Joins in PostgreSQL

Introduction:

PostgreSQL is an open-source, multi-platform relational database system that supports Windows, Linux, and Unix. Renowned for its reliability, performance, and rich feature set, PostgreSQL is designed with object-oriented capabilities.It utilises multi-version concurrency control (MVCC) to manage concurrent operations effectively, ensuring data integrity and consistency.

JOIN : A JOIN clause is used to combine rows from two or more tables, based on a related column between them.

Let's look at a selection from the product table:

Then, look at a selection from the categories table:

Notice that the category_id column in the product table refers to the category_id in the categories table. The relationship between the two tables above is the category_id column.

Then, we can create the following SQL statement (with a JOIN), that selects records that have matching values in both tables:

SELECT product_id, product_name, category_name

FROM product

JOIN categories ON product.category_id = categories.category_id;

Here are the different types of the Joins in PostgreSQL:

INNER JOIN: Returns records that have matching values in both tables.

For example : If we see the diagram below there are 2 tables, Table A and Table B. As per Inner Join, it returns matching records from Table A and Table B.

The INNER JOIN keyword selects records that have matching values in both tables.

Advantages of Inner Join

• Reduced Data Duplication: Inner joins only return rows that have matching values in both tables being joined, which can reduce the amount of duplicate data returned in the result set.

• Efficient Query Execution: Since inner joins only involve rows that match in both tables, they can be more efficient in terms of query execution time compared to outer joins.

• Data Accuracy: Inner joins only return rows that have matching values in both tables, which can improve data accuracy by excluding irrelevant or mismatched data.

Disadvantages of Inner Join

• Data Loss: Since inner joins only return rows that have matching values in both tables, some data may be lost if there are no matching values.

• Query Complexity: Inner joins can become complex and difficult to write and understand when working with multiple tables.

• Overlapping Data: In some cases, inner joins may return overlapping data that needs not to be duplicated in post-processing.

LEFT JOIN: Returns all records from the left table, and the matched records from the right table. Left Join in SQL language is used to retrieve all the data or records from the left table and the matching data or records from the right table. In some cases, if there is no match of data or the record as per the given query by the programmer, then the left join will still display or return the rows from the left table and will show the NULL values for the columns of the right table.

For example : If we see the diagram below there are 2 tables, Table A and Table B. As per Left Join, Table A returns all records and matched records from Table B.

RIGHT JOIN: Returns all records from the right table, and the matched records from the left table.Right Join in SQL is used to retrieve all the data or records that are stored from the rightmost table and the matching records from the leftmost table. In some cases, there might be a situation where there are no matches of the data, then in this case Right Join will still include the rows from the right table but it will display the null values for the columns that are associated with the left table.

For example : If we see the diagram below there are 2 tables, Table A and Table B. As per Right Join, Table B returns all records and matched records from Table A.

FULL JOIN: Returns all records when there is a match in either the left or right table.

Full Join returns all the rows from both the tables. When no matching rows exist for the row in the left table, the columns of the right table are null padded. Similarly, when no matching rows exist for the row in the right table, the columns of the left table are null padded. Full join is the union of left join and right join.

For example : If we see the diagram below there are 2 tables, Table A and Table B. As per full join, Table A and Table B returns all records from both tables where there is a match in either of the table.

CROSS JOIN : The CROSS JOIN keyword matches ALL records from the "left" table with EACH record from the "right" table.

For example : If we see the diagram below there are 2 tables, Table A and Table B. As per Cross Join, Table A and Table B returns all records from the left table with each record from the right table. 

That means that all records from the "right" table will be returned for each record in the "left" table.

Normally, cross joins are not commonly used in SQL Server or any other database system, as they can quickly generate a large number of rows and result in performance issues. It's important to use cross joins judiciously and with care, especially when dealing with large tables or complex queries.

Cross joins can have serious performance implications, especially when working with large tables or complex queries. Here are some performance considerations to keep in mind when using cross-joins in SQL:

1. Cross joins can quickly generate many rows in the result set, especially when dealing with tables with many rows. This can cause performance issues, such as slower query execution times and increased memory usage.

2. Cross joins can cause resource contention, as they may require a lot of CPU, memory, and disk I/O resources to process. This can impact the performance of other queries running on the same server or database.

3. Cross joins can result in data duplication, as the same rows may appear multiple times in the result set. This can cause issues when aggregating or analyzing the data.

To mitigate these performance considerations, using cross joins judiciously and with care is important. It's often better to use other join types, such as inner joins or outer joins, that can be more efficient and selective. If cross-joins are necessary, consider limiting the number of rows returned using filtering conditions or other techniques and optimizing the query to minimize resource usage and improve query performance.