How to write SQL queries

How to write SQL queries

Executive Summary

This document is a short tutorial of the Structured Query Language (also known as SQL). SQL allows users to access data in relational database management systems, such as Oracle, Microsoft SQL Server, Access, and many others, by allowing users to describe the data the user wishes to see. SQL also allows users to define the data in a database, and manipulate that data.
This document describes how to use SQL, and provides examples as well. The SQL used in this document is ANSI, or standard SQL.
It is recommended to print out this page, so that you can easily refer back to it as needed.

Content Summary

  • Basics of the SELECT Statement
  • Conditional Selection
  • Relational Operators
  • Compound Conditions
  • Using LIKE
  • Joins
  • Keys
  • Performing a Join
  • Eliminating Duplicates
  • Aliases & In/Subqueries
  • Aggregate Functions
  • Views
  • Creating New Tables
  • Altering Tables
  • Adding Data
  • Deleting Data
  • Updating Data
  • Indexes
  • More Subqueries
  • UNION & Outer Joins
  • Common SQL Questions
  • non-standard SQL
  • Syntax Summary

Basics of the SELECT Statement

In a relational database, data is stored in tables. An example table could relate to the Social Security Number, Name, and Address:

SSN FirstName LastName Address City State
512687458 Joe Smith 83 First Street Howard Ohio
758420012 Mary Scott 842 Vine Ave. Losantiville Ohio
102254896 Sam Jones 33 Elm St. Paris New York
876512563 Sarah Ackerman 440 U.S. 110 Upton Michigan

Now, let’s say you want to see the address of each employee. Use the SELECT statement, like so:

SELECT FirstName, LastName, Address, City, State FROM EmployeeAddressTable;

The following is the results of your query on the database:

First Name Last Name Address City State
Joe Smith 83 First Street Howard Ohio
Mary Scott 842 Vine Ave. Losantiville Ohio
Sam Jones 33 Elm St. Paris New York
Sarah Ackerman 440 U.S. 110 Upton Michigan

To explain what you just did, you asked for all of the data in the

EmployeeAddressTable, and specifically, you asked for the columns called FirstName, LastName, Address, City, and State. Note that column names and table names do not have spaces…they must be typed as one word; and that the statement ends with a semicolon (;). The general form for a SELECT statement, retrieving all of the rows in the table is:

SELECT ColumnName, ColumnName, … FROM TableName;

To get all columns of a table without typing all column names, use:

SELECT * FROM TableName;

Each database management system (DBMS) and database software has different methods for logging in to the database and entering SQL commands; see the local computer guru to help you get onto the system, so that you can use SQL properly.


Conditional Selection

To further discuss the SELECT statement, let’s look at a new example table (for hypothetical purposes only):

EmployeeIDNo Salary Benefits Position
010 75000 15000 Manager
105 65000 15000 Manager
152 60000 15000 Manager
215 60000 12500 Manager
244 50000 12000 Staff
300 45000 10000 Staff
335 40000 10000 Staff
400 32000 7500 Entry-Level
441 28000 7500 Entry-Level


Relational Operators

There are six Relational Operators in SQL, and after introducing them, we’ll see how they’re used:

= Equal
<> or != (see manual) Not Equal
< Less Than
> Greater Than
<= Less Than or Equal To
>= Greater Than or Equal To

The WHERE clause is used to specify that only certain rows of the table are displayed, based on the criteria described in that WHERE clause. It is most easily understood by looking at a couple of examples.

If you wanted to see the EMPLOYEEIDNO’s of those making at or over $50,000, use the following:



WHERE SALARY >= 50000;

Notice that the >= (greater than or equal to) sign is used, as we wanted to see those who made greater than $50,000, or equal to $50,000, listed together. This displays:








The WHERE description, SALARY >= 50000, is known as a condition (an operation which evaluates to True or False). The same can be done for text columns:



This displays the ID Numbers of all Managers. Generally, with text columns, stick to equal to or not equal to, and make sure that any text that appears in the statement is surrounded by single quotes (‚).


More Complex Conditions: Compound Conditions / Logical Operators

The AND operator joins two or more conditions, and displays a row only if that row’s data satisfies ALL conditions listed (i.e. all conditions hold true). For example, to display all staff making over $40,000, use:




The OR operator joins two or more conditions, but returns a row if ANY of the conditions listed hold true. To see all those who make less than $40,000 or have less than $10,000 in benefits, listed together, use the following query:



WHERE SALARY < 40000 OR BENEFITS < 10000; AND & OR can be combined, for example:



WHERE POSITION = ‚Manager‘ AND SALARY > 60000 OR BENEFITS > 12000;

First, SQL finds the rows where the salary is greater than $60,000 and the position column is equal to Manager, then taking this new list of rows, SQL then sees if any of these rows satisfies the previous AND condition or the condition that the Benefits column is greater then $12,000. Subsequently, SQL only displays this second new list of rows, keeping in mind that anyone with Benefits over $12,000 will be included as the OR operator includes a row if either resulting condition is True. Also note that the AND operation is done first.

