In 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.
Category: Relational
4 Options for Extracting the First Value from a List in DuckDB
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 readingCreate a TIMESTAMP Value in DuckDB with MAKE_TIMESTAMP()
The 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.
Understanding the JSON_GROUP_STRUCTURE() Function in DuckDB
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.
How to Use and Store Dates in SQLite
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 readingA Quick Look at DuckDB’s JSON_STRUCTURE() Function
In 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.
3 Easy Ways to Calculate the Median Value from a List in DuckDB
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 readingHow EPOCH_NS() Works in DuckDB
DuckDB provides an epoch_ns() function that returns the Unix epoch time from a given date/time value in nanoseconds.
Unix epoch time is typically expressed as the number of seconds that have elapsed since January 1, 1970 (UTC), but it can also be expressed in other units, such as milliseconds, microseconds, and nanoseconds. DuckDB has specific functions for each of these units, with epoch_ns() being the function that returns it in nanoseconds.
Understanding DuckDB’s JSON_VALID() Function
Most of the major RDBMSs that support JSON also include a json_valid() function for checking the validity of a JSON document, and DuckDB’s no exception. Like the other RDBMSs, DuckDB’s implementation of json_valid() checks its argument and returns a boolean value to indicate whether or not it’s valid JSON.
RPAD() Alternative: Applying Right Padding in SQL Server
The SQL rpad() function has been widely implemented across many major RDBMSs, including MySQL, Oracle, PostgreSQL, and MariaDB, to name just a few. But when it comes to SQL Server, we have a problem. SQL Server doesn’t currently provide us with an rpad() function.
But that’s not to say we can’t apply right padding in SQL Server. SQL Server still provides us with enough tools to get the job done. With a bit of work, we can get a similar result to what we might be able to achieve with rpad(). It may not be as elegant as a simple rpad() function, but at least it’s an option.