Our great sponsors
- Onboard AI - Learn any GitHub repo in 59 seconds
- InfluxDB - Collect and Analyze Billions of Data Points in Real Time
- SaaSHub - Software Alternatives and Reviews
-
simdjson
Parsing gigabytes of JSON per second : used by Facebook/Meta Velox, WatermelonDB, Apache Doris, Milvus, StarRocks
Daniel Lemire’s simdjson probably belongs in this discussion and I would be surprised if it is not the fastest tool by some margin.
-
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.
-
Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
-
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 "..."`)?
-
-
> 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.
-
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.
-
InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
-
-
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`.
-
msgspec
A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
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. -
