Move Your Database To The Data And Speed Up Your Analytics With DuckDB
Data Engineering Podcast
The Hive Protocol Is Not a Good Idea, Maybe?
In order to pull this from spark to your python shell, spark, in its infinite wisdom, cyralizes the data using the hive protocol. The hive protocol uses thriftting herd colametater, which is not a good idea and means that you have to use mud plodly instead of spark. You know, i think that's more true than people want to believe, absolutely how much performance we leave on the table.
Summary
When you think about selecting a database engine for your project you typically consider options focused on serving multiple concurrent users. Sometimes what you really need is an embedded database that is blazing fast for single user workloads. DuckDB is an in-process database engine optimized for OLAP applications to speed up your analytical queries that meets you where you are, whether that’s Python, R, Java, even the web. In this episode, Hannes Mühleisen, co-creator and CEO of DuckDB Labs, shares the motivations for creating the project, the myriad ways that it can be used to speed up your data projects, and the detailed engineering efforts that go into making it adaptable to any environment. This is a fascinating and humorous exploration of a truly useful piece of technology.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Hannes Mühleisen about DuckDB, an in-process embedded database engine for columnar analytics
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what DuckDB is and the story behind it?
- Where did the name come from?
- What are some of the use cases that DuckDB is designed to support?
- The interface for DuckDB is similar (at least in spirit) to SQLite. What are the deciding factors for when to use one vs. the other?
- How might they be used in concert to take advantage of their relative strengths?
- What are some of the ways that DuckDB can be used to better effect than options provided by different language ecosystems?
- Can you describe how DuckDB is implemented?
- How has the design and goals of the project changed or evolved since you began working on it?
- What are some of the optimizations that you have had to make in order to support performant access to data that exceeds available memory?
- Can you describe a typical workflow of incorporating DuckDB into an analytical project?
- What are some of the libraries/tools/systems that DuckDB might replace in the scope of a project or team?
- What are some of the overlooked/misunderstood/under-utilized features of DuckDB that you would like to highlight?
- What is the governance model and plan long-term sustainability of the project?
- What are the most interesting, innovative, or unexpected ways that you have seen DuckDB used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on DuckDB?
- When is DuckDB the wrong choice?
- What do you have planned for the future of DuckDB?
Contact Info
- Hannes Mühleisen
- @hfmuehleisen on Twitter
- Website
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- DuckDB
- CWI
- SQLite
- OLAP == Online Analytical Processing
- Duck Typing
- ZODB
- Teradata
- HTAP == Hybrid Transactional/Analytical Processing
- Pandas
- Apache Arrow
- Julia Language
- Voltron Data
- Parquet
- Thrift
- Protobuf
- Vectorized Query Processor
- LLVM
- DuckDB Labs
- DuckDB Foundation
- MIT Open Courseware (OCW)
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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