A database is a structured collection of data that is organized and stored in a way that allows efficient retrieval and manipulation of the data. A database typically contains one or more tables or files that are related to each other in some way.
A database management system (DBMS) is used to manage and manipulate data within a database. A DBMS is a software system that provides tools for creating, storing, retrieving, updating, and deleting data in a database.
Examples of popular DBMSs include MySQL, Oracle, Microsoft SQL Server, and PostgreSQL.
Databases can be used to store various types of data, such as customer information, sales data, financial records, product catalogs, and so on. Databases can also be used for a wide range of applications, such as e-commerce, online banking, inventory management, and healthcare.
There are a variety of database types, and which type you use will be dependent on the type of data you wish to store. Let’s look at a few popular database types:
These databases are laid out in rows and columns, store and provide data in multiple tables, and allow you to identify and access the data in relation to one another. All relational databases use SQL. Microsoft SQL Server is an example of a relational database management system.
Any database that does not use SQL as its primary language. These databases are better suited for those who do not want their data as structured. CouchDB is an example of a NoSQL database.
In order to access and manipulate data in a database, users typically use a structured query language (SQL), which is a programming language designed for managing data in a relational database. SQL is used to create, modify, and retrieve data in a database.
SQL also allows users to perform complex queries and analyze large volumes of data quickly and efficiently.
Some of the most common uses of SQL include:
SQL is used to retrieve data from a database using various query techniques, such as SELECT, FROM, WHERE, and JOIN. This allows users to filter, sort, and aggregate data in a meaningful way.
SQL is used to add new records to a database or update existing records. This is done using the INSERT, UPDATE, and DELETE commands.
SQL is used to create and modify database structures, such as tables, indexes, and constraints. This allows users to define the structure and relationships of the data in a database.
SQL is used to manage user access and security in a database, such as creating users, granting and revoking privileges, and setting up security policies.
SQL is used to perform complex data analysis tasks, such as aggregating and summarizing data, calculating statistics, and generating reports.
In SQL, a statement is a command or instruction that is used to interact with a database. SQL statements are used to perform various operations on a database, such as querying data, inserting new records, updating existing records, deleting records, creating tables, altering tables, and so on.
SQL statements are categorized into different types based on their functionality. Some common types of SQL statements include:
These statements are used to define the database schema, such as creating tables, altering tables, and dropping tables. Examples: CREATE, ALTER, DROP, etc.
These statements are used to manipulate data in the database, such as inserting data, updating data, deleting data, and querying data. Examples: INSERT, UPDATE, DELETE, SELECT, etc.
These statements are used to control access to the database, such as granting and revoking permissions. Examples: GRANT, LOCK, REVOKE, etc.
These statements are used to control transactions in the database, such as committing transactions and rolling back transactions. Examples: COMMIT, ROLLBACK, SAVEPOINT, etc.
RDBMS stands for Relational Database Management System. The key difference here, compared to DBMS, is that RDBMS stores data in the form of a collection of tables, and relations can be defined between the common fields of these tables. Examples: MySQL, Microsoft SQL Server, Oracle, IBM DB2, etc.
In SQL, a table is a collection of data organized in rows and columns. Each row represents a single record, and each column represents a specific data element or attribute of that record. Tables are the basic building blocks of a relational database, and are used to store and organize data in a structured way.
For example, a table might be used to store customer data for an e-commerce website, with each row representing a single customer and each column representing a different attribute of that customer, such as their name, address, and email address.
Tables can be created using the CREATE TABLE statement in SQL, which allows users to specify the name of the table, the names and data types of the columns, and any constraints on the data. Once a table has been created, data can be inserted into the table using the INSERT statement, and queried using the SELECT statement.
Tables can be modified using the ALTER TABLE statement, which allows users to add, remove, or modify columns, change data types, and add or remove constraints. They can also be dropped using the DROP TABLE statement, which deletes the table and all of its data from the database.
In SQL, a primary key is a column or a set of columns in a table that uniquely identifies each record in the table. The primary key serves as a unique identifier for each row in the table and is used to enforce data integrity constraints.
Some key characteristics of a primary key include:
Each value in the primary key column(s) must be unique, meaning that no two rows can have the same value for the primary key.
Each value in the primary key column(s) must be non-null, meaning that no row can have a null value for the primary key.
The primary key value(s) must not change over the lifetime of the row, as this would cause data integrity issues.
The primary key should consist of as few columns as possible while still ensuring uniqueness.
In most SQL database management systems, a primary key is created by specifying the PRIMARY KEY constraint on one or more columns when creating a table. The primary key can also be added to an existing table using the ALTER TABLE statement.
