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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association pull_request body repo type active_lock_reason performed_via_github_app reactions draft state_reason
836829560 MDU6SXNzdWU4MzY4Mjk1NjA= 248 support for Apache Arrow / parquet files I/O mhalle 649467 open 0     1 2021-03-20T14:59:30Z 2021-10-28T23:46:48Z   NONE  

I just started looking at Apache Arrow using pyarrow for import and export of tabular datasets, and it looks quite compelling. It might be worth looking at for sqlite-utils and/or datasette.

As a test, I took a random jsonl data dump of a dataset I have with floats, strings, and ints and converted it to arrow's parquet format using the naive pyarrow.parquet.write_file() command, which has automatic type inferrence. It compressed down to 7% of the original size. Conversion of a 26MB JSON file and serializing it to parquet was eyeblink instantaneous. Parquet files are portable and can be directly imported into pandas and other analytics software.

The only hangup is the automatic type inference of the naive reader. It's great for general laziness and for parsing JSON columns (it correctly interpreted a table of mine with a JSON array). However, I did get an exception for a string column where most entries looked integer-like but had a couple values that weren't -- the reader tried to coerce all of them for some reason, even though the JSON type is string. Since the writer optionally takes a schema, it shouldn't be too hard to grab the sqlite header types. With some additional hinting, you might get datetime columns and JSON, which are native Arrow types.

Somewhat tangentially, someone even wrote an sqlite vfs extension for Parquet: https://cldellow.com/2018/06/22/sqlite-parquet-vtable.html

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