[gdal-dev] Read a /vsigzip/ csv.gz all at once

[Erik Schnetter]

The Julia wrapper (ArchGDAL.jl) for `getfield` calls `OGR_FD_GetFieldDefn` and several related function (to get the type of the field etc.). Are these possibly expensive operations in GDAL?

Any C function in GDAL can easily be called from Julia. Which C function would get all fields at once? I assume that e.g. `OGR_F_GetFieldAsDoubleList` would not work; this would be for values that are themselves lists?

The Julia code for `getfield` spends quite a bit of work to find out the type of the field. This includes a bit of reference counting, allocating small structures on the heap, registering finalizers for them etc. This could be avoided by adding a Julia wrapper that calls `getfield` repeatedly (even from Julia, calling C has no overhead by itself) for a range of integers. This would avoid the additional overhead having to do with handling types, and the Julia/GDAL reference counting. Even, is that what you had in mind?

-erik

> On Jun 24, 2025, at 11:01, Even Rouault via gdal-dev <gdal-dev at lists.osgeo.org> wrote:
> 
> Hi,
> 
> I don't know anything about Julia but I'd suspect that there must be something particularly slow in the way it interacts with C. For comparison,  "time python3 swig/python/gdal-utils/osgeo_utils/samples/ogrinfo.py  /vsigzip//vsicurl/https://bulk.meteostat.net/v2/hourly/2022/08554.csv.gz  -al > /dev/null" that does essentially your loop, and also prints on stdout, runs in 1.5 seconds (compared to native ogrinfo that runs in 0.7 s). Perhaps you could write a Julia wrapper to get all fields of feature at once and return whatever dictionary or equivalent data structure is idiomatic (and efficient )in Julia ? Also are you sure your Julia wrapper is built with optimization enabled?
> 
> Even
> 
> Le 24/06/2025 à 16:33, Joaquim Manuel Freire Luís via gdal-dev a écrit :
>> Hi,
>>  
>> Im trying to read files like 
>> https://bulk.meteostat.net/v2/hourly/2022/08554.csv.gz
>>  
>> in my Julia wrapper. The point is that, although I’m kind off succeeding, the hole operation is very slow.
>> What I’m doing (code not committed yet so can’t post a link) is to read like this
>>  
>> layer = getlayer(dataset, 0)
>> for f in layer
>>                for k = 1: Gdal.nfield(f)
>>                               Gdal.getfield(f, k-1)
>> …
>>  
>> This works but it’s extremely slow because each “getfield” takes about 1e-4 seconds and the file has ~8 k rows, each with 13 fields. That amounts to > 10 sec.
>>  
>> I’ve searched but couldn’t find a way to read the entire file at once (which takes 1e-2 seconds if I read it, locally, with a gzip wrapper) and return it as a single string array that I could parse later.
>>  
>> Is that possible? 
>>  
>> Thanks
>>  
>> Joaquim
>> 
>> 
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