DuckDB, an in-memory analytical SQL database management system, provides a good selection of functions for data manipulation and analysis. One of the these functions is list().
The list() function enables users to group multiple values into a single list, allowing for more advanced data aggregation and operations.
In this article, we’ll explore how the list() function works, along with some simple examples.
DuckDB has a min_by() function that’s similar to the min() function, but with a twist. While the min() function simply returns the minimum value, the min_by() function finds the row with the minimum value in one column and returns the corresponding value from another column at that row.
Here are some examples that demonstrate how it works.
Continue readingIn SQL databases, COUNT() is a commonly used aggregation function that returns the number of rows in a group. In this article, I run some examples of the COUNT() function in DuckDB. DuckDB is a high-performance analytical database system that’s designed to be fast, reliable, portable, and easy to use.
DuckDB includes a reservoir_quantile() function that allows us to compute approximate quantiles efficiently. It provides the approximate quantile using reservoir sampling. This function can be handy when working with large datasets where exact quantile computation would be too slow or resource-intensive.
In this article, we will explore how the reservoir_quantile() function works, along with examples to demonstrate its usage.
DuckDB has a date_add() function, which allows us to add a specified time interval to a date or timestamp. This article looks at how the date_add() function works in DuckDB, including its syntax, usage, and examples.
DuckDB is an in-process SQL OLAP database management system designed for analytical workloads. It is known for its speed, efficiency, and ease of use. One of the many functions DuckDB provides is current_date, which is useful for working with date-related data.
In this article, we’ll look at how the current_date function works, along with some straightforward examples.
DuckDB is an in-process SQL OLAP database management system designed for analytical workloads. One of its handy features is the ability to compute approximate quantiles efficiently using the approx_quantile() function. This function is particularly useful when working with large datasets where exact quantile computation would be computationally expensive.
In this article, we will explore how the approx_quantile() function works, its syntax, and provide examples to demonstrate its usage.
DuckDB is an in-process SQL OLAP database management system designed for fast analytical queries. One of its handy features is the approx_count_distinct() function, which provides an approximate count of distinct values in a column. This function is particularly useful when working with large datasets where an exact count would be computationally expensive.
In this article, we’ll explore how approx_count_distinct() works, its benefits, and how to use it with some simple examples.
DuckDB is a fast and reliable analytical database that provides us with a wide range of aggregate functions that can help us perform analytical queries. One of these aggregate functions is the ARBITRARY() function, which returns the first value from the group.
In this post, we’ll take a look at how the ARBITRARY() function works in DuckDB
DuckDB has a min() function just like most RDBMSs that returns the minimum value from a set of values. However, DuckDB’s implementation also allows us to return the bottom n minimum values, which is not something we see in most other RDBMSs.
This article presents some examples of DuckDB’s implementation of the min() function, so as to demonstrate its basic usage, as well as its bottom n functionality.