DuckDB has a fsum() function that can be used instead of the regular sum() function in order to get more accurate results. fsum() calculates the sum using a floating point summation method known as Kahan summation (or compensated summation).
This method helps reduce the accumulation of rounding errors that can occur when summing many floating point numbers when using the regular sum() function.
In analytical SQL workloads, expressing weighted averages can sometimes involve verbose expressions such as combining the sum() function with other operators. DuckDB streamlines this with its native weighted_avg() aggregate function, allowing us to compute weighted averages directly and efficiently. The weighted_avg() function enhances both clarity and speed when dealing with data where values contribute unequally — such as population-adjusted metrics or revenue-weighted scores.
This article explores the weighted_avg() in DuckDB, along with examples to demonstrate its usage.
If you’re getting an error in DuckDB that includes “list dimensions must be equal“, it appears that you’re performing a list operation that requires the lists to be the same dimension, but the lists are of different dimensions.
To fix this error, be sure to use lists of equal dimensions when performing the operation.
Continue readingDate/time arithmetic, such as adding one or more minutes to a date/time value, is an operation we often need to perform when using SQL databases such as DuckDB. As with most RDBMSs, DuckDB makes this easy for us to achieve.
Here are two options for adding one or more minutes to a date, timestamp, or time value.
Continue readingDuckDB offers a variety of utility functions to help us better understand the data that we need to work with. Among these functions, typeof() serves as a useful tool for type inspection and validation. In this article, we’ll explore how this function works, along with examples that demonstrate its usage.
Encountering errors while converting between data types can be frustrating when working with SQL databases like DuckDB. But it usually means that something’s wrong. In most cases these errors occur because we’re trying to perform an impossible conversion, like from a number to a date or something.
But sometimes errors can get in the way, especially when we’re trying to convert a bunch of values. Sometimes it would be better for the system to return NULL for such failed conversions than to return an error and mess up the whole operation. Fortunately, we can do this.
Continue readingDuckDB’s list_aggregate() function is a handy tool for performing grouped aggregations over lists. It allows us to apply any aggregate function (like sum, avg, min, max, count, etc.) to a list column as if each list item were a row.
The way it works is that we pass the list as the first argument, followed by the name of an aggregate function we want to apply to that list. The function will return its result as if it were the named aggregate function.
Continue readingSometimes when working with DuckDB, we need to find the maximum value from a given list. This can often be the case when working with lists that contain numeric data. Fortunately DuckDB provides a few options for us for doing this.
Continue readingMost SQL databases provide a cast() function and DuckDB is no exception. The purpose of the cast() function is to convert a value from one data type to another. This can also be referred to as casting the value as the other data type.
Whenever we use DuckDB’s generate_series() function, the results are returned in a list. But sometimes we might want the results to be returned in a table instead of a list, so that each value in the series is presented as a separate row in a table (as opposed to being an item in a list). In such cases we would need to “unnest” the results.