devkuma – Database

SQL Basics | SQL

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

SQL (Structured Query Language) is a special-purpose programming language designed to manage data in relational database management systems (RDBMS). It was created for searching and managing data in relational database management systems, creating and modifying database schemas, and controlling access to database objects. Many database-related programs adopt SQL as a standard.

Types of commands

SQL commands are divided into data definition (DDL), data manipulation (DML), and data control (DCL).

Data Definition Language (DDL)
Data Manipulation Language (DML)
Data Control Language (DCL)

Database Index

kc@example.com (kc kim) — Sun, 19 Dec 2021 01:05:00 +0900

What Is an Index?

An index is a data structure that increases the speed of operations on a table.
An index can be created by using one column or multiple columns in a table.
It provides the basis not only for fast search operations but also for efficient ordering related to record access.
An index is a data structure that improves search speed in a database table by using additional write operations and storage space.
If we want to find specific content in a book, searching every page takes a long time. For that reason, authors add an index at the front or back of a book, and a database index is like a book index.
In databases as well, searching all data in a table takes a long time, so a data structure containing data and the location of the data is created to support fast lookups.
If you query a column without an index, a Full Scan that searches the entire table is performed. Full Scan compares and searches the whole table, so it is slow.

Two Ways to Search Data in a Database

FTS(Full Table Scan)
- A method that searches a table from beginning to end.
Index Scan
- A method that searches an index and accesses the table for the corresponding data.

Index Data Structures

Hash table
- Implements an index based on a hash generated from column values.
- Search is very fast because the time complexity is O(1).
- Sequential search for continuous data, such as inequalities (<, >), is not possible.
B+Tree
- A data structure that improves a B-Tree, where each node has two or more child nodes.
- It makes sequential search easier by connecting the leaf nodes of a BTree with a LinkedList.
- Although it has worse time complexity, O(log2n), than a hash table, it is used more commonly than hash tables.

SQL Basics | SQL | Comments

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

Comments are used to explain logic or disable code. Comments do not affect the code. SQL provides two types of comments.

Single-line comment

A single-line comment uses --.

--Select all:
SELECT * FROM customer;

Multi-line comment

Everything from /* to */ is treated as a comment.

/*Select all the columns
of all the records
in the customers table:*/
SELECT * FROM customer;

SQL Basics

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

This section explains SQL.

Database Transaction

kc@example.com (kc kim) — Sun, 19 Dec 2021 01:05:00 +0900

What Is a Database Transaction?

A transaction is a logical unit of work performed to change the state of a database.
It is a unit of work that manipulates data in a database.
A transaction can perform several operations.
If even one operation fails while a transaction is running, the entire transaction is rolled back. If all operations succeed, a commit is performed.

What Is Transaction ACID?

Atomicity
- Ensures that one transaction is not partially executed or interrupted.
Consistency
- Means that even when a transaction completes successfully, the database remains in the same valid state as before the work.
Isolation
- Ensures that other work cannot interfere while a Transaction is being performed.
Durability
- Means that successfully completed transactions must be reflected permanently.

Notes When Using Transactions

Minimize the scope of a transaction.
- It is important to minimize the scope of a transaction.
- Because the number of database connections is limited, the time a connection is held should be minimized.
- Otherwise, other services may have to wait to use the connection.

What Are the Types and Characteristics of Transaction Isolation Levels?

READ UNCOMMITTED
- When executing a SELECT query, data that has not been committed by another transaction can be read.
- Reading uncommitted data is called a dirty read.
- Be careful because data that has only been inserted and may be rolled back, meaning data that has not been committed, can be read.
READ COMMITTED
- Read Committed guarantees a level where only committed data is visible during SELECT, and most DBMSs use Read Committed as the default.
- Read Committed guarantees that dirty reads, which occur in Read Uncommitted, do not occur.
- Even if a transaction has not performed COMMIT, the value may already be reflected in the DB. To guarantee data before COMMIT, the process of restoring uncommitted queries is needed. In other words, Consistent Read must be performed at this point.
- The problem with Read Committed is that it does not guarantee that data remains the same each time SELECT is performed within a single transaction. This is because if another transaction has committed that data, Read Committed returns the committed data.
- For this reason, Read Committed is also called Non-repeatable Read.
REPEATABLE READ
- Unlike Read Committed, Repeatable Read guarantees that values read do not change even if SELECT is performed repeatedly within a transaction.
- A Repeatable Read transaction records the time when the first SELECT is performed, and for all subsequent SELECTs, performs Consistent Read based on that point in time.
- Therefore, even if another transaction commits during the transaction, newly committed data is not visible.
- This is because it reads the snapshot created at the first SELECT.
SERIALIZABLE
- Serializable provides the highest isolation level, as if all work were processed in one transaction.
- A common issue with both Read Committed and Repeatable Read is that Phantom Read can occur.
  - What is Phantom Read?
    - Phantom Read refers to a case where an UPDATE command in one transaction is lost or overwritten, or where after UPDATE and COMMIT, a subsequent query shows unexpected values or data has been lost.
- Unlike those levels, in SERIALIZABLE, all SELECT queries are automatically changed to SELECT … FOR SHARE, which can prevent several situations that Repeatable Read cannot prevent.

Commit and Rollback

Commit
- When a transaction ends, it notifies the transaction manager that it has completed.
Rollback
- When transaction processing ends abnormally and breaks database consistency, rollback means canceling all operations.

SQL Basics | DDL: Data Definition Language

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

DDL (Data Definition Language) is used to define or manipulate database schemas. It defines, changes, and deletes SCHEMA, DOMAIN, TABLE, VIEW, and INDEX objects with the following commands.

Database Normalization

kc@example.com (kc kim) — Sun, 19 Dec 2021 01:05:00 +0900

DB Normalization

Normalization is the process of splitting one table into two or more tables in order to transform it into a stable data structure that minimizes data inconsistency and data duplication while securing maximum data stability, without data loss or the introduction of unnecessary information.

Purpose of DB Normalization

Maintain data integrity
- Minimize the storage space needed for data storage and eliminate anomalies caused by data insertion, update, and deletion.
Maximize the stability of the data structure.

