DuckCon 2023 - 2nd edition

The DuckDB team is excited to invite you all for our second DuckCon user group meeting. It will take place the day before FOSDEM in Brussels on Feb 3rd, 2023, at the Hilton Hotel.

In this edition, we will have the DuckDB creators Hannes Mühleisen, and Mark Raasveldt, talking about the current state of DuckDB and future plans. We will also have one talk regarding building out-of-tree extensions from DuckDB contributors Pedro Holanda, and Sam Ansmink and one invited talk from MotherDuck’s Founding Engineers, Boaz Leskes and Yves Le Maout. In addition, we will have a session with lightning talks from our users. We will close the event with a networking session with food and drinks where users are invited to interact directly with the DuckDB team.

Announcing DuckDB 0.6.0

The DuckDB team is happy to announce the latest DuckDB version (0.6.0) has been released. This release of DuckDB is named “Oxyura” after the White-headed duck (Oxyura leucocephala) which is an endangered species native to Eurasia.

To install the new version, please visit the installation guide. Note that the release is still being rolled out, so not all artifacts may be published yet. The full release notes can be found here.

Lightweight Compression in DuckDB

TLDR: DuckDB supports efficient lightweight compression that is automatically used to keep data size down without incurring high costs for compression and decompression.

When working with large amounts of data, compression is critical for reducing storage size and egress costs. Compression algorithms typically reduce data set size by 75-95%, depending on how compressible the data is. Compression not only reduces the storage footprint of a data set, but also often improves performance as less data has to be read from disk or over a network connection.

Modern Data Stack in a Box with DuckDB

TLDR: A fast, free, and open-source Modern Data Stack (MDS) can now be fully deployed on your laptop or to a single machine using the combination of DuckDB, Meltano, dbt, and Apache Superset.

This post is a collaboration with Jacob Matson and cross-posted on dataduel.co.

Summary

There is a large volume of literature (1, 2, 3) about scaling data pipelines. “Use Kafka! Build a lake house! Don’t build a lake house, use Snowflake! Don’t use Snowflake, use XYZ!” However, with advances in hardware and the rapid maturation of data software, there is a simpler approach. This article will light up the path to highly performant single node analytics with an MDS-in-a-box open source stack: Meltano, DuckDB, dbt, & Apache Superset on Windows using Windows Subsystem for Linux (WSL). There are many options within the MDS, so if you are using another stack to build an MDS-in-a-box, please share it with the community on the DuckDB Twitter, GitHub, or Discord, or the dbt slack! Or just stop by for a friendly debate about our choice of tools!

Querying Postgres Tables Directly From DuckDB

TLDR: DuckDB can now directly query queries stored in PostgreSQL and speed up complex analytical queries without duplicating data.

Persistent Storage of Adaptive Radix Trees in DuckDB

TLDR: DuckDB uses Adaptive Radix Tree (ART) Indexes to enforce constraints and to speed up query filters. Up to this point, indexes were not persisted, causing issues like loss of indexing information and high reload times for tables with data constraints. We now persist ART Indexes to disk, drastically diminishing database loading times (up to orders of magnitude), and we no longer lose track of existing indexes. This blog post contains a deep dive into the implementation of ART storage, benchmarks, and future work. Finally, to better understand how our indexes are used, I’m asking you to answer the following survey. It will guide us when defining our future roadmap.

Range Joins in DuckDB

TL;DR: DuckDB has fully parallelised range joins that can efficiently join millions of range predicates.

Range intersection joins are an important operation in areas such as temporal analytics, and occur when two inequality conditions are present in a join predicate. Database implementations often rely on slow O(N^2) algorithms that compare every pair of rows for these operations. Instead, DuckDB leverages its fast sorting logic to implement two highly optimized parallel join operators for these kinds of range predicates, resulting in 20-30x faster queries. With these operators, DuckDB can be used effectively in more time-series-oriented use cases.

