# Benchmarking ways to write/load DataFrames IndexedTables to disk

**URL:** <https://discourse.julialang.org/t/benchmarking-ways-to-write-load-dataframes-indexedtables-to-disk/8973>\
**Category:** Data\
**Created:** [February 10, 2018, 1:13pm UTC](https://discourse.julialang.org/t/benchmarking-ways-to-write-load-dataframes-indexedtables-to-disk/8973 "2018-02-10T13:13:55Z")\
**Posts on this page:** 1\
**Showing post:** 37

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**Author:** ![xiaodai](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/xiaodai/32/15937_2.png) [@xiaodai](https://discourse.julialang.org/u/xiaodai)\
**Post date:** [February 19, 2018, 11:54am UTC](https://discourse.julialang.org/t/benchmarking-ways-to-write-load-dataframes-indexedtables-to-disk/8973/37 "2018-02-19T11:54:08Z")

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![1m](https://global.discourse-cdn.com/julialang/original/3X/1/2/122c547d86df4013374b6fb678cec19ce01a017f.png)

Updated with @zhangliye’s code. R’s feather implementation is quite a bit faster than Julia’s. This can probably be improved. Actually data.table’s `fwrite` is actually very very fast and is competitive with `fst`.

@davidanthoff Looks like CSV.jl has a reasonably fast reader, on par with Pandas and data.table, in this case which is reading in 1m rows with 9 columns of mixed string, float and integer types. I am interested to test this out on a largish real-world dataset e.g. Fannie Mae to see how it stacks up, [last time I tried it](https://discourse.julialang.org/t/my-experiences-reading-csvs-from-the-fannie-mae-datasets/8737) it didn’t compare so favourably.

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