To generalize this process, SQL performs the AND operation(s) to determine the rows where the AND operation(s) hold true (remember: all of the conditions are true), then these results are used to compare with the OR conditions, and only display those remaining rows where any of the conditions joined by the OR operator hold true (where a condition or result from an AND is paired with another condition or AND result to use to evaluate the OR, which evaluates to true if either value is true). Mathematically, SQL evaluates all of the conditions, then evaluates the AND pairs, and then evaluates the OR’s (where both operators evaluate left to right).

To look at an example, for a given row for which the DBMS is evaluating the SQL statement Where clause to determine whether to include the row in the query result (the whole Where clause evaluates to True), the DBMS has evaluated all of the conditions, and is ready to do the logical comparisons on this result:

True AND False OR True AND True OR False AND False

First simplify the AND pairs:

False OR True OR False

Now do the OR’s, left to right:

True OR False


The result is True, and the row passes the query conditions. Be sure to see the next section on NOT’s, and the order of logical operations. I hope that this section has helped you understand AND’s or OR’s, as it’s a difficult subject to explain briefly (especially when you write a version and the editor loses the changes–on multiple occasions no less).

To perform OR’s before AND’s, like if you wanted to see a list of employees making a large salary (>$50,000) or have a large benefit package (>$10,000), and that happen to be a manager, use parentheses:



WHERE POSITION = ‚Manager‘ AND (SALARY > 50000 OR BENEFIT > 10000);



An easier method of using compound conditions uses IN or BETWEEN. For example, if you wanted to list all managers and staff:



WHERE POSITION IN (‚Manager‘, ‚Staff‘);

or to list those making greater than or equal to $30,000, but less than or equal to $50,000, use:




To list everyone not in this range, try:




Similarly, NOT IN lists all rows excluded from the IN list.

Additionally, NOT’s can be thrown in with AND’s & OR’s, except that NOT is a unary operator (evaluates one condition, reversing its value, whereas, AND’s & OR’s evaluate two conditions), and that all NOT’s are performed before any AND’s or OR’s.

SQL Order of Logical Operations (each operates from left to right)

  1. NOT
  2. AND
  3. OR

Using LIKE

Look at the EmployeeStatisticsTable, and say you wanted to see all people whose last names started with L; try:




The percent sign (%) is used to represent any possible character (number, letter, or punctuation) or set of characters that might appear after the L. To find those people with LastName’s ending in L, use ‚%L‘, or if you wanted the L in the middle of the word, try ‚%L%‘. The ‚%‘ can be used for any characters in the same position relative to the given characters. NOT LIKE displays rows not fitting the given description. Other possiblities of using LIKE, or any of these discussed conditionals, are available, though it depends on what DBMS you are using; as usual, consult a manual or your system manager or administrator for the available features on your system, or just to make sure that what you are trying to do is available and allowed. This disclaimer holds for the features of SQL that will be discussed below. This section is just to give you an idea of the possibilities of queries that can be written in SQL.


In this section, we will only discuss inner joins, and equijoins, as in general, they are the most useful. For more information, try the SQL links at the bottom of the page.

Good database design suggests that each table lists data only about a single entity, and detailed information can be obtained in a relational database, by using additional tables, and by using a join.

First, take a look at these example tables:

OwnerID OwnerLastName OwnerFirstName
01 Jones Bill
02 Smith Bob
15 Lawson Patricia
21 Akins Jane
50 Fowler Sam


OwnerID ItemDesired
02 Table
02 Desk
21 Chair
15 Mirror


SellerID BuyerID Item
01 50 Bed
02 15 Table
15 02 Chair
21 50 Mirror
50 01 Desk
01 21 Cabinet
02 21 Coffee Table
15 50 Chair
01 15 Jewelry Box
02 21 Pottery
21 02 Bookcase
50 01 Plant Stand



First, let’s discuss the concept of keys. A primary key is a column or set of columns that uniquely identifies the rest of the data in any given row. For example, in the AntiqueOwners table, the OwnerID column uniquely identifies that row. This means two things: no two rows can have the same OwnerID, and, even if two owners have the same first and last names, the OwnerID column ensures that the two owners will not be confused with each other, because the unique OwnerID column will be used throughout the database to track the owners, rather than the names.