Normalization Process

First Normal Form
- Decomposes data so that every attribute value has an atomic value.
Second Normal Form
- Satisfies First Normal Form and decomposes data so that non-primary-key attributes are fully functionally dependent on the primary key.
  - Here, full functional dependency means that a subset of the primary key does not determine another value.
Third Normal Form
- Satisfies Second Normal Form and decomposes data so that non-primary-key attributes are directly dependent on the primary key (non-transitive dependency).
  - Here, transitive dependency means that A->B->C holds, and decomposing it into A,B and B,C is Third Normal Form.
BCNF Normalization
- Satisfies Third Normal Form and decomposes data so that when the functional dependency X->Y holds, every determinant X becomes a candidate key.

SQL Basics | DDL: Data Definition Language | DATABASE

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

CREATE DATABASE

The CREATE DATABASE statement is used to create a database for the first time.

CREATE DATABASE syntax

CREATE DATABASE [database_name];

CREATE DATABASE example

CREATE DATABASE devkuma;

DROP DATABASE

The DROP DATABASE statement is used to delete a database.

DROP DATABASE syntax

DROP DATABASE databasename;

DROP DATABASE example

CREATE DATABASE devkuma;

Database Tuning

kc@example.com (kc kim) — Sun, 19 Dec 2021 01:05:00 +0900

DB Optimizer

A DB optimizer is a core engine inside a DBMS that creates the optimal processing path to execute SQL as quickly and efficiently as possible.

Types
- Rule-based optimizer, cost-based optimizer
Process
- Finds candidate execution plans to execute the query submitted by the user.
- Estimates the expected cost of each execution plan through object statistics and system statistics.
- Compares each execution plan and selects the one with the lowest cost.

Hint

A hint is an instruction statement for tuning SQL.
When the optimizer cannot process an SQL statement with the optimal plan, the developer directly provides the optimal execution plan.
Hints can be written after SELECT, and there are various hint clauses such as INDEX and PARALLEL.

Database Tuning and Methods

DB tuning means improving the performance of a database system by adjusting the database structure, the database itself, the operating system, and related elements.
Tuning can proceed in the stages of DB design tuning > DBMS tuning > SQL tuning.

Step 1: DB Design Tuning (Modeling Perspective)

Tuning methods
- Design with performance in mind during the database design stage
- Data modeling, index design
- Data files, tablespace design
- Database capacity estimation
Tuning examples
- Denormalization, distributed file placement

Step 2: DBMS Tuning (Environment Perspective)

Tuning methods
- Specify memory or block size with performance in mind
- Perspective related to CPU and memory I/O
Tuning examples
- Buffer size
- Cache size

Step 3: SQL Tuning (Application Perspective)

Tuning methods
- Consider performance when writing SQL
- Join, Indexing, SQL Execution Plan
Tuning examples
- Hash / Join

SQL Basics | DDL: Data Definition Language | TABLE

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

A table is the basic structure for storing data in a database. A table is divided into columns and rows. Each row represents one record, and each column represents part of that record. For example, if a table stores customer data, its columns may include name, address, and date of birth. When defining a table, you specify each column name and the data type of that field.

Data can take many forms. It may be an integer, such as 1; a real number, such as 0.55; a string, such as 'sql'; a date and time, such as '2000-JAN-25 03:22:22'; or binary data. These forms are called data types. When defining a table, you must define the data type for each column. For example, the data type of a name field may be char(50), which represents a string of 50 characters. However, data types differ by database, so check the documentation for the database you are using when defining a table.

CREATE TABLE

The CREATE TABLE statement is used to create a table.

CREATE TABLE syntax

CREATE TABLE "table_name" (
    "field_name1" "data_type",
    "field_name2" "data_type",
    "field_name3" "data_type",
    ...
);

For example, to create customer data, enter SQL as shown below.

CREATE TABLE customer (
    first_name char(50),
    last_name char(50),
    address char(50),
    city char(50),
    country char(25),
    birth_date datetime
);

DROP TABLE

The DROP TABLE statement is used to delete a data table.

There are cases where a table must be removed from a database. If it is not removed when needed, it can cause serious problems because the database administrator (DBA) may not be able to manage the database efficiently. SQL provides the DROP TABLE statement to remove a table. The DROP TABLE statement is written as follows.

DROP TABLE "table_name";

To delete a table, enter the following.

DROP TABLE customer;

TRUNCATE TABLE

When you delete a table with DROP TABLE, the entire table disappears. In contrast, when you use TRUNCATE TABLE, all data in the table is removed, but the table itself remains.

TRUNCATE TABLE "table_name";

To delete the data in a table, enter the following.

TRUNCATE TABLE customer;

Database Lock

kc@example.com (kc kim) — Mon, 11 Jul 2022 09:26:00 +0900

What Is a Lock?

A lock is a method for guaranteeing ordering between transactions.
Here, a transaction means the smallest unit that can be processed independently.
Because each DBMS implements locks in different ways, you need to understand the concept of locks for each DBMS.

What Is a DB Lock?

A DB lock controls access when multiple transactions try to access one piece of data at the same time.

Types of DB Locks

Lock types include Shared Lock and Exclusive Lock, which are also called Read Lock and Write Lock, respectively.

Shared Lock(LS, Shared Lock)
- A lock used when a transaction reads data. It can read data but cannot write data.
Exclusive Lock(LX, Exclusive Lock)
- A lock used when a transaction reads and writes data. It can read and write data.

Exclusive Control

Optimistic Locking

Exclusive control from an optimistic perspective that assumes multiple people will rarely change a resource at almost the same time.

Pessimistic Locking

Exclusive control from a pessimistic perspective that assumes multiple people frequently change the same resource.

SQL Basics | DDL: Data Definition Language | VIEW

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

A view can be seen as a virtual table. The difference from a table is that data is physically stored in a table, while a view does not store data in the structure created from the table.

CREATE VIEW

The statement for creating a view is as follows.

CREATE VIEW "VIEW_NAME" AS "SQL statement";

The SQL statement can be any SQL.

