In this article we look at how to automatically enclose values in single quotes when outputting DuckDB query results as a list. Using this method, each value is formatted as SQL literals. This can be useful when you want to escape the values for SQL or for some other purpose.
We also look at how CSV output is sometimes quoted with double quotes, and how we can adjust the results by doing things like removing the headers and changing the list separator.
Continue readingIf you’re using DuckDB and you need to check which version you’re running, here are some quick and easy methods to do so.
Continue readingPerforming date/time arithmetic is a common operation when using SQL databases such as DuckDB. As with most RDBMSs, DuckDB makes these types of operations relatively straightforward for us.
Here are two options for adding one or more seconds to a date, timestamp, or time value.
Continue readingDuckDB’s SAMPLE clause is a handy feature that allows us to work with a random subset of our data. This is particularly useful when dealing with large datasets where processing the entire dataset might be time-consuming or unnecessary for exploratory data analysis, testing queries, or creating representative samples.
When we use this clause, we can specify the absolute number of rows to return, or a percentage of rows. We also have an option of sampling method to use.
Continue readingOne of SQLite’s unique features is the WITHOUT ROWID table, which can be used to optimize performance and storage in specific scenarios.
While it’s easy enough to create a WITHOUT ROWID table (just add WITHOUT ROWID to the definition), how to identify a WITHOUT ROWID table might not be so obvious.
In this article, we’ll start by briefly revising what WITHOUT ROWID tables are and how they differ from ordinary tables. Then we’ll look at how to identify these tables by using SQLite’s PRAGMA commands.
If you’re getting an error that reads “Conversion Error: Could not convert …etc” while using the COALESCE() function in DuckDB, it appears that you’re using arguments with incompatible types.
To fix this issue, try using CAST() or TRY_CAST() to ensure that all arguments are compatible. Alternatively, make sure the arguments to COALESCE() are of the same type (or at least, compatible types).
If you’ve ever used the strptime() function to create a timestamp in DuckDB, you may be aware that it will return an error if it can’t construct the timestamp from the format string/s provided.
While such an error could be useful in some situations, it could also be annoying in others.
Fortunately, DuckDB also provides the try_strptime() function, which will suppress any error that we might ordinarily get in such cases. This function returns null instead of an error.
When working with date and time data in DuckDB, calculating the difference between two dates is a common requirement. Whether we’re determining a person’s age from their birthdate or measuring the duration between two events, DuckDB’s age() function provides a straightforward solution. This function returns an interval representing the difference between two timestamps or dates, making it especially useful for time-based analyses.
In this article, we’ll explore how to use the age() function in DuckDB. We’ll cover its syntax, and provide some simple examples.
If you’re getting an error that reads “Conversion Error: extract specifier “monthname” not recognized” in DuckDB, it appears that you’re using a function like extract() or date_part() to try to get the month name from a date.
These functions don’t accept a monthname specifier, and so that’s why the error occurs. Fortunately, DuckDB provides a monthname() function, and so you could try that instead. Also, the strftime() function has a format specifier for the month name, and so that’s another option.
So to fix this issue, try the monthname() or strftime() function instead.
In DuckDB, ISFINITE() is a function for checking whether values are finite. DuckDB supports infinite values, and so we can use this function to check whether a value is infinite or not. This can be useful when working with floating-point data that might contain special values like NaN (Not a Number) or infinity. We can also use it on date and timestamp values.