DuckDB has a today() function that returns the current date. It’s similar to the current_date function, which does the same thing.
Here, we’ll look at how the today() function works, along with some basic examples.
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3 Ways to Get the Weighted Average in DuckDB
Weighted averages are common calculations in data analysis, allowing us to assign different levels of importance to individual values in our dataset. Unlike simple averages, where each value has equal impact, weighted averages let us incorporate the relative significance of each observation. This is particularly valuable for scenarios like calculating GPA (where courses have different credit weights), investment portfolio returns (where assets have varying allocations), or quality ratings (where reviewers have different expertise levels).
In this article, we’ll explore three ways of calculating weighted averages in DuckDB.
A Quick Look at LIMIT & OFFSET in DuckDB
Most database management systems (DBMSs) provide us with a means of restricting the number of rows returned by a query to a fixed number of rows, or to a percentage of the data set. In many cases this is done with a LIMIT clause (although some DBMSs provide other methods, such as SQL Server’s TOP clause).
When it comes to DuckDB, the LIMIT clause is what’s implemented for this functionality.
Extract All Values From a JSON Document With DuckDB’s JSON_TRANSFORM() Function
The json_transform() function in DuckDB is a handy tool for converting JSON strings into structured data types like STRUCT, MAP, and LIST. This allows you to directly query and manipulate nested JSON data using standard SQL, making it much easier to work with complex JSON objects and arrays.
Think of it as a way to cast your JSON data into a more usable, typed format within your database.
Using JSON_TYPE() in DuckDB to Get the Data Type of a Given JSON Value
DuckDB’s json_type() function is a useful utility for inspecting JSON data structures. It helps us determine what type of data we’re working with at any level within a JSON document. This function can be handy when we need to validate data types or handle different JSON structures programmatically.
JSON_EXTRACT() vs JSON_EXTRACT_STRING() in DuckDB: What’s the Difference?
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.
Get the Number of Elements in a JSON Array with JSON_ARRAY_LENGTH() in DuckDB
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.
Create 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.
A 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.