# JuliaDB Benchmarks
**URL:** https://discourse.julialang.org/t/juliadb-benchmarks/20710
**Category:** Performance
**Tags:** announcement
**Created:** [February 12, 2019, 4:09pm UTC](https://discourse.julialang.org/t/juliadb-benchmarks/20710 "2019-02-12T16:09:33Z")
**Posts on this page:** 5
**Page:** 1
### Author:  [@joshday](https://discourse.julialang.org/u/joshday)
#### Post date: [February 12, 2019, 4:09pm UTC](https://discourse.julialang.org/t/juliadb-benchmarks/20710/1 "2019-02-12T16:09:33Z")
I’ve started gathering some reproducible benchmarks at [GitHub - JuliaData/JuliaDB\_Benchmarks: Reproducible benchmarks for JuliaDB](https://github.com/joshday/JuliaDB_Benchmarks) (huge shout out to [https://github.com/oxinabox/DataDeps.jl](https://github.com/oxinabox/DataDeps.jl) for simplifying the reproducible part).
I would happily accept PRs of more examples! If you even just have a pointer to an interesting dataset you think JuliaDB would work well for, I’d love to hear about it!
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### Author:  [@anon67531922](https://discourse.julialang.org/u/anon67531922)
#### Post date: [February 12, 2019, 4:16pm UTC](https://discourse.julialang.org/t/juliadb-benchmarks/20710/2 "2019-02-12T16:16:46Z")
It is really great to have this, but it definitely highlights some room for improvement 😅
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### Author:  [@nalimilan](https://discourse.julialang.org/u/nalimilan)
#### Post date: [February 12, 2019, 4:25pm UTC](https://discourse.julialang.org/t/juliadb-benchmarks/20710/3 "2019-02-12T16:25:09Z")
It would be interesting to add JuliaDB to [Database-like ops benchmark](https://h2oai.github.io/db-benchmark).
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### Author:  [@piever](https://discourse.julialang.org/u/piever)
#### Post date: [February 12, 2019, 8:01pm UTC](https://discourse.julialang.org/t/juliadb-benchmarks/20710/4 "2019-02-12T20:01:22Z")
> [@anon67531922](#):
>
> It is really great to have this, but it definitely highlights some room for improvement 😅
I’m actually not sure: it may well be that `groupby` in pandas is special-casing `mean` and `sum` to use online algorithms, i.e. not extracting the vector corresponding to the group but just computing the sum while iterating through it (or so I understood from the discussion when comparing performance with DataFrames) in which case the correct performance comparison would be with `groupreduce` that indeed performs quite well. I’d be curious to see what happens with a custom user defined “reducing” function in `groupby` where this optimization is no longer available.
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### Author:  [@xiaodai](https://discourse.julialang.org/u/xiaodai)
#### Post date: [February 12, 2019, 8:11pm UTC](https://discourse.julialang.org/t/juliadb-benchmarks/20710/5 "2019-02-12T20:11:55Z")
The fannie mae data requires a login the it’s large at almost 2 billion rows