The use of a primary key in SQL tables allows for efficient querying and manipulation of data, as well as enforcing referential integrity with other tables through the use of foreign keys.
In SQL, a foreign key is a field or set of fields in a table that refers to the primary key of another table. A foreign key establishes a link between two tables, allowing data to be associated between them.
For example, consider two tables: orders and customers. The orders table might have a foreign key column called customer_id, which refers to the id primary key column in the customers table. This allows each order in the orders table to be associated with a specific customer in the customers table.
Some key characteristics of a foreign key include:
The foreign key establishes a referential integrity constraint between the two tables, ensuring that data in the orders table is consistent with data in the customers table.
A foreign key can be used to represent a one-to-many relationship between two tables, where one record in the parent table can have many associated records in the child table.
Cascade actions can be defined on foreign keys to specify what should happen when records in the parent table are deleted or updated. For example, a cascade delete action might delete all associated records in the child table when a record in the parent table is deleted.
Foreign keys can be defined using the FOREIGN KEY constraint when creating a table or using the ALTER TABLE statement to modify an existing table. Foreign keys can also be used in combination with indexes to improve the performance of queries that join multiple tables.
This statement is used to create a new table in the database. For example, the following statement creates a new table called customers with columns for customer ID, name, and email address:
CREATE TABLE customers (
id INT PRIMARY KEY,
name VARCHAR(50),
email VARCHAR(50)
);
This statement is used to modify the structure of an existing table, such as adding a new column or changing the data type of column. For example, the following statement adds a new column called phone to the customers table:
ALTER TABLE customers
ADD COLUMN phone VARCHAR(20);
This statement is used to delete an entire table from the database. For example, the following statement deletes the customers table:
DROP TABLE customers;
This statement is used to delete all data from a table without deleting the table itself. For example, the following statement deletes all data from the customers table:
TRUNCATE TABLE customers;
This statement is used to create an index on one or more columns of a table, which can improve the performance of queries that filter or sort data based on those columns. For example, the following statement creates an index on the name column of the customers table:
CREATE INDEX idx_customers_name ON customers(name);
This statement is used to retrieve data from one or more tables in the database. For example, the following statement retrieves all columns from the customers table:
SELECT * FROM customers;
If we wish to select specific column(s) from the table then we need to specified those columns, separated by comma
SELECT name, email FROM customers;
This statement is used to insert new data into a table. For example, the following statement inserts a new record into the customers table:
INSERT INTO customers (name, email)
VALUES ('John Smith', 'john@example.com');
This statement is used to modify existing data in a table. For example, the following statement updates the email address of the customer with an ID of 1 in the "customers" table:
UPDATE customers
SET email = 'newemail@example.com'
WHERE id = 1;
This statement is used to delete data from a table. For example, the following statement deletes the record for the customer with an ID of 2 from the "customers" table:
DELETE FROM customers
WHERE id = 2;
This statement is used to perform an upsert operation, which inserts a new row if it does not exist or updates an existing row if it does. For example, the following statement inserts a new record into the customers table if a record with the same email address does not exist, or updates the existing record if it does:
MERGE INTO customers AS target
USING (VALUES ('john@example.com', 'John Smith')) AS source (email, name)
ON target.email = source.email
WHEN NOT MATCHED THEN
INSERT (email, name)
VALUES (source.email, source.name)
WHEN MATCHED THEN
UPDATE SET name = source.name;
This statement is used to give users specific privileges to access objects in the database. For example, the following statement grants SELECT and INSERT privileges on the customers table to a user named user1:
GRANT SELECT, INSERT ON customers TO user1;
This statement is used to remove user privileges from objects in the database. For example, the following statement revokes the SELECT privilege on the customers table from the user named user1:
REVOKE SELECT ON customers FROM user1;
This statement is used to deny access to objects in the database, overriding any granted privileges. For example, the following statement denies the DELETE privilege on the orders table to the user named user2:
DENY DELETE ON orders TO user2;
This statement is used to permanently save changes made to the database since the last COMMIT or ROLLBACK statement. For example, the following statement commits all changes made in the current transaction:
COMMIT;
This statement is used to undo changes made to the database since the last COMMIT or ROLLBACK statement. For example, the following statement rolls back all changes made in the current transaction:
ROLLBACK;
- Create table with a single field as primary key in a new table
CREATE TABLE Students (
ID INT NOT NULL
Name VARCHAR(255)
PRIMARY KEY (ID)
);
- Create table with multiple fields as primary key in a new table
CREATE TABLE Students (
ID INT NOT NULL
LastName VARCHAR(255)
FirstName VARCHAR(255) NOT NULL,
CONSTRAINT PK_Student
PRIMARY KEY (ID, FirstName)
);
- Set a column as primary key in already existing table
ALTER TABLE Students
ADD PRIMARY KEY (ID);
- Set multiple columns as primary key in already existing table
ALTER TABLE Students
ADD CONSTRAINT PK_Student /* Naming a Primary Key */
PRIMARY KEY (ID, FirstName);
- Adding a foreign key to a new table. In this example, a new table orders is created with a foreign key constraint that references the customer_id column in the customers table.