A foreign key is a column in a table where that column is a primary key of another table, which means that any data in a foreign key column must have corresponding data in the other table where that column is the primary key. In DBMS-speak, this correspondence is known as referential integrity. For example, in the Antiques table, both the BuyerID and SellerID are foreign keys to the primary key of the

AntiqueOwners table (OwnerID; for purposes of argument, one has to be an Antique Owner before one can buy or sell any items), as, in both tables, the ID rows are used to identify the owners or buyers and sellers, and that the OwnerID is the primary key of the AntiqueOwners table. In other words, all of this ID data is used to refer to the owners, buyers, or sellers of antiques, themselves, without having to use the actual names.


Performing a Join

The purpose of these keys is so that data can be related across tables, without having to repeat data in every table–this is the power of relational databases. For example, you can find the names of those who bought a chair without having to list the full name of the buyer in the Antiques table…you can get the name by relating those who bought a chair with the names in the AntiqueOwners table through the use of the OwnerID, which relates the data in the two tables. To find the names of those who bought a chair, use the following query:




Note the following about this query…notice that both tables involved in the relation are listed in the FROM clause of the statement. In the WHERE clause, first notice that the ITEM = ‚Chair‘ part restricts the listing to those who have bought (and in this example, thereby owns) a chair. Secondly, notice how the ID columns are related from one table to the next by use of the BUYERID = OWNERID clause. Only where ID’s match across tables and the item purchased is a chair (because of the AND), will the names from the AntiqueOwners table be listed. Because the joining condition used an equal sign, this join is called an equijoin. The result of this query is two names: Smith, Bob & Fowler, Sam.

Dot notation refers to prefixing the table names to column names, to avoid ambiguity, as such:





As the column names are different in each table, however, this wasn’t necessary.


DISTINCT and Eliminating Duplicates

Let’s say that you want to list the ID and names of only those people who have sold an antique. Obviously, you want a list where each seller is only listed once–you don’t want to know how many antiques a person sold, just the fact that this person sold one (for counts, see the Aggregate Function section below). This means that you will need to tell SQL to eliminate duplicate sales rows, and just list each person only once. To do this, use the DISTINCT keyword.

First, we will need an equijoin to the AntiqueOwners table to get the detail data of the person’s LastName and FirstName. However, keep in mind that since the SellerID column in the Antiques table is a foreign key to the AntiqueOwners table, a seller will only be listed if there is a row in the AntiqueOwners table listing the ID and names. We also want to eliminate multiple occurences of the SellerID in our listing, so we use DISTINCT on the column where the repeats may occur.

To throw in one more twist, we will also want the list alphabetized by LastName, then by FirstName (on a LastName tie), then by OwnerID (on a LastName and FirstName tie). Thus, we will use the ORDER BY clause:





In this example, since everyone has sold an item, we will get a listing of all of the owners, in alphabetical order by last name. For future reference (and in case anyone asks), this type of join is considered to be in the category of inner joins.


Aliases & In/Subqueries

In this section, we will talk about Aliases, In and the use of subqueries, and how these can be used in a 3-table example. First, look at this query which prints the last name of those owners who have placed an order and what the order is, only listing those orders which can be filled (that is, there is a buyer who owns that ordered item):







This gives:

Last Name Item Ordered

——— ————

Smith   Table

Smith   Desk

Akins   Chair

Lawson  Mirror

There are several things to note about this query:

  1. First, the ‚Last Name‘ and ‚Item Ordered‘ in the Select lines gives the headers on the report.
  2. The OWN & ORD are aliases; these are new names for the two tables listed in the FROM clause that are used as prefixes for all dot notations of column names in the query (see above). This eliminates ambiguity, especially in the equijoin WHERE clause where both tables have the column named OwnerID, and the dot notation tells SQL that we are talking about two different OwnerID’s from the two different tables.
  3. Note that the Orders table is listed first in the FROM clause; this makes sure listing is done off of that table, and the AntiqueOwners table is only used for the detail information (Last Name).
  4. Most importantly, the AND in the WHERE clause forces the In Subquery to be invoked (‚= ANY‘ or ‚= SOME‘ are two equivalent uses of IN). What this does is, the subquery is performed, returning all of the Items owned from the

Antiques table, as there is no WHERE clause. Then, for a row from the Orders table to be listed, the ItemDesired must be in that returned list of Items owned from the Antiques table, thus listing an item only if the order can be filled from another owner. You can think of it this way: the subquery returns a set of Items from which each ItemDesired in the Orders table is compared; the In condition is true only if the ItemDesired is in that returned set from the Antiques table.

  1. Also notice, that in this case, that there happened to be an antique available for each one desired…obviously, that won’t always be the case. In addition, notice that when the IN, ‚= ANY‘, or ‚= SOME‘ is used, that these keywords refer to any possible row matches, not column matches…that is, you cannot put multiple columns in the subquery Select clause, in an attempt to match the column in the outer Where clause to one of multiple possible column values in the subquery; only one column can be listed in the subquery, and the possible match comes from multiple row values in that one column, not vice-versa.

