Simon Willison’s Weblog

datasette-studio. I’ve been thinking for a while that it might be interesting to have a version of Datasette that comes bundled with a set of useful plugins, aimed at expanding Datasette’s default functionality to cover things like importing data and editing schemas.

This morning I built the very first experimental preview of what that could look like. Install it using pipx:

pipx install datasette-studio

I recommend pipx because it will ensure datasette-studio gets its own isolated environment, independent of any other Datasette installations you might have.

Now running “datasette-studio” instead of “datasette” will get you the version with the bundled plugins.

The implementation of this is fun—it’s a single pyproject.toml file defining the dependencies and setting up the datasette-studio CLI hook, which is enough to provide the full set of functionality.

Is this a good idea? I don’t know yet, but it’s certainly an interesting initial experiment. # 18th February 2024, 8:38 pm

Data analysis with SQLite and Python. I turned my 2hr45m workshop from PyCon into the latest official tutorial on the Datasette website. It includes an extensive handout which should be useful independently of the video itself. # 2nd July 2023, 4:48 pm

Making SQLite extensions pip install-able (via) Alex Garcia figured out how to bundle a compiled SQLite extension in a Python wheel (building different wheels for different platforms) and publish them to PyPI. This is a huge leap forward in terms of the usability of SQLite extensions, which have previously been pretty difficult to actually install and run. Alex also created Datasette plugins that depend on his packages, so you can now “datasette install datasette-sqlite-regex” (or datasette-sqlite-ulid, datasette-sqlite-fastrand, datasette-sqlite-jsonschema) to gain access to his custom SQLite extensions in your Datasette instance. It even works with “datasette publish --install” to deploy to Vercel, Fly.io and Cloud Run. # 6th February 2023, 7:44 pm

Deploying Python web apps as AWS Lambda functions. After literally years of failed half-hearted attempts, I finally managed to deploy an ASGI Python web application (Datasette) to an AWS Lambda function! Here are my extensive notes. # 19th September 2022, 4:05 am

Where does all the effort go? Looking at Python core developer activity (via) Łukasz Langa used Datasette to explore 28,780 pull requests made to the CPython GitHub repository, using some custom Python scripts (and sqlite-utils) to load in the data. # 18th October 2021, 8:21 pm

How to install and upgrade Datasette using pipx (via) I’ve been using pipx to run Datasette for a while now—it’s a neat Python packaging tool which installs a Python CLI command with all of its dependencies in its own isolated virtual environment. Today, thanks to Twitter, I figured out how to install and upgrade plugins in the same environment—so I added a section to the Datasette installation documentation about it. # 4th May 2020, 7:23 pm

Datasette 0.31. Released today: this version adds compatibility with Python 3.8 and breaks compatibility with Python 3.5. Since Glitch support Python 3.7.3 now I decided I could finally give up on 3.5. This means Datasette can use f-strings now, but more importantly it opens up the opportunity to start taking advantage of Starlette, which makes all kinds of interesting new ASGI-based plugins much easier to build. # 12th November 2019, 6:11 am

Ministry of Silly Runtimes: Vintage Python on Cloud Run (via) Cloud Run is an exciting new hosting service from Google that lets you define a container using a Dockerfile and then run that container in a “scale to zero” environment, so you only pay for time spent serving traffic. It’s similar to the now-deprecated Zeit Now 1.0 which inspired me to create Datasette. Here Dustin Ingram demonstrates how powerful Docker can be as the underlying abstraction by deploying a web app using a 25 year old version of Python 1.x. # 9th April 2019, 5:33 pm

Datasette unit tests: monkeytype_call_traces (via) Faceted browse against every function call that occurs during the execution of Datasette’s test suite. I used Instagram’s MonkeyType tool to generate this, which can run Python code and generates a SQLite database of all of the traced calls. It’s intended to be used to automatically add mypy annotations to your code, but since it produces a SQLite database as a by-product I’ve started exploring the intermediary format using Datasette. Generating this was as easy as running “monkeytype run `which pytest`” in the Datasette root directory. # 2nd August 2018, 9:03 pm

XARs: An efficient system for self-contained executables (via) Really interesting new open source project from Facebook: a XAR is a new way of packaging up a Python executable complete with its dependencies and resources such that it can be distributed and executed elsewhere as a single file. It’s kind of like a Docker container without Docker—it uses the SquashFS compressed read-only filesystem. I can’t wait to try this out with Datasette. # 13th July 2018, 7 pm