Simon Willison’s Weblog


12 items tagged “duckdb”


An example running DuckDB in ChatGPT Code Interpreter (via) I confirmed today that DuckDB can indeed be run inside ChatGPT Code Interpreter (aka "data analysis"), provided you upload the correct wheel file for it to install. The wheel file it needs is currently duckdb-1.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl from the PyPI releases page - I asked ChatGPT to identify its platform, and it said that it needs manylinux2014_x86_64.whl wheels.

Once the wheel in installed ChatGPT already knows enough of the DuckDB API to start performing useful operations with it - and any brand new features in 1.0 will work if you tell it how to use them.

# 17th July 2024, 9:04 pm / ai, duckdb, generative-ai, chatgpt, llms, code-interpreter

Using DuckDB for Embeddings and Vector Search (via) Sören Brunk's comprehensive tutorial combining DuckDB 1.0, a subset of German Wikipedia from Hugging Face (loaded using Parquet), the BGE M3 embedding model and DuckDB's new vss extension for implementing an HNSW vector index.

# 15th June 2024, 2:39 pm / ai, duckdb, embeddings

DuckDB 1.0 (via) Six years in the making. The most significant feature in this milestone is stability of the file format: previous releases often required files to be upgraded to work with the new version.

This release also aspires to provide stability for both the SQL dialect and the C API, though these may still change with sufficient warning in the future.

# 3rd June 2024, 1:23 pm / databases, sql, duckdb

DuckDB as the New jq (via) The DuckDB CLI tool can query JSON files directly, making it a surprisingly effective replacement for jq. Paul Gross demonstrates the following query:

select license->>'key' as license, count(*) from 'repos.json' group by 1

repos.json contains an array of {"license": {"key": "apache-2.0"}..} objects. This example query shows counts for each of those licenses.

# 21st March 2024, 8:36 pm / sql, jq, duckdb

Announcing DuckDB 0.10.0. Somewhat buried in this announcement: DuckDB has Fixed-Length Arrays now, along with array_cross_product(a1, a2), array_cosine_similarity(a1, a2) and array_inner_product(a1, a2) functions.

This means you can now use DuckDB to find related content (and other tricks) using vector embeddings!

Also notable: “DuckDB can now attach MySQL, Postgres, and SQLite databases in addition to databases stored in its own format. This allows data to be read into DuckDB and moved between these systems in a convenient manner, as attached databases are fully functional, appear just as regular tables, and can be updated in a safe, transactional manner.”

# 13th February 2024, 5:57 pm / databases, mysql, postgresql, sql, sqlite, duckdb, embeddings

Fastest Way to Read Excel in Python (via) Haki Benita produced a meticulously researched and written exploration of the options for reading a large Excel spreadsheet into Python. He explored Pandas, Tablib, Openpyxl, shelling out to LibreOffice, DuckDB and python-calamine (a Python wrapper of a Rust library). Calamine was the winner, taking 3.58s to read 500,00 rows—compared to Pandas in last place at 32.98s.

# 3rd January 2024, 8:04 pm / excel, pandas, python, rust, duckdb, haki-benita


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 / machine-learning, sql, sqlite, ai, duckdb


Querying Postgres Tables Directly From DuckDB (via) I learned a lot of interesting PostgreSQL tricks from this write-up of the new DuckDB feature that allows it to run queries against PostgreSQL servers directly. It works using COPY (SELECT ...) TO STDOUT (FORMAT binary) which writes rows to the protocol stream in efficient binary format, but splits the table being read into parallel fetches against page ranges and uses SET TRANSACTION SNAPSHOT ... in those parallel queries to ensure they see the same transactional snapshot of the database.

# 3rd October 2022, 2:27 pm / databases, postgresql, duckdb

Notes on the SQLite DuckDB paper

SQLite: Past, Present, and Future is a newly published paper authored by Kevin P. Gaffney, Martin Prammer and Jignesh M. Patel from the University of Wisconsin-Madison and D. Richard Hipp, Larry Brasfield and Dan Kennedy from the core SQLite engineering team.

[... 1,021 words]


DuckDB-Wasm: Efficient Analytical SQL in the Browser (via) First SQLite, now DuckDB: options for running database engines in the browser using WebAssembly keep on growing. DuckDB means browsers now have a fast, intuitive mechanism for querying Parquet files too. This also supports the same HTTP Range header trick as the SQLite demo from a while back, meaning it can query large databases loaded over HTTP without downloading the whole file.

# 29th October 2021, 3:25 pm / sqlite, parquet, webassembly, duckdb

Querying Parquet using DuckDB (via) DuckDB is a relatively new SQLite-style database (released as an embeddable library) with a focus on analytical queries. This tutorial really made the benefits click for me: it ships with support for the Parquet columnar data format, and you can use it to execute SQL queries directly against Parquet files—e.g. “SELECT COUNT(*) FROM ’taxi_2019_04.parquet’”. Performance against large files is fantastic, and the whole thing can be installed just using “pip install duckdb”. I wonder if faceting-style group/count queries (pretty expensive with regular RDBMSs) could be sped up with this?

# 25th June 2021, 10:40 pm / python, parquet, duckdb


DuckDB (via) This is a really interesting, relatively new database. It’s kind of a weird hybrid between SQLite and PostgreSQL: it uses the PostgreSQL parser but models itself after SQLite in that databases are a single file and the code is designed for use as an embedded library, distributed in a single amalgamation C++ file (SQLite uses a C amalgamation). It features a “columnar-vectorized query execution engine” inspired by MonetDB (also by the DuckDB authors) and is hence designed to run analytical queries really quickly. You can install it using “pip install duckdb”—the resulting module feels similar to Python’s sqlite3, and follows roughly the same DBAPI pattern.

# 19th September 2020, 11:43 pm / databases, postgresql, sqlite, duckdb