Whew! That’s enough on the topic of complex SELECT queries for now. Now on to other SQL statements.

Miscellaneous SQL Statements

Aggregate Functions

I will discuss five important aggregate functions: SUM, AVG, MAX, MIN, and

COUNT. They are called aggregate functions because they summarize the results of a query, rather than listing all of the rows.

  • SUM () gives the total of all the rows, satisfying any conditions, of the given column, where the given column is numeric.
  • AVG () gives the average of the given column.
  • MAX () gives the largest figure in the given column.
  • MIN () gives the smallest figure in the given column.
  • COUNT(*) gives the number of rows satisfying the conditions.

Looking at the tables at the top of the document, let’s look at three examples:



This query shows the total of all salaries in the table, and the average salary of all of the entries in the table.



This query gives the smallest figure of the Benefits column, of the employees who are Managers, which is 12500.



This query tells you how many employees have Staff status (3).



In SQL, you might (check your DBA) have access to create views for yourself. What a view does is to allow you to assign the results of a query to a new, personal table, that you can use in other queries, where this new table is given the view name in your FROM clause. When you access a view, the query that is defined in your view creation statement is performed (generally), and the results of that query look just like another table in the query that you wrote invoking the view. For example, to create a view:


Now, write a query using this view as a table, where the table is just a listing of all Items Desired from the Orders table:



This query shows all SellerID’s from the Antiques table where the Item in that table happens to appear in the Antview view, which is just all of the Items Desired in the Orders table. The listing is generated by going through the Antique Items one-by-one until there’s a match with the Antview view. Views can be used to restrict database access, as well as, in this case, simplify a complex query.


Creating New Tables

All tables within a database must be created at some point in time…let’s see how we would create the Orders table:



This statement gives the table name and tells the DBMS about each column in the table. Please note that this statement uses generic data types, and that the data types might be different, depending on what DBMS you are using. As usual, check local listings. Some common generic data types are:

  • Char(x) – A column of characters, where x is a number designating the maximum number of characters allowed (maximum length) in the column.
  • Integer – A column of whole numbers, positive or negative.
  • Decimal(x, y) – A column of decimal numbers, where x is the maximum length in digits of the decimal numbers in this column, and y is the maximum number of digits allowed after the decimal point. The maximum (4,2) number would be 99.99.
  • Date – A date column in a DBMS-specific format.
  • Logical – A column that can hold only two values: TRUE or FALSE.

One other note, the NOT NULL means that the column must have a value in each row. If NULL was used, that column may be left empty in a given row.


Altering Tables

Let’s add a column to the Antiques table to allow the entry of the price of a given Item:


The data for this new column can be updated or inserted as shown later.


Adding Data

To insert rows into a table, do the following:

INSERT INTO ANTIQUES VALUES (21, 01, ‚Ottoman‘, 200.00);

This inserts the data into the table, as a new row, column-by-column, in the predefined order. Instead, let’s change the order and leave Price blank:


VALUES (01, 21, ‚Ottoman‘);


Deleting Data

Let’s delete this new row back out of the database:


WHERE ITEM = ‚Ottoman‘;

But if there is another row that contains ‚Ottoman‘, that row will be deleted also. Let’s delete all rows (one, in this case) that contain the specific data we added before:




Updating Data

Let’s update a Price into a row that doesn’t have a price listed yet:


This sets all Chair’s Prices to 500.00. As shown above, more WHERE conditionals, using AND, must be used to limit the updating to more specific rows. Also, additional columns may be set by separating equal statements with commas.


Miscellaneous Topics


Indexes allow a DBMS to access data quicker (please note: this feature is nonstandard/not available on all systems). The system creates this internal data structure (the index) which causes selection of rows, when the selection is based on indexed columns, to occur faster. This index tells the DBMS where a certain row is in the table given an indexed-column value, much like a book index tells you what page a given word appears. Let’s create an index for the OwnerID in the AntiqueOwners column:



To get rid of an index, drop it:


By the way, you can also ‚drop‘ a table, as well (careful!–that means that your table is deleted). In the second example, the index is kept on the two columns, aggregated together–strange behavior might occur in this situation…check the manual before performing such an operation.

Some DBMS’s do not enforce primary keys; in other words, the uniqueness of a column is not enforced automatically. What that means is, if, for example, I tried to insert another row into the AntiqueOwners table with an OwnerID of 02, some systems will allow me to do that, even though, we do not, as that column is supposed to be unique to that table (every row value is supposed to be different). One way to get around that is to create a unique index on the column that we want to be a primary key, to force the system to enforce prohibition of duplicates:




One special use of GROUP BY is to associate an aggregate function (especially COUNT; counting the number of rows in each group) with groups of rows. First, assume that the Antiques table has the Price column, and each row has a value for that column. We want to see the price of the most expensive item bought by each owner. We have to tell SQL to group each owner’s purchases, and tell us the maximum purchase price:




Now, say we only want to see the maximum purchase price if the purchase is over $1000, so we use the HAVING clause:





More Subqueries

Another common usage of subqueries involves the use of operators to allow a Where condition to include the Select output of a subquery. First, list the buyers who purchased an expensive item (the Price of the item is $100 greater than the average price of all items purchased):





The subquery calculates the average Price, plus $100, and using that figure, an OwnerID is printed for every item costing over that figure. One could use DISTINCT OWNERID, to eliminate duplicates.

