Hi Max,

Theoretically, for a small number of elements, usually somewhere between 3
and 30 depending on implementations, a linear search is faster than a hash
search, especially in the Pharo dictionary hash search implementation.

Efficient dictionary implementations are usually bucket-based. The
dictionary holds a certain number of buckets, and based on the key hash,
the bucket where the key value is present is determined. Small buckets are
linear (arrays or linked list). Large buckets are typically balanced binary
trees (red-black trees typically). Under a certain number of elements there
is a single bucket, which means a linear search is performed, as for the
SmallDictionary. When it grows the dictionary search becomes a combination
between a hash search and a linear or tree search.

Pharo dictionary search is first hash-based, then all the buckets are
represented next to each other in the same arrays and a linear search is
performed there, leading to many collisions and slower search time
(especially when the element is not found), sometimes the code searches
over multiple buckets because the dictionary is too full or there are too
many near-collisions. The memory consumption is competitive with the
advanced implementations though (precise measurements would need to be
made).

Method dictionaries are represented differently to optimize the look-up
logic.

If you want to improve things and have one dictionary implementation
instead of two, implement or look for a bucket based dictionary and put it
in the base image instead of Dictionary. This is quite some work since
there are many APIs to port. You can look at the Pinnochio implementation,
it's quite good but they've not implemented large buckets.



On Fri, Jun 8, 2018 at 8:46 AM, Max Leske <maxleske at gmail.com> wrote:

> Hi,
>
> I was messing around with SmallDictionary when I suddenly realised that I
> can't find a single reason to use it over a normal Dictionary. While its
> name and class comment imply that it is somehow an optimised Dictionary, I
> don't see any measurement where that actually holds up. The following was
> run in a Pharo 7 image on a recent VM (see below):
>
> | d |
> d := SmallDictionary new.
> d sizeInMemory. "24"
> [100000 timesRepeat: [
>         1 to: 100 do: [ :i | d at:i put: i] ] ] timeToRun. "0:00:00:05.226"
>
> [100000 timesRepeat: [
>         d at: 48 ifAbsent: [] ] ] timeToRun. "0:00:00:00.041"
>
>
>
> | d |
> d := Dictionary new.
> d sizeInMemory. "16"
> [100000 timesRepeat: [
>         1 to: 100 do: [ :i | d at:i put: i] ] ] timeToRun. "0:00:00:00.385"
> [100000 timesRepeat: [
>         d at: 48 ifAbsent: [] ] ] timeToRun.  "0:00:00:00.006"
>
>
> As you can see, SmallDictionary is 8 bytes larger per instance and
> significantly faster while reading and writing (I know that this isn't a
> good benchmark but it suffices to make my point).
>
>
> Is anyone aware of a reason for hanging on to SmallDictionary? I'm also
> curious to know how SmallDictionary came to be. There must have been some
> advantage over Dictionary at some point in the past.
>
>
> Cheers,
> Max
>
>
>
>
>
> Image version: Pharo 7.0
> Build information: Pharo-7.0+alpha.build.961.sha.
> a69e72a97136bc3f93831584b6efa2b1703deb84 (32 Bit)
>
> VM version: CoInterpreter VMMaker.oscog- nice.2281 uuid:
> 4beeaee7-567e-1a4b-b0fb-bd95ce302516 Nov 27 2017
> StackToRegisterMappingCogit VMMaker.oscog-nice.2283 uuid:
> 2d20324d-a2ab-48d6-b0f6-9fc3d66899da Nov 27 2017
> VM: 201711262336 https://github.com/OpenSmalltalk/opensmalltalk-vm.git $
> Date: Mon Nov 27 00:36:29 2017 +0100 $ Plugins: 201711262336
> https://github.com/OpenSmalltalk/opensmalltalk-vm.git $
>
> OS: macOS 10.13.5
> Machine: MacBook Pro (13-inch, 2016, Four Thunderbolt 3 Ports)
>
>


-- 
Clément Béra
https://clementbera.github.io/
https://clementbera.wordpress.com/
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