HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
JuliaData
CSV.jl
Julia2020–2020
JuliaData / CSV.jl

CSV.jl

Utility library for working with CSV and other delimited files in the Julia programming language

3Distinct people
3Mentions
0Explicit recommenders
511GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Utility library for working with CSV and other delimited files in the Julia programming language

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week3 dated mentions

Observed mentions: 2020-08-20 16:20:06 UTC to 2020-10-11 17:33:16 UTC. Weeks start Monday, UTC.

0122020-08-17 00:00:00 UTC to 2020-08-24 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsAug 172020-08-24 00:00:00 UTC to 2020-08-31 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-08-31 00:00:00 UTC to 2020-09-07 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-09-07 00:00:00 UTC to 2020-09-14 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsSep 72020-09-14 00:00:00 UTC to 2020-09-21 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-09-21 00:00:00 UTC to 2020-09-28 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-09-28 00:00:00 UTC to 2020-10-05 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-10-05 00:00:00 UTC to 2020-10-12 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsOct 5

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Aug 172020-08-17 00:00:00 UTC2020-08-24 00:00:00 UTC110
Aug 242020-08-24 00:00:00 UTC2020-08-31 00:00:00 UTC000
Aug 312020-08-31 00:00:00 UTC2020-09-07 00:00:00 UTC000
Sep 72020-09-07 00:00:00 UTC2020-09-14 00:00:00 UTC000
Sep 142020-09-14 00:00:00 UTC2020-09-21 00:00:00 UTC000
Sep 212020-09-21 00:00:00 UTC2020-09-28 00:00:00 UTC000
Sep 282020-09-28 00:00:00 UTC2020-10-05 00:00:00 UTC000
Oct 52020-10-05 00:00:00 UTC2020-10-12 00:00:00 UTC202

What Hacker News says

3 sources
I posted this comment on lobste.rs when this came up:[1] It kind of looks like Julia’s CSV parser is cheating: https://github.com/JuliaData/CSV.jl/blob/9f6ef108d195f85daa5... It’s doing parallel parsing, but I’m pretty sure their technique won’t work for all inputs. Namely, they try to hop around the CSV data and chunk it up, and then parse each chunk in a separate thread AIUI. But you can’t do this in general because of quoting. If you read the code around where I linked, you can see they try to be a bit speculative and avoid common failures (“now we read the next 5 rows and see if we get the right # of columns”), but that isn’t going to be universally correct. It might be a fair trade off to make, since CSV data that fails there is probably quite rare. But either I’m misunderstanding their optimization or they aren’t being transparent a
Read the rest
bout it. I don’t see this downside anywhere in the README or the benchmark article. [1] - https://lobste.rs/s/zksa0f/loading_csv_file_at_speed_limit_n...
In CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster than Python and R
EDIT: Be sure to read the reply from the author of the package. I do think Julia has promise, but it is still early days. In the last 2 months there have been problems with: * Throwing rows away: https://github.com/JuliaData/CSV.jl/issues/720 * Incorrect float parsing: https://github.com/JuliaData/CSV.jl/issues/714 At there are unfixed issues to do with parsing large files ( https://github.com/JuliaData/CSV.jl/issues/749 , https://github.com/JuliaData/CSV.jl/issues/734 ). While Julia has become much more stable than it used to be, it is still in many places an evolving system and you (in my limited experience) should be prepared to hit issues.
In CSV Reader Benchmarks: Julia Reads CSVs 10-20x Faster than Python and R

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.