Andy Brownsword has a procedure:
Let’s recap what we’re doing here:
Large append-heavy tables – like logs or audits – often don’t have a useful index on the timestamp. These types of tables do however have a strong correlation between their clustering key and the timestamp due to chronological inserts.
A binary search approach splits the table in half to narrow down the search space with each iteration. By abusing the incremental relationship between the clustering key and timestamps, we can quickly zero in on the point in time we’re after. If you want to see the mechanics, check out last week’s post.
I love the approach for log tables, assuming that a timestamp is part of the filter. This is a clever application of a very common computer science algorithm to database operations.