CREATE TABLE orders (
order_id INT PRIMARY KEY,
customer_id INT,
order_date DATE,
FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);
- Adding a foreign key to an already existing table. In this example, an existing orders table is altered to add a foreign key constraint named fk_customer_id that references the customer_id column in the customers table.
ALTER TABLE orders
ADD CONSTRAINT fk_customer_id
FOREIGN KEY (customer_id) REFERENCES customers(customer_id);
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
- NOT NULL - Restricts NULL value from being inserted into a column.
- CHECK - Verifies that all values in a field satisfy a condition.
- DEFAULT - Automatically assigns a default value if no value has been specified for the field.
- UNIQUE - Ensures unique values to be inserted into the field.
- INDEX - Indexes a field providing faster retrieval of records.
- PRIMARY KEY - Uniquely identifies each record in a table.
- FOREIGN KEY - Ensures referential integrity for a record in another table.
A UNIQUE constraint ensures that all values in a column are different. This provides uniqueness for the column(s) and helps identify each row uniquely.
Unlike primary key, there can be multiple unique constraints defined per table. The code syntax for UNIQUE is quite similar to that of PRIMARY KEY and can be used interchangeably.
- Create table with a single field as unique
CREATE TABLE Students (
ID INT NOT NULL UNIQUE
Name VARCHAR(255)
);
- Create table with multiple fields as unique
CREATE TABLE Students (
ID INT NOT NULL
LastName VARCHAR(255)
FirstName VARCHAR(255) NOT NULL
CONSTRAINT PK_Student
UNIQUE (ID, FirstName)
);
- Set a column as unique
ALTER TABLE Students
ADD UNIQUE (ID);
- Set multiple columns as unique
ALTER TABLE Students
ADD CONSTRAINT PK_Student /* Naming a unique constraint */
UNIQUE (ID, FirstName);
In SQL, a join is a way of combining rows from two or more tables based on a related column between them.
The basic syntax of a join statement is as follows:
SELECT column_name(s)
FROM table1
JOIN table2
ON table1.column_name = table2.column_name;
Here, table1 and table2 are the names of the tables being joined, and column_name is the name of the column used
to join the tables.
The JOIN keyword is used to specify the type of join being performed. The most common types of joins are INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
- INNER JOIN returns only the rows where there is a match in both tables based on the specified join condition.
- LEFT JOIN returns all the rows from the left table and the matching rows from the right table. If there is no match in the right table, the result will contain null values.
- RIGHT JOIN returns all the rows from the right table and the matching rows from the left table. If there is no match in the left table, the result will contain null values.
- FULL OUTER JOIN returns all the rows from both tables, with null values where there is no match in either table.
INNER JOIN is a type of SQL join used to combine rows from two or more tables based on a related column between them. An INNER JOIN returns only the rows that have matching values in both tables, and the result set only includes the columns that are specified in the SELECT statement.
Here's an example of how to use INNER JOIN to join two tables:
Suppose we have two tables, orders and customers, with the following columns:
orders table:
order_id | order_date | customer_id | order_total
customers table:
customer_id | first_name | last_name | email
We can use INNER JOIN to join these tables on the customer_id column and select certain columns from both tables, like this:
SELECT orders.order_id, orders.order_date, customers.first_name, customers.last_name
FROM orders
INNER JOIN customers
ON orders.customer_id = customers.customer_id;
In this example, we are selecting the order_id, order_date, first_name, and last_name columns from both the orders
and customers tables. We use the ON keyword to specify the join condition, which is that the customer_id column in the
orders table must match the customer_id column in the customers table.
The result of this query will be a table that contains only the rows where there is a matching value in both tables, with the specified columns from each table. This can be useful when we need to combine data from two or more tables to get a more complete picture of the data we are working with.
Note that if there are rows in one table that do not have matching values in the other table, those rows will not appear in the result set when using INNER JOIN. If you want to include all the rows from one table, even if there are no matching values in the other table, you can use LEFT JOIN or RIGHT JOIN instead.
