Thanks. I appreciate your efforts. Looking forward to 0.4.1. in that case.
Am Dienstag, 27. Oktober 2015 06:30:32 UTC+1 schrieb Jacob Quinn: > > Just a quick follow-up here: after some benchmarking of my own on a > windows machine, the culprit ended up being a deathly slow `strtod` system > library function on windows. It takes a few hoops to get the performance > right, which I discovered is already done in Base Julia, it just wasn't > exported. > > My PR to Base Julia <https://github.com/JuliaLang/julia/pull/13641> has > been accepted and is backport pending, so once Julia 0.4.1 is released, > CSV.jl will be updated to use the new code and will require that version of > Julia to enable similar great performance cross-platform. > > -Jacob > > On Wed, Oct 14, 2015 at 3:51 AM, bernhard <[email protected] <javascript:> > > wrote: > >> with readtable the julia process goes up to 6.3 GB and stays there. It >> takes 95 seconds. (@time shows "373M, allocations: 13GB, 7% GC time") >> I will try Jacob's approach again. >> >> >> Am Mittwoch, 14. Oktober 2015 10:59:06 UTC+2 schrieb Milan Bouchet-Valat: >>> >>> Le mercredi 14 octobre 2015 à 00:15 -0700, Grey Marsh a écrit : >>> > Done with the testing in the cloud instance. >>> > It works and the timings in my case >>> > >>> > 58.346345 seconds (694.00 M allocations: 12.775 GB, 2.63% gc time) >>> > >>> > result of "top" command: VIRT: 11.651g RES: 3.579g >>> > >>> > ~13gb memory for a 900mb file! >>> > Thanks to Jacob atleast I was able check that the process works. >>> As Yichao noted, at no point in the import did Julia use 13GB of RAM. >>> That's the total amount of memory that was allocated and freed by >>> pieces (694M of them). You'd need to watch the Julia process while >>> working to see what's the maximum value of RES when importing. >>> >>> >>> Regards >>> >>> > On Wednesday, October 14, 2015 at 12:10:02 PM UTC+5:30, bernhard >>> > wrote: >>> > > Jacob >>> > > >>> > > I do run into the same issue as Grey. the step >>> > > ds = DataStreams.DataTable(f); >>> > > gets stuck. >>> > > I also tried this with a smaller file (150MB) which I have. This >>> > > file is read by readtable in 15s. But the DataTable function >>> > > freezes. I use 0.4 on Windows 7. >>> > > >>> > > I note that your code did work on a tiny file though (40 lines or >>> > > so). >>> > > I do get a dataframe, but when I show it (by simply typing df, or >>> > > dump(df)) Julia crashes... >>> > > >>> > > Bernhard >>> > > >>> > > >>> > > Am Mittwoch, 14. Oktober 2015 06:54:16 UTC+2 schrieb Grey Marsh: >>> > > > I am using Julia 0.4 for this purpose, if that's what is meant by >>> > > > "0.4 only". >>> > > > >>> > > > On Wednesday, October 14, 2015 at 9:53:09 AM UTC+5:30, Jacob >>> > > > Quinn wrote: >>> > > > > 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_1 >>> > > > > > 931","VAR_1932","VAR_1933","VAR_1934","target"],[Int64,DataSt >>> > > > > > reams.PointerString,Int64,Int64,Int64,DataStreams.PointerStri >>> > > > > > ng,Int64,Int64,DataStreams.PointerString,DataStreams.PointerS >>> > > > > > tring >>> > > > > > >> … >>> > > > > > >> >>> > > > > > Int64,Int64,Int64,Int64,Int64,Int64,Int64,Int64,DataStreams.P >>> > > > > > ointerString,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:1 >>> > > > > > 64 >>> > > > > > >>>> in readtable! at >>> > > > > > >>>> >>> > > > > > C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:7 >>> > > > > > 67 >>> > > > > > >>>> in readtable at >>> > > > > > >>>> >>> > > > > > C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:8 >>> > > > > > 47 >>> > > > > > >>>> in readtable at >>> > > > > > >>>> >>> > > > > > C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:8 >>> > > > > > 93 >>> > > > > > >>>> >>> > > > > > >>>> >>> > > > > > >>>> >>> > > > > > >>>> >>> > > > > > >>>> >>> > > > > > >>>> 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? >>> > > > > > >>>>> > >>> > > > > > >>>>> > >>> > > > > > >> >>> > > > > > >> >>> > > > > > > >>> > > > > > >>> > > > > >>> >> >
