~41K pages/sec peak throughput.
Key choices: memory-mapped I/O, SIMD string search, parallel page extraction, streaming output. Handles CID fonts, incremental updates, all common compression filters.
~5,000 lines, no dependencies, compiles in <2s.
Why it's fast:
- Memory-mapped file I/O (no read syscalls)
- Zero-copy parsing where possible
- SIMD-accelerated string search for finding PDF structures
- Parallel extraction across pages using Zig's thread pool
- Streaming output (no intermediate allocations for extracted text)
- XRef tables and streams (PDF 1.5+)
- Incremental PDF updates (/Prev chain)
- FlateDecode, ASCII85, LZW, RunLength decompression
- Font encodings: WinAnsi, MacRoman, ToUnicode CMap
- CID fonts (Type0, Identity-H/V, UTF-16BE with surrogate pairs)a better speed comparison would either be multi-process pdfium (since pdfium was forked from foxit before multi-thread support, you can't thread it), multi-threaded foxit, or something like syncfusion (which is quite fast and supports multiple threads). Or even single thread pdfium vs single thread your-code.
These were always the fastest/best options. I can (and do) achieve 41k pages/sec or better on these options.
The other thing it doesn't appear you mention is whether you handle putting the words in reading order (IE how they appear on the page), or only stream order (which varies in its relation to apperance order) .
If it's only stream order, sure, that's really fast to do. But also not anywhere near as helpful as reading order, which is what other text-extraction engines do.
Looking at the code, it looks like the code to do reading order exists, but is not what is being benchmarked or used by default?
If so, this is really comparing apples and oranges.
From https://github.com/Lulzx/zpdf/blob/main/src/main.zig it looks like the help text cites an unimplemented "-j" option to enable multiple threads.
There is a "--parallel" option, but that is only implemented for the "bench" command.
I haven't tested without SIMD.
Are you using LLMs for parts of the coding?
What's your work flow when approaching a new project like this?
I can't talk about the code, but the readme and commit messages are most likely LLM-generated.
And when you take into account that the first commit happened just three hours ago, it feels like the entire project has been vibe coded.
You didn't. Claude did. Like it did write this comment.
And you didn't even bother testing it before submitting, which is insulting to everyone.
You avoid an unnecessary copy. Normal read system call gets the data from disk hardware into the kernel page cache and then copies it into the buffer you provide in your process memory. With mmap, the page cache is mapped directly into your process memory, no copy.
All running processes share the mapped copy of the file.
There are a lot of downsides to mmap: you lose explicit error handling and fine-grained control of when exactly I/O happens. Consult the classic article on why sophisticated systems like DBMSs do not use mmap: https://db.cs.cmu.edu/mmap-cidr2022/
74910,74912c187768,187779
< [Example 1: If you want to use the code conversion facetcodecvt_utf8to output tocouta UTF-8 multibyte sequence
< corresponding to a wide string, but you don't want to alter the locale forcout, you can write something like:\237 D.27.21954
\251ISO/IECN4950wstring_convert<std::codecvt_utf8<wchar_t>> myconv;
< std::string mbstring = myconv.to_bytes\050L"Hello\134n"\051;
---
>
> [Example 1: If you want to use the code conversion facet codecvt_utf8 to output to cout a UTF-8 multibyte sequence
> corresponding to a wide string, but you don’t want to alter the locale for cout, you can write something like:
>
> § D.27.2
> 1954
>
> © ISO/IEC
> N4950
>
> wstring_convert<std::codecvt_utf8<wchar_t>> myconv;
> std::string mbstring = myconv.to_bytes(L"Hello\n");
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0200020D02030208020901F90203020901FF0203020502080003012B020001F9012B020001F901FA0205020A01FD01FE0208
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https://github.com/Lulzx/zpdf/blob/main/python/tests/test_zp...
the licensing is a huge blocker for using mupdf in non-OSS tools, so it's very nice to see this is MIT
python bindings would be good too
also, added python bindings.
as others have commented, I think while this is a nice portfolio piece, I would worry about its longevity as a vibe coded project
- commit message: LLM-generated.
