL),
  (6, 'wave6', 'Event', 8, NULL),
  (7, 'wave7', 'Event', 8, '2024-01-01 00:00:00'::TIMESTAMP),
  (8, 'wave8', 'Event', 9, NULL),
  (9, 'wave9', 'Event', 9, '2024-01-01 00:00:00'::TIMESTAMP);

The following demonstrates using COUNT(DISTINCT ...) FILTER (WHERE ...) to report counts for multiple conditions within a single query. Without DISTINCT, the query would report incorrect counts for events that have both invitations and waves when either has more than one associated with the event.

SELECT
  e.id AS event_id,
  e.name,
  COUNT(DISTINCT i.id) AS total_invitations,
  COUNT(DISTINCT i.id) FILTER (WHERE i.status IN (4, 5, 6)) AS total_validated,
  COUNT(DISTINCT w.id) AS total_waves,
  COUNT(DISTINCT w.id) FILTER (WHERE w.scheduled_at IS NOT NULL) AS scheduled_waves
FROM
  events e
  LEFT JOIN invitations i
    ON i.event_id = e.id
  LEFT JOIN invitation_waves w
    ON w.wavable_type = 'Event' AND
       w.wavable_id = e.id
GROUP BY
  e.id
ORDER BY
  e.id;

Running the query with the test data produces the following results:

event_id	name	total_invitations	total_validated	total_waves	scheduled_waves
1	0 0 0 0	0	0	0	0
2	1 0 0 0	1	0	0	0
3	2 1 0 0	2	1	0	0
4	0 0 1 0	0	0	1	0
5	0 0 2 1	0	0	2	1
6	1 0 1 0	1	0	1	0
7	2 1 1 0	2	1	1	0
8	1 0 2 1	1	0	2	1
9	2 1 2 1	2	1	2	1

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Postgresql – sum case with left join

Option 1

Option 2 (with DISTINCT)

Option 3 (sub select) ✅

Answers

Option 2 (with `DISTINCT`)