SQL SYNTAX'S,We can get Good Knowlede

SQL SYNTAX

Select Statement
SELECT "column_name" FROM "table_name"
Distinct
SELECT DISTINCT "column_name"
FROM "table_name"
Where
SELECT "column_name"
FROM "table_name"
WHERE "condition"
And/Or
SELECT "column_name"
FROM "table_name"
WHERE "simple condition"
{[AND|OR] "simple condition"}+
In
SELECT "column_name"
FROM "table_name"
WHERE "column_name" IN ('value1', 'value2', ...)
Between
SELECT "column_name"
FROM "table_name"
WHERE "column_name" BETWEEN 'value1' AND 'value2'
Like
SELECT "column_name"
FROM "table_name"
WHERE "column_name" LIKE {PATTERN}
Order By
SELECT "column_name"
FROM "table_name"
[WHERE "condition"]
ORDER BY "column_name" [ASC, DESC]
Count
SELECT COUNT("column_name")
FROM "table_name"
Group By
SELECT "column_name1", SUM("column_name2")
FROM "table_name"
GROUP BY "column_name1"
Having
SELECT "column_name1", SUM("column_name2")
FROM "table_name"
GROUP BY "column_name1"
HAVING (arithematic function condition)
Create Table Statement
CREATE TABLE "table_name"
("column 1" "data_type_for_column_1",
"column 2" "data_type_for_column_2",
... )
Drop Table Statement
DROP TABLE "table_name"
Truncate Table Statement
TRUNCATE TABLE "table_name"
Insert Into Statement
INSERT INTO "table_name" ("column1", "column2", ...)
VALUES ("value1", "value2", ...)
Update Statement
UPDATE "table_name"
SET "column_1" = [new value]
WHERE {condition}
Delete From Statement
DELETE FROM "table_name"
WHERE {condition}

SQL COMMANDS
SQL SELECT

What do we use SQL commands for? A common use is to select data from the tables located in a database. Immediately, we see two keywords: we need to SELECT information FROM a table. (Note that a table is a container that resides in the database where the data is stored. For more information about how to manipulate tables, go to the Table Manipulation Section). Hence we have the most basic SQL structure:

SELECT "column_name" FROM "table_name"


To illustrate the above example, assume that we have the following table:
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

We shall use this table as an example throughout the tutorial (this table will appear in all sections). To select all the stores in this table, we key in,
SELECT store_name FROM Store_Information
Result:

store_name

Los Angeles

San Diego

Los Angeles

Boston

Multiple column names can be selected, as well as multiple table names.

SQL DISTINCT
The SELECT keyword allows us to grab all information from a column (or columns) on a table. This, of course, necessarily mean that there will be redundancies. What if we only want to select each DISTINCT element? This is easy to accomplish in SQL. All we need to do is to add DISTINCT after SELECT. The syntax is as follows:
SELECT DISTINCT "column_name"
FROM "table_name"
For example, to select all distinct stores in Table Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we key in,
SELECT DISTINCT store_name FROM Store_Information
Result:

store_name

Los Angeles

San Diego

Boston

SQL WHERE
Next, we might want to conditionally select the data from a table. For example, we may want to only retrieve stores with sales above $1,000. To do this, we use the WHERE keyword. The syntax is as follows:
SELECT "column_name"
FROM "table_name"
WHERE "condition"
For example, to select all stores with sales above $1,000 in Table Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we key in,
SELECT store_name
FROM Store_Information
WHERE Sales > 1000



Result:

store_name

Los Angeles

SQL AND OR
In the previous section, we have seen that the WHERE keyword can be used to conditionally select data from a table. This condition can be a simple condition (like the one presented in the previous section), or it can be a compound condition. Compound conditions are made up of multiple simple conditions connected by AND or OR. There is no limit to the number of simple conditions that can be present in a single SQL statement.
The syntax for a compound condition is as follows:
SELECT "column_name"
FROM "table_name"
WHERE "simple condition"
{[AND|OR] "simple condition"}+
The {}+ means that the expression inside the bracket will occur one or more times. Note that AND and OR can be used interchangeably. In addition, we may use the parenthesis sign () to indicate the order of the condition.
For example, we may wish to select all stores with sales greater than $1,000 or all stores with sales less than $500 but greater than $275 in Table Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
San Francisco	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we key in,
SELECT store_name
FROM Store_Information
WHERE Sales > 1000
OR (Sales < 500 AND Sales > 275)



