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.

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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).

Fastest table sort in the West - Redesigning DuckDB’s sort

TLDR: DuckDB, a free and Open-Source analytical data management system, has a new highly efficient parallel sorting implementation that can sort much more data than fits in main memory.

Database systems use sorting for many purposes, the most obvious purpose being when a user adds an ORDER BY clause to their query. Sorting is also used within operators, such as window functions. DuckDB recently improved its sorting implementation, which is now able to sort data in parallel and sort more data than fits in memory. In this post, we will take a look at how DuckDB sorts, and how this compares to other data management systems.

Querying Parquet with Precision using DuckDB

TLDR: DuckDB, a free and open source analytical data management system, can run SQL queries directly on Parquet files and automatically take advantage of the advanced features of the Parquet format.

Apache Parquet is the most common “Big Data” storage format for analytics. In Parquet files, data is stored in a columnar-compressed binary format. Each Parquet file stores a single table. The table is partitioned into row groups, which each contain a subset of the rows of the table. Within a row group, the table data is stored in a columnar fashion.

Efficient SQL on Pandas with DuckDB

TLDR: DuckDB, a free and open source analytical data management system, can efficiently run SQL queries directly on Pandas DataFrames.

Recently, an article was published advocating for using SQL for Data Analysis. Here at team DuckDB, we are huge fans of SQL. It is a versatile and flexible language that allows the user to efficiently perform a wide variety of data transformations, without having to care about how the data is physically represented or how to do these data transformations in the most optimal way.

Testing out DuckDB's Full Text Search Extension

TLDR: DuckDB now has full-text search functionality, similar to the FTS5 extension in SQLite. The main difference is that our FTS extension is fully formulated in SQL. We tested it out on TREC disks 4 and 5.

Searching through textual data stored in a database can be cumbersome, as SQL does not provide a good way of formulating questions such as “Give me all the documents about Mallard Ducks”: string patterns with LIKE will only get you so far. Despite SQL’s shortcomings here, storing textual data in a database is commonplace. Consider the table products (id INT, name VARCHAR, description VARCHAR) - it would be useful to search through the name and description columns for a website that sells these products.