Previously we just looked at the raw count of probed keys. Now, we
compute the average and max of both the probe _distance_ measured in the
number of _groups_ probed, and the number of probe _compares_ measured
in the compares required _before_ finding the matching entry.
This lets us understand the relative impact of probe-distance vs. tag
collisions on a given set of benchmark keys. Some of this is motivated
by considering additional optimization techniques similar to those used
in Boost's table and the F14 table from Facebook/Meta.
---------
Co-authored-by: Richard Smith <richard@metafoo.co.uk>
The hash table design is heavily based on Abseil's ["Swiss
Tables"][swiss-tables] design. It uses an array of bytes storing
metadata about each entry and an array of entries where each is a pair
of key and value. The metadata byte consists of 7-bits of hash of the
key (distinct from the bits used to index the table), and one bit
indicating the presence of a special entry -- either empty or deleted.
[swiss-tables]: https://abseil.io/about/design/swisstables
There are a large range of optimizations and other nuanced aspects of
this hash table design and implementation, a good point to understand
that context is `raw_hashtable.h` which has an overview of the design
and references to various other files for relevant details.
---------
Co-authored-by: josh11b <15258583+josh11b@users.noreply.github.com>
Chandler CarruthandRichard Smith