Result:

store_name

Los Angeles

San Francisco

SQL IN
In SQL, there are two uses of the IN keyword, and this section introduces the one that is related to the WHERE clause. When used in this context, we know exactly the value of the returned values we want to see for at least one of the columns. The syntax for using the IN keyword is as follows:
SELECT "column_name"
FROM "table_name"
WHERE "column_name" IN ('value1', 'value2', ...)
The number of values in the parenthesis can be one or more, with each values separated by comma. Values can be numerical or characters. If there is only one value inside the parenthesis, this commend is equivalent to
WHERE "column_name" = 'value1'
For example, we may wish to select all records for the Los Angeles and the San Diego stores in Table Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
San Francisco	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we key in,
SELECT *
FROM Store_Information
WHERE store_name IN ('Los Angeles', 'San Diego')




Result:

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999

SQL IN
Whereas the IN keyword help people to limit the selection criteria to one or more discrete values, the BETWEEN keyword allows for selecting a range. The syntax for the BETWEEN clause is as follows:
SELECT "column_name"
FROM "table_name"
WHERE "column_name" BETWEEN 'value1' AND 'value2'
This will select all rows whose column has a value between 'value1' and 'value2'.
For example, we may wish to select view all sales information between January 6, 1999, and January 10, 1999, in Table Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
San Francisco	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we key in,
SELECT *
FROM Store_Information
WHERE Date BETWEEN 'Jan-06-1999' AND 'Jan-10-1999'
Note that date may be stored in different formats in different databases. This tutorial simply choose one of the formats.



Result:

store_name	Sales	Date
San Diego	$250	Jan-07-1999
San Francisco	$300	Jan-08-1999
Boston	$700	Jan-08-1999

SQL BETWEEN
Whereas the IN keyword help people to limit the selection criteria to one or more discrete values, the BETWEEN keyword allows for selecting a range. The syntax for the BETWEEN clause is as follows:
SELECT "column_name"
FROM "table_name"
WHERE "column_name" BETWEEN 'value1' AND 'value2'
This will select all rows whose column has a value between 'value1' and 'value2'.
For example, we may wish to select view all sales information between January 6, 1999, and January 10, 1999, in Table Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
San Francisco	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we key in,
SELECT *
FROM Store_Information
WHERE Date BETWEEN 'Jan-06-1999' AND 'Jan-10-1999'


Note that date may be stored in different formats in different databases. This tutorial simply choose one of the formats.
Result:

store_name	Sales	Date
San Diego	$250	Jan-07-1999
San Francisco	$300	Jan-08-1999
Boston	$700	Jan-08-1999

LIKE is another keyword that is used in the WHERE clause. Basically, LIKE allows you to do a search based on a pattern rather than specifying exactly what is desired (as in IN) or spell out a range (as in BETWEEN). The syntax for is as follows:
SELECT "column_name"
FROM "table_name"
WHERE "column_name" LIKE {PATTERN}
{PATTERN} often consists of wildcards. Here are some examples:

• 'A_Z': All string that starts with 'A', another character, and end with 'Z'. For example, 'ABZ' and 'A2Z' would both satisfy the condition, while 'AKKZ' would not (because there are two characters between A and Z instead of one).

• 'ABC%': All strings that start with 'ABC'. For example, 'ABCD' and 'ABCABC' would both satisfy the condition.

• '%XYZ': All strings that end with 'XYZ'. For example, 'WXYZ' and 'ZZXYZ' would both satisfy the condition.

• '%AN%': All string that contain the pattern 'AN' anywhere. For example, 'LOS ANGELES' and 'SAN FRANCISCO' would both satisfy the condition.

