Database Management Systems
DuckDB 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.
If you’re getting an error that reads “Binder Error: ‘list_has_all’ cannot compare lists of different types” in DuckDB, it appears that you’re passing two different types of lists to the list_has_all() function.
DuckDB’s list_has_all() function accepts two lists as arguments. These need to be of the same type.
To fix this issue, make sure that both lists are of the same type.
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.
DuckDB provides us with an easy way to add one or more months to date values, as well as subtract from them. This applies to the various date related data types, such as DATE and TIMESTAMP.
Actually, DuckDB provides us with a choice; We can use the + operator or the date_add() function.
If you’re getting an error that reads something like “Sample method System cannot be used with a discrete sample count” when using the SAMPLE clause in DuckDB, it looks like you’re specifying an invalid sampling method for the context with which you’re using the SAMPLE clause. Perhaps you’re using system or bernoulli, when you should be using reservoir.
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.
If you’re getting an error that reads something like “Binder Error: Could not find key “…” in struct” when using the array_extract() function in DuckDB, it could be that you’re specifying a non-existent key.
When using DuckDB’s array_extract() function to extract a value from a struct, we must provide a key that actually exists in the struct. Otherwise we’ll end up with an error like the above one.
To fix this issue, be sure to specify a key that actually exists in the struct.
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.
DuckDB 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.