sqlite-utils
ultrajson
sqlite-utils | ultrajson | |
---|---|---|
35 | 3 | |
1,523 | 4,250 | |
- | 0.5% | |
8.1 | 6.9 | |
24 days ago | 5 days ago | |
Python | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
sqlite-utils
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Ask HN: High quality Python scripts or small libraries to learn from
https://github.com/simonw/sqlite-utils
So, his code might not be a good place to find best patterns (for ex, I don't think they are fully typed), but his repos are very pragmatic, and his development process is super insightful (well documented PRs for personal repos!). Best part, he blogs about every non-trivial update, so you get all the context!
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Why you should probably be using SQLite
Sounds like your problem is with SQLAlchemy, not with SQLite.
My https://sqlite-utils.datasette.io library might be a better fit for you. It's a much thinner abstraction than SQLAlchemy.
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Welcome to Datasette Cloud
There are a few things you can do here.
SQLite is great at JSON - so I often dump JSON structures in a TEXT column and query them using https://www.sqlite.org/json1.html
I also have plugins for running jq() functions directly in SQL queries - https://datasette.io/plugins/datasette-jq and https://github.com/simonw/sqlite-utils-jq
I've been trying to drive the cost of turning semi-structured data into structured SQL queries down as much as possible with https://sqlite-utils.datasette.io - see this tutorial for more: https://datasette.io/tutorials/clean-data
This is also an area that I'm starting to explore with LLMs. I love the idea that you could take a bunch of messy data, tell Datasette Cloud "I want this imported into a table with this schema"... and it does that.
I have a prototype of this working now, I hope to turn it into an open source plugin (and Datasette Cloud feature) pretty soon. It's using this trick: https://til.simonwillison.net/gpt3/openai-python-functions-d...
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SQLite Functions for Working with JSON
I've baked a ton of different SQLite tricks - including things like full-text indexing support and advanced alter table methods - into my sqlite-utils CLI tool and Python library: https://sqlite-utils.datasette.io
My Datasette project provides tools for exploring, analyzing and publishing SQLite databases, plus ways to expose them via a JSON API: https://datasette.io
I've also written a ton of stuff about SQLite on my two blogs:
- https://simonwillison.net/tags/sqlite/
- https://til.simonwillison.net/sqlite
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Show HN: Trogon – An automatic TUI for command line apps
This is really fun. I have an experimental branch of my sqlite-utils CLI tool (which has dozens of sub-commands) running with this now and it really did only take 4 lines of code - I'm treating Trogon as an optional dependency because people using my package as a Python library rather than a CLI tool may not want the extra installed components:
https://github.com/simonw/sqlite-utils/commit/ec12b780d5dcd6...
There's an animated GIF demo of the result here: https://github.com/simonw/sqlite-utils/issues/545#issuecomme...
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I'm sure I'm being stupid.. Copying data from an API and making a database
My project https://datasette.io/ is ideal for this kind of thing. You can use https://sqlite-utils.datasette.io/ to load JSON data into a SQLite database, then publish it with Datasette.
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Just: A Command Runner
I've been using this for about six months now and I absolutely love it.
Make never stuck for me - I couldn't quite get it to fit inside my head.
Just has the exact set of features I want.
Here's one example of one of my Justfiles: https://github.com/simonw/sqlite-utils/blob/fc221f9b62ed8624... - documented here: https://sqlite-utils.datasette.io/en/stable/contributing.htm...
I also wrote about using Just with Django in this TIL: https://til.simonwillison.net/django/just-with-django
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Ask HN: What Do You Use for a Personal Database
SQLite with the open source toolchain I've been building over the past five years:
https://datasette.io as the interface for running queries against (and visualizing) my data.
https://sqlite-utils.datasette.io/ as a set of tools for creating and modifying my databases (inserting JSON or CSV data, enabling full text search text)
https://dogsheep.github.io as a suite of tools for importing my personal data - see also this talk I gave about that project: https://simonwillison.net/2020/Nov/14/personal-data-warehous...
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The Perfect Commit
Here's an example: https://github.com/simonw/sqlite-utils/pull/468
> After identifying about 7 commits (with pretty basic/useless messages, and no PR link!), I then had to find the corresponding PRs based on timestamps, and search the PR history for PRs merged around those timestamps.
Not sure if this would save any time, but it is possible to search PRs by commit. For example, say git blame led me to this commit: https://github.com/simonw/sqlite-utils/commit/129141572f249e...
I could have found PR #373 via this search: https://github.com/simonw/sqlite-utils/pulls?q=bb16f52681b6d...
> I thus treat PRs as ephemeral
I think I see what you're saying but as others have pointed out, sometimes you want to add screenshots etc to the context, and you can't capture this kind of info in commit messages. So then you have two choices: issues or PRs.
> Then any review comments are preferably not addressed directly in the PR
I would think that sometimes you really do want to have a back and forth conversation in the PR, rather than just a "make this change" -> "ok done" type of feedback loop.
I view the PR as an decent place for all of this because it's basically a commit of commits, capturing the related changes/conversation/context all in a single place at the point of merge.
ultrajson
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Processing JSON 2.5x faster than simdjson with msgspec
ujson
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Benchmarking Python JSON serializers - json vs ujson vs orjson
For most cases, you would want to go with python’s standard json library which removes dependencies on other libraries. On other hand you could try out ujsonwhich is simple replacement for python’s json library. If you want more speed and also want dataclass, datetime, numpy, and UUID instances and you are ready to deal with more complex code, then you can try your hands on orjson
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The fastest tool for querying large JSON files is written in Python (benchmark)
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
What are some alternatives?
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.
sqliteviz - Instant offline SQL-powered data visualisation in your browser
greenpass-covid19-qrcode-decoder - An easy tool for decoding Green Pass Covid-19 QrCode
ImportExcel - PowerShell module to import/export Excel spreadsheets, without Excel
python-rapidjson - Python wrapper around rapidjson
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
Fast JSON schema for Python - Fast JSON schema validator for Python.
q - q - Run SQL directly on delimited files and multi-file sqlite databases
PyLD - JSON-LD processor written in Python
Scoop - A command-line installer for Windows.
pysimdjson - Python bindings for the simdjson project.