Creating a VIEW

For example, suppose you have the following table.

customer table

Column name	Data type
first_name	char(50)
last_name	char(50)
address	char(50)
city	char(50)
country	char(25)
birth_date	datetime

To create a view that contains the three fields first_name, last_name, and country from this table, enter the following.

CREATE VIEW v_customer
AS SELECT first_name, last_name, country
FROM customer;

This creates a view named v_customer.

v_customer view

Column name	Data type
first_name	char(50)
last_name	char(50)
country	char(25)

VIEW created by joining tables

You can also join two tables by using a view. This lets users check the information they need through a view without directly combining two different tables. Suppose you have the following two tables.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Geography table

region_name	store_name
East	Boston
East	New York
West	Los Angeles
West	San Diego

You can create a view that includes sales by region with the following command.

CREATE VIEW v_region_sales
AS SELECT A1.region_name region, SUM(A2.sales) sales
FROM geography A1, store_Information A2
WHERE A1.store_name = A2.store_name
GROUP BY A1.region_name;

This creates a view named v_region_sales. The view contains sales data for each region. To retrieve data from this view, enter the following.

SELECT * FROM v_region_sales;

The result of the command above is as follows.

region	sales
east	700
west	2050

DROP VIEW

Use the DROP VIEW command to delete an existing view.

DROP VIEW "[database_name.]VIEW_NAME";

SQL Basics | DDL: Data Definition Language | INDEX

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

An index helps retrieve data from a table quickly and efficiently. However, while retrieval becomes faster, operations such as INSERT and UPDATE can become slower, so care is required. It is recommended to create indexes on tables that are queried frequently.

CREATE INDEX

CREATE INDEX index_name
ON table_name (column1, column2, ...);

DROP INDEX

DROP INDEX [ IF EXISTS ] index_name;

SQL Basics | DDL: Data Definition Language | Primary Key

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

Each value of a primary key is unique within a table. In other words, a primary key is used to uniquely identify each row of data in a table. A primary key can be a field from the original data or a generated field that is unrelated to the original data. A primary key can include one or more fields. When a primary key includes multiple fields, it is called a composite key.

A primary key can be set when creating a new table with CREATE TABLE, or when changing the structure of an existing table with ALTER TABLE.

Setting a primary key when creating a table

The following are examples of setting a primary key when creating a new table.

MySQL:

CREATE TABLE customer  (
  sid INTEGER,
  last_name VARCHAR(30),
  first_name VARCHAR(30),
  PRIMARY KEY (sid)
);

Oracle:

CREATE TABLE Customer (
  sid integer PRIMARY KEY,
  last_name VARCHAR(30),
  first_name VARCHAR(30)
);

SQL Server:

CREATE TABLE Customer (
  sid INTEGER PRIMARY KEY,
  last_name VARCHAR(30),
  first_name VARCHAR(30)
);

Setting a primary key when changing a table structure

The following are examples of setting a primary key when changing the structure of an existing table.

MySQL:

ALTER TABLE customer ADD PRIMARY KEY (sid);

Oracle:

ALTER TABLE customer ADD PRIMARY KEY (sid);

SQL Server:

ALTER TABLE customer ADD PRIMARY KEY (sid);

Before setting a primary key with ALTER TABLE, make sure the field used as the primary key is set to NOT NULL. In other words, the field must always contain data.

SQL Basics | DDL: Data Definition Language | Foreign Key

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

A foreign key is used to ensure referential integrity by pointing to the primary key field of one or more other tables. In other words, it ensures that only allowed data values are stored in the database.

Foreign key example

For example, suppose there are two tables. One is the customer table, which records all customer data, and the other is the orders table, which records all customer orders. There is one constraint: every customer in the order data must exist in the customer table. Here, you set a foreign key on the orders table, and that foreign key references the primary key of the customer table. This ensures that every customer in the orders table exists in the customer table. In other words, the orders table contains data for customers that exist in the customer table.

The structure of the two tables is as follows.

customer table

Field name	Property
sid	Primary key
last_name
first_name

orders table

Field name	Property
order_id	Primary key
order_date
customer_sid	Foreign key
amount

In the example above, the customer_sid field in the orders table is a foreign key that points to the sid field in the customer table.

The following are several ways to specify a foreign key when creating the orders table.

MySQL:

CREATE TABLE orders  (
    order_id INTEGER,
    order_date DATE,
    customer_sid INTEGER,
    amount DOUBLE,
    PRIMARY KEY (order_id),
    FOREIGN KEY (customer_sid) REFERENCES customer (sid)
);

Oracle:

CREATE TABLE orders (
    order_id INTEGER PRIMARY KEY,
    order_date DATE,
    customer_sid INTEGER REFERENCES customer (sid),
    amount DOUBLE
);

SQL Server:

CREATE TABLE orders  (
    order_id integer PRIMARY KEY,
    order_date DATETIME,
    customer_sid INTEGER REFERENCES customer(sid),
    amount DOUBLE
);

Next, look at examples of specifying a foreign key by changing the table structure.
Here, assume that the orders table has already been created and no foreign key has been specified.

MySQL:

ALTER TABLE orders
ADD FOREIGN KEY (customer_sid) REFERENCES customer (sid);

Oracle:

ALTER TABLE orders
ADD (CONSTRAINT fk_orders1) FOREIGN KEY (customer_sid) REFERENCES customer(sid);

SQL Server:

ALTER TABLE orders
ADD FOREIGN KEY (customer_sid) REFERENCES customer (sid);

SQL Basics | DCL: Data Control Language

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

DCL (Data Control Language) is a language for controlling data. It is used to define data security, integrity, recovery, and concurrency control.

COMMIT: Applies the result of a transaction
ROLLBACK: Cancels a transaction and restores it to its original state
GRANT: Grants privileges to a user
REVOKE: Revokes user privileges

TCL

Some sources separate COMMIT and ROLLBACK, which are commands that control transactions within DCL, and refer to them as TCL (Transaction Control Language).

SQL Basics | DML: Data Manipulation Language

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

SQL Basics | DML: Data Manipulation Language | SELECT

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The SELECT statement is used to retrieve data from a database.

The returned data is stored in a result set.

SELECT syntax

SELECT column_name1, column_name2, ... FROM table_name;

Here, column_name1, column_name2, and so on are the names of the fields in the table from which data will be selected. They are called columns. To select all available fields from a table, use the following syntax.