List the Last Names of those in the AntiqueOwners table, ONLY if they have bought an item:





The subquery returns a list of buyers, and the Last Name is printed for an Antique Owner if and only if the Owner’s ID appears in the subquery list (sometimes called a candidate list). Note: on some DBMS’s, equals can be used instead of IN, but for clarity’s sake, since a set is returned from the subquery, IN is the better choice.

For an Update example, we know that the gentleman who bought the bookcase has the wrong First Name in the database…it should be John:





WHERE ITEM = ‚Bookcase‘);

First, the subquery finds the BuyerID for the person(s) who bought the Bookcase, then the outer query updates his First Name.

Remember this rule about subqueries: when you have a subquery as part of a WHERE condition, the Select clause in the subquery must have columns that match in number and type to those in the Where clause of the outer query. In other words, if you have ‚WHERE ColumnName = (SELECT…);‘, the Select must have only one column in it, to match the ColumnName in the outer Where clause, and they must match in type (both being integers, both being character strings, etc.).



EXISTS uses a subquery as a condition, where the condition is True if the subquery returns any rows, and False if the subquery does not return any rows; this is a nonintuitive feature with few unique uses. However, if a prospective customer wanted to see the list of Owners only if the shop dealt in Chairs, try:





WHERE ITEM = ‚Chair‘);

If there are any Chairs in the Antiques column, the subquery would return a row or rows, making the EXISTS clause true, causing SQL to list the Antique Owners. If there had been no Chairs, no rows would have been returned by the outside query.

ALL is another unusual feature, as ALL queries can usually be done with different, and possibly simpler methods; let’s take a look at an example query:






This will return the largest priced item (or more than one item if there is a tie), and its buyer. The subquery returns a list of all Prices in the Antiques table, and the outer query goes through each row of the Antiques table, and if its Price is greater than or equal to every (or ALL) Prices in the list, it is listed, giving the highest priced Item. The reason ‚>=‘ must be used is that the highest priced item will be equal to the highest price on the list, because this Item is in the Price list.


UNION & Outer Joins (briefly explained)

There are occasions where you might want to see the results of multiple queries together, combining their output; use UNION. To merge the output of the following two queries, displaying the ID’s of all Buyers, plus all those who have an Order placed:





Notice that SQL requires that the Select list (of columns) must match, column-bycolumn, in data type. In this case BuyerID and OwnerID are of the same data type (integer). Also notice that SQL does automatic duplicate elimination when using UNION (as if they were two ’sets‘); in single queries, you have to use DISTINCT.

The outer join is used when a join query is ‚united‘ with the rows not included in the join, and are especially useful if constant text ‚flags‘ are included. First, look at the query:

SELECT OWNERID, ‚is in both Orders & Antiques‘




SELECT BUYERID, ‚is in Antiques only‘




The first query does a join to list any owners who are in both tables, and putting a tag line after the ID repeating the quote. The UNION merges this list with the next list. The second list is generated by first listing those ID’s not in the Orders table, thus generating a list of ID’s excluded from the join query. Then, each row in the Antiques table is scanned, and if the BuyerID is not in this exclusion list, it is listed with its quoted tag. There might be an easier way to make this list, but it’s difficult to generate the informational quoted strings of text.

This concept is useful in situations where a primary key is related to a foreign key, but the foreign key value for some primary keys is NULL. For example, in one table, the primary key is a salesperson, and in another table is customers, with their salesperson listed in the same row. However, if a salesperson has no customers, that person’s name won’t appear in the customer table. The outer join is used if the listing of all salespersons is to be printed, listed with their customers, whether the salesperson has a customer or not–that is, no customer is printed (a logical NULL value) if the salesperson has no customers, but is in the salespersons table. Otherwise, the salesperson will be listed with each customer.

Another important related point about Nulls having to do with joins: the order of tables listed in the From clause is very important. The rule states that SQL ‚adds‘ the second table to the first; the first table listed has any rows where there is a null on the join column displayed; if the second table has a row with a null on the join column, that row from the table listed second does not get joined, and thus included with the first table’s row data. This is another occasion (should you wish that data included in the result) where an outer join is commonly used. The concept of nulls is important, and it may be worth your time to investigate them further.


