rolling joins data.table in R - r

Question

I am trying to understand a little more about the way rolling joins work and am having some confusion, I was hoping somebody could clarify this for me. To take a concrete example:
dt1 <- data.table(id=rep(1:5, 10), t=1:50, val1=1:50, key="id,t")
dt2 <- data.table(id=rep(1:5, 2), t=1:10, val2=1:10, key="id,t")
I expected this to produce a long data.table where the values in dt2 are rolled:
dt1[dt2,roll=TRUE]
Instead, the correct way to do this seems to be:
dt2[dt1,roll=TRUE]
Could someone explain to me more about how joining in data.table works as I am clearly not understanding it correctly. I thought that dt1[dt2,roll=TRUE] corresponded to the sql equivalent of select * from dt1 right join dt2 on (dt1.id = dt2.id and dt1.t = dt2.t), except with the added functionality locf.
Additionally the documentation says:
X[Y] is a join, looking up X's rows using Y (or Y's key if it has one)
as an index.
This makes it seem that only things in X should be returned an the join being done is an inner join, not outer. What about in the case when roll=T but that particular id does not exist in dt1? Playing around a bit more I can't understand what value is being placed into the column.

Answer 1

That quote from the documentation appears to be from FAQ 1.12 What is the difference between X[Y] and merge(X,Y). Did you find the following in ?data.table and does it help?
roll Applies to the last join column, generally a date but can be any
ordered variable, irregular and including gaps. If roll=TRUE and i's
row matches to all but the last x join column, and its value in the
last i join column falls in a gap (including after the last
observation in x for that group), then the prevailing value in x is
rolled forward. This operation is particularly fast using a modified
binary search. The operation is also known as last observation carried
forward (LOCF). Usually, there should be no duplicates in x's key, the
last key column is a date (or time, or datetime) and all the columns
of x's key are joined to. A common idiom is to select a
contemporaneous regular time series (dts) across a set of identifiers
(ids): DT[CJ(ids,dts),roll=TRUE] where DT has a 2-column key (id,date)
and CJ stands for cross join.
rolltolast Like roll but the data is not rolled forward past the last
observation within each group defined by the join columns. The value
of i must fall in a gap in x but not after the end of the data, for
that group defined by all but the last join column. roll and
rolltolast may not both be TRUE.
In terms of left/right analogies to SQL joins, I prefer to think about that in the context of FAQ 2.14 Can you explain further why data.table is inspired by A[B] syntax
in base. That's quite a long answer so I won't paste it here.