Simon Willison’s Weblog

Online gradient descent written in SQL (via) Max Halford trains an online gradient descent model against two years of AAPL stock data using just a single advanced SQL query. He built this against DuckDB—I tried to replicate his query in SQLite and it almost worked, but it gave me a “recursive reference in a subquery” error that I was unable to resolve. # 7th March 2023, 6:56 pm

konstantint/SKompiler (via) A tool for compiling trained SKLearn models into other representations —including SQL queries and Excel formulas. I’ve been pondering the most light-weight way to package a simple machine learning model as part of a larger application without needing to bundle heavy dependencies, this set of techniques looks ideal! # 2nd October 2022, 11:56 pm

Efficient Pagination Using Deferred Joins (via) Surprisingly simple trick for speeding up deep OFFSET x LIMIT y pagination queries, which get progressively slower as you paginate deeper into the data. Instead of applying them directly, apply them to a “select id from ...” query to fetch just the IDs, then either use a join or run a separate “select * from table where id in (...)” query to fetch the full records for that page. # 16th August 2022, 5:35 pm

The Checkered Flag Diagram for visualizing SQL joins. I really like this alternative to Venn diagrams for showing the difference between different types of SQL join (left join, right join, cross join etc). # 20th July 2022, 1:16 pm

How Materialize and other databases optimize SQL subqueries. Jamie Brandon offers a survey of the state-of-the-art in optimizing correlated subqueries, across a number of different database engines. # 15th May 2022, 8:24 pm

SQLModel. A new project by FastAPI creator Sebastián Ramírez: SQLModel builds on top of both SQLAlchemy and Sebastián’s Pydantic validation library to provide a new ORM that’s designed around Python 3’s optional typing. The real brilliance here is that a SQLModel subclass is simultaneously a valid SQLAlchemy ORM model AND a valid Pydantic validation model, saving on duplicate code by allowing the same class to be used both for form/API validation and for interacting with the database. # 24th August 2021, 11:16 pm

Bare columns in an aggregate queries. This is a really nice SQL tweak implemented in SQLite: If you run a query like “SELECT a, b, max(c) FROM tab1 GROUP BY a” SQLite will find the row with the highest value for c and use the columns of that row as the returned values for the other columns mentioned in the query. # 10th August 2021, 1:29 am

Django SQL Dashboard 1.0 (via) As part of my ongoing attempt to be braver about 1.0 releases (crucial if you want to do semantic versioning properly) I’ve released version 1.0 of Django SQL Dashboard, my Datasette-inspired app for Django that adds an interface for running read-only, bookmarkable SQL queries against a PostgreSQL database. The new version adds a column cog menu providing shortcuts for changing the sort order, counting distinct values and performing a group-by/count against column values. # 1st July 2021, 5:44 pm

Hierarchical Structures in PostgreSQL (via) Two techniques I hadn’t seen before: the first is to define a materialized view using a CTE that offers efficient tree queries against a PostgreSQL array of path components (plus a trigger to update the materialized view), the second is with the PostgreSQL ltree extension which ships as part of PostgreSQL and hence should be widely available. # 25th June 2021, 5:19 pm

The humble hash aggregate (via) Today I learned that “hash aggregate” is the name for the algorithm where you split a list of tuples on a common key, run an aggregation against each resulting group and combine the results back together again—I’d previously thought if this in terms of map/reduce but hash aggregate is a much older term used widely by SQL engines—I’ve seen it come up in PostgreSQL explain query output (for GROUP BY) before but didn’t know what it meant. # 6th June 2021, 4:03 pm

Practical SQL for Data Analysis (via) This is a really great SQL tutorial: it starts with the basics, but quickly moves on to a whole array of advanced PostgreSQL techniques—CTEs, window functions, efficient sampling, rollups, pivot tables and even linear regressions executed directly in the database using regr_slope(), regr_intercept() and regr_r2(). I picked up a whole bunch of tips for things I didn’t know you could do with PostgreSQL here. # 4th May 2021, 3:11 am

Everything You Always Wanted To Know About GitHub (But Were Afraid To Ask) (via) ClickHouse by Yandex is an open source column-oriented data warehouse, designed to run analytical queries against TBs of data. They’ve loaded the full GitHub Archive of events since 2011 into a public instance, which is a great way of both exploring GitHub activity and trying out ClickHouse. Here’s a query I just ran that shows number of watch events per year, for example:

SELECT toYear(created_at) as yyyy, count() FROM github_events WHERE event_type = ’WatchEvent’ group by yyyy # 5th January 2021, 1:02 am

Some SQL Tricks of an Application DBA (via) This post taught me so many PostgreSQL tricks that I hadn’t seen before. Did you know you can start a transaction, drop an index, run explain and then rollback the transaction (cancelling the index drop) to see what explain would look like without that index? Among other things I also learned what the “correlation” database statistic does: it’s a measure of how close-to-sorted the values in a specific column are, which helps PostgreSQL decide if it should do an index scan or a bitmap scan when making use of an index. # 29th July 2020, 7:04 pm

Get Started—Materialize. Materialize is a really interesting new database—“a streaming SQL materialized view engine”. It builds materialized views on top of streaming data sources (such as Kafka)—you define the view using a SQL query, then it figures out how to keep that view up-to-date automatically as new data streams in. It speaks the PostgreSQL protocol so you can talk to it using the psql tool or any PostgreSQL client library. The “get started” guide is particularly impressive: it uses a curl stream of the Wikipedia recent changes API, parsed using a regular expression. And it’s written in Rust, so installing it is as easy as downloading and executing a single binary (though I used Homebrew). # 1st June 2020, 10:11 pm

Using SQL to Look Through All of Your iMessage Text Messages (via) Dan Kelch shows how to access the iMessage SQLite database at ~/Library/Messages/chat.db—it’s protected under macOS Catalina so you have to enable Full Disk Access in the privacy settings first. I usually use the macOS terminal app but I installed iTerm for this because I’d rather enable full disk access to a separate terminal program than let anything I’m running in my regular terminal take advantage of it. It worked! Now I can run “datasette ~/Library/Messages/chat.db” to browse my messages. # 22nd May 2020, 4:45 pm

How Super Graph compiles GraphQL to a single SQL query. Super Graph is a GraphQL server that compiles arbitrarily nested GraphQL queries to “a single fast SQL query”. I’ve always wondered how that could possible work, so I asked author Vikram Rangnekar for an example of a compiled query—it turns out it uses a brilliant sequence of JSON aggregations to glue together results from nested subqueries and left outer joins. # 16th April 2020, 10:52 pm

SQL is a better API language than GraphQL – Convince me otherwise (via) A dumb tweet I posted this morning blew up today and ended up on the Hacker News homepage. # 16th April 2020, 10:44 pm

Generated Columns in SQLite (via) SQLite 3.31.0 released today, and generated columns are the single most notable new feature. PostgreSQL 12 added these in October 2019, and MySQL has had them since 5.7 in October 2015. MySQL and SQLite both offer either “stored” or “virtual” generated columns, with virtual columns being calculated at runtime. PostgreSQL currently only supports stored columns. # 24th January 2020, 4:20 am

athena-sqlite (via) Amazon Athena is the AWS tool for querying data stored in S3—as CSV, JSON or Apache Parquet files—using SQL. It’s an interesting way of buliding a very cheap data warehouse on top of S3 without having to run any additional services. Athena recently added a query federation SDK which lets you define additional custom data sources using Lambda functions. Damon Cortesi used this to write a custom connector for SQLite, which lets you run queries against data stored in SQLite files that you have uploaded to S3. You can then run joins between that data and other Athena sources. # 18th December 2019, 9:05 am