In some cases, ApplyFilter can be significantly faster. On the other hand, it doesn't
matter as much as you might think on the mutex imports, because we've already sucked
most of the time out of those.
Added additional mutex sample data and batches of it so we can
confirm that overwrite works. It didn't work, so that needed to be fixed.
Couple of things:
(1) Wasn't updating "last value seen" so the check for an unsorted list
didn't work.
(2) Also didn't handle the case where there were to-clear values higher
than any to-set value.
This could result in bits not getting cleared, which could result in
there being more than N bits to clear for N new bits. And that could cause
really strange problems when the input slices were parts of a single
larger slice, because bit positions to clear could get shoved in as
possible columns in a future batch.
For the Rows benchmark, we were continuing to use the original writable
transaction, meaning RBF was spending all its time looking up dirty
pages in the transaction's dirty page cache rather than working with
the disk in any way. It wasn't clear whether this was hurting or
helping performance, but it was clear that it wasn't testing the
"real" workload use case, where queries are done against the RBF
file rather than the dirty page cache.
Modify the benchmark to test it both ways for comparison. Answer:
The RBF file is faster than the in-memory map (!).
This reduces noticably the cost of reading leaf cells, by passing
a single pointer down the stack instead of the entire data structure
up the stack. It's only a few percent overall, but it's noticeable.
This gives RBF an ApplyFilter that can run without instantiating containers
when the filter it's using doesn't need them instantiated. We can also seek
ahead in cases where we know the next key we care about is not just the next
key numerically.
This is a partial solution to a nasty performance problem, which is that
a ContainerIterator has to *generate* all the containers. With roaring, this
was cheap because they already exist in memory; with transactional backends,
it's an allocation per container, *even for the containers we don't use*.
This design admits filters which can distinguish between answers they
can give just based on keys and times when they actually need containers
instantiated, and can also give hints as to future answers -- saying "yes"
or "no" to entire rows at a time, or indicating when they're done.
This is only part of the solution; we also need a Tx API hook for
doing scans like this which doesn't rely on ContainerIterator.
- use short_txkey for rbf
- short_txkey breaks a bunch of bolt_test.go, so leave it on (long) txkey for now.
- remove SliceOfShards method from Tx interface
moved lonquerytime from cluster into server and moved cluster.longquerytime into top level config
kept cluster.longquerytime for backwards compatibility, favored if both longquerytime options are present
