I'm not sure where the conflict in what's written online is, but by virtue of being designed such that data structures do not require memory buffers to be RAM resident (i.e. can reference memory maps), we are set up well to process larger-than-memory datasets. In C++ at least we are putting the pieces in place to be able to do efficient query execution on on-disk datasets, and it may already be possible in Rust with DataFusion.
On Thu, Oct 22, 2020 at 2:11 PM Chris Nuernberger <[email protected]> wrote: > > There are ways to handle datasets larger than memory. mmap'ing one or more > arrow files and going from there is a pathway forward here: > > https://techascent.com/blog/memory-mapping-arrow.html > > How this maps to other software ecosystems I don't know but many have mmap > support. > > On Thu, Oct 22, 2020 at 12:47 PM Jacek Pliszka <[email protected]> > wrote: >> >> I believe it would be good if you define your use case. >> >> I do handle larger than memory datasets with pyarrow with the use of >> dataset.scan but my use case is very specific as I am repartitioning >> and cleaning a bit large datasets. >> >> BR, >> >> Jacek >> >> czw., 22 paź 2020 o 20:39 Jacob Zelko <[email protected]> napisał(a): >> > >> > Hi all, >> > >> > Very basic question as I have seen conflicting sources. I come from the >> > Julia community and was wondering if Arrow can handle larger-than-memory >> > datasets? I saw this post by Wes McKinney here discussing that the tooling >> > is being laid down: >> > >> > Table columns in Arrow C++ can be chunked, so that appending to a table is >> > a zero copy operation, requiring no non-trivial computation or memory >> > allocation. By designing up front for streaming, chunked tables, appending >> > to existing in-memory tabler is computationally inexpensive relative to >> > pandas now. Designing for chunked or streaming data is also essential for >> > implementing out-of-core algorithms, so we are also laying the foundation >> > for processing larger-than-memory datasets. >> > >> > ~ Apache Arrow and the “10 Things I Hate About pandas” >> > >> > And then in the docs I saw this: >> > >> > The pyarrow.dataset module provides functionality to efficiently work with >> > tabular, potentially larger than memory and multi-file datasets: >> > >> > A unified interface for different sources: supporting different sources >> > and file formats (Parquet, Feather files) and different file systems >> > (local, cloud). >> > Discovery of sources (crawling directories, handle directory-based >> > partitioned datasets, basic schema normalization, ..) >> > Optimized reading with predicate pushdown (filtering rows), projection >> > (selecting columns), parallel reading or fine-grained managing of tasks. >> > >> > Currently, only Parquet and Feather / Arrow IPC files are supported. The >> > goal is to expand this in the future to other file formats and data >> > sources (e.g. database connections). >> > >> > ~ Tabular Datasets >> > >> > The article from Wes was from 2017 and the snippet on Tabular Datasets is >> > from the current documentation for pyarrow. >> > >> > Could anyone answer this question or at least clear up my confusion for >> > me? Thank you! >> > >> > -- >> > Jacob Zelko >> > Georgia Institute of Technology - Biomedical Engineering B.S. '20 >> > Corning Community College - Engineering Science A.S. '17 >> > Cell Number: (607) 846-8947