SELECT * FROM table_name;

SELECT examples

Column example

SELECT column1, column2 FROM table_name;

Example

SELECT * FROM table_name;

You can load multiple columns at once, and you can select several pieces of data from a table.

SELECT demo

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

sql

SELECT store_name FROM store_information;

Result

store_name
Los Angeles
San Diego
Los Angeles
Boston

SQL Basics | DML: Data Manipulation Language | DISTINCT

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The DISTINCT keyword removes duplicate rows from the result of a SELECT statement.

With a SELECT statement, you can load all data from one or more fields in a table. Even when the same value is repeated, all data can be loaded. In data processing, you often need to know which different values exist. In other words, rather than the number of times each value appears, you need to know which distinct values are present in the table or field. SQL makes this easy. Add DISTINCT after SELECT.

DISTINCT syntax

The DISTINCT statement is as follows.

SELECT DISTINCT column_name1, column_name2, ...
FROM table_name;

DISTINCT example

For example, suppose you want to find the names of different stores in the following Store_Information table.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT DISTINCT store_name FROM store_information;

The result is as follows.

store_name
Los Angeles
San Diego
Boston

SQL Basics | DML: Data Manipulation Language | WHERE

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The WHERE clause is used to filter records. It extracts only records that satisfy the specified condition.

You do not always load all data from a table. Often, you load data selectively. For example, if you need to retrieve only data with sales of $1,000 or more, use the WHERE clause.

WHERE syntax

The WHERE clause is as follows.

SELECT column_name1, column_name2, ...
FROM table_name
WHERE condition;

WHERE example

Suppose you need to retrieve data with sales of $1,000 or more from the following table.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT Store_Name
FROM Store_Information
WHERE Sales > 1000;

The result is as follows.

Store_Name
Los Angeles

SQL Basics | DML: Data Manipulation Language | AND, OR

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The previous page explained how to load data that meets a condition from a table with the WHERE clause. A condition can be simple, as in the previous example, or complex. A complex condition combines two or more simple conditions with AND or OR. A single SQL statement can use any number of simple conditions.

AND | OR syntax

The AND | OR statement is as follows.

SELECT "field_name"
FROM "table_name"
WHERE "condition"
{[AND|OR] "condition"}+;

{}+ means that the condition inside {} can occur one or more times. Here, it shows that adding an AND condition or adding an OR condition can occur one or more times. You can also use parentheses to indicate the priority order of conditions.

AND | OR example

Suppose you need to retrieve data with sales of $1,000 or more from the following table.

Suppose you need to retrieve all data from the Store_Information table where Sales is $1,000 or more, or where Sales is between $500 and $275.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
San Francisco	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT store_name
FROM store_information
WHERE Sales > 1000
OR (sales < 500 AND Sales > 275);

The result is as follows.

store_name
Los Angeles
San Francisco

SQL Basics | DML: Data Manipulation Language | IN, NOT IN

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

SQL uses the IN operator in two situations. This page explains one of them: the situation related to WHERE. For this usage, you must know at least one required value. All known values are then entered in the IN clause.

IN, NOT IN syntax

The IN clause is as follows.

SELECT "field_name"
FROM "table_name"
WHERE "field_name" IN ('value1', 'value2', ...);

There is one or more value inside the parentheses, and the values are separated by commas. A value can be a number or a string. If there is only one value inside the parentheses, it is equivalent to the following.

WHERE "field_name" = 'value1'

To query the opposite of the IN clause, use NOT IN.

SELECT "field_name"
FROM "table_name"
WHERE "field_name" NOT IN ('value1', 'value2', ...);

IN example

For example, suppose you want to retrieve all data that includes Los Angeles or San Diego from the store_information table.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT *
FROM store_information
WHERE store_name IN ('Los Angeles', 'San Diego');

The result is as follows.

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018

NOT IN example

Conversely, to retrieve all data excluding Los Angeles and San Diego, use the following.

SELECT *
FROM store_information
WHERE store_name NOT IN ('Los Angeles', 'San Diego');

The result is as follows.

store_name	sales	txn_date
Boston	700	Jan-08-2018

SQL Basics | DML: Data Manipulation Language | BETWEEN

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The IN operator retrieves matching values from a database by limiting them to one or more discrete values. BETWEEN retrieves matching values from a database within a certain range.

BETWEEN syntax

SELECT "field_name"
FROM "table_name"
WHERE "field_name" BETWEEN 'value1' AND 'value2';

This lets you query data whose field value is between value1 and value2.

BETWEEN example

For example, suppose you want to retrieve data from the store_information table between January 6, 2018 and January 10, 2018.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
San Francisco	300	Jan-08-2018
Boston	700	Jan-08-2018

Note: Depending on the database, dates may be stored in different ways. The format shown here is one of those storage methods.

Enter the following command.

SELECT *
FROM Store_Information
WHERE Txn_Date BETWEEN 'Jan-06-2018' AND 'Jan-10-2018';

The result is as follows.

store_name	sales	txn_date
San Diego	250	Jan-07-2018
San Francisco	300	Jan-08-2018
Boston	700	Jan-08-2018

SQL Basics | DML: Data Manipulation Language | LIKE

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

LIKE is another operator used in the WHERE clause. Basically, LIKE lets you find the data you need according to a pattern.

LIKE syntax

The LIKE clause syntax is as follows.

SELECT "field_name"
FROM "table_name"
WHERE "field_name" LIKE {pattern};

{pattern} includes wildcards. The following are some examples.

‘A_Z’: A string that starts with A, has any single character after it, and ends with Z. ABZ and A2Z match this pattern, but AKKZ does not because there are two characters between A and Z, not one.
‘ABC%’: A string that starts with ABC. For example, ABCD and ABCABC match this pattern.
’%XYZ’: A string that ends with XYZ. For example, WXYZ and ZZXYZ match this pattern.
’%AN%’: A string that contains AN. For example, LOS ANGELES and SAN FRANCISCO match this pattern.

