Performance issues with evaluation of custom tree data structure in Julia - memory-management

Question

I am implementing a Binary Tree in Julia. The binary tree has nodes and leafs. The nodes point to left and right children, which are also nodes/leafs objects. The following code exemplifies the data structure:
using TimerOutputs
mutable struct NodeLeaf
isleaf::Bool
value::Union{Nothing,Float64}
split::Union{Nothing,Float64}
column::Union{Nothing,Int64}
left::Union{Nothing,NodeLeaf}
right::Union{Nothing,NodeLeaf}
end
function evaluate(node::NodeLeaf, x)::Float64
while !node.isleaf
if x[node.column] < node.split
node = node.left
else
node = node.right
end
end
return node.value
end
function build_random_tree(max_depth)
if max_depth == 0
return NodeLeaf(true, randn(), randn(), rand(1:10), nothing, nothing)
else
return NodeLeaf(false, randn(), randn(), rand(1:10), build_random_tree(max_depth - 1), build_random_tree(max_depth - 1))
end
end
function main()
my_random_tree = build_random_tree(4)
#timeit to "evaluation" for i in 1:1000000
evaluate(my_random_tree, randn(10))
end
end
const to = TimerOutput()
main()
show(to)
I notice that a lot of allocations occur in the evaluate function, but I don't see the reason why this is the case:
julia mytree.jl
───────────────────────────────────────────────────────────────────────
Time Allocations
─────────────────────── ────────────────────────
Tot / % measured: 476ms / 21.6% 219MiB / 62.7%
Section ncalls time %tot avg alloc %tot avg
───────────────────────────────────────────────────────────────────────
evaluation 1 103ms 100.0% 103ms 137MiB 100.0% 137MiB
───────────────────────────────────────────────────────────────────────
As I increase the evaluation loop, the allocation continues to increase without bound. Can anybody explain why allocation grows so much and please suggest how to avoid this issue? Thanks.
EDIT
I simplified too much the code for the example. The actual code is accessing DataFrames, so the main looks like this:
using DataFrames
function main()
my_random_tree = build_random_tree(7)
df = DataFrame(A=1:1000000)
for i in 1:9
df[!, string(i)] = collect(1:1000000)
end
#timeit to "evaluation" for i in 1:size(df, 1)
evaluate(my_random_tree, #view df[i, :])
end
end
I expect this to yield 0 allocations, but that isn't true:
julia mytree.jl
───────────────────────────────────────────────────────────────────────
Time Allocations
─────────────────────── ────────────────────────
Tot / % measured: 551ms / 20.5% 305MiB / 45.0%
Section ncalls time %tot avg alloc %tot avg
───────────────────────────────────────────────────────────────────────
evaluation 1 113ms 100.0% 113ms 137MiB 100.0% 137MiB
───────────────────────────────────────────────────────────────────────%
On the other hand, if I use a plain array I don't get allocations:
function main()
my_random_tree = build_random_tree(7)
df = randn(1000000, 10)
#timeit to "evaluation" for i in 1:size(df, 1)
evaluate(my_random_tree, #view df[i, :])
end
end
julia mytree.jl
───────────────────────────────────────────────────────────────────────
Time Allocations
─────────────────────── ────────────────────────
Tot / % measured: 465ms / 5.7% 171MiB / 0.0%
Section ncalls time %tot avg alloc %tot avg
───────────────────────────────────────────────────────────────────────
evaluation 1 26.4ms 100.0% 26.4ms 0.00B - % 0.00B
───────────────────────────────────────────────────────────────────────%

Answer 1

The thing that allocates is randn not evaluation. Switch to randn!:
julia> using Random
julia> function main()
my_random_tree = build_random_tree(4)
x = randn(10)
#allocated for i in 1:1000000
evaluate(my_random_tree, randn!(x))
end
end
main (generic function with 1 method)
julia> main()
0
EDIT
Solution with DataFrames.jl:
function bar(mrt, nti)
#timeit to "evaluation" for nt in nti
evaluate(mrt, nt)
end
end
function main()
my_random_tree = build_random_tree(7)
df = DataFrame(A=1:1000000)
for i in 1:9
df[!, string(i)] = collect(1:1000000)
end
bar(my_random_tree, Tables.namedtupleiterator(df))
end