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Daniel Lemire’s simdjson probably belongs in this discussion and I would be surprised if it is not the fastest tool by some margin.
https://github.com/simdjson/simdjson
For me OjG (https://github.com/ohler55/ojg) has been great. I regularly use it on files that can not be loaded into memory. The best JSON file format for multiple record is one JSON document per record all in the same file. OjG doesn't care if they are on different lines. It is fast (https://github.com/ohler55/compare-go-json) and uses a fairly complete JSONPath implementation for searches. Similar to jq but using JSONPath instead of a proprietary query language.
I am biased though as I wrote OjG to handle what other tools were not able to do.
For me OjG (https://github.com/ohler55/ojg) has been great. I regularly use it on files that can not be loaded into memory. The best JSON file format for multiple record is one JSON document per record all in the same file. OjG doesn't care if they are on different lines. It is fast (https://github.com/ohler55/compare-go-json) and uses a fairly complete JSONPath implementation for searches. Similar to jq but using JSONPath instead of a proprietary query language.
I am biased though as I wrote OjG to handle what other tools were not able to do.
I asked about this on the Github issue regarding these benchmarks as well.
I'm curious as to why libraries like ultrajson[0] and orjson[1] weren't explored. They aren't command line tools, but neither is pandas right? Is it perhaps because the code required to implement the challenges is large enough that they are considered too inconvenient to use through the same way pandas was used (ie, `python -c "..."`)?
[0] https://github.com/ultrajson/ultrajson
> resulting in large programs with lots of boilerplate
That was what I was trying to say when I said "the code required to implement the challenges is large enough that they are considered too inconvenient to use". This makes sense to me.
Thank you for this benchmark! I'll probably switch to spyql now from jq.
> So, orjson is part of the reason why a python-based tool outperforms tools written in C, Go, etc and deserves credit.
Yes, I definitely think this is worth mentioning upfront in the future, since, IIUC, orison's core uses Rust (the serde library, specifically). The initial title gave me the impression that a pure-Python json parsing-and-querying solution was the fastest out there, which I find misleading.
A parallel I think is helpful is saying something like "the fastest BERT implementation is written Python[0]". While the linked implementation is written in Python, it offloads the performance critical parts to C/C++ through TensorFlow.
[0] https://github.com/google-research/bert
My main problem with jq was finding the paths to the nodes, so I wrote catj (https://github.com/soheilpro/catj) to help with that.
json: 113.79130696877837 ms
While `orjson`, is faster than `ujson`/`json` here, it's only ~6% faster (in this benchmark). `simdjson` and `msgspec` (my library, see https://jcristharif.com/msgspec/) are much faster due to them avoiding creating PyObjects for fields that are never used.
If spyql's query engine can determine the fields it will access statically before processing, you might find using `msgspec` for JSON gives a nice speedup (it'll also type check the JSON if you know the type of each field). If this information isn't known though, you may find using `pysimdjson` (https://pysimdjson.tkte.ch/) gives an easy speed boost, as it should be more of a drop-in for `orjson`.
json: 113.79130696877837 ms
While `orjson`, is faster than `ujson`/`json` here, it's only ~6% faster (in this benchmark). `simdjson` and `msgspec` (my library, see https://jcristharif.com/msgspec/) are much faster due to them avoiding creating PyObjects for fields that are never used.
If spyql's query engine can determine the fields it will access statically before processing, you might find using `msgspec` for JSON gives a nice speedup (it'll also type check the JSON if you know the type of each field). If this information isn't known though, you may find using `pysimdjson` (https://pysimdjson.tkte.ch/) gives an easy speed boost, as it should be more of a drop-in for `orjson`.
"Datasette" (from Django co-creator) can take tabular data (SQLite, CSV, JSON, etc) and generate a REST/GraphQL API with visualization tools from it:
https://github.com/simonw/datasette
From the same author, "sqlite-utils" can take tabular data and create SQLite table definitions and rows from them:
https://github.com/simonw/sqlite-utils
"Pipe JSON (or CSV or TSV) directly into a new SQLite database file, automatically creating a table with the appropriate schema"
> * What sort of JSON "meta-formats" are the most important/common for you? E.g. in a file you could have object-per-line, object-of-arrays, array-of-objects, or in an SQL context you could have object-per-row or object-of-arrays-as-table, etc). I'd love to hear about others that are important to you.
"Datasette" (from Django co-creator) can take tabular data (SQLite, CSV, JSON, etc) and generate a REST/GraphQL API with visualization tools from it:
https://github.com/simonw/datasette
From the same author, "sqlite-utils" can take tabular data and create SQLite table definitions and rows from them:
https://github.com/simonw/sqlite-utils
"Pipe JSON (or CSV or TSV) directly into a new SQLite database file, automatically creating a table with the appropriate schema"
> * What sort of JSON "meta-formats" are the most important/common for you? E.g. in a file you could have object-per-line, object-of-arrays, array-of-objects, or in an SQL context you could have object-per-row or object-of-arrays-as-table, etc). I'd love to hear about others that are important to you.