When it comes to concatenating lists in DuckDB, we can use the list_concat() function (and its aliases), or the list concatenation operator (||). Often when we have a choice like this, we can use either option interchangeably. But that isn’t quite true in this case.
There’s an important difference between list_concat() and the || operator in DuckDB, and it all comes down to how it handles NULL values.
DuckDB provides a handful of functions for getting data from JSON documents. We can use them as long as the JSON extension is installed and loaded (which it is in most distributions). One such function for getting data from a JSON document is json_value(). This function extracts scalar data from the specified path in the JSON document. If the value isn’t scalar, then a NULL value is returned.
DuckDB has a json_extract_string() function that works similar to json_extract(), except that it returns its result as a string (varchar). The json_extract() function, on the other hand, returns its result as JSON.
The purpose of these two functions is to extract data from a JSON document. We’ll focus on the json_extract_string() function in this article.
DuckDB has a json_extract() function that extracts JSON data from a JSON document. It enables us to get JSON values from within the JSON document, rather than returning the whole document itself. This article takes a quick look at the function along with some examples of usage.
When working with dates in DuckDB, some common tasks we might need to perform include extracting date parts from a date or timestamp value. For example we might want to extract the year from a date. Fortunately, DuckDB provides us with an abundance of options for doing that.
In this article, we’ll look at five different functions extract the year from a date in DuckDB.
DuckDB provides us with a bunch of epoch...() functions that enable us to get the Unix epoch time from a given date/time value. Different functions return their result using different units (for example seconds, milliseconds, etc). The epoch_us() function returns its result in microseconds.
Unix epoch time is typically expressed as the number of seconds that have elapsed since January 1, 1970 (UTC), but epoch_us() function returns the equivalent amount in microseconds.
Most DuckDB distributions are shipped with the json extension, which enables us to work with JSON documents. DuckDB provides a bunch of JSON functions, including JSON_EXISTS(). The JSON_EXISTS() function allows us to check whether a specific path exists within a JSON document.
This article explores how this function works, along with examples.
In DuckDB, the epoch_ms() function serves a dual purpose. It converts timestamp values into Unix epoch time in milliseconds and also performs the reverse operation, transforming Unix epoch time values back into timestamps.
Unix epoch time is typically expressed as the number of seconds that have elapsed since January 1, 1970 (UTC), but this function returns the equivalent amount in milliseconds.
The function is similar to the epoch() function, which returns its result in seconds. However, the epoch() function only works in one direction; it converts a timestamp value to epoch time, but it doesn’t work the other way around like epoch_ms() can.
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.