SQL (Structured Query Language) is a database computer language designed for managing data in relational database management systems (RDBMS). Its scope includes data query and update, schema creation and modification, and data access control.
SQL Statement Types
SQL Statement Types DML - stands for Data Manipulation Language, it’s the part of SQL that deals with querying updating and inserting records in tables and views. DDL – stands for Data Definition Language, it’s the part of SQL that deals with the construction and alteration of database structures like tables, views and the further entities inside these tables like columns. It may be used to set the properties of columns as well.
SQL Statement Types Examples: DML DDL • SELECT … FROM … WHERE …. • CREATE [TABLE | VIEW | ROLE … • INSERT INTO …. WHERE … • DROP [TABLE | VIEW | ROLE … • DELETE FROM …. WHERE … • GRANT …. TO …. ON … • UPDATE … WHERE …. • ALTER [TABLE | COLUMN …
SQL Data Types
SQL Data Types
SQL Data Types (Descriptions)
SQL Data Types (Descriptions)
Aggregate Functions assist with the summarization of large volumes of data. They return a single result row based on groups of rows, rather than on single rows. Aggregate functions can appear in select lists and in ORDER BY and HAVING clauses.
Aggregate Functions Function Access SQL Server MySQL SQL Server Oracle Sum SUM SUM SUM SUM SUM Average AVG AVG AVG AVG AVG Count COUNT COUNT COUNT COUNT, COUNT_BIG* COUNT COUNT(*) Standard STDEV STDEV STD, STDEV STDEVP, VAR, VARP STDEV, Deviation STDEV_POP, STDEV_SAMP Minimum MIN MIN MIN MIN MIN Maximum MAX MAX MAX MAX MAX Others CHECKSUM, MEDDAN, LAST, CHECKSUM_AGG GROUPING BINARY_CHECKSUM
Aggregate Functions • Ef ect of NULL on aggregate function output – No ef ect, NULL values are eliminated from the result set. – Except for the COUNT function, NULL values are grouped and returned as part of the result set. • Use GROUP BY when using aggregate functions in a multiple column in a select statement • Use HAVING when using aggregate functions are referred to in the condition.
Aggregate Functions SELECT state, count(*) FROM test WHERE state IN(‘CA’,’LA’) GROUP BY state Which is better?? ORDER BY state •WHERE CLAUSE restricts the number of rows that are counted SELECT state, count(*) •HAVING is applied to the result set last after all rows are FROM test counted. GROUP BY state HAVING state IN(‘CA’,’LA’) ORDER BY state
Aggregate Functions SELECT state, SUM(baldue) SELECT state, SUM(baldue) FROM test FROM test WHERE state IN(‘CA’,’LA’) WHERE state IN(‘CA’,’LA’) GROUP BY state GROUP BY state ORDER BY state HAVING SUM(baldue) > 0 ORDER BY state State Sum(baldue) ===== =========== CA 250.00 State Sum(baldue) CO 58.75 ===== =========== GA 3987.50 CA 250.00 LA 0.0 MN 510.00 NY 589.50 TX 62.00 VT 439.00
NULL What is it?? • NULL is not a data value • Indicates the absence of data • Using NULL in any equation yields NULL as the result. • Must modify NULL via a function to produce non nul result. – NULLIF() - SQL Server, MySQL – NVL() - Oracle – CASE statement • Checking for NULL – ISNULL() - SQL Server, MySQL – IFNULL() – MySQL
Comparison Operators & Keywords
Comparison Operators & Keywords • Operators =, !=*, <, >, <=, >= Oracle *not ISO standard >, <, >=, <>, !=*, !<*, !>* T-SQL *not ISO standard =, >, <, >=, <>, !=*, !<*, !>*, -=*, ->*, -<* T-SQL *not ISO standard • Keywords – LIKE – BETWEEN, IN – EXISTS
Comparison Operators & Keywords • Precedence – Operators with higher precedence are applied first – When operators have the same precedence they are handled dif erently depending on the db. • Oracle – applied in no particular order • SQL Server – applied in left to right order – Parenthesis can be used the control the order of precedence. • Evaluation is done from innermost set to outer when nested.
Set Operations let you combine rows from different sets, locate which rows exist in both sets, or find which rows exist in one set but not the other.
