Database Management Systems
DuckDB is a high-performance in-process analytical database management system, often referred to as the “SQLite for analytics.” Its Command Line Interface (CLI) is a useful tool for executing SQL queries, managing databases, and exploring DuckDB’s features.
This article provides a comprehensive guide to the basics of using DuckDB’s CLI, helping beginners and advanced users alike understand how to leverage it effectively.
Continue readingThis article takes a look at DuckDB, a modern database management system (DBMS) designed specifically for data analysis and processing.
Often described as the “SQLite for analytics,” DuckDB is lightweight, embeddable, and highly efficient, making it a popular choice for those working with structured data.
Whether you’re considering DuckDB for your projects or simply curious about its capabilities, this article will give you a basic overview of what it is and how it fits into the data analytics landscape.
Continue readingIf you’re getting an error that reads “AUTOINCREMENT not allowed on WITHOUT ROWID tables” in SQLite, it appears that you’re trying to define a column as an AUTOINCREMENT in a WITHOUT ROWID table.
SQLite doesn’t allow us to create AUTOINCREMENT columns on WITHOUT ROWID tables.
To address this issue, either remove the AUTOINCREMENT attribute or remove the WITHOUT ROWID from the table definition.
Despite its simplicity, SQLite includes advanced optimization techniques to ensure efficient query execution. One of the lesser-known but potentially useful features it offers is the unlikely() function, a tool designed to guide SQLite’s query planner in making better decisions.
This article looks at how the unlikely() function works in SQLite.
SQLite provides a range of tools to help optimize query execution. One of these is the likely() function, which helps developers guide the query planner by hinting at the probability of certain conditions being true.
In this article, we’ll look at the likely() function, understanding how it works, its syntax, some use cases, and its impact on query performance.
SQLite is a lightweight, serverless database engine widely used for its simplicity and flexibility. Among its many functions, the likelihood() function is a handy option that allows you to influence query planning by providing hints about the probability of certain conditions being true.
This article explores the details of the likelihood() function, its syntax, use cases, and a practical example to demonstrate its application.
If you’re getting an error that reads something like “unknown datatype for (columnname): “DATE”” in SQLite, it appears that you’re trying to define a column as a DATE type in a strict table.
SQLite doesn’t support the DATE type, however, this error should only occur on strict tables (i.e. a table defined as STRICT).
To fix this issue, either use a supported data type or make the table a regular (non-strict) table.
Continue readingSQLite is a lightweight, self-contained SQL database engine known for its simplicity and versatility. In version 3.37.0, SQLite introduced strict tables, offering stricter type enforcement compared to its regular tables.
This feature allows developers to define tables with precise data types, ensuring better data consistency.
In this article, we’ll look at the six supported data types for strict tables—INT, INTEGER, REAL, TEXT, BLOB, and ANY—and provide simple examples to illustrate their usage.
The ACOSH() function in SQLite calculates the inverse hyperbolic cosine of a given number. The inverse hyperbolic cosine of a number is the value whose hyperbolic cosine equals that number.
The COSH() function in SQLite calculates the hyperbolic cosine of a number, which is similar to the regular cosine function, but for hyperbolic geometry.