- README: LLM-generated.
I'm not convinced that projects vibe coded over the evening deserve the HN front page…
Edit: and of course the author's blog is also full of AI slop…
2026 hasn't even started I already hate it.
Using AI lazily is a problem though. Writing code has never been the most important part of software development, making sure that the code does what the user needs is what takes most of the time. But from the github issues and the comment here from the few who have tested the tool, it looka like the author didn't even test the AI output on real PDF.
If you use AI to build in 3 month something that would have taken a year without it, then cool. But here we're talking about someone who's spending 2-3 hours every other day building a new fake software project to pad his resume. This isn't something anyone should endorse.
If it's really better than what we had before, what does it matter how it was made? It's literally hacked together with the tools of the day (LLMs) isn't that the very hacker ethos? Patching stuff together that works in a new and useful way.
5x speed improvements on pdf text extraction might be great for some applications I'm not aware of, I wouldn't just dismiss it out of hand because the author used $robot to write the code.
Presumably the thought to make the thing in the first place and decide what features to add and not add was more important than how the code is generated?
That's a very big if. The whole point is that what we had before was made slowly. This was made quickly. In itself it's not better but what it typically means is hours and hours of testing. Going through painful problems that highlight idiosyncrasies of the problem space. Things that are really weird and specific to whatever the tool is trying to address.
In such cases we can be expect that with very little time very few things were tested and tested properly (including a comment mentioned how tests were also generated). "We" the audience of potentially interested users have then to do that work (as plenty did commenting on that post).
IMHO what you bring forward is precisely that :
- can the new "solution" actually pass ALL the tests the previous one did? More?
This should be brought to the top and the actual compromises can then be understood, "we" can then decide if it's "better" for our context. In some cases faster with lossy output is actually better, in others absolutely not. The difference between the new and the old solutions isn't binary and have no visibility on that is what makes such a process nothing more than yet another showcase that LLMs can indeed produce "something" that is absolutely boring while consuming a TON of resources, including our own attention.
TL;DR: there should be test "harness" made by 3rd parties (or from well known software it is the closest too) that an LLM generated piece of code should pass before being actually compared.
zpdf extract texbook.pdf | grep -m1 Stanford
DONALD E. KNUTHStanford UniversityIllustrations byDon't disagree but in specific case, per the author, project was made via Claude Code. Although could as well be that Zig is better as LLM target. Noticed many new vibe projects decide to use Zig as target.
Contrasted with python, which is interpreted, has a clunky runtime, minimal optimizations, and all sorts of choices that result in slow, redundant, and also slow, performance.
The price for performance is safety checks, redundancy, how badly wrong things can go, and so on.
A good compromise is luajit - you get some of the same aggressive optimizations, but in an interpreted language, with better-than-c performance but interpreted language convenience, access to low level things that can explode just as spectacularly as with zig or c, but also a beautiful language.
It may be easier to write code that runs faster in Zig than in C under similar build optimization levels, because writing high performance C code looks a lot like writing idiomatic Zig code. The Zig standard library offers a lot of structures like hash maps, SIMD primitives, and allocators with different performance characteristics to better fit a given use-case. C application code often skips on these things simply because it is a lot more friction to do in C than in Zig.
machine code, not https://en.wikipedia.org/wiki/Bytecode
> The price for performance is safety checks
In Zig, non-ReleaseFast build modes have significant safety checks.
> luajit ... with better-than-c performance
No.
fpdf
jpdf
cpdf
cpppdf
bfpdf
ppdf
...
opdf
I'm curious about the trade-offs mentioned in the comments regarding Unicode handling. For document analysis pipelines (like extracting text from technical documentation or research papers), robust Unicode support is often critical.
Would be interesting to see benchmarks on different PDF types - academic papers with equations, scanned documents with OCR layers, and complex layouts with tables. Performance can vary wildly depending on the document structure.