Set Operations • UNION [DISTINCT | ALL] SELECT … FROM …. WHERE … UNION SELECT … FROM … WHERE … • INTERSECT [DISTINCT | ALL] SELECT … FROM …. WHERE … INTERSECT SELECT … FROM … WHERE … • EXCEPT [DISTINCT | ALL] SELECT … FROM …. WHERE … EXCEPT SELECT … FROM … WHERE …
Set Operators • Multiple select statements can be combined using different set operators in the same statement. SELECT … FROM …. WHERE … INTERSECT SELECT … FROM … WHERE … UNION SELECT … FROM … WHERE …
Set Operators • In a UNION both statements must have same number of columns. • NULL values are considered equal. • Precedence – Operators with higher precedence are applied first – When operators have the same precedence they are handled dif erently depending on the db. • Oracle – applied in no particular order • SQL Server – applied in left to right order – Parenthesis, (), can be used the control the order of precedence. • Evaluation is done from innermost set to outer when nested.
CASE Statement SELECT (CASE <expression> WHEN <value1> THEN <result1> WHEN <value2> THEN <result2> … WHEN <valueN> THEN <resultN> [ ELSE <else-result> ] END) FROM … WHERE …
CASE Statement • Used to provide if-then-else type logic to SQL • Uses two types case expressions; simple and search 1. Simple expressions provides only an equality check 2. Search expressions allows the use of comparison operators (AND, OR) between each boolean expression. • Can be used in queries or sub queries
Mathematical Operators & Functions
Mathematical Operators & Functions • Operators (Division, Subtraction, Multiplication, Addition) – Operators with higher precedence are applied first • Multiply, Divide and Modulo have higher precedence than Addition and Subtraction – Operators with the same precedence are handled differently depending on the db. • Oracle – applied in no particular order • SQL Server – applied in left to right order – Parenthesis, (), are used to control the order of precedence. • Evaluation is done from innermost set to outer when nested.
Mathematical Operators & Functions • Functions – absolute value [ABS] – rounding [ROUND] – square root [SQRT] – sign values [SIGN] – ceiling [CEIL] and floor [FLOOR] – exponential value [SIN,COS,TAN] • Can be used in most SQL statements (select, update, etc)
Joins are one of the basic constructions of SQL and Databases and as such they combine records from two or more database tables into one set of rows with the same source columns. These columns can originate from either of the joined tables as well as be formed using expressions and built-in or user-defined functions.
Join Types • INNER Only rows satisfying selection criteria from both joined tables are returned. • OUTER 1. Left - remaining row from the left joined table are returned along with nul s instead of actual right hand joined table rows. 2. Right - remaining row from the right joined table are returned along with nulls instead of actual left hand joined table rows. 3. Full – remaining rows from both right and left table are returned. • SELF Single table is joined to itself; the second table is same as the first but renamed using an alias. • CROSS (cartesian product)* Al rows are returned from both tables, no condition is specified, or the condition is always true.
Joins Syntax Cross Join: SELECT <column list> FROM <left joined table>, <right joined table> Inner Join: SELECT <column list> FROM <left joined table>, <right joined table> WHERE <join condition> SELECT <column list> FROM <left joined table> [INNER] JOIN <right joined table> ON <join condition>
Joins Syntax Outer Join: SELECT <column list> FROM <left joined table>, <right joined table> WHERE <join condition> SELECT <column list> FROM <left joined table> LEFT | RIGHT | FULL [OUTER] JOIN <right joined table> ON <join condition>
Joins Syntax Outer Join (Oracle): SELECT <column list> FROM <left joined table>, <right joined table> WHERE <left joined column> (+) = <right joined table column> Outer Join (DB2): SELECT <column list> FROM <left joined table>, <right joined table> WHERE <left joined column> #= <right joined table column>
Joins – Example (Data)
Cross Join – Examples
Inner Join - Examples
Outer Joins - Examples
Subqueries are queries that are nested inside a SELECT, INSERT, UPDATE or DELETE statement, or inside of another subquery. A subquery can be used anywhere an expression is allowed .
Subqueries • A subquery must be enclosed in the parenthesis. • A subquery must be put in the right hand of the comparison operator • A subquery cannot contain a ORDER-BY clause. • A query can contain more than one sub-queries.
Subqueries • 3 Subquery Types 1. Single-row subquery - where the subquery returns only one row. 2. Multiple-row subquery - where the subquery returns multiple rows. 3. Multiple column subquery - where the subquery returns multiple columns. • Another name for these query types is: Correlated Subquery.
