Oh yes, I forgot to mention that the CSV/DataStreams code is 0.4 only. Definitely interested to hear about any results/experiences though.
-Jacob On Tue, Oct 13, 2015 at 10:11 PM, Yichao Yu <[email protected]> wrote: > On Wed, Oct 14, 2015 at 12:02 AM, Grey Marsh <[email protected]> wrote: > > @Jacob, I tried your approach. Somehow it got stuck in the "@time ds = > > DataStreams.DataTable(f)" line. After 15 minutes running, julia is using > > ~500mb and 1 cpu core with no sign of end. The memory use has been almost > > same for the whole duration of 15 minutes. I'm letting it run, hoping > that > > it finishes after some time. > > > > From your run, I can see it needs 12gb memory which is higher than my > > machine memory of 8gb. could it be the problem? > > 12GB is the total number of memory ever allocated during the timing. A > lot of them might be intermediate results that are freed by the GC. > Also, from the output of @time, it looks like 0.4. > > > > > On Wednesday, October 14, 2015 at 2:28:09 AM UTC+5:30, Jacob Quinn wrote: > >> > >> I'm hesitant to suggest, but if you're in a bind, I have an experimental > >> package for fast CSV reading. The API has stabilized somewhat over the > last > >> week and I'm planning a more broad release soon, but I'd still consider > it > >> alpha mode. That said, if anyone's willing to give it a drive, you just > need > >> to > >> > >> Pkg.add("Libz") > >> Pkg.add("NullableArrays") > >> Pkg.clone("https://github.com/quinnj/DataStreams.jl";) > >> Pkg.clone("https://github.com/quinnj/CSV.jl";) > >> > >> With the original file referenced here I get: > >> > >> julia> reload("CSV") > >> > >> julia> f = CSV.Source("/Users/jacobquinn/Downloads/train.csv";null="NA") > >> CSV.Source: "/Users/jacobquinn/Downloads/train.csv" > >> delim: ',' > >> quotechar: '"' > >> escapechar: '\\' > >> null: "NA" > >> schema: > >> > DataStreams.Schema(UTF8String["ID","VAR_0001","VAR_0002","VAR_0003","VAR_0004","VAR_0005","VAR_0006","VAR_0007","VAR_0008","VAR_0009" > >> … > >> > "VAR_1926","VAR_1927","VAR_1928","VAR_1929","VAR_1930","VAR_1931","VAR_1932","VAR_1933","VAR_1934","target"],[Int64,DataStreams.PointerString,Int64,Int64,Int64,DataStreams.PointerString,Int64,Int64,DataStreams.PointerString,DataStreams.PointerString > >> … > >> > Int64,Int64,Int64,Int64,Int64,Int64,Int64,Int64,DataStreams.PointerString,Int64],145231,1934) > >> dateformat: Base.Dates.DateFormat(Base.Dates.Slot[],"","english") > >> > >> > >> julia> @time ds = DataStreams.DataTable(f) > >> 43.513800 seconds (694.00 M allocations: 12.775 GB, 2.55% gc time) > >> > >> > >> You can convert the result to a DataFrame with: > >> > >> function DataFrames.DataFrame(dt::DataStreams.DataTable) > >> cols = dt.schema.cols > >> data = Array(Any,cols) > >> types = DataStreams.types(dt) > >> for i = 1:cols > >> data[i] = DataStreams.column(dt,i,types[i]) > >> end > >> return DataFrame(data,Symbol[symbol(x) for x in dt.schema.header]) > >> end > >> > >> > >> -Jacob > >> > >> On Tue, Oct 13, 2015 at 2:40 PM, feza <[email protected]> wrote: > >>> > >>> Finally was able to load it, but the process consumes a ton of > memory. > >>> julia> @time train = readtable("./test.csv"); > >>> 124.575362 seconds (376.11 M allocations: 13.438 GB, 10.77% gc time) > >>> > >>> > >>> > >>> On Tuesday, October 13, 2015 at 4:34:05 PM UTC-4, feza wrote: > >>>> > >>>> Same here on a 12gb ram machine > >>>> > >>>> _ > >>>> _ _ _(_)_ | A fresh approach to technical computing > >>>> (_) | (_) (_) | Documentation: http://docs.julialang.org > >>>> _ _ _| |_ __ _ | Type "?help" for help. > >>>> | | | | | | |/ _` | | > >>>> | | |_| | | | (_| | | Version 0.5.0-dev+429 (2015-09-29 09:47 UTC) > >>>> _/ |\__'_|_|_|\__'_| | Commit f71e449 (14 days old master) > >>>> |__/ | x86_64-w64-mingw32 > >>>> > >>>> julia> using DataFrames > >>>> > >>>> julia> train = readtable("./test.csv"); > >>>> ERROR: OutOfMemoryError() > >>>> in resize! at array.jl:452 > >>>> in readnrows! at > >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:164 > >>>> in readtable! at > >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:767 > >>>> in readtable at > >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:847 > >>>> in readtable at > >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:893 > >>>> > >>>> > >>>> > >>>> > >>>> > >>>> On Tuesday, October 13, 2015 at 3:47:58 PM UTC-4, Yichao Yu wrote: > >>>>> > >>>>> > >>>>> On Oct 13, 2015 2:47 PM, "Grey Marsh" <[email protected]> wrote: > >>>>> > >>>>> Which julia version are you using. There's sime gc tweak on 0.4 for > >>>>> that. > >>>>> > >>>>> > > >>>>> > I was trying to load the training dataset from springleaf marketing > >>>>> > response on Kaggle. The csv is 921 mb, has 145321 row and 1934 > columns. My > >>>>> > machine has 8 gb ram and julia ate 5.8gb+ memory after that I > stopped julia > >>>>> > as there was barely any memory left for OS to function properly. > It took > >>>>> > about 5-6 minutes later for the incomplete operation. I've windows > 8 64bit. > >>>>> > Used the following code to read the csv to Julia: > >>>>> > > >>>>> > using DataFrames > >>>>> > train = readtable("C:\\train.csv") > >>>>> > > >>>>> > Next I tried to to load the same file in python: > >>>>> > > >>>>> > import pandas as pd > >>>>> > train = pd.read_csv("C:\\train.csv") > >>>>> > > >>>>> > This took ~2.4gb memory, about a minute time > >>>>> > > >>>>> > Checking the same in R again: > >>>>> > df = read.csv('E:/Libraries/train.csv', as.is = T) > >>>>> > > >>>>> > This took 2-3 minutes and consumes 3.5gb mem on the same machine. > >>>>> > > >>>>> > Why such discrepancy and why Julia even fails to load the csv > before > >>>>> > running out of memory? If there is any better way to get the file > loaded in > >>>>> > Julia? > >>>>> > > >>>>> > > >> > >> > > >
