You probably don't want to hear this, but the best option to speed up
SELECT DISTINCT is to avoid
DISTINCT to begin with. In many cases (not all!) it can be avoided with better database-design or better queries.
GROUP BY is faster, because it takes a different code path.
In your particular case, it doesn't seem like you can get rid of
DISTINCT (well, see below). But you can support the query with a special index if you have many queries of that kind:
CREATE INDEX foo ON events (project_id, "time", user_id);
In Postgres 11 or later, you can use an actual "covering" index like:
CREATE INDEX foo ON events (project_id, "time") INCLUDE (user_id);
user_id is only useful if you get index-only scans out of this. See:
Would remove the expensive
Bitmap Heap Scan from your query plan, which consumes 90% of the query time.
EXPLAIN shows 2,491 distinct users out of half a million qualifying rows. This won't become super-fast, no matter what you do, but it can be substantially faster. With around 200 rows per user, emulating an index skip scan on above index might pay. The range condition on
time complicates matters, and 200 rows per user is still a moderate number. So not sure. See:
Either way, if time intervals in your queries are always the same, a
MATERIALIZED VIEW folding
(project_id, <fixed time interval>) would go a long way. No chance there with varying time intervals, though. Maybe you could at least fold users per hour or some other minimum time unit, and that would buy enough performance to warrant the considerable overhead. Can be combined with either query style.
Most probably, the predicates on
"time" should really be:
AND "time" >= '2015-01-11 8:00:00'
AND "time" < '2015-02-10 8:00:00';
time as identifier. It's a reserved word in standard SQL and a basic type in Postgres.