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- INSERT Statements 11775
- INSERT statements are the standard way of loading data into a relational database. When using INSERT statements, the values are supplied row-by-row. While simple, there is significant overhead involved in parsing and processing individual I
...NSERT statements. This makes lots of individual row-by-row insertions very inefficient for bulk insertion. Bestpractice As a rule-of-thumb, avoid using lots of individual row-by-row INSERT statements when inse... - https://man.mtool.work/duckdb/2.0/data/insert.html
- Jupyter Notebooks 8798
- DuckDB's Python client can be used directly in Jupyter notebooks with no additional configuration, as the first section shows. Additional libraries can simplify SQL query development; the rest of this guide uses the JupySQL package. See oth
...andas and then use matplotlib or seaborn for plotting. This approach requires loading all data into memory which is highly inefficient. The plotting module in JupySQL runs computations in the SQL engine. This delegates memory management to the engine and en... - https://man.mtool.work/duckdb/2.0/guides/python/jupyter.html
- JSON Processing Functions 7310
- JSON Extraction Functions There are two extraction functions, which have their respective operators. The operators can only be used if the string is stored as the JSON logical type. These functions support the same two location notations as
...ARCHAR","species":["VARCHAR"],"coolness":"DOUBLE","hair":"BOOLEAN"} Transforming JSON to Nested Types In many cases, it is inefficient to extract values from JSON one-by-one. Instead, we can “extract” all values at once, transforming JSON to the nested type... - https://man.mtool.work/duckdb/2.0/data/json/json_functions.html
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