Re-posted from: https://bkamins.github.io/julialang/2022/04/08/fast2.html
Introduction
It was April 1st, when I was writing my last post, but my friends, after
having read it, told me that it was not very funny. So today I get back to boring
style.
In the previous post I was comparing lookup speed in vector vs set in Julia and
considered a scenario when lookup in vector was faster. This is not something
that normally we should expect, but clearly I must have chosen the benchmark
settings in a way that lead to such a result.
Today let me go back to that example and discuss it in more detail.
The post was written under Julia 1.7.0, DataFrames.jl 1.3.2,
BenchmarkTools.jl 1.3.1, and Plots.jl 1.27.1.
The experiment
Let me remind you the experiment setting. I tested the lookup performance for
collections from size 10 to 40. I used random strings and then performed a check
if every value stored in the collection is indeed present in it.
Here is the test code I used with one twist. In my original post I have measured
total time of lookup. This time I measure time of lookup per single element
(the difference is in the plot command where now I divide everything by df.i).
using DataFrames
using BenchmarkTools
using Random
using Plots
f(x, r) = all(in(x), r) # function testing lookup speed
df = DataFrame()
Random.seed!(1234)
for i in 10:40
v = [randstring(rand(1:1000)) for _ in 1:i] # randomly generate a Vector
s = Set(v) # turn it to Set for comparison
@assert length(s) == i # make sure we had no duplicates
t1 = @belapsed f($s, $v)
t2 = @belapsed f($v, $v)
# display the intermetiate results so that I can monitor the progress
@show (i, t1, t2)
push!(df, (;i, t1, t2))
end
plot(df.i, [df.t1 df.t2] ./ df.i;
labels=["Set" "Vector"],
legend=:topleft,
xlabel="collection size",
ylabel="time per lookup in seconds")
The code produces the following plot:
We can see that the lookup time of one element in Set is roughly constant, but
for Vector it grows linearly. This is expected. Amortized time of random
lookup in Set is O(1) while for Vector it is O(n), where n is
collection size.
Last week I have computed total time, which is roughly linear for Set while it
is quadratic for Vector. However, I have scaled the plot in a way that it was
not very visible that the curve for Vector is a parabola.
Now we can clearly see that we can expect that for larger collections Set will
be faster. Let us pick i = 1000 as an example (not very large number, but big
enough):
julia> i = 1000;
julia> v = [randstring(rand(1:1000)) for _ in 1:i];
julia> s = Set(v);
julia> @assert length(s) == i
julia> @belapsed f($s, $v)
8.8383e-5
julia> @belapsed f($v, $v)
0.001030348
As you can see now Vector is much slower.
But why was Vector faster than Set for the original data?
Understanding lookup process in Set and in Vector
When you search for a value in a Set Julia roughly does the following steps:
- compute
hashof the value you look for; - check if
Setcontains some values that have the same hash (there might be
more than one such value due to collisions); - if yes then check if any of them are equal to the value we have provided.
When you search for a value in a Vector the process is simpler: Julia checks
sequentially checks elements of a vector if they are equal to the value you
passed. So as you can see the extra cost of using Set is that you need to
perform hashing.
In my example I have generated the data using the following expression:
[randstring(rand(1:1000)) for _ in 1:i]. As you can see these are random
strings that in general can be quite long (up to 1000 characters). Hashing such
strings is expensive. The additional trick I did was to generate the strings so
that with high probability they have a different length. I did this to make
sure that string comparison is fast. As you can check in the implementation of
isequal in Julia it falls back to == in this case, which for String is:
function ==(a::String, b::String)
pointer_from_objref(a) == pointer_from_objref(b) && return true
al = sizeof(a)
return al == sizeof(b) && 0 == _memcmp(a, b, al)
end
We can observe that if strings have unequal lengths they are compared very fast.
In summary, I have baked the example so that hashing is expensive but comparison
is cheap. If I used shorter strings, e.g. of length 2, then even for i = 10
on the average Set lookup would be faster than Vector lookup:
julia> i = 10;
julia> v = [randstring(2) for _ in 1:i];
julia> s = Set(v);
julia> @assert length(s) == i
julia> @belapsed f($s, $v)
1.47515625e-7
julia> @belapsed f($v, $v)
1.8996439628482973e-7
Of course for very short collections, like e.g. length 1 Set will be slower
even in this case as it still would compute hash.
Understanding volatility of Set timing
An additional issue that we have noticed last week is smoothness of Vector
plot and volatility of Set plot. What is the reason for this? There are two
factors here in play:
- For each
iI use random strings of different lengths. This means that
hashing cost differs per experiment. On the other hand forVectorwe do
not need to do hashing and, as I commented above, we can immediately decide
that strings are different just by looking at their length. - For different
iSethas different percentage of occupied slots, which
means that number of hash collisions that can happen differs.
Conclusions
My takeaways from today’s post are:
- benchmarking is useful, but it is important to understand the data structures
and algorithms that one uses; - when doing visualization it is really important to think what measures to use
so that the plots are informative; - and finally:
Set’s not dead (although sometimes indeed it is not an optimal
data structure; in general I recommend you to have a look at the options
available in DataStructures.jl).
