An INNER JOIN is a term used in SQL (Structured Query Language) to combine rows from two (or more) tables based on a related column between them.

Example:

You have two tables:

Table: Customers

Table: Orders

Now you want to know which customers have placed orders. You only want the customers who exist in both tables.

SQL with INNER JOIN:

SELECT Customers.Name, Orders.Product
FROM Customers
INNER JOIN Orders ON Customers.CustomerID = Orders.CustomerID;

Result:

Explanation:

• Clara didn’t place any orders → not included.

• The order with CustomerID 4 doesn’t match any customer → also excluded.

In short:

An INNER JOIN returns only the rows with matching values in both tables.

An explicit join is a clear and direct way to define a join in an SQL query, where the type of join (such as INNER JOIN, LEFT JOIN, RIGHT JOIN, or FULL OUTER JOIN) is explicitly stated.

Example of an explicit join:

SELECT *
FROM customers
INNER JOIN orders
ON customers.customer_id = orders.customer_id;

This makes it clear:

• Which tables are being joined (customers, orders)

• What kind of join is used (INNER JOIN)

• What the join condition is (ON customers.customer_id = orders.customer_id)

In contrast: Implicit join

An implicit join is the older style, using a comma in the FROM clause, and putting the join condition in the WHERE clause:

SELECT *
FROM customers, orders
WHERE customers.customer_id = orders.customer_id;

This works the same, but it's less clear and not ideal for complex queries.

Benefits of explicit joins:

• More readable and structured, especially with multiple tables

• Clear separation of join conditions (ON) and filter conditions (WHERE)

• Recommended in modern SQL development

An implicit join is a way of joining tables in SQL without using the JOIN keyword explicitly. Instead, the join is expressed using the WHERE clause.

Example of an implicit join:

SELECT *
FROM customers, orders
WHERE customers.customer_id = orders.customer_id;

In this example, the tables customers and orders are joined using a condition in the WHERE clause.

In contrast, an explicit join looks like this:

SELECT *
FROM customers
JOIN orders ON customers.customer_id = orders.customer_id;

Differences:

When is an implicit join used?

It was common in older SQL code, but explicit joins are recommended today, as they are clearer, easier to maintain, and less error-prone, especially in complex queries involving multiple tables.

A Materialized View is a special type of database object that stores the result of a SQL query physically on disk, unlike a regular view which is computed dynamically every time it’s queried.

Key Characteristics of a Materialized View:

• Stored on disk: The result of the query is saved, not just the query definition.

• Faster performance: Since the data is precomputed, queries against it are typically much faster.

• Needs refreshing: Because the underlying data can change, a materialized view must be explicitly or automatically refreshed to stay up to date.

Comparison: View vs. Materialized View

Example:

-- Creating a materialized view in PostgreSQL
CREATE MATERIALIZED VIEW top_customers AS
SELECT customer_id, SUM(order_total) AS total_spent
FROM orders
GROUP BY customer_id;

To refresh the data:

REFRESH MATERIALIZED VIEW top_customers;

When to use it?

• For complex aggregations that are queried frequently

• When performance is more important than real-time accuracy

• In data warehouses or reporting systems

ACID is an acronym that describes four key properties essential for the reliability of database transactions in a database management system (DBMS). These properties ensure the integrity of data and the consistency of the database even in the event of errors or system crashes. ACID stands for:

1. Atomicity:

   • Every transaction is treated as an indivisible unit. This means that either the entire transaction is completed successfully, or none of it is. If any part of the transaction fails, the entire transaction is rolled back, and the database remains in a consistent state.

2. Consistency:

   • Every transaction takes the database from one consistent state to another consistent state. This means that after a transaction completes, all integrity constraints of the database are satisfied. Consistency ensures that no transaction leaves the database in an invalid state.

3. Isolation:

   • Transactions are executed in isolation from each other. This means that the execution of one transaction must appear as though it is the only transaction running in the system. The results of a transaction are not visible to other transactions until the transaction is complete. This prevents concurrent transactions from interfering with each other and causing inconsistencies.

4. Durability:

   • Once a transaction is completed (i.e., committed), its changes are permanent, even in the event of a system failure. Durability is typically ensured by writing changes to non-volatile storage such as disk drives.

Example for Clarification

Consider a bank database with two accounts: Account A and Account B. A transaction transfers 100 euros from Account A to Account B. The ACID properties ensure the following:

• Atomicity: If the transfer fails for any reason (e.g., a system crash), the entire transaction is rolled back. Account A is not debited, and Account B does not receive any funds.
• Consistency: The transaction ensures that the total amount of money in both accounts remains the same before and after the transaction (assuming no other factors are involved). If Account A initially had 200 euros and Account B had 300 euros, the total balance of 500 euros remains unchanged after the transaction.
• Isolation: If two transfers occur simultaneously, they do not interfere with each other. Each transaction sees the database as if it is the only transaction running.
• Durability: Once the transaction is complete, the changes are permanent. Even if a power failure occurs immediately after the transaction, the new balances of Account A and Account B are preserved.

Importance of ACID

The ACID properties are crucial for the reliability and integrity of database transactions, especially in systems dealing with sensitive data, such as financial institutions, e-commerce platforms, and critical business applications. They help prevent data loss and corruption, ensuring that data remains consistent and trustworthy.