Friendlier SQL with DuckDB

An elegant user experience is a key design goal of DuckDB. This goal guides much of DuckDB’s architecture: it is simple to install, seamless to integrate with other data structures like Pandas, Arrow, and R Dataframes, and requires no dependencies. Parallelization occurs automatically, and if a computation exceeds available memory, data is gracefully buffered out to disk. And of course, DuckDB’s processing speed makes it easier to get more work accomplished.

However, SQL is not famous for being user-friendly. DuckDB aims to change that! DuckDB includes both a Relational API for dataframe-style computation, and a highly Postgres-compatible version of SQL. If you prefer dataframe-style computation, we would love your feedback on our roadmap. If you are a SQL fan, read on to see how DuckDB is bringing together both innovation and pragmatism to make it easier to write SQL in DuckDB than anywhere else. Please reach out on GitHub or Discord and let us know what other features would simplify your SQL workflows. Join us as we teach an old dog new tricks!

Parallel Grouped Aggregation in DuckDB

TL;DR: DuckDB has a fully parallelized aggregate hash table that can efficiently aggregate over millions of groups.

Grouped aggregations are a core data analysis command. It is particularly important for large-scale data analysis (“OLAP”) because it is useful for computing statistical summaries of huge tables. DuckDB contains a highly optimized parallel aggregation capability for fast and scalable summarization.

Jump straight to the benchmarks?

DuckDB Time Zones: Supporting Calendar Extensions

TLDR: The DuckDB ICU extension now provides time zone support.

Time zone support is a common request for temporal analytics, but the rules are complex and somewhat arbitrary. The most well supported library for locale-specific operations is the International Components for Unicode (ICU). DuckDB already provided collated string comparisons using ICU via an extension (to avoid dependencies), and we have now connected the existing ICU calendar and time zone functions to the main code via the new TIMESTAMP WITH TIME ZONE (or TIMESTAMPTZ for short) data type. The ICU extension is pre-bundled in DuckDB’s Python and R clients and can be optionally installed in the remaining clients.

In this post, we will describe how time works in DuckDB and what time zone functionality has been added.

DuckDB quacks Arrow: A zero-copy data integration between Apache Arrow and DuckDB

TLDR: The zero-copy integration between DuckDB and Apache Arrow allows for rapid analysis of larger than memory datasets in Python and R using either SQL or relational APIs.

This post is a collaboration with and cross-posted on the Arrow blog.

DuckDB - The Lord of Enums:
The Fellowship of the Categorical and Factors.

String types are one of the most commonly used types. However, often string columns have a limited number of distinct values. For example, a country column will never have more than a few hundred unique entries. Storing a data type as a plain string causes a waste of storage and compromises query performance. A better solution is to dictionary encode these columns. In dictionary encoding, the data is split into two parts: the category and the values. The category stores the actual strings, and the values stores a reference to the strings. This encoding is depicted below.

Fast Moving Holistic Aggregates

TLDR: DuckDB, a free and Open-Source analytical data management system, has a windowing API that can compute complex moving aggregates like interquartile ranges and median absolute deviation much faster than the conventional approaches.

In a previous post, we described the DuckDB windowing architecture and mentioned the support for some advanced moving aggregates. In this post, we will compare the performance various possible moving implementations of these functions and explain how DuckDB’s performant implementations work.

DuckDB-Wasm: Efficient Analytical SQL in the Browser

TLDR: DuckDB-Wasm is an in-process analytical SQL database for the browser. It is powered by WebAssembly, speaks Arrow fluently, reads Parquet, CSV and JSON files backed by Filesystem APIs or HTTP requests and has been tested with Chrome, Firefox, Safari and Node.js. You can try it in your browser at shell.duckdb.org or on Observable.

Windowing in DuckDB

TLDR: DuckDB, a free and Open-Source analytical data management system, has a state-of-the-art windowing engine that can compute complex moving aggregates like inter-quartile ranges as well as simpler moving averages.

Window functions (those using the OVER clause) are important tools for analysing data series, but they can be slow if not implemented carefully. In this post, we will take a look at how DuckDB implements windowing. We will also see how DuckDB can leverage its aggregate function architecture to compute useful moving aggregates such as moving inter-quartile ranges (IQRs).