Common SQL Questions–Advanced Topics

  1. Why can’t I just ask for the first three rows in a table? –Because in relational databases, rows are inserted in no particular order, that is, the system inserts them in an arbitrary order; so, you can only request rows using valid SQL features, like ORDER BY, etc.
  2. What is this DDL and DML I hear about? –DDL (Data Definition Language) refers to (in SQL) the Create Table statement…DML (Data Manipulation Language) refers to the Select, Update, Insert, and Delete statements.
  3. Aren’t database tables just files? –Well, DBMS’s store data in files declared by system managers before new tables are created (on large systems), but the system stores the data in a special format, and may spread data from one table over several files. In the database world, a set of files created for a database is called a tablespace. In general, on small systems, everything about a database (definitions and all table data) is kept in one file.
  4. (Related question) Aren’t database tables just like spreadsheets? –No, for two reasons. First, spreadsheets can have data in a cell, but a cell is more than just a row-column-intersection. Depending on your spreadsheet software, a cell might also contain formulas and formatting, which database tables cannot have (currently). Secondly, spreadsheet cells are often dependent on the data in other cells. In databases, ‚cells‘ are independent, except that columns are logically related (hopefully; together a row of columns describe an entity), and, other than primary key and foreign key constraints, each row in a table is independent from one another.
  5. How do I import a text file of data into a database? –Well, you can’t do it directly…you must use a utility, such as Oracle’s SQL*Loader, or write a program to load the data into the database. A program to do this would simply

go through each record of a text file, break it up into columns, and do an Insert into the database.

  1. What is a schema? –A schema is a logical set of tables, such as the Antiques database above…usually, it is thought of as simply ‚the database‘, but a database can hold more than one schema. For example, a star schema is a set of tables where one large, central table holds all of the important information, and is linked, via foreign keys, to dimension tables which hold detail information, and can be used in a join to create detailed reports.
  2. Show me an example of an outer join. –Well, from the questions I receive, this is an extremely common example, and I’ll show you both the Oracle and Access queries…

Think of the following Employee table (the employees are given numbers, for simplicity):

Name Department
1         10
2         10
3 20
4 30
5 30

Now think of a department table:


Now suppose you want to join the tables, seeing all of the employees and all of the departments together…you’ll have to use an outer join which includes a null employee to go with Dept. 40.


In the book, ‚Oracle: the Complete Reference‘, about outer joins, ‚think of the (+), which must immediately follow the join column of the table, as saying add an extra (null) row anytime there’s no match‘. So, in Oracle, try this query (the + goes on Employee, which adds the null row on no match):


Select E.Name, D.Department

From Department D, Employee E

Where E.Department(+) = D.Department;


This is a left (outer) join, in Access:

SELECT DISTINCTROW Employee.Name, Department.Department

FROM Department LEFT JOIN Employee ON

Department.Department = Employee.Department;


And you get this result:

Name Department
1 10
2 10
3         20
4         30
5         30
  1. What are some general tips you would give to make my SQL queries and databases better and faster (optimized)? o You should try, if you can, to avoid expressions in Selects, such as SELECT ColumnA + ColumnB, etc. The query optimizer of the database, the portion of the DBMS that determines the best way to get the required data out of the database itself, handles expressions in such a way that would normally require more time to retrieve the data than if columns were normally selected, and the expression itself handled programmatically.
    • Minimize the number of columns included in a Group By clause.
    • If you are using a join, try to have the columns joined on (from both tables) indexed.
    • When in doubt, index. o Unless doing multiple counts or a complex query, use COUNT(*) (the number of rows generated by the query) rather than COUNT(Column_Name).
  2. What is normalization? –Normalization is a technique of database design that suggests that certain criteria be used when constructing a table layout (deciding what columns each table will have, and creating the key structure), where the idea is to eliminate redundancy of non-key data across tables. Normalization is usually referred to in terms of forms, and I will introduce only the first three, even though it is somewhat common to use other, more advanced forms (fourth, fifth, Boyce-Codd, etc).


First Normal Form refers to moving data into separate tables where the data in each table is of a similar type, and by giving each table a primary key.


Putting data in Second Normal Form involves removing to other tables data that is only dependent of a part of the key. For example, if I had left the names of the Antique Owners in the items table, that would not be in Second Normal Form because that data would be redundant; the name would be repeated for each item owned; as such, the names were placed in their own table. The names themselves don’t have anything to do with the items, only the identities of the buyers and sellers.


Third Normal Form involves getting rid of anything in the tables that doesn’t depend solely on the primary key. Only include information that is dependent on the key, and move off data to other tables that are independent of the primary key, and create a primary keys for the new tables.


There is some redundancy to each form, and if data is in 3NF (shorthand for 3rd normal form), it is already in 1NF and 2NF. In terms of data design then, arrange data so that any non-primary key columns are dependent only on the whole primary key. If you take a look at the examples above, you will see that the way then to navigate through the database is through joins using common key columns.