Let's say we have the following table:
Table Store_Information

store_name	Sales	Date
LOS ANGELES	$1500	Jan-05-1999
SAN DIEGO	$250	Jan-07-1999
SAN FRANCISCO	$300	Jan-08-1999
BOSTON	$700	Jan-08-1999

We want to find all stores whose name contains 'AN'. To do so, we key in,
SELECT *
FROM Store_Information
WHERE store_name LIKE '%AN%'

Result:

store_name	Sales	Date
LOS ANGELES	$1500	Jan-05-1999
SAN DIEGO	$250	Jan-07-1999
SAN FRANCISCO	$300	Jan-08-1999

SQL ORDER BY
So far, we have seen how to get data out of a table using SELECT and WHERE commands. Often, however, we need to list the output in a particular order. This could be in ascending order, in descending order, or could be based on either numerical value or text value. In such cases, we can use the ORDER BY keyword to achieve our goal.
The syntax for an ORDER BY statement is as follows:
SELECT "column_name"
FROM "table_name"
[WHERE "condition"]
ORDER BY "column_name" [ASC, DESC]
The [] means that the WHERE statement is optional. However, if a WHERE clause exists, it comes before the ORDER BY clause. ASC means that the results will be shown in ascending order, and DESC means that the results will be shown in descending order. If neither is specified, the default is ASC.
It is possible to order by more than one column. In this case, the ORDER BY clause above becomes
ORDER BY "column_name1" [ASC, DESC], "column_name2" [ASC, DESC]
Assuming that we choose ascending order for both columns, the output will be ordered in ascending order according to column 1. If there is a tie for the value of column 1, we the sort in ascending order by column 2.
For example, we may wish to list the contents of Table Store_Information by dollar amount, in descending order:

Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
San Francisco	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we key in,
SELECT store_name, Sales, Date
FROM Store_Information
ORDER BY Sales DESC

Result:

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
Boston	$700	Jan-08-1999
San Francisco	$300	Jan-08-1999
San Diego	$250	Jan-07-1999

AGGREATE FUNCTIONS
In addition to column name, we may also use column position (based on the SQL query) to indicate which column we want to apply the ORDER BY clause. The first column is 1, second column is 2, and so on. In the above example, we will achieve the same results by the following command:
SELECT store_name, Sales, Date
FROM Store_Information
ORDER BY 2 DESC
Since we have started dealing with numbers, the next natural question to ask is if it is possible to do math on those numbers, such as summing them up or taking their average. The answer is yes! SQL has several arithematic functions, and they are:

- AVG
- COUNT
- MAX
- MIN
- SUM

The syntax for using functions is,
SELECT "function type"("column_name")
FROM "table_name"
For example, if we want to get the sum of all sales from the following table,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we would type in
SELECT SUM(Sales) FROM Store_Information

Result:

SUM(Sales)

$2750

$2750 represents the sum of all Sales entries: $1500 + $250 + $300 + $700.
In addition to using functions, it is also possible to use SQL to perform simple tasks such as addition (+) and subtraction (-). For character-type data, there are also several string functions available, such as concatenation, trim, and substring functions. Different RDBMS vendors have different string functions implementations, and it is best to consult the references for your RDBMS to see how these functions are used.

SQL  COUNT
Another arithmetic function is COUNT. This allows us to COUNT up the number of row in a certain table. The syntax is,
SELECT COUNT("column_name")
FROM "table_name"
For example, if we want to find the number of store entries in our table,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we'd key in
SELECT COUNT(store_name)
FROM Store_Information
Result:

Count(store_name)

4

COUNT and DISTINCT can be used together in a statement to fetch the number of distinct entries in a table. For example, if we want to find out the number of distinct stores, we'd type,
SELECT COUNT(DISTINCT store_name)
FROM Store_Information
Result:

Count(DISTINCT store_name)

3

SQL GROUP BY
Now we return to the aggregate functions. Remember we used the SUM keyword to calculate the total sales for all stores? What if we want to calculate the total sales for each store? Well, we need to do two things: First, we need to make sure we select the store name as well as total sales. Second, we need to make sure that all the sales figures are grouped by stores. The corresponding SQL syntax is,
SELECT "column_name1", SUM("column_name2")
FROM "table_name"
GROUP BY "column_name1"
Let's illustrate using the following table,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

