thank you so much! this was an annoyance of mine for so long. edit: any chance you make a brew package? if you'd like I'd be happy to PR it in.

It’s crazy how long we’ve gone without a tool like this. This is huge. Thank you for finally building this!

Apart from some visual glitches, this is an INSTANT BUY !

Note: must the Windows binary really be 78MB ?

Can DuckDB be included in the tool, so you can run queries directly from the UI? [that would avoid opening DBeaver whenever you need that kind of feature]

Looks like a nice tool, but failed for me when reading a geoparquet file created using duckdb.

Looks great!

Another seemingly extremely similar project released in the last few days: https://github.com/raulcd/datanomy

This is very impressive. Look forward to using this

Nice work—this hits a real pain point with Parquet. My main use case is debugging partitioned datasets on S3 with schema drift and skew, where I care about: which files/partitions have schema mismatches, weird row-group stats (all-null, out-of-range, huge skew), and doing that via metadata only.

Right now parqeye looks mainly single-file focused. Do you have plans for a “dataset mode” that takes a dir/S3 prefix and surfaces per-file/row-group summaries (row counts, min/max, null %, schema diffs vs a reference file) using just Parquet stats so it scales to tens of GB? Or do you see parqeye intentionally staying a single-file inspector?

Such a cool idea!! So helpful

tried it out. love it.

Yours looks much better for your use case, but fwiw you can do it in a single command with duckdb too (but not interactive etc.):

    duckdb -c "from 'foo.parquet'"

but maybe still useful for other formats or multi-file or remote situations

What is really missing for parquet's wide adoption is support in Excel.

Very nice that it can show the metadata. If you rather focus on the data itself, a Swiss army knife in the terminal is VisiData [1] . It works with many formats from CSV to Parquet. You'd need to install Pyarrow I think to read Parquet files. VisiData is great to not only peek into the file but filter it, sort, compute simple metrics and even can plot a histogram or scatterplot for ex. I avoided a lot of Jupyter notebooks by using VisiData :)

[1] https://www.visidata.org/

I found a similar tool called nail-parquet[1] which has some nice query functions. I packaged[2] it up for nixpkgs but it’s stuck in merge limbo…

[1] https://github.com/Vitruves/nail-parquet [2] https://github.com/NixOS/nixpkgs/pull/449066

Similar tool for JSONL files: I built JSONL Viewer Pro after repeatedly crashing VS Code trying to inspect multi-GB training datasets and IoT device logs with nested objects.

Native Mac/Windows app with multi-threaded parsing (simdjson), automatic nested object flattening, and handles 10M+ rows instantly.

For HN: Use code HN100 for free access

https://iotdatasystems.gumroad.com/

Built with C++ for native performance (~6MB app, not Electron).

Would love feedback from folks working with large JSONL files.

Beautiful, I'm currently deep into getting our data into iceberg from firehose and I'm really curious what metadata is written, are bloomfilters being written for the columns i want? Has my compaction and sort jobs helped min-max statistics on those columns?

Will take a look when i get to my laptop!

This looks very handy, thank you for working on this and making it open source.

I did submit a feature request for vi keybindings; though I could look into contributing this myself if I find a bit of spare time.

The other thing that surprised me was the size of the binaries: 90MB for a TUI tool (x64 Linux)? I wonder what the bulk of that is? Is there an issue with LTO? An other commenter noticed as well.

It also looks like you are building against a relatively recent glibc (2.34), which limits compatibility with older systems. Building against an older glibc can be hard to do, so I am not faulting you here, and you do provide a musl fallback, which is appreciated (mandatory notice that the musl allocator can dramatically degrade the performance of rust programs, just in case you were not aware of this).

A few more ideas for improvements (you probably already have your own laundry list):

- Mouse support?

- Seeing that you do have graphs, it would be fun to see a scatter plot as well as a distribution plot under statistics in the "Row Groups" tab (though you probably pull these from the metadata, so that would require further processing, which may be out of scope).

what was wrong with using a python repl with pyarrow/polars/duckdb for this?

This looks beautiful but we're heavily invested in s3 so I'll wait for remote support

Isn't this what we have spreadsheets for?

Also allows you to do computations on the data in place.

Great! I worked a lot with parquet like 5 years ago. The frustration and tilt working with the tooling was immense. Thank you for building this, it feels like resolving some old knot in my soul.

Some kind soul made this repository then, and I found it on like the 13th page of Google while in the depths of despair. It is my most treasured GitHub star, a the shining beacon that saved me. I see it has saved 17 other people too.

https://github.com/casidiablo/parquet-tools-for-dumb-people-...

It's unfortunate that Python and R don't really have any out-of-the-box means of opening data files from arguments, but if you do this kind of stuff on a daily basis it's something that you can set up. My not directly usable examples below.

Python (uv + dataiter, but easy to modify for pandas or polars): https://github.com/otsaloma/dataiter/blob/master/bin/di-open

R (as per comment, requires also ~/.Rprofile code, nanoparquet in this case): https://github.com/otsaloma/R-tools/blob/master/r-load

Nice tool!

BTW, you can use duckdb with their ui plugin to have an interactive view of your data, not only parquet.