Two other important points in database design are using good, consistent, logical, full-word names for the tables and columns, and the use of full words in the database itself. On the last point, my database is lacking, as I use numeric codes for identification. It is usually best, if possible, to come up with keys that are, by themselves, self-explanatory; for example, a better key would be the first four letters of the last name and first initial of the owner, like JONEB for Bill Jones (or for tiebreaking purposes, add numbers to the end to differentiate two or more people with similar names, so you could try JONEB1, JONEB2, etc.).

  1. What is the difference between a single-row query and a multiple-row query and why is it important to know the difference? –First, to cover the obvious, a single-row query is a query that returns one row as its result, and a multiplerow query is a query that returns more than one row as its result. Whether a query returns one row or more than one row is entirely dependent on the design (or schema) of the tables of the database. As query-writer, you must be aware of the schema, be sure to include enough conditions, and structure your SQL statement properly, so that you will get the desired result (either one row or multiple rows). For example, if you wanted to be sure that a query of the AntiqueOwners table returned only one row, consider an equal condition

of the primary key-column, OwnerID.


Three reasons immediately come to mind as to why this is important. First, getting multiple rows when you were expecting only one, or vice-versa, may mean that the query is erroneous, that the database is incomplete, or simply, you learned something new about your data. Second, if you are using an update or delete statement, you had better be sure that the statement that you write performs the operation on the desired row (or rows)…or else, you might be deleting or updating more rows than you intend. Third, any queries written in Embedded SQL must be carefully thought out as to the number of rows returned. If you write a single-row query, only one SQL statement may need to be performed to complete the programming logic required. If your query, on the other hand, returns multiple rows, you will have to use the Fetch statement, and quite probably, some sort of looping structure in your program will be required to iterate processing on each returned row of the query.

  1. What are relationships? –Another design question…the term ‚relationships‘ (often termed ‚relation‘) usually refers to the relationships among primary and foreign keys between tables. This concept is important because when the tables of a relational database are designed, these relationships must be defined because they determine which columns are or are not primary or foreign keys. You may have heard of an Entity-Relationship Diagram, which is a graphical view of tables in a database schema, with lines connecting related columns across tables. See the sample diagram at the end of this section or some of the sites below in regard to this topic, as there are many different ways of drawing E-R diagrams. But first, let’s look at each kind of relationship…

A One-to-one relationship means that you have a primary key column that is related to a foreign key column, and that for every primary key value, there is one foreign key value. For example, in the first example, the

EmployeeAddressTable, we add an EmployeeIDNo column. Then, the EmployeeAddressTable is related to the EmployeeStatisticsTable (second example table) by means of that EmployeeIDNo. Specifically, each employee in the EmployeeAddressTable has statistics (one row of data) in the EmployeeStatisticsTable. Even though this is a contrived example, this is a ’11‘ relationship. Also notice the ‚has‘ in bold…when expressing a relationship, it is important to describe the relationship with a verb.


The other two kinds of relationships may or may not use logical primary key and foreign key constraints…it is strictly a call of the designer. The first of these is the one-to-many relationship (‚1-M‘). This means that for every column value in one table, there is one or more related values in another table. Key constraints may be added to the design, or possibly just the use of some sort of identifier column may be used to establish the relationship. An example would be that for every OwnerID in the AntiqueOwners table, there are one or more (zero is permissible too) Items bought in the Antiques table (verb: buy).


Finally, the many-to-many relationship (‚M-M‘) does not involve keys generally, and usually involves idenifying columns. The unusual occurence of a ‚M-M‘ means that one column in one table is related to another column in another table, and for every value of one of these two columns, there are one or more related values in the corresponding column in the other table (and vice-versa), or more a common possibility, two tables have a 1-M relationship to each other (two relationships, one 1-M going each way). A [bad] example of the more common situation would be if you had a job assignment database, where one table held one row for each employee and a job assignment, and another table held one row for each job with one of the assigned employees. Here, you would have multiple rows for each employee in the first table, one for each job assignment, and multiple rows for each job in the second table, one for each employee assigned to the project. These tables have a M-M: each employee in the first table has many job assignments from the second table, and each job has many employees assigned to it from the first table. What are some important non-standard SQL features (extremely common question)? –Well, see the next section…


Non-standard SQL…’check local listings‘

  • INTERSECT and MINUS are like the UNION statement, except that INTERSECT produces rows that appear in both queries, and MINUS produces rows that result from the first query, but not the second.
  • Report Generation Features: the COMPUTE clause is placed at the end of a query to place the result of an aggregate function at the end of a listing, like COMPUTE SUM (PRICE); Another option is to use break logic: define a break to divide the query results into groups based on a column, like BREAK ON BUYERID. Then, to produce a result after the listing of a group, use

COMPUTE SUM OF PRICE ON BUYERID. If, for example, you used all three of these clauses (BREAK first, COMPUTE on break second, COMPUTE overall sum third), you would get a report that grouped items by their BuyerID, listing the sum of Prices after each group of a BuyerID’s items, then, after all groups are listed, the sum of all Prices is listed, all with SQL-generated headers and lines.