When the last example above is used with the store_information table, it works as follows.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
San Francisco	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT *
FROM Store_Information
WHERE Store_Name LIKE '%AN%';

The result is as follows.

store_name	sales	txn_date
LOS ANGELES	1500	Jan-05-2018
SAN DIEGO	250	Jan-07-2018
SAN FRANCISCO	300	Jan-08-2018

SQL Basics | DML: Data Manipulation Language | IS NULL, IS NOT NULL

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

These conditions check whether a field value is empty, that is, whether it is NULL.

IS NULL, IS NOT NULL syntax

The IS NULL condition is as follows.

SELECT "field_name"
FROM "table_name"
WHERE "field_name" IS NULL

To query the opposite of the IS NULL condition, use the following.

SELECT "field_name"
FROM "table_name"
WHERE "field_name" IS NOT NULL

IS NULL example

Given the store_information table below, suppose sales is empty.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston

Enter the following command.

SELECT *
FROM store_information
WHERE sales IS NULL

The result is as follows.

store_name	sales	txn_date
Boston

IS NOT NULL example

When sales is not empty, use the following.

SELECT *
FROM store_information
WHERE sales IS NOT NULL

The result is as follows.

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018

SQL Basics | DML: Data Manipulation Language | EXISTS

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

EXISTS returns rows when the condition executed in WHERE has a result.

EXISTS syntax

SELECT "field_name1"
FROM "table_name1"
WHERE EXISTS
(SELECT "field_name2" FROM "table_name2" WHERE "search_condition");

EXISTS example

Given the following two tables, suppose you want to retrieve information for stores in the East region.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

geography table

region_name	store_name
East	Boston
East	New York
West	Los Angeles
West	San Diego

To retrieve information for stores in the East region, use the following.

SELECT *
FROM store_information A1
WHERE EXISTS
(SELECT * FROM geography WHERE region_name = 'East' AND store_name = A1.store_name);

The result is as follows.

store_name	sales	txn_date
Boston	700	Jan-08-2018

SQL Basics | DML: Data Manipulation Language | ORDER BY

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The ORDER BY keyword is used to sort a result set in ascending or descending order.

By default, ORDER BY sorts records in ascending order. To sort records in descending order, use the DESC keyword.

ORDER BY syntax

SELECT column_name1, column_name2, ...
FROM table_name
ORDER BY column_name1, column_name2, ... ASC|DESC;

ORDER BY examples

Example 1

SELECT * FROM customers
ORDER BY country;

Example 2

SELECT * FROM customers
ORDER BY country, customer_name;

Example 3

SELECT * FROM customers
ORDER BY country ASC, customer_name DESC;

SQL Basics | DML: Data Manipulation Language | GROUP BY

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The GROUP BY clause is used to group rows that have the same value.

Returning to aggregate functions, suppose you calculated all Sales with the SUM command. If you want to calculate Sales for each Store_Name, you need to do two things. First, select the store_name and Sales fields. Second, calculate sales for each store_name. The syntax is as follows.

GROUP BY syntax

SELECT "field1", SUM("field2")
FROM "table_name"
GROUP BY "field1";

For example:

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT store_name, SUM(sales)
FROM store_Information
GROUP BY store_name;

The result is as follows.

store_name	SUM(sales)
Los Angeles	1800
San Diego	250
Boston	700

When you select more than one field and at least one of them includes a function calculation, you must use GROUP BY. In this case, check the other fields together with GROUP BY. In other words, after identifying the field that includes the function, put the other selected field in the GROUP BY clause.

SQL Basics | DML: Data Manipulation Language | HAVING

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

This section explains how to apply conditions to function values.

For example, suppose you want to retrieve only stores with sales of $1,500 or more. This cannot be done with WHERE alone. In that case, use a command such as HAVING. In general, the HAVING clause appears at the end of an SQL statement. SQL that includes a HAVING clause does not necessarily have to include a GROUP BY clause.

HAVING syntax

The HAVING statement syntax is as follows.

SELECT "column1", SUM("field2")
FROM "table_name"
GROUP BY "field1"
HAVING (function_condition);

Note: The GROUP BY clause is not always required.

HAVING example

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Given the table above, enter the following statement.

SELECT store_name, SUM(sales)
FROM store_Information
GROUP BY store_name
HAVING SUM(sales) > 1500;

The result of the statement above is as follows.

store_name	SUM(sales)
Los Angeles	1800

SQL Basics | DML: Data Manipulation Language | AS - Aliases

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

AS stands for alias. It is used to provide a temporary name, or alias, for a table or a table field. Aliases are often used to make column names easier to read, and they exist only while a query is running.

In short, field aliases are used to make SQL results easier to understand. In the example, summing sales produces the field name SUM(sales). That is fine in this case, but if the field is not a simple sum and becomes a complex expression, the field name also becomes complex. By using an alias for such a field, the field name shown in the result becomes easier to understand.

The second type of alias is a table alias. To add an alias to a table, put one space after the table name in the FROM clause and specify the alias. This is very useful when reading data from different tables with SQL.

AS syntax

Field and table aliases are specified as follows.

SELECT "table_alias"."field1" (AS) "field_alias"
FROM "table_name" (AS) "table_alias";

Basically, both aliases are specified after a table name or field name with one space. You can also explicitly write AS.

AS example

Consider the following store_information table.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Use GROUP BY as follows and add a field alias and table alias.

SELECT A1.store_name Store, SUM(A1.sales) 'Total Sales'
FROM store_information A1
GROUP BY A1.store_name;

The result is as follows.

Store	Total Sales
Los Angeles	1800
San Diego	250
Boston	700

The data itself is the same in the result, but the field headings have changed. This is the use of field aliases. Instead of the heading “Sum (Sales)” for the second field, the heading “Total Sales” is displayed. Clearly, “Total Sales” shows the meaning of the field more clearly than “Sum(sales)”.

SQL Basics | DML: Data Manipulation Language | JOIN

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

JOIN is used to combine tables with other tables.

The following are several types of JOIN in SQL.

(INNER) JOIN: Returns records that have matching values in both tables.
LEFT (OUTER) JOIN: Returns all records from the left table and matching records from the right table.
RIGHT (OUTER) JOIN: Returns all records from the right table and matching records from the left table.
FULL (OUTER) JOIN: Returns all records from the left or right table.