Subqueries Correlated Subqueries – Are dependent on the their outer query* – Will be executed many times while it’s outer queries is being processed, running once for each row selected by the outer query. – Can be in the HAVING OR WHERE clauses
Subqueries SELECT store_name, sum(quantity) store_sales, (SELECT sum(quantity) FROM sales)/ (SELECT count(*) FROM store) avg_sales FROM store s, sales sl WHERE s.store_key = sl.store_key HAVING sum(quantity) > (SELECT sum(quantity) from sales)/(select count(*) FROM store) GROUP BY store_name;
Views • A view is a virtual table based on the result-set of a SQL statement • Views contain rows and columns, just like a real table. The fields in a view are fields from one or more real tables in the database. • Views (virtual) always show up-to-date data. The db engine recreates the data using the view’s SQL statement every time a user queries the view.
Views • Why create views?? – Hide data complexity – Security – Save space • Read-only vs. Update – Usually only a single table can be updated (for inherently updateable views). – For multi-table views usually the tables whose unique index is accessed in the view can be updated. – For some DBs only one table in the view may be updated. – INSTEAD OF database triggers can be created on any view to make updatable.
View Syntax • Create CREATE VIEW <view name> AS SELECT <column list> FROM <table list> WHERE expression • Update CREATE OR REPLACE VIEW <view name> AS SELECT <column list> FROM <table list> WHERE expression • Drop DROP VIEW <view name>
View Syntax (Updatable Views) • INSTEAD OF Triggers Syntax CREATE OR REPLACE TRIGGER <trigger name> INSTEAD OF < INSERT | UPDATE | DELETE > FOR EACH ROW BEGIN … END <trigger name>
View Syntax (Updatable Views) • INSTEAD OF Triggers Example CREATE OR REPLACE TRIGGER ioft_emp_perm INSTEAD OF DELETE ON employee_permission_view FOR EACH ROW BEGIN
DELETE FROM dept_code
WHERE dept_code = :OLD.dept_code;
SET dept_code = NULL, mod_user_id = USER, mod_user_date = SYSDATE
WHERE dept_code = :OLD.dept_code;
DELETE FROM test
WHERE test = 'Z'; END ioft_emp_perm;
Materialized Views are created in the same way that ordinary views are created from a query. The resulting data set is cached and can be updated from the original database tables.
Materialized Views Why use Materialized Views?? – Data warehousing. Stage tables, summarization (pre-calculation) – More ef icient access than ordinary views. – Have al the advantages of database tables. Names (depending on DB) – Materialized View (Oracle) – Materialized Query (DB2). – Indexed Views (SQL Server). – Flexviews (MySQL).
Materialized Views Types of Materialized Views 1. Read only – Cannot be updated 2. Updatable – Can be update even when disconnected from the master site – Are refreshed on demand – Consume fewer resources 3. Writable – Created with the FOR UPDATE clause – Changes are lost when view is refreshed
Materialized Views - Syntax ORACLE: CREATE MATERIALIZED VIEW <name> TABLESPACE <tablespace> BUILD <IMMEDIATE | DEFERRED> AS SELECT … ; DB2: CREATE TABLE <name> AS ( SELECT … ) [INITIALLY DEFERRED] REFRESH DEFERRED IN <tablespace> INDEXES IN <tablespace>;
Materialized Views - Syntax SQL SERVER: CREATE VIEW <name> WITH SCHEMABINDING AS SELECT … ;
Inline Views are select statements in the FROM-clause of another select statement. In- line views are commonly used to simplify queries by removing join operations and condensing several separate queries into a single query.
Inline Views • Act as tables in select statements • Can be columns in some DBs (SQL Server, Oracle) • Known as Nested Subqueries in DB2 • Cannot be indexed
Inline Views - Examples Inline View as Table: SELECT s.first_name FROM employee s INNER JOIN (SELECT ID FROM title WHERE job_title = 'tester') d ON s.ID = d.ID; …OR… SELECT s.first_name FROM employee s, (SELECT ID FROM title WHERE job_title = 'tester') d WHERE s.ID = d.ID;
Inline Views - Examples Inline View as Column: SELECT cu CompanyName, (SELECT MIN(OrderDate) FROM Orders o WHERE o.CustomerID = cu.CustomerID) AS OrderDate FROM Customers cu;
Optimizing Select Clauses
Optimizing Select Clauses • Why?? – Performance improvement • Order of listed tables and views – Search largest tables as few times as possible • Use of functions (or not) – Avoid using functions in the where clause • Use of database hints/options (Oracle vs. SQL Server) – Index selection – Search (join) type – Order of tables in join