We want to find total sales for each store. To do so, we would key in,
SELECT store_name, SUM(Sales)
FROM Store_Information
GROUP BY store_name
Result:

store_name	SUM(Sales)
Los Angeles	$1800
San Diego	$250
Boston>	$700

SQL HAVING
The GROUP BY keyword is used when we are selecting multiple columns from a table (or tables) and at least one arithmetic operator appears in the SELECT statement. When that happens, we need to GROUP BY all the other selected columns, i.e., all columns except the one(s) operated on by the arithmetic operator.
Another thing people may want to do is to limit the output based on the corresponding sum (or any other aggregate functions). For example, we might want to see only the stores with sales over $1,500. Instead of using the WHERE clause in the SQL statement, though, we need to use the HAVING clause, which is reserved for aggregate functions. The HAVING clause is typically placed near the end of the SQL statement, and a SQL statement with the HAVING clause may or may not include the GROUP BY clause. The syntax for HAVING is,
SELECT "column_name1", SUM("column_name2")
FROM "table_name"
GROUP BY "column_name1"
HAVING (arithmetic function condition)
Note: the GROUP BY clause is optional.
In our example, table Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

we would type,
SELECT store_name, SUM(sales)
FROM Store_Information
GROUP BY store_name
HAVING SUM(sales) > 1500
Result:

store_name		SUM(Sales)
Los Angeles		$1800

SQL ALIAS
We next focus on the use of aliases. There are two types of aliases that are used most frequently: column alias and table alias. In short, column aliases exist to help organizing output. In the previous example, whenever we see total sales, it is listed as SUM(sales). While this is comprehensible, we can envision cases where the column heading can be complicated (especially if it involves several arithmetic operations). Using a column alias would greatly make the output much more readable.
The second type of alias is the table alias. This is accomplished by putting an alias directly after the table name in the FROM clause. This is convenient when you want to obtain information from two separate tables (the technical term is 'perform joins'). The advantage of using a table alias when doing joins is readily apparent when we talk about joins.
Before we get into joins, though, let's look at the syntax for both the column and table aliases:
SELECT "table_alias"."column_name1" "column_alias"
FROM "table_name" "table_alias"
Briefly, both types of aliases are placed directly after the item they alias for, separate by a white space. We again use our table, Store_Information,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

We use the same example as that in the SQL GROUP BY section, except that we have put in both the column alias and the table alias:
SELECT A1.store_name Store, SUM(A1.Sales) "Total Sales"
FROM Store_Information A1
GROUP BY A1.store_name
Result:

Store		Total Sales
Los Angeles		$1800
San Diego		$250
Boston		$700

Notice that difference in the result: the column titles are now different. That is the result of using the column alias. Notice that instead of the somewhat cryptic "Sum(Sales)", we now have "Total Sales", which is much more understandable, as the column header. The advantage of using a table alias is not apparent in this example. However, they will become evident in the next section.

SQL JOIN
Now we want to look at joins. To do joins correctly in SQL requires many of the elements we have introduced so far. Let's assume that we have the following two tables,




Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

Table Geography

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

and we want to find out sales by region. We see that table Geography includes information on regions and stores, and table Store_Information contains sales information for each store. To get the sales information by region, we have to combine the information from the two tables. Examining the two tables, we find that they are linked via the common field, "store_name". We will first present the SQL statement and explain the use of each segment later:
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
Result:

