Question 1

How to import a CSV file into DuckDB using SQL?

Accepted Answer

You can directly reference the CSV file in the FROM clause, like SELECT * FROM 'myfile.csv'. This eliminates the need for separate import steps and is documented in the README's data import section for simplicity.

Question 2

DuckDB vs SQLite: which is better for data analysis?

Accepted Answer

DuckDB is optimized for analytical queries with columnar storage, offering better performance for complex analytics on large datasets, while SQLite is more general-purpose with row-based storage suited for transactional workloads.

Question 3

Can I use DuckDB with Python pandas?

Accepted Answer

Yes, DuckDB has deep integration with pandas, allowing you to run SQL queries directly on pandas DataFrames. This is supported through the Python client and enhances data manipulation workflows.

Question 4

What are the performance benchmarks for DuckDB?

Accepted Answer

DuckDB is designed for high-performance analytics, and benchmarks often show it outperforming traditional databases for analytical tasks. Specific benchmarks can be run using the benchmark_runner tool as mentioned in the development section.

Question 5

Is DuckDB suitable for web applications with multiple users?

Accepted Answer

No, DuckDB is not ideal for web apps requiring high concurrency, as it's an embedded database. For such scenarios, a client-server database like PostgreSQL is recommended due to better multi-user support.

Question 6

How to handle nested data types like arrays in DuckDB?

Accepted Answer

DuckDB supports complex data types including arrays, structs, and maps, with SQL functions for manipulation. Refer to the SQL reference documentation for examples on querying and transforming these types.

DuckDB

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