When you’re building or maintaining a relational database, objects rarely live in isolation. Tables support views, views feed reports, procedures call other procedures, and constraints tie data together. These relationships are called dependencies, and they can be hard or soft.
The difference boils down to how strictly the database enforces the relationship.
A “columns mismatch” error in SQL usually happens when the number of values you’re trying to insert doesn’t line up with the number of columns in the table. It’s not a complicated issue, but it can be an easy one to overlook, especially when working with tables that evolve over time or when you skip specifying column names in your INSERT statements.
Understanding why the error occurs makes it simple to avoid, and a few small habits can help keep your SQL inserts clean and reliable.
In programming, the word idempotent describes an operation that produces the same result no matter how many times it is executed. When applied to SQL, idempotence refers to queries or commands that don’t introduce unexpected changes if you run them repeatedly.
The whole idea is that after the first execution, additional executions should leave the database in the same final state. Not just error-free, but stable and predictable. This concept is especially important when writing scripts that may be re-executed, such as database migrations or automated deployments.
Julian day is a concept you might occasionally encounter in SQL code or database operations, particularly when working with date and time functions. While it may seem esoteric at first, understanding Julian day can be incredibly useful for handling date calculations, especially in fields like astronomy, data analysis, and historical research.
This article looks at the origins, calculations, and practical applications of Julian day, including examples of converting between Julian day and other date formats in SQL.
In relational database management systems (RDBMSs), constraints are nifty tools that we can use to ensure the integrity, accuracy, and reliability of the data stored in our database.
Constraints can enforce rules at the column and table levels, guiding how data can be inserted, updated, or deleted. Whether you’re defining the uniqueness of a value, establishing relationships between tables, or ensuring that critical fields are never left blank, constraints play an important role in the design of relational databases.
Probably the most widely known key type in SQL is the primary key, which is chosen to uniquely identify each row in a table. Perhaps next is the foreign key, which is used to establish a relationship between tables.
But there are more key types than this, and the differences between them can be subtle, but important. Here we’ll look at nine of the various key types in SQL.
Probably the most common way to join tables in SQL is with the ON clause. But that’s not the only way.
We can also join with the USING clause, which can be more concise, while providing the same or similar results. And there’s also the concept of a natural join, which is more concise again.
Let’s take a look at these three join options and compare them side by side.
SQL (Structured Query Language) is the standard language for managing and manipulating relational databases. Whether you’re just starting your journey in data management or looking to refresh your skills, understanding the basic SQL statements is crucial.
This article will walk you through seven fundamental SQL statement examples that are pretty much standard across most major Relational Database Management Systems (RDBMSs).
In SQL, a UNIQUE key is a column or set of columns that can uniquely identify a row in a table. These are also candidate keys. Only one candidate key can become the primary key for a table. All other candidate keys can then be referred to as UNIQUE keys, given they can uniquely identify a row in a table.
Perhaps one of the lesser-known clauses when it comes to SQL joins is the USING clause. While the more widely-used ON clause allows us to explicitly specify join conditions, the USING clause simplifies the syntax when the join is based on columns with the same name in both tables.
In this article, we’ll dive into the USING clause, compare it to the ON clause, and look at examples that illustrate the difference.
