This slows down insert to be log2(N), where N is
the total number of objects in the store. The upside
is that it also removes the terrible worst case
scenarios for insert.
* Added copyright notices to files
* Added Apache 2.0 License file with permission from Kresten/Trifork
* Changed the handle from "Db" to "Tree" because... it made me feel better
* Other minor changes here and there
Sync blocks insert/lookup while doing a range
query, but still buffers the results in a
per-range query process.
Async fold runs on a hard link copy of the
underlying tree data.
This commit and also fixes a number of bugs
related to folding; it was not taking nursery
data into account.
First implementation of range fold
Range folding doesn't prohibit insert/lookup or
merge operations, but each level can only have
one range fold operation active.
Thus, worst case active range folds can double
space requirements, because it holds hard-linked
copies of used btree files.
A dict is open and with keys if its size is greater than 0. Go through
the dicts one by one with lists:any/2 and use that to optimize the
check. Ought to run faster than traversing the full set.
To make failing keys work we must be able to produce a key in fewer
failures then currently. So encode the picker for non-empty trees
directly so it is much faster and doesn't try to do it by generating a
case and then asking "is this one good enough?". A simple
dict:filter/2 does it.
