In modern relational database systems, efficiency and optimization are key to handling large datasets with speed and agility. One powerful feature that PostgreSQL offers to further this goal is the use of generated columns. Generated columns are special columns that are always computed from other columns within the same table, thereby allowing for more complex calculations and data representations without requiring additional storage space or manual data entry. This feature, introduced in PostgreSQL 12, provides a dynamic method to maintain derived data, which can be crucial for performance, maintainability, and data integrity. In this detailed guide, we will delve deeply into the concept of generated columns in PostgreSQL, how to use them, and best practices to make the most of this powerful functionality.
Understanding Generated Columns
Generated columns come in two forms: stored and virtual. A stored generated column computes its value when a row is inserted or updated, and the result is stored on disk alongside other columns. Since it is stored physically, reading a stored generated column requires no additional computation. In contrast, a virtual generated column calculates its value on the fly whenever it’s queried, so it doesn’t consume disk space because the value is not stored. The choice between stored and virtual depends on the specific requirements of your application and your performance considerations.
Creating Generated Columns
Let’s see how to create generated columns. The syntax to create a table with generated columns in PostgreSQL is straightforward. Here’s a basic example:
CREATE TABLE employee (
id SERIAL PRIMARY KEY,
first_name VARCHAR(100),
last_name VARCHAR(100),
full_name VARCHAR(255) GENERATED ALWAYS AS (first_name || ' ' || last_name) STORED
);
In this example, the ‘full_name’ column is generated by concatenating the ‘first_name’ and ‘last_name’ columns, with a space in between. If we were to insert data into the ’employee’ table, PostgreSQL would automatically generate the ‘full_name’ for us.
Inserting Data into Tables with Generated Columns
INSERT INTO employee (first_name, last_name) VALUES ('John', 'Doe');
When we query the data, the ‘full_name’ column will show the generated value:
SELECT id, full_name FROM employee;
This would yield:
id | full_name
----+-----------
1 | John Doe
(1 row)
Updating Data with Generated Columns
It is important to note that you cannot directly update a generated column. Any attempt to do so will result in an error. The values of generated columns are automatically computed from the other columns they depend on. If you need to change the value of a generated column, you must update the source columns.
Using Indexes with Generated Columns
One significant advantage of generated columns is that they can be indexed. This is particularly useful when you need to speed up queries that involve the generated column. For example, if you frequently search for employees by their full name, you can create an index on the ‘full_name’ generated column:
CREATE INDEX idx_full_name ON employee USING btree (full_name);
An index on a generated column works just as it does on a regular column, making queries more efficient.
Considerations for Generated Columns
When using generated columns, there are several considerations to keep in mind. While virtual generated columns do not consume extra disk space, they do require additional CPU resources each time they are accessed. Therefore, it is essential to consider the trade-offs between computation time and storage space, especially when dealing with large tables and complex calculations.
Also, consider the dependencies between columns. Since a generated column is computed from other columns, changes to the source columns will affect the generated column. This is important for understanding how updates to your data model may impact generated columns and, by extension, queries and indexes that rely on them.
Advanced Usage: Complex Expressions and Constraints
Generated columns can use complex expressions, not just simple concatenation. They can include any immutable function or expression that PostgreSQL supports. Here’s an example with a mathematical calculation:
CREATE TABLE measurements (
length_cm INT,
width_cm INT,
area_sq_cm INT GENERATED ALWAYS AS (length_cm * width_cm) STORED
);
You can even use generated columns in constraints to enforce specific conditions:
CREATE TABLE time_tracking (
start_time TIMESTAMP WITHOUT TIME ZONE,
end_time TIMESTAMP WITHOUT TIME ZONE,
duration INTERVAL GENERATED ALWAYS AS (end_time - start_time) STORED,
CHECK (duration >= '00:00:00')
);
In this table, the ‘duration’ column is computed from ‘start_time’ and ‘end_time’, and there is a check constraint ensuring that the duration is never negative.
Conclusion
Generated columns introduce a powerful way to keep your PostgreSQL data consistent, efficient, and intelligently managed. By offloading computation to the database and leveraging indexing on these columns, performance gains can be substantial. Moreover, they help maintain a single source of truth by preventing data duplication and reducing the risk of errors that can arise from manual calculations. As with any database feature, application developers and database administrators should assess the impact on their unique use cases but, in many situations, generated columns will be a valuable tool in their PostgreSQL arsenal.