You can also control the returned values as follows.

JOIN syntax

INNER JOIN

SELECT column_name(s)
FROM table1
INNER JOIN table2 ON table1.column_name = table2.column_name;

LEFT (OUTER) JOIN

SELECT column_name(s)
FROM table1
LEFT JOIN table2 ON table1.column_name = table2.column_name;

RIGHT (OUTER) JOIN

SELECT column_name(s)
FROM table1
RIGHT JOIN table2 ON table1.column_name = table2.column_name;

FULL (OUTER) JOIN

SELECT column_name(s)
FROM table1
FULL OUTER JOIN table2 ON table1.column_name = table2.column_name;

INNER JOIN example

This is an example of SQL JOIN.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

geography table

region_name	store_name
East	Boston
East	New York
West	Los Angeles
West	San Diego

An inner join returns data only when matching values exist in both tables.

We will examine sales by region_name. The geography table shows stores in each region, and the store_information table shows sales for each store. To check sales by region, combine the data from these two tables. Looking closely, you can see that the two tables can be joined by the store_name field.

SELECT A1.region_name REGION, SUM(A2.sales) SALES
FROM geography A1 JOIN store_information A2 ON A1.store_name = A2.store_name
GROUP BY A1.region_name;

Or:

SELECT A1.region_name REGION, SUM(A2.sales) SALES
FROM geography A1, store_information A2
WHERE A1.store_name = A2.store_name
GROUP BY A1.region_name;

The result is as follows.

REGION	SALES
East	700
West	2050

In the first line, SQL selects two fields. The first is the region_name field from the geography table, aliased as REGION, and the second is the sales field from the store_information table, aliased as SALES. Here, the table alias for Geography is A1 and the alias for store_information is A2. Without table aliases, the first line would look like this.

SELECT Geography.region_name REGION, SUM(Store_Information.Sales) SALES

As you can see, writing out all table names makes the statement longer and more complex. Table aliases make SQL statements easier to understand, especially when a statement includes multiple tables.

Next, in the FROM clause on the second line, the store_name field in the geography table is connected to the store_name field in the store_information table with JOIN.

OUTER JOIN example

Use OUTER JOIN when a data value in one table does not appear at all in another table.

Outer join syntax can differ by database. For example, Oracle places (+) after every table loaded in the WHERE clause to indicate that the table’s data is required.

We will retrieve sales for each store. If an inner join is used, data for the New York store, which does not exist in the store_information table, is omitted. In that case, use an outer join.

The Oracle outer join used here is as follows.

SELECT A1.store_name, SUM(A2.sales) SALES
FROM geography A1, store_information A2
WHERE A1.store_name = A2.store_name (+)
GROUP BY A1.store_name;

The result is as follows.

store_name	SALES
Boston	700
New York
Los Angeles	1800
San Diego	250

When there is no matching data in the second table, SQL inserts a NULL value. In this example, because New York does not exist in the store_information table, its SALES field becomes NULL.

SQL Basics | DML: Data Manipulation Language | Subquery

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

You can put another SQL statement inside a single SQL statement. When another SQL statement is inserted in a WHERE clause or HAVING clause, you can use a subquery. Subqueries are first used to combine tables, and sometimes a subquery is the only way to join two tables.

Subquery

A subquery is written as follows.

SELECT "field1" FROM "table1"
WHERE "field2" [comparison_operator] (SELECT "field1" FROM "table2" WHERE "condition");

Comparison operators include =, >, <, >=, and <=, as well as string operators such as LIKE.

Subquery example

This is an example related to SQL joins.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

geography table

region_name	store_name
East	Boston
East	New York
West	Los Angeles
West	San Diego

To use a subquery to calculate all sales for stores in the West region, use the following SQL.

SELECT SUM(sales) FROM store_information
WHERE store_name IN
(SELECT store_name FROM geography
WHERE region_name = 'West');

The result is as follows.

SUM(sales)
2050

In this example, the two tables are not joined directly, but the total sales for stores in the West region can still be calculated. First, it checks which stores are in the West region. Then it calculates and sums the sales for those stores.

SQL Basics | DML: Data Manipulation Language | UNION, UNION ALL

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The UNION operator is used to combine the result sets of two or more SELECT statements.

UNION combines the results of two SQL statements. In that sense, UNION is somewhat similar to JOIN because both commands can retrieve data from multiple tables. However, UNION has a limitation: the fields produced by the two SQL statements must use the same data types. Also, when UNION is used, duplicate data is not output, similar to SELECT DISTINCT.

Conditions for UNION

Each SELECT statement in a UNION must have the same number of columns.
The columns must have similar data types.
The columns in each SELECT statement must also be in the same order.

UNION ALL is also used to combine the results of two SQL statements. The difference from UNION is that UNION ALL displays all data that matches the condition even when data values are duplicated.

UNION, UNION ALL syntax

UNION is used as follows.

UNION syntax

[SQL statement 1]
UNION
[SQL statement 2];

Two SQL statements are entered here.

UNION ALL is used as follows.

UNION ALL syntax

[SQL statement 1]
UNION ALL
[SQL statement 2];

Except for adding ALL, it is the same as UNION.

UNION example

Suppose you have the following two tables.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

internet_sales table

txn_date	sales
Jan-07-2018	250
Jan-10-2018	535
Jan-11-2018	320
Jan-12-2018	750

To check all sales dates, use the following SQL statement.

SELECT txn_date FROM store_information
UNION
SELECT txn_date FROM internet_sales;

The execution result is as follows.

txn_date
Jan-05-2018
Jan-07-2018
Jan-08-2018
Jan-10-2018
Jan-11-2018
Jan-12-2018

One thing to note is that using SELECT DISTINCT txn_date in the SQL statement, or in both clauses where applicable, produces the same result.

UNION ALL example

Now retrieve the dates that have store sales and internet sales. In this case, use the following SQL statement.

