DuckDB User Survey Analysis

Earlier this year, we conducted a survey in the DuckDB community. We were mostly curious about the following topics:

1. How do people use DuckDB?
2. Where do people use DuckDB?
3. What do they like about DuckDB?
4. What improvements would they like to see in future releases?

The survey was open for about three weeks. More than 500 people submitted their answers, and we raffled 20 t-shirts and hoodies among the participants.

Summary

We summarize the key findings of the survey below:

• Users run DuckDB most often on laptops but servers are also very popular.
• The most popular clients are the Python API and the standalone CLI client.
• Most users don't have huge data sets but they appreciate high performance very much.
• Users would like to see performance optimizations related to time series and partitioned data.
• DuckDB is popular among data engineers, analysts and scientists, and also among software engineers.

Let's dive into the details!

Using DuckDB

Environments

We asked users about the environment where DuckDB is deployed and found that most of them, 87%, run DuckDB on their laptops. This is in line with the vision that originally drove the creation of DuckDB: creating a system that harnesses the power of hardware available in modern end-user devices. 29% run DuckDB on desktop workstations, and 58% run it on servers (see the breakdown later in the “Server Types” section).

Clients

Unsurprisingly, DuckDB is most often used from Python (73%), followed by the standalone command-line application (47%). The third spot is hotly contested with R, WebAssembly (!) and Java all achieving around 14%, followed by Node.js (Javascript) at 9%

The next few places, with 6-7% each, are occupied by ODBC, Rust, and Go. Finally, Arrow (ADBC) rounds off the top 10 with 5%.

Operating Systems

We found that most users, 61%, run DuckDB on Linux servers. These deployments include cloud instances, on-premises installations, and continuous integration (CI) runners. Windows desktop and macOS have a similar share of users, 41–45%. A further 9% run DuckDB on Windows servers.

We found the number of Linux desktop users quite striking. While the overall market share of Linux desktop is around 4.5%, 29% of respondents indicated that they run DuckDB on Linux desktop! We suspect that this is thanks to DuckDB's popularity among data engineers, who often use Linux desktop due to its customizability and similarity to the Linux server-based deployment environments.

Server Types

As we discussed in the “Environments” section, DuckDB is often run on servers. But how big are these servers, and where are they operated? Both small servers (less than 16 GB of memory) and medium-sized servers (16-512 GB of memory) are popular, with 56% and 61% of users reporting that they run DuckDB on these. About 14% of respondents run DuckDB on servers with more than 0.5TB of memory.

Regarding where the servers run, on-premises deployments and AWS are neck-and-neck with 27%. They are followed by two other clouds, Microsoft Azure and the Google Cloud Platform. Finally, about 4% of users run DuckDB on Hetzner servers.

Data

Data Formats

We inquired about the data formats used when working with DuckDB. Parquet is the most popular format: 79% of users reporting to use it. CSV is a close second with 73%. JSON is also popular with vanilla JSON achieving 42% and NDJSON 11%. About ⅓ reported to use Arrow.

Dataset Sizes

We asked users about the size of the largest dataset they processed with DuckDB. We defined dataset size as the size of the data when stored in uncompressed CSV format. For Parquet files and DuckDB database files, we asked users to approximate the CSV size by multiplying their file sizes by 5.

The responses showed that only a few respondents use DuckDB to process Big Data. For ¾ of users, their largest dataset size was less than 100 GB data, 20% of users processed a dataset between 100 GB and 1 TB, and approximately 5% of the users ventured into the 1 TB+ territory. About 1% processed 10 TB+ datasets. These findings are in line with statistics derived from a recent RedShift usage dataset by Jordan Tigani of MotherDuck, and the recent analysis of the Snowflake and RedShift datasets by George Fraser of Fivetran.

While these results obviously are somewhat biased – users who need to crunch through huge datasets may not work with DuckDB (yet!) –, the skew towards smaller datasets is quite significant and shows that many real-world use cases can be tackled using small to medium-sized datasets. The results also show that DuckDB can solve many problems on datasets larger than 1 TB.

Features

Most Liked Features

We were curious: what do users like most about DuckDB? The plot shows the most frequent responses:

The most liked feature is high performance. Users also enjoy file format support (CSV, Parquet, JSON, etc.), ease of use, extensive SQL support (including friendly SQL) and in-memory integrations such as support for Pandas, Arrow and NumPy. Finally, users mentioned low memory usage, protocol support (e.g., HTTPS, S3), database integrations, and portability.

Feature Requests

We asked users about the features that they'd most like to see in future DuckDB versions. The most popular requests are listed in the table below:

Feature	Percentage
Improved partitioning and optimizations related to partitioning	39%
Improved support for time series and optimizations for pre-sorted data	35%
Support for materialized views	28%
Support for vector search	24%
Support for attaching to database systems via ODBC	24%
Support for time travel queries (query the database as of a given time)	23%
Support for the Delta Lake format	22%
Improved support for Iceberg (including writes)	17%

We are happy to report that, since the survey was conducted pre-v1.0.0 and DuckDB is now at version 1.1.1, some of these requests are already a reality:

• Reading Delta Lake is now possible via the delta extension.
• Vector search is now supported via the vss extension.

For the rest of the requested features, several ones are in the making at DuckDB Labs. Stay tuned!

User Roles

We asked respondents to indicate their main roles in their organization. The top-5 answers were as follows:

It's no surprise that DuckDB is popular in the “data” roles: 26% of the respondents are data engineers, 14% are data scientists, and 9% are data analysts. The form had a surprisingly high share of software engineers, 23%. Finally, about 2% of respondents indicated that their primary role is DBA.

Conclusion

We would like to thank all participants for taking the time to complete the survey. We will use the answers to guide the future development of DuckDB, and we hope that readers of this analysis find it informative as well.