LEFT OUTER JOIN is a type of SQL join used to combine rows from two or more tables based on a related column between them. A LEFT OUTER JOIN returns all the rows from the left table and the matching rows from the right table, and if there is no match in the right table, the result set will contain NULL values for the right table columns.
Here's an example of how to use LEFT OUTER JOIN to join two tables:
Suppose we have two tables, orders and customers, with the following columns:
orders table:
order_id | order_date | customer_id | order_total
customers table:
customer_id | first_name | last_name | email
We can use LEFT OUTER JOIN to join these tables on the customer_id column and select certain columns from both tables,
like this:
SELECT orders.order_id, orders.order_date, customers.first_name, customers.last_name
FROM orders
LEFT OUTER JOIN customers
ON orders.customer_id = customers.customer_id;
In this example, we are selecting the order_id, order_date, first_name, and last_name columns from both the orders
and customers tables. We use the ON keyword to specify the join condition, which is that the customer_id column in the
orders table must match the customer_id column in the customers table.
The result of this query will be a table that contains all the rows from the orders table, and only the matching rows
from the customers table, with NULL values for the first_name and last_name columns of the customers table if
there is no match. This can be useful when we want to include all the rows from one table, even if there are no matching
values in the other table.
A RIGHT OUTER JOIN, also known as a right join or right outer join, is a type of database join that returns all the rows from the right table and only the matching rows from the left table. If there is no match in the left table, then NULL values are returned.
Here's an example to illustrate how a RIGHT OUTER JOIN works:
Suppose you have two tables: a students table and a grades table. The students table has the following columns:
student_id, first_name, and last_name. The grades table has the following columns: student_id, grade.
students table:
|------------|------------|-----------|
| student_id | first_name | last_name |
|------------|------------|-----------|
| 1 | John | Doe |
| 2 | Jane | Smith |
| 3 | Bob | Johnson |
grades table:
|------------|-------|
| student_id | grade |
|------------|-------|
| 1 | 90 |
| 2 | 85 |
| 4 | 95 |
To get a list of all students and their grades (if they have any), you can use a RIGHT OUTER JOIN with the students
table as the right table and the grades table as the left table:
SELECT students.first_name, students.last_name, grades.grade
FROM students
RIGHT OUTER JOIN grades
ON students.student_id = grades.student_id;
This would produce the following result:
|------------|-----------|-------|
| first_name | last_name | grade |
|------------|-----------|-------|
| John | Doe | 90 |
| Jane | Smith | 85 |
| NULL | NULL | 95 |
Notice that all the rows from the grades table are included in the result, even though there is no matching student in
the students table for the third row. In that case, NULL values are returned for the first_name and last_name columns.
A FULL OUTER JOIN, also known as a full join, is a type of database join that returns all the rows from both tables and combines them into a single result set. If there is no match in either the left or the right table, then NULL values are returned.
Here's an example to illustrate how a FULL OUTER JOIN works:
Suppose you have two tables: a students table and a grades table. The students table has the following columns:
student_id, first_name, and last_name. The grades table has the following columns: student_id, grade.
students table:
+------------+------------+-----------+
| student_id | first_name | last_name |
+------------+------------+-----------+
| 1 | John | Doe |
| 2 | Jane | Smith |
| 3 | Bob | Johnson |
+------------+------------+-----------+
grades table:
+------------+-------+
| student_id | grade |
+------------+-------+
| 1 | 90 |
| 2 | 85 |
| 4 | 95 |
+------------+-------+
To get a list of all students and their grades (if they have any), you can use a FULL OUTER JOIN:
SELECT students.first_name, students.last_name, grades.grade
FROM students
FULL OUTER JOIN grades
ON students.student_id = grades.student_id;
This would produce the following result:
+------------+-----------+-------+
| first_name | last_name | grade |
+------------+-----------+-------+
| John | Doe | 90 |
| Jane | Smith | 85 |
| Bob | Johnson | NULL |
| NULL | NULL | 95 |
+------------+-----------+-------+
Notice that all the rows from both tables are included in the result, even though there is no matching student in the
students table for the fourth row and no matching grade in the grades table for the third row. In those cases,
NULL values are returned for the columns that do not have matching values.
A self join is a type of join in which a table is joined with itself. In other words, a self join is a way to combine rows from the same table based on a related column.
To perform a self join, we need to give two different aliases to the same table in the SQL statement. Each alias represents a separate instance of the table, and we can use them to reference the different columns within the table.