SELECT txn_date FROM store_information
UNION ALL
SELECT txn_date FROM internet_sales;

The result is as follows.

txn_date
Jan-05-2018
Jan-07-2018
Jan-08-2018
Jan-08-2018
Jan-07-2018
Jan-10-2018
Jan-11-2018
Jan-12-2018

SQL Basics | DML: Data Manipulation Language | INTERSECT

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

INTERSECT is similar to UNION and is used to process the results of two SQL statements. The difference is that UNION is basically like OR: if a value exists in the first statement or the second statement, it is selected and output. INTERSECT is closer to AND: a value is selected only when it exists in both the first and second statements. UNION is a union, while INTERSECT is an intersection.

INTERSECT syntax

The INTERSECT command is as follows.

[SQL statement 1]
INTERSECT
[SQL statement 2];

INTERSECT example

Suppose you have the following two tables.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

internet_sales table

txn_date	sales
Jan-07-2018	250
Jan-10-2018	535
Jan-11-2018	320
Jan-12-2018	750

To check which dates have both store sales and internet sales, use the following SQL statement.

SELECT txn_date FROM store_information
INTERSECT
SELECT txn_date FROM internet_sales;

The SQL execution result is as follows.

txn_date
Jan-07-2018

Note that the INTERSECT command displays each distinct value only once.

SQL Basics | DML: Data Manipulation Language | MINUS

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

MINUS is a command used with two SQL statements. MINUS first checks the result of the first SQL statement. It then checks whether the retrieved result exists in the result of the second SQL statement. If it does, that data is removed and does not appear in the final result. Data that does not appear in the first SQL statement’s result is excluded.

MINUS syntax

The MINUS command is as follows.

[SQL statement 1]
MINUS
[SQL statement 2];

MINUS example

Suppose you have the following two tables.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

internet_sales table

txn_date	sales
Jan-07-2018	250
Jan-10-2018	535
Jan-11-2018	320
Jan-12-2018	750

To check which dates have store sales but no internet sales, use the following SQL statement.

SELECT txn_date FROM store_information
MINUS
SELECT txn_date FROM Internet_sales;

The SQL execution result is as follows.

txn_date
Jan-05-2018
Jan-08-2018

Jan-05-2018, Jan-07-2018, and Jan-08-2018 are returned by SELECT txn_date FROM store_information. Among them, Jan-07-2018 exists in the result of SELECT txn_date FROM internet_sales, so it does not appear in the final result.

Note that the MINUS statement displays each distinct value only once based on the statement result.

SQL Basics | DML: Data Manipulation Language | INSERT

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

Basically, there are two ways to insert data into a table. One inserts one row at a time. The other inserts multiple rows at once.

INSERT syntax

The most basic SQL for inserting data is as follows.

INSERT INTO "table_name" ("field1", "field2", ...)
VALUES ("value1", "value2", ...);

If the order of the fields in the table matches the order of the entered values, the field names can be omitted.

INSERT INTO "table_name"
VALUES ("value1", "value2", ...);

You can also insert multiple rows at once with SELECT. Unlike the previous example, the SELECT command specifies the data to insert into the table. If you are wondering whether that means the data comes from another table, that is not necessarily the case. SQL for inserting multiple rows at once is as follows.

INSERT INTO table2
SELECT * FROM table1
WHERE condition;

This is the most basic form. The SQL statement can also include joins, aliases, and clauses such as WHERE, GROUP BY, and HAVING.

INSERT example: inserting one row

Suppose you have a table with the following structure.

store_information table

Field name	Data type
store_name	char(50)
sales	float
txn_date datetime

To insert sales data of $900 for the Los Angeles store on Jan-10-2018 into this table, enter the following SQL statement.

INSERT INTO store_information (store_name, sales, txn_Date)
VALUES ('Los Angeles', 900, 'Jan-10-2018');

Or you can enter it as follows.

INSERT INTO store_information VALUES ('Los Angeles', 900, 'Jan-10-2018');

INSERT example: inserting multiple rows

Suppose you want to insert sales data from 2017 into the store_information table, and that data is obtained from the sales_information table. Enter the following SQL.

INSERT INTO store_information (store_name, sales, txn_date)
SELECT store_name, sales, txn_date
FROM sales_information
WHERE Year(txn_date) = 2017;

Here, a SQL Server function is used to retrieve the year from the date, but the syntax differs by database. For example, in Oracle, you can use WHERE TO_CHAR(Txn_Date, 'yyyy') = 2017.

SQL Basics | DML: Data Manipulation Language | UPDATE

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

Use the UPDATE command when modifying data in a table.

UPDATE syntax

The basic SQL for an UPDATE statement is as follows.

UPDATE "table_name"
SET "field1" = [new_value]
WHERE "condition";

You can also modify multiple fields at the same time.

UPDATE "table_name"
SET "field1" = [value1], "field2" = [value2]
WHERE "condition";

UPDATE example

Suppose you have the following table.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Later, you find that the actual sales for the Los Angeles store on 2018/01/08 were $500, not the $300 stored in the table. Use the following SQL to modify the data.

UPDATE store_information
SET Sales = 500
WHERE Store_Name = 'Los Angeles'
AND Txn_Date = 'Jan-08-2018';

If you query the table again, you can confirm that it has been updated as follows.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	500	Jan-08-2018
Boston	700	Jan-08-2018

In this example, only one row matches the condition in the WHERE clause. If multiple rows match the condition, all matching rows are modified.

SQL Basics | DML: Data Manipulation Language | DELETE

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

You can delete some data directly from a database. Use DELETE to delete data.

DELETE syntax

DELETE FROM "table_name"
WHERE "condition";

DELETE example

Consider the following example table.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

To delete all data for Los Angeles, use the following SQL statement.

DELETE FROM store_information
WHERE store_name = 'Los Angeles';

After running this SQL, the table becomes as follows.

store_name	sales	txn_date
San Diego	250	Jan-07-2018
Boston	700	Jan-08-2018

SQL Basics | Functions

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

In databases, much of the data is represented as numbers, and functions are used for numeric operations. For example, they can calculate the sum of those numbers or compute an average.

AVG
COUNT
MAX
MIN
SUM

Function syntax

Functions are used as follows.

SELECT "function_name"("field_name")
FROM "table_name";

SQL Basics | Functions | AVG

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

AVG calculates the average of the data retrieved from a table.

