for (uint i=0; i\<4; i++)
> v\[n\]\[i\] = (rand() % 100) / 100.0;
> //
> t-\>Fill();
> }
>
> t-\>Write();
> f-\>Close();
>
> return 0;
> }
> \`\`\`
>
> then, you can try (only \`\*\_X\` branches)
> \`\`\`julia
> @time let
> f = TFile(joinpath("test\_tree.root"))
> t = f\["mytree"\]
> t.p1\_X + t.p2\_X
> end
> \`\`\`
> will take forever (11 sec.).
>
> Surely faster to convert the whole tree and, then, access.
> \`\`\`
> @btime let
> f = TFile(joinpath("test\_tree.root"))
> t = f\["mytree"\]\[1:end\]
> t.p1\_X + t.p2\_X
> end # 351.006 ms
> \`\`\`
>
> Creating the three with only needed branches is the fastest option
> \`\`\`julia
> @btime let
> f = TFile(joinpath("test\_tree.root"))
> t = f\["mytree"\]
> tT = getindex(t, eachindex(t), \["p1\_X", "p2\_X"\])
> #
> tT.p1\_X + tT.p2\_X
> end # 328.482 ms
> \`\`\`
> The difference between the last two scales with the number of branches.
The file has 300\_000 rows, the size of 10MB
Some more retails are below
the `Particle` is a simple mutable struct, the `process` function creates a named tuple of for particles
```julia
using UpROOT
using Parameters
#
@with_kw mutable struct Particle
E::Float64
px::Float64
py::Float64
pz::Float64
end
#
process(row) =
(pb=Particle(
px = row.pBeamX,
py = row.pBeamY,
pz = row.pBeamZ,
E = row.pBeamE),
pr = Particle(
px = row.pRecoilX,
py = row.pRecoilY,
pz = row.pRecoilZ,
E = row.pRecoilE),
pπ = Particle(
px = row.pPimX,
py = row.pPimY,
pz = row.pPimZ,
E = row.pPimE),
pη = Particle(
px = row.pEtaX,
py = row.pEtaY,
pz = row.pEtaZ,
E = row.pEtaE))
```
---
### Author:  [@misha\_mikhasenko](https://discourse.julialang.org/u/misha_mikhasenko)
#### Post date: [August 11, 2020, 9:18am UTC](https://discourse.julialang.org/t/tables-jl-uproot-jl-and-the-speed/44737/2 "2020-08-11T09:18:15Z")
Pin @oschulz. Any ideas?
Thanks
---
### Author:  [@misha\_mikhasenko](https://discourse.julialang.org/u/misha_mikhasenko)
#### Post date: [August 11, 2020, 9:28am UTC](https://discourse.julialang.org/t/tables-jl-uproot-jl-and-the-speed/44737/3 "2020-08-11T09:28:23Z")
Just found a typo in the second execution:
it should be `process.(tr)` instead of `process.(t)`. Then it takes 1s
What remains unclear why processing the tree directly is so much slower. It contains
the only branches that I use
---
### Author:  [@misha\_mikhasenko](https://discourse.julialang.org/u/misha_mikhasenko)
#### Post date: [August 11, 2020, 9:34am UTC](https://discourse.julialang.org/t/tables-jl-uproot-jl-and-the-speed/44737/4 "2020-08-11T09:34:46Z")
Well, here is the shortest representation of the problem:
A few orders of magnitude difference. Interesting.
---
### Author:  [@misha\_mikhasenko](https://discourse.julialang.org/u/misha_mikhasenko)
#### Post date: [August 11, 2020, 10:09am UTC](https://discourse.julialang.org/t/tables-jl-uproot-jl-and-the-speed/44737/5 "2020-08-11T10:09:28Z")
Conversion of the TTree (in disk) to Table (in memory) is done when `getindex` is called.
It seems that the conversion is happening for every index when broadcast
---
### Author:  [@oschulz](https://discourse.julialang.org/u/oschulz)
#### Post date: [August 11, 2020, 10:26am UTC](https://discourse.julialang.org/t/tables-jl-uproot-jl-and-the-speed/44737/6 "2020-08-11T10:26:54Z")
Probably best if we discuss this on your GibHub issue ([https://github.com/JuliaHEP/UpROOT.jl/issues/12](https://github.com/JuliaHEP/UpROOT.jl/issues/12)), in case we need to make changes to UpROOT.jl