Here's an example of a self join:
Suppose we have a table named employees with the following columns:
employee_id | first_name | last_name | manager_id
The manager_id column contains the ID of the employee's manager. We can use a self join to find the names of all employees
and their managers:
SELECT e.first_name AS employee_name, m.first_name AS manager_name
FROM employees e
JOIN employees m
ON e.manager_id = m.employee_id;
In this query, we are joining the employees table with itself using the manager_id and employee_id columns. We are using
aliases e and m to represent the two instances of the employees table.
The result of this query will be a list of all employees and their managers, with the names of the employees in one column and the names of their managers in another column.
Self joins are particularly useful when working with hierarchical data, such as organizational charts or family trees, where a parent or ancestor can be represented by a row in the same table as its children or descendants.
A CROSS JOIN is a type of join operation in a relational database that returns the Cartesian product of the two tables. In other words, it returns all possible combinations of rows from both tables.
Here's an example to illustrate how a CROSS JOIN works:
Suppose you have two tables: a colors table and a sizes table. The colors table has the following rows: red, blue,
and green. The sizes table has the following rows: small, medium, and large. A CROSS JOIN between these two tables
would return all possible combinations of rows:
colors table:
+-------+
| color |
+-------+
| red |
| blue |
| green |
+-------+
sizes table:
+--------+
| size |
+--------+
| small |
| medium |
| large |
+--------+
To perform a CROSS JOIN, you would execute the following SQL query:
SELECT * FROM colors CROSS JOIN sizes;
This would produce the following result:
+-------+--------+
| color | size |
+-------+--------+
| red | small |
| red | medium |
| red | large |
| blue | small |
| blue | medium |
| blue | large |
| green | small |
| green | medium |
| green | large |
+-------+--------+
Notice that all possible combinations of rows from the colors and sizes tables are returned, resulting in a total of
9 rows. This can be useful for generating test data or for finding all possible combinations of two sets of data.
However, it can also be computationally expensive, especially for large tables.
In SQL, an index is a database object that can speed up the retrieval of data from a table. An index is similar to an index in a book, in that it provides a way to quickly locate specific information within a table.
An index is created on one or more columns of a table, and it stores a copy of the indexed columns in a separate structure. When a query is executed that involves the indexed column(s), the database engine uses the index to quickly locate the relevant rows, rather than scanning the entire table. This can greatly improve the performance of queries that involve large tables or that require frequent searches for specific values.
Here's an example to illustrate how an index works:
Suppose you have a students table with the following columns: student_id, first_name, last_name, and age.
If you frequently need to search for students by their last name, you can create an index on the last_name column:
CREATE INDEX idx_last_name ON students(last_name);
This creates an index named idx_last_name on the last_name column of the students table. The index contains a copy
of the last name values from each row in the table, along with a pointer to the corresponding row in the table.
When you execute a query that involves the last_name column, such as:
SELECT * FROM students WHERE last_name = 'Smith';
The database engine can use the index to quickly locate all the rows in the students table where the last name is 'Smith',
without scanning the entire table. This can significantly improve the performance of the query, especially if the table
contains a large number of rows.
However, it's important to note that creating too many indexes or indexing the wrong columns can actually degrade performance, as it can increase the overhead of maintaining the indexes and may cause the database engine to use inefficient query plans.
Therefore, it's important to carefully consider which columns to index and to monitor the performance of queries after creating indexes to ensure they are actually improving performance.
Data integrity refers to the accuracy and consistency of data stored in a database. In SQL, data integrity is maintained through the use of various constraints and rules that ensure that data in a database remains valid, consistent, and reliable. The following are some key aspects of data integrity in SQL:
This ensures that each row in a table has a unique identifier (i.e., primary key) that distinguishes it from all other rows in the table.
This ensures that relationships between tables are maintained correctly. It involves enforcing constraints on foreign keys to ensure that they match the primary keys of the related tables.
This ensures that data values stored in a table are valid and consistent. It involves enforcing data type constraints, range checks, and other rules to ensure that data values are within acceptable limits.
These are rules that limit the values that can be entered in a specific column. For example, a check constraint might limit the range of values that can be entered into a date column, or it might ensure that a column can only contain certain values.
These are rules that specify whether a column can have a null value. For example, a column with a NOT NULL constraint cannot have null values.
Maintaining data integrity is crucial for ensuring the accuracy and reliability of data stored in a database. It helps to prevent data inconsistencies, errors, and other issues that can lead to incorrect results, data corruption, and other problems. Therefore, it's important to define and enforce data integrity rules in SQL databases to ensure that data remains consistent and reliable over time.