"Data warehouse"-like SQLite store design - sql

Question

I am interested in designing a SQL-based (SQLite, actually) storage for an application processing a large number of similar data entries. For this example, let it be a chat messages storage.
The application has to provide the capabilities of filtering and analyzing the data by message participants, tags, etc., all of those implying N-to-N relationships.
So, the schema (kind of star) will look something like:
create table messages (
message_id INTEGER PRIMARY KEY,
time_stamp INTEGER NOT NULL
-- other fact fields
);
create table users (
user_id INTEGER PRIMARY KEY,
-- user dimension data
);
create table message_participants (
user_id INTEGER references users(user_id),
message_id INTEGER references messages(message_id)
);
create table tags (
tag_id INTEGER PRIMARY KEY,
tag_name TEXT NOT NULL,
-- tag dimension data
);
create table message_tags (
tag_id INTEGER references tags(tag_id),
message_id INTEGER references messages(message_id)
);
-- etc.
So, all good and well, until I have to perform analytic operations and filtering based on the N-to-N dimensions. Given millions of rows in the messages table and thousands in the dimensions (there are more than shown in the example), all the joins are simply too much a performance hit.
For example, I would like to analyze the number of messages each user participated in, given the data is filtered based on selected tags, selected users and other aspects:
select U.user_id, U.user_name, count(1)
from messages as M
join message_participants as MP on M.message_id=MP.message_id
join user as U on MP.user_id=U.user_id
where
MP.user_id not in ( /* some user ID's set */ )
and M.time_stamp between #StartTime and #EndTime
and
-- more fact table fields filtering
and message_id in
(select message_id
from message_tags
where tag_id in ( /* some tag ID's set */ ))
and
-- more N-to-N filtering
group by U.user_id
I am constrained to SQL and, specifically, SQLite. And I do use indices on the tables.
I there some way I don't see to improve the schema, maybe a clever way to de-normalize it?
Or maybe there is a way to somehow index the dimension keys inside the message row (I thought about using FTS capabilities but not sure if searching the textual index and joining on the results will provide any performance leverage)?

Answer 1

Too long to put in a comment, and might help with performance but isn't exactly a direct answer to your question (your schema seems fine): have you tried messing with your query itself?
I often see that kind of subselect filter for many-to-many, and I have found that on large queries like this I frequently see improvements in performance from running a CTE/join rather than a where blag in (subselect):
;with tagMesages as (
select distinct message_id
from message_tags
where tag_id in ( /* some tag ID's set */ )
) -- more N-to-N filtering
select U.user_id, U.user_name, count(1)
from messages as M
join message_participants as MP on M.message_id=MP.message_id
join user as U on MP.user_id=U.user_id
join tagMesages on M.message_id = tagMesages.message_id
where
MP.user_id not in ( /* some user ID's set */ )
and M.time_stamp between #StartTime and #EndTime
and
-- more fact table fields filtering
group by U.user_id
We can tell they're the same, but the query planner can sometimes find this more helpful
Disclaimer: I don't do SQLite, I do SQL Server, so sorry if I've made some obvious (or otherwise) error.