REGION		SALES
East		$700
West		$2050

The first two lines tell SQL to select two fields, the first one is the field "region_name" from table Geography (aliased as REGION), and the second one is the sum of the field "Sales" from table Store_Information (aliased as SALES). Notice how the table aliases are used here: Geography is aliased as A1, and Store_Information is aliased as A2. Without the aliasing, the first line would become
SELECT Geography.region_name REGION, SUM(Store_Information.Sales) SALES
which is much more cumbersome. In essence, table aliases make the entire SQL statement easier to understand, especially when multiple tables are included.
Next, we turn our attention to line 3, the WHERE statement. This is where the condition of the join is specified. In this case, we want to make sure that the content in "store_name" in table Geography matches that in table Store_Information, and the way to do it is to set them equal. This WHERE statement is essential in making sure you get the correct output. Without the correct WHERE statement, a Cartesian Join will result. Cartesian joins will result in the query returning every possible combination of the two (or whatever the number of tables in the FROM statement) tables. In this case, a Cartesian join would result in a total of 4 x 4 = 16 rows being returned

SQL OUTER JOIN
Previously, we had looked at left join, or inner join, where we select rows common to the participating tables to a join. What about the cases where we are interested in selecting elements in a table regardless of whether they are present in the second table? We will now need to use the SQL OUTER JOIN command.
The syntax for performing an outer join in SQL is database-dependent. For example, in Oracle, we will place an "(+)" in the WHERE clause on the other side of the table for which we want to include all the rows.
Let's assume that we have the following two tables,
Table Store_Information

store_name	Sales	Date
Los Angeles	$1500	Jan-05-1999
San Diego	$250	Jan-07-1999
Los Angeles	$300	Jan-08-1999
Boston	$700	Jan-08-1999

Table Geography

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

and we want to find out the sales amount for all of the stores. If we do a regular join, we will not be able to get what we want because we will have missed "New York," since it does not appear in the Store_Information table. Therefore, we need to perform an outer join on the two tables above:
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
Note that in this case, we are using the Oracle syntax for outer join.
Result:

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

Note: NULL is returned when there is no match on the second table. In this case, "New York" does not appear in the table Store_Information, thus its corresponding "SALES" column is NULL.

SQL CONCATENATE
Sometimes it is necessary to combine together (concatenate) the results from several different fields. Each database provides a way to do this:

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

The syntax for CONCAT() is as follows:
CONCAT(str1, str2, str3, ...): Concatenate str1, str2, str3, and any other strings together. Please note the Oracle CONCAT() function only allows two arguments -- only two strings can be put together at a time using this function. However, it is possible to concatenate more than two strings at a time in Oracle using '||'.
Let's look at some examples. Assume we have the following table:




Table Geography

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';
Result:
'EastBoston'
Example 2:
Oracle:
SELECT region_name || ' ' || store_name FROM Geography
WHERE store_name = 'Boston';
Result:
'East Boston'
Example 3:
SQL Server:
SELECT region_name + ' ' + store_name FROM Geography
WHERE store_name = 'Boston';
Result:
'East Boston'

SQL SUBSTRING
The Substring function in SQL is used to grab a portion of the stored data. This function is called differently for the different databases:

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

The most frequent uses are as follows (we will use SUBSTR() here):

SUBSTR(str,pos): Select all characters from <str> starting with position <pos>. Note that this syntax is not supported in SQL Server.
SUBSTR(str,pos,len): Starting with the <pos>th character in string <str> and select the next <len> characters.
Assume we have the following table:
Table Geography

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';
Result:
's Angeles'
Example 2:
SELECT SUBSTR(store_name,2,4)
FROM Geography
WHERE store_name = 'San Diego';
Result:
'an D'
SQL TRIM FUNCTION
The TRIM function in SQL is used to remove specified prefix or suffix from a string. The most common pattern being removed is white spaces. This function is called differently in different databases:

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

The syntax for these trim functions are:
TRIM([[LOCATION] [remstr] FROM ] str): [LOCATION] can be either LEADING, TRAILING, or BOTH. This function gets rid of the [remstr] pattern from either the beginning of the string or the end of the string, or both. If no [remstr] is specified, white spaces are removed.
LTRIM(str): Removes all white spaces from the beginning of the string.
RTRIM(str): Removes all white spaces at the end of the string.
Example 1:
SELECT TRIM('   Sample   ');
Result:
'Sample'
Example 2:
SELECT LTRIM('   Sample   ');
Result:
'Sample   '
Example 3:
SELECT RTRIM('   Sample   ');
Result:
'   Sample'

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