AVG syntax

SELECT AVG("field_name")
FROM "table_name";

AVG example

For example, suppose you want to find the average of the sales field in the table below.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT AVG(sales)
FROM store_information

The result is as follows.

AVG(sales)
687.5000

SQL Basics | Functions | COUNT

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

COUNT can retrieve how many rows of data are returned from a table.

COUNT syntax

SELECT COUNT("field_name")
FROM "table_name";

COUNT example

For example, suppose you want to find how many non-empty values exist in the store_name field of the table below.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT COUNT(store_name)
FROM store_information
WHERE store_name IS NOT NULL;

The result is as follows.

COUNT(store_name)
4

IS NOT NULL indicates that this field is not NULL, meaning it is not empty.

COUNT and DISTINCT are used together when you want to remove duplicate data from a table and count how many distinct values remain. For example, to count how many store_name values exist in the table, enter the following.

SELECT COUNT(DISTINCT store_name)
FROM store_information;

The result is as follows.

COUNT(DISTINCT store_name)
3

SQL Basics | Functions | MAX, MIN

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

MAX and MIN return the maximum and minimum values of data retrieved from a table.

MAX, MIN syntax

The function for finding the maximum value is as follows.

SELECT MAX("field_name")
FROM "table_name";

The function for finding the minimum value is as follows.

SELECT MIN("field_name")
FROM "table_name";

MAX(), MIN() example

For example, suppose you want to find the maximum and minimum values of the sales field in the table below.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT MAX(sales), MIN(sales)
FROM store_information

The result can be obtained as follows.

MAX(sales)	MIN(sales)
1500	250

SQL Basics | Functions | SUM

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

SUM can retrieve the total of data returned from a table.

SUM syntax

SELECT SUM("field_name")
FROM "table_name";

SUM example

For example, suppose you want to calculate the total of the sales field in the example table.

store_information table

store_name	sales	txn_date
Los Angeles	1500	Jan-05-2018
San Diego	250	Jan-07-2018
Los Angeles	300	Jan-08-2018
Boston	700	Jan-08-2018

Enter the following command.

SELECT SUM(sales) FROM store_information;

The result is as follows.

SUM(sales)
2750

2750 is the total of all sales fields: 1500 + 250 + 300 + 700.

In addition to functions, SQL can also perform simple mathematical operations such as addition (+) and subtraction (-). SQL also has several string processing functions for character data, such as concatenation, trim, and substring. Functions differ by database. To check how to use those functions, refer to the information for the database you are using.

SQL Basics | Functions | Substring

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

In SQL, the substring function is used to read part of the data in a field. The function name differs by database.

MySQL: SUBSTR(), SUBSTRING()
Oracle: SUBSTR()
SQL Server: SUBSTRING()

It is generally used as follows. Here, SUBSTR() is used as an example.

SUBSTR(str, pos)

Reads all characters from the pos position in str. Note that this syntax does not apply to SQL Server.

SUBSTR(str, pos, len)

Reads len characters from the pos position in str.

Substring example

Suppose you have the following table.

geography table

region_name	store_name
East	Boston
East	New York
West	Los Angeles
West	San Diego

Example 1

SELECT SUBSTR(store_name, 3)
FROM geography
WHERE store_name = 'Los Angeles';

The result is as follows.

's Angeles'

Example 2

SELECT SUBSTR(store_name, 2, 4)
FROM geography
WHERE store_name = 'San Diego';

The result is as follows.

'an D'

SQL Basics | Functions | Concatenate

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

Sometimes you need to combine data from different fields. Each database has a way to achieve this.

MySQL: CONCAT()
Oracle: CONCAT(), ||
SQL Server: +

Concatenate syntax

CONCAT() is used as follows.

CONCAT(string1, string2, string3, ...)

It combines string1, string2, string3, and so on. Note that Oracle’s CONCAT() accepts two parameters. In other words, it can combine only two strings at a time. To combine multiple strings at once, use ||.

Concatenate example

For example, suppose you have the following table.

geography table

region_name	store_name
East	Boston
East	New York
West	Los Angeles
West	San Diego

Example 1

MySQL / Oracle:

SELECT CONCAT(region_name, store_name) FROM geography
WHERE store_name = 'Boston';

The result is as follows.

'EastBoston'

Example 2

Oracle:

SELECT region_name || ' ' || store_name FROM geography
WHERE store_name = 'Boston';

The result is as follows.

'East Boston'

Example 3

SQL Server:

SELECT region_name + ' ' + store_name FROM geography
WHERE store_name = 'Boston';

The result is as follows.

'East Boston'

SQL Basics | Functions | Trim

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

The SQL function TRIM is used to remove part of the beginning or end of a string. Commonly, the function name differs by database.

MySQL: TRIM(), RTRIM(), LTRIM()
Oracle: RTRIM(), LTRIM()
SQL Server: RTRIM(), LTRIM()

Each trim function is as follows.

TRIM([[position] [remove_string] FROM] string)

The value entered for [position] is LEADING (beginning), TRAILING (end), or BOTH (beginning and end). This function removes [remove_string] from the beginning, end, or both beginning and end of the string. If [remove_string] is not specified, spaces are removed.

LTRIM (string)

Removes spaces from the beginning of the string.

RTRIM (string)

Removes spaces from the end of the string.

Trim example

TRIM() example

SELECT TRIM('   Sample   ');

The result is as follows.

'Sample'

LTRIM() example

SELECT LTRIM('   Sample   ');

The result is as follows.

'Sample   '

RTRIM() example

SELECT RTRIM ('   Sample   ');

The result is as follows.

'   Sample'

SQL Basics | Functions | Replace

kc@example.com (kc kim) — Sat, 17 Jun 2017 18:14:13 +0900

In SQL, the Replace function is used to replace part of the data in a field. The function name differs by database.

MySQL: REPLACE()

Replace syntax

REPLACE() is used as follows.

REPLACE('string', 'string_to_replace', 'replacement_string')

Replace example

SELECT REPLACE('abcdefg', 'abc', 'aaa');

The result is as follows.

| REPLACE('abcdefg', 'abc', 'aaa') |
|--|
| aaadefg |