  • In addition to the above listed aggregate functions, some DBMS’s allow more functions to be used in Select lists, except that these functions (some character functions allow multiple-row results) are to be used with an

individual value (not groups), on single-row queries. The functions are to be used only on appropriate data types, also. Here are some Mathematical Functions:

ABS(X) Absolute value-converts negative numbers to positive, or leaves positive numbers alone
CEIL(X) X is a decimal value that will be rounded up.
FLOOR(X)           X is a decimal value that will be rounded down.
GREATEST(X,Y) Returns the largest of the two values.
LEAST(X,Y) Returns the smallest of the two values.
MOD(X,Y) Returns the remainder of X / Y.
POWER(X,Y) Returns X to the power of Y.
Rounds X to Y decimal places. If Y is omitted, X is rounded


to the nearest integer.

SIGN(X)                Returns a minus if X < 0, else a plus.
SQRT(X)               Returns the square root of X.
Character Functions
LEFT(<string>,X) Returns the leftmost X characters of the string.
RIGHT(<string>,X) Returns the rightmost X characters of the string.
UPPER(<string>) Converts the string to all uppercase letters.
LOWER(<string>) Converts the string to all lowercase letters.
INITCAP(<string>) Converts the string to initial caps.
LENGTH(<string>) Returns the number of characters in the string.
<string>||<string> Combines the two strings of text into one, concatenated string, where the first string is immediately followed by the second.
LPAD(<string>,X,’*‘) Pads the string on the left with the * (or whatever character is inside the quotes), to make the string X characters long.
RPAD(<string>,X,’*‘) Pads the string on the right with the * (or whatever character is inside the quotes), to make the string X characters long.
SUBSTR(<string>,X,Y) Extracts Y letters from the string beginning at position X.
NVL(<column>,<value>) The Null value function will substitute <value> for any NULLs for in the <column>. If the current value
of <column> is not NULL, NVL has no effect.


Syntax Summary – For Advanced Users

Here are the general forms of the statements discussed in this tutorial, plus some extra important ones (explanations given). REMEMBER that all of these statements may or may not be available on your database system, so check the DBMS documentation regarding its availability:


SPECIFICATION[S]…see Create Table); –allows you to add or delete a column or columns from a table, or change the specification (data type, etc.) on an existing column; this statement is also used to change the physical specifications of a table (how a table is stored, etc.), but these definitions are DBMS-specific, so read the documentation. Also, these physical specifications are used with the Create Table statement, when a table is first created. In addition, only one option can be performed per Alter Table statement–either add, drop, OR modify in a single statement.

COMMIT; –makes changes made to some database systems permanent (since the last COMMIT; known as a transaction)


ON <TABLE NAME> (<COLUMN LIST>); –UNIQUE is optional; within brackets.


(<COLUMN NAME> <DATA TYPE> [(<SIZE>)] <COLUMN CONSTRAINT>, …other columns); (also valid with ALTER TABLE)

–where SIZE is only used on certain data types (see above), and constraints include the following possibilities (automatically enforced by the DBMS; failure causes an error to be generated):

  1. NULL or NOT NULL (see above)
  2. UNIQUE enforces that no two rows will have the same value for this column
  3. PRIMARY KEY tells the database that this column is the primary key column (only used if the key is a one column key, otherwise a PRIMARY KEY (column, column, …) statement appears after the last column definition.
  4. CHECK allows a condition to be checked for when data in that column is updated or inserted; for example, CHECK (PRICE > 0) causes the system to check that the Price column is greater than zero before accepting the value…sometimes implemented as the CONSTRAINT statement.
  5. DEFAULT inserts the default value into the database if a row is inserted without that column’s data being inserted; for example, BENEFITS INTEGER DEFAULT = 10000
  6. FOREIGN KEY works the same as Primary Key, but is followed by:

REFERENCES <TABLE NAME> (<COLUMN NAME>), which refers to the referential primary key.





ROLLBACK; –Takes back any changes to the database that you have made, back to the last time you gave a Commit command…beware! Some software uses automatic committing on systems that use the transaction features, so the Rollback command may not work.







[ORDER BY <ORDERING COLUMN(S)> [ASC|DESC]]; –where ASC|DESC allows the ordering to be done in ASCending or DESCending order



[WHERE <CONDITION>]; –if the Where clause is left out, all rows will be updated according to the Set statement