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Question

I want to aggregate statistics for each app in the table apps

I have the following query, but for some reason all the results return 0

select
    a.id,
    'support' as domain,
    'summary' as type,
    90 as interval,
    json_build_object(
        'new', count(new),
        'closed', count(closed_c),
        'reply_rate', count(reply_rate),
        'median_response', max(median_response.response_time)
    ) as data
from apps a
full join (
    SELECT * from conversations c
    WHERE c.started_at::date > (current_date - (90  || ' days')::interval)::date
) as new on new.app_id = a.id
full join (
    SELECT * from conversations c
    WHERE c.closed_at::date > (current_date - (90  || ' days')::interval)::date
) as closed_c on closed_c.app_id = a.id
full join (
    SELECT * from conversations c
    WHERE c.started_at::date > (current_date - (90  || ' days')::interval)::date AND c.first_response_at is not null
) as reply_rate on reply_rate.app_id = a.id
full join (
    SELECT c.app_id, extract(epoch from (c.first_response_at - c.started_at)) as response_time, ntile(2) OVER (ORDER BY (c.first_response_at - c.started_at)) AS bucket FROM conversations c
    WHERE c.started_at::date > (current_date - (90  || ' days')::interval)::date AND c.first_response_at is not null
) as median_response on median_response.app_id = a.id
where a.test = false
group by a.id

Answer 1

I can't tell exactly why everything is zero, but

#1: full join should be replaced by left join (due to the where a.test = false )

#2: as you access the same table four times with different conditions this can be probably replaced by a single Select using conditional aggregation.

Check if this returns the correct counts and then Left Join it to apps .

select
    app_id,
    sum(new),
    sum(closed_c),
    sum(reply_rate),
    max(case when bucket = 1 then response_time end)
from
 (
    SELECT app_id,

       1 as new,

       case when c.closed_at::date > (current_date - (90  || ' days')::interval)::date then 1 else 0 end as closed_c,

       case when c.first_response_at is not null then 1 else 0 end as reply_rate,

       extract(epoch from (c.first_response_at - c.started_at)) as response_time, 

       ntile(2) OVER (ORDER BY (c.first_response_at - c.started_at)) AS bucket
    FROM conversations c
    -- assuming that closed_at is always after started_at
    WHERE c.started_at::date > (current_date - (90  || ' days')::interval)::date 
 ) as dt
group by app_id

Answer 2

Why are you doing so many joins when you don't use not a single column from those tables?

If you just need to count, you can do just like that:

select
    a.id,
    'support' as domain,
    'summary' as type,
    90 as interval,
    json_build_object(
        'new',              (SELECT count(*)                                                      from conversations c WHERE c.app_id = a.id and c.started_at::date  > current_date - 90),
        'closed',           (SELECT count(*)                                                      from conversations c WHERE c.app_id = a.id and c.closed_at::date   > current_date - 90),
        'reply_rate',       (SELECT count(*)                                                      from conversations c WHERE c.app_id = a.id and c.started_at::date  > current_date - 90 and c.first_response_at is not null),
        'median_response',  (SELECT max(extract(epoch from (c.first_response_at - c.started_at))) from conversations c WHERE c.app_id = a.id and c.started_at::date  > current_date - 90 and c.first_response_at is not null)
    ) as data
from apps a
where a.test = false

Also, why are you using interval to add/subtract days into a date type?

You could just do current_date - 90 .

I recommend you create some indexes in conversations too:

create index i_conversations_started_at on conversations (id, started_at::date); 
create index i_conversations_closed_at on conversations (id, closed_at::date);