You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I do not remember if it was reported before (as similar issues were reported), but reading the file instagram_posts.csv that can be found in https://www.kaggle.com/datasets/shmalex/instagram-dataset using 4 threads leaves 15GB memory leak (after destroying all the visible variables that reference to the read file) + GC.gc() is very slow all the time.
When doing the same on a single thread all is OK, i.e. after removing references to the read file and doing GC.gc memory is back to the previous level.
Configuration: Win11, Julia 1.8.2, CSV.jl 0.10.4.
The text was updated successfully, but these errors were encountered:
@bkamins, can you check your script/workflow on #1046? I believe we're probably also running into a similar issue in Arrow.jl w/ multithreaded reading/writing. It's probably also worth considering for DataFrames.jl and any other packages utilizing Threads.@spawn.
I do not remember if it was reported before (as similar issues were reported), but reading the file instagram_posts.csv that can be found in https://www.kaggle.com/datasets/shmalex/instagram-dataset using 4 threads leaves 15GB memory leak (after destroying all the visible variables that reference to the read file) +
GC.gc()
is very slow all the time.When doing the same on a single thread all is OK, i.e. after removing references to the read file and doing
GC.gc
memory is back to the previous level.Configuration: Win11, Julia 1.8.2, CSV.jl 0.10.4.
The text was updated successfully, but these errors were encountered: