16 items tagged “duckdb”
2024
Turning Your Root URL Into a DuckDB Remote Database. Fun idea from Drew Breunig: DuckDB supports attaching existing databases that are accessible over HTTP using their URL. Drew suggests creating vanity URLs using your root domain, detecting the DuckDB user-agent and serving the database file directly - allowing tricks like this one:
ATTACH 'https://steplist.app/' AS steplist;
SELECT * FROM steplist.lists;
Foursquare Open Source Places: A new foundational dataset for the geospatial community (via) I did not expect this!
[...] we are announcing today the general availability of a foundational open data set, Foursquare Open Source Places ("FSQ OS Places"). This base layer of 100mm+ global places of interest ("POI") includes 22 core attributes (see schema here) that will be updated monthly and available for commercial use under the Apache 2.0 license framework.
The data is available as Parquet files hosted on Amazon S3.
Here's how to list the available files:
aws s3 ls s3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/
I got back places-00000.snappy.parquet through places-00024.snappy.parquet, each file around 455MB for a total of 10.6GB of data.
I ran duckdb and then used DuckDB's ability to remotely query Parquet on S3 to explore the data a bit more without downloading it to my laptop first:
select count(*) from 's3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/places-00000.snappy.parquet';
This got back 4,180,424 - that number is similar for each file, suggesting around 104,000,000 records total.
Update: DuckDB can use wildcards in S3 paths (thanks, Paul) so this query provides an exact count:
select count(*) from 's3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/places-*.snappy.parquet';
That returned 104,511,073 - and Activity Monitor on my Mac confirmed that DuckDB only needed to fetch 1.2MB of data to answer that query.
I ran this query to retrieve 1,000 places from that first file as newline-delimited JSON:
copy (
select * from 's3://fsq-os-places-us-east-1/release/dt=2024-11-19/places/parquet/places-00000.snappy.parquet'
limit 1000
) to '/tmp/places.json';
Here's that places.json file, and here it is imported into Datasette Lite.
Finally, I got ChatGPT Code Interpreter to convert that file to GeoJSON and pasted the result into this Gist, giving me a map of those thousand places (because Gists automatically render GeoJSON):
Wikidata is a Giant Crosswalk File.
Drew Breunig shows how to take the 140GB Wikidata JSON export, use sed 's/,$//' to convert it to newline-delimited JSON, then use DuckDB to run queries and extract external identifiers, including a query that pulls out 500MB of latitude and longitude points.
Conflating Overture Places Using DuckDB, Ollama, Embeddings, and More.
Drew Breunig's detailed tutorial on "conflation" - combining different geospatial data sources by de-duplicating address strings such as RESTAURANT LOS ARCOS,3359 FOOTHILL BLVD,OAKLAND,94601 and LOS ARCOS TAQUERIA,3359 FOOTHILL BLVD,OAKLAND,94601.
Drew uses an entirely offline stack based around Python, DuckDB and Ollama and finds that a combination of H3 geospatial tiles and mxbai-embed-large embeddings (though other embedding models should work equally well) gets really good results.
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.
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.
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.
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.
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.
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.
2023
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.
2022
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.
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]2021
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.
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?
2020
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.