你也可以只用 SQL 来解决这个问题（如果你对这样的答案感兴趣的话）。
Range types 提供了很好的 functions and operators 进行计算。
当有子范围时，这些解决方案将为您提供行，其中有给定组中的一些失踪人员（它会给您确切的子范围以及所需人数中缺少多少人)。
简单的方法：
您想尝试类似的方法。您需要选择count() 所基于的某个区间（我选择了5 minutes）：
select g.id group_id, i start_time, i + interval '5 minutes' end_time, g.required_people - count(a.id)
from groups g
cross join generate_series(g.start_time, g.end_time, interval '5 minutes') i
left join people_assignments a on a.group_id = g.id
where tsrange(a.start_time, a.end_time) && tsrange(i, i + interval '5 minutes')
group by g.id, i
having g.required_people - count(a.id) > 0
order by g.id, i
但请注意，当缺少的子范围小于 5 minutes 时，您将无法检测到它们。前任user1 分配给11:00-11:56，而user2 分配给11:59-13:00，他们似乎在11:00-13:00 的组中（因此11:56-11:59 的缺失子范围将不被注意）。
注意：间隔越短（你选择的）结果就越精确（而且慢！）。
http://rextester.com/GRC64969
艰难的道路：
您可以使用custom aggregates 或recursive CTEs 即时累积结果
with recursive r as (
-- start with "required_people" as "missing_required_people" in the whole range
select 0 iteration,
id group_id,
array[]::int[] used_assignment_ids,
-- build a json map, where keys are the time ranges
-- and values are the number of missing people for that range
jsonb_build_object(tsrange(start_time, end_time), required_people) required_people_per_time_range
from groups
where required_people > 0
and id = 1 -- query parameter
union all
select r.iteration + 1,
r.group_id,
r.used_assignment_ids || a.assignment_id,
d.required_people_per_time_range
from r
-- join a single assignment to the previous iteration, where
-- the assigment's time range overlaps with (at least one) time range,
-- where there is still missing people. when there are no such time range is
-- found in assignments, the "recursion" (which is really just a loop) stops
cross join lateral (
select a.id assignment_id, tsrange(start_time, end_time) time_range
from people_assignments a
cross join (select key::tsrange time_range from jsonb_each(r.required_people_per_time_range)) j
where a.group_id = r.group_id
and a.id <> ALL (r.used_assignment_ids)
and tsrange(start_time, end_time) && j.time_range
limit 1
) a
-- "partition" && accumulate all remaining time ranges with
-- the one found in the previous step
cross join lateral (
-- accumulate "partition" results
select jsonb_object_agg(u.time_range, u.required_people) required_people_per_time_range
from (select key::tsrange time_range, value::int required_people
from jsonb_each_text(r.required_people_per_time_range)) j
cross join lateral (
select u time_range, j.required_people - case when u && a.time_range then 1 else 0 end required_people
-- "partition" the found time range with all existing ones, one-by-one
from unnest(case
when j.time_range @> a.time_range
then array[tsrange(lower(j.time_range), lower(a.time_range)), a.time_range, tsrange(upper(a.time_range), upper(j.time_range))]
when j.time_range && a.time_range
then array[j.time_range * a.time_range, j.time_range - a.time_range]
else array[j.time_range]
end) u
where not isempty(u)
) u
) d
),
-- select only the last iteration
l as (
select group_id, required_people_per_time_range
from r
order by iteration desc
limit 1
)
-- unwind the accumulated json map
select l.group_id, lower(time_range) start_time, upper(time_range) end_time, missing_required_people
from l
cross join lateral (
select key::tsrange time_range, value::int missing_required_people
from jsonb_each_text(l.required_people_per_time_range)
) j
-- select only where there is still some missing people
-- this is optional, if you omit it you'll also see row(s) for sub-ranges where
-- there is enough people in the group (these rows will have zero,
-- or negative amount of "missing_required_people")
where j.missing_required_people > 0
http://rextester.com/GHPD52861