DuckDB provides us with a good selection of functions for working with date/time values. Among them is date_part(), which we can use to extract specific components from dates, timestamps, and intervals.
In this article, we’ll look how the date_part() function works, along with some basic examples.
DuckDB has a string_agg() function, which allows us to concatenate strings from a group of rows into a single string. This function can be useful when we need to aggregate text data in a meaningful way.
In this article, we’ll explore how the string_agg() function works, along with some simple examples to demonstrate its usage.
DuckDB has a function called list_distinct() that enables us to remove duplicate values from a list. Any value that appears more than once is “deduped” and is returned once in the resulting list.
The function also removes any NULL values.
Continue readingWhen working with DuckDB, we may need to determine whether a specific value exists within a list or array. Fortunately, DuckDB offers four synonymous functions that enable us to accomplish this.
Continue readingDuckDB has an unnest() function that we can use to unnest lists and structs. Well, it can also be applied to NULL, but that’ll return an empty result.
By “unnest” I mean it takes the list or struct, and it returns its contents as rows in a table. You might say that it converts lists and structs into tables, where each item in the list or struct becomes a row in the table.
Below are examples of using DuckDB’s unnest() function to unnest lists and structs.
One feature that sets SQLite apart from most other RDBMSs is the concept of WITHOUT ROWID tables. This is an optimization feature designed to improve performance and reduce storage space for certain use cases.
This article explores what WITHOUT ROWID tables are, how they work, their benefits, and when to use them.
The GREATEST() function in DuckDB is a versatile utility that returns the greatest value from a list of expressions. The function works across various data types and provides flexible comparison capabilities for data analysis tasks.
This article takes a look at DuckDB’s GREATEST() function, along with some simple examples.
When working with lists in DuckDB, we sometimes need to check whether a list contains specific elements. The list_has_all() function is a handy tool that allows us to verify if all elements of one list exist within another. This function is particularly useful in filtering queries, data validation, and advanced list-based operations.
In this article, we’ll explore how list_has_all() works in DuckDB.
DuckDB has a flatten() function that we can use to flatten nested lists. The function concatenates a list of lists into a single list. So whether the outer list contains just one list or multiple lists, we can use the flatten() function to flatten them into one list.
However, it only goes one level deep, so that’s something to keep in mind.
Continue readingDuckDB provides us with a bunch of list concatenation functions that do exactly the same thing; concatenate two lists. Actually, they’re all synonyms and so they can all be used interchangeably. There’s also a more general concatenation function that can also be used on lists.
So this article presents five functions that we can use to concatenate lists.
Continue reading