DuckDB has a json_extract() function that extracts JSON from a JSON document. DuckDB also has a function called json_extract_string() that does a similar thing, but extracts its value as a string.
This article takes a look at these two functions side by side.
Continue readingDuckDB provides us with a few options for slicing lists. Slicing a list is where we take a sublist or “slice” from the list, rather than the whole list. We specify the start and end position for which to return the slice. We can also specify a step, which allows us to skip one or more elements along the slice.
Continue readingWhen it comes to converting between data types, DuckDB performs implicit conversions when required, while also enabling us to perform explicit conversions. Implicit conversions are performed automatically by DuckDB when we do some other operation, such as use a function that requires its argument/s in a different data type than the one we’re providing. Regarding explicit conversions, we have the option of using a function like cast() or try_cast(), or using the shorthand method.
In this article we’ll take a quick look at how to convert between data types using the shorthand method.
Continue readingIn DuckDB, the json_array_length() function is used to determine the number of elements in a JSON array. This function can return the count of items in the top-level array or in a nested array within it. It’s especially useful when working with complex JSON structures where arrays are embedded inside objects or other arrays. By combining it with JSON path expressions, you can target specific portions of the data for more precise analysis.
When working with lists in DuckDB, sometimes you might want to return just the first value, rather than the whole list. Fortunately, DuckDB provides us with several ways to do this.
Continue readingThe make_timestamp() function in DuckDB is a handy tool for creating timestamp values from individual components. It allows us to construct timestamp values using two alternative approaches; by specifying the number of milliseconds from epoch, or by specifying the year, month, day, hour, minute, and second components separately.
Let’s take a look at the make_timestamp() function, along with some examples.
The json_group_structure() function in DuckDB is an aggregate function that inspects all JSON values within a group and returns a JSON representation of their structure. It essentially infers a “schema” for the JSON objects in that group. This can be useful for understanding the shape and consistency of your JSON data.
SQLite is a lightweight, self-contained relational database management system that is widely used due to its simplicity and portability. However, one notable aspect of SQLite is its lack of a dedicated DATE or DATETIME storage class. Instead, SQLite stores date and time values as integers, real numbers, or text, depending on how the developer chooses to manage these values.
In this article, we’ll explore how to effectively use and store dates in SQLite, and provide examples that may help you implement date management in your projects.
Continue readingIn DuckDB, the json_structure() is used to parse and introspect JSON data, returning the structure of a JSON document. This is especially useful when you’re working with semi-structured JSON data and you want to understand its schema — including nested keys, arrays, and types — without manually inspecting the raw JSON.
While we might typically use DuckDB’s median() function to get the median value from a column, when it comes to getting the median value from a list, we need to modify this slightly. A list can contain many values, and so we need a way to calculate the median of all of those values, rather than a single value like we’d normally be doing when calculating the median value from a column.
Fortunately, DuckDB provides us with several easy options for achieving this. Below are three options for getting the median value from a list in DuckDB.
Continue reading