pysimdjson
datasette
Our great sponsors
pysimdjson | datasette | |
---|---|---|
6 | 187 | |
628 | 8,881 | |
- | - | |
5.3 | 9.2 | |
3 months ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pysimdjson
- Analyzing multi-gigabyte JSON files locally
-
I Use C When I Believe in Memory Safety
Its magic function wrapping comes at a cost, trading ease of use for runtime performance. When you have a single C++ function to call that will run for a "long" time, pybind all the way. But pysimdjson tends to call a single function very quickly, and the overhead of a single function call is orders of magnitude slower than with cython when being explit with types and signatures. Wrap a class in pybind11 and cython and compare the stack trace between the two, and the difference is startling.
Ex: https://github.com/TkTech/pysimdjson/issues/73
-
Processing JSON 2.5x faster than simdjson with msgspec
simdjson
-
[package-find] lsp-bridge
You are aware of simdjson being available in python if you really need some json crunching, albeit json module in Python is implemented in C itself, so I don't think understand why do you think Python is slow there?
-
The fastest tool for querying large JSON files is written in Python (benchmark)
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`.
-
How I cut GTA Online loading times by 70%
I don't think JSON is really the problem - parsing 10MB of JSON is not so slow. For example, using Python's json.load takes about 800ms for a 47MB file on my system, using something like simdjson cuts that down to ~70ms.
datasette
-
Ask HN: High quality Python scripts or small libraries to learn from
Simon Willison's github would be a great place to get started imo -
https://github.com/simonw/datasette
- Show HN: TextQuery – Query and Visualize Your CSV Data in Minutes
-
Little Data: How do we query personal data? (2013)
I'm a fan on simonw's datasette/dogsheep ecosystem https://datasette.io/
-
LaTeX and Neovim for technical note-taking
I use Anki the exact same way. After a lifetime of learning I have accepted that I will never read over anything I write for myself voluntarily - so my two options are:
1. Write an article so good I can publish it and look it over myself later on. I did this last year with https://andrew-quinn.me/fzf/, for example.
2. Create Anki cards out of the material. Use the builtin Card Browser or even https://datasette.io/ on the underlying SQLite database in a pinch to search for my notes any time I have to.
-
Daily Price Tracking for Trader Joes
Were you aware of, or tempted by https://datasette.io/ for creating your solution?
- SQLite-Web: Web-based SQLite database browser written in Python
-
Ask HN: What two software products should have a kid?
Browsing HN, GitHub and the like we get to see a huge variety of software products and code bases.
I often see products and think - if this product X, got together with Y, it would be pretty cool - kind of like if they had a kid together.
Not too literally, but more on the conceptual level - my level of programming is low.
E.g. Just some....
- pocketable.io & datasette (+with some more charting) [https://pocketbase.io, https://datasette.io]
-
Ask HN: Looking for a project to volunteer on? (February 2024)
You might like the Datasette project: https://datasette.io/
I don't think they are desperate for contributions but it's a welcoming environment and a fun project to hack on. You'll learn a lot just from reading the source and the incredibly informative PRs. The creator is a really talented developer with a great blog which shows up on the HN front page often.
-
Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
-
What We Watched: A Netflix Engagement Report – About Netflix
> uploads of boring raw excel data and receive a nice UI
https://datasette.io/
What are some alternatives?
orjson - Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
nocodb - 🔥 🔥 🔥 Open Source Airtable Alternative
cysimdjson - Very fast Python JSON parsing library
duckdb - DuckDB is an in-process SQL OLAP Database Management System
ultrajson - Ultra fast JSON decoder and encoder written in C with Python bindings
sql.js-httpvfs - Hosting read-only SQLite databases on static file hosters like Github Pages
Fast JSON schema for Python - Fast JSON schema validator for Python.
litestream - Streaming replication for SQLite.
lupin is a Python JSON object mapper - Python document object mapper (load python object from JSON and vice-versa)
Sequel-Ace - MySQL/MariaDB database management for macOS
PyValico - Small python wrapper around https://github.com/rustless/valico
beekeeper-studio - Modern and easy to use SQL client for MySQL, Postgres, SQLite, SQL Server, and more. Linux, MacOS, and Windows.