General References on SQL (structured query language) SQL tutorial.

Transcription

1 Week 8 Relational Databases Reading DBI - Database programming with Perl Appendix A and B, Ch 1-5 General References on SQL (structured query language) SQL tutorial A complete SQL course with SQL interpreter MariaDB (open source fork of MySQL) Tutorialspoint - Biol Practical Biocomputing 1

2 RDBMS Relational Database Management System Proposed in the 1970s First commercial system 1981 Today databases are a pervasive element of commercial, government, and scientific information systems Open source free systems MySQL PostgreSQL Commercial Oracle Microsoft SQL Server IBM DB2 document store, column store systems Biol Practical Biocomputing 2

3 Information in relational databases is stored in tables Tables are broken up into rows and columns A database is a collection of one or more tables A RDBMS may contain multiple databases Table: Employee Column names Name Jane Doe Richard Roe Edgar Poe Address 127 Elm St. 915 W. State St. 666 Usher House Rows Columns values Biol Practical Biocomputing 3

4 Other terminology you may see Table relation file Row tuple record Column attribute field Name Jane Doe Richard Roe Edgar Poe Address 127 Elm St. 915 W. State St. 666 Usher House Phone Employee Name Address Phone Another representation of a table Biol Practical Biocomputing 4

5 Basics Each row of a table describes one thing (an entity) Each row has a unique identifier or primary key Order of the rows is arbitrary information is looked up using primary key RDBMS have highly optimized functions to look up indexed information Biol Practical Biocomputing 5

6 SQL structured query language Used to get information into and out of relational databases Official standard ANSI SQL (1992) Many vendor specific extensions Beware! Use of extensions ties you to a particular vendor Biol Practical Biocomputing 6

7 SQL Structured Query Language A high level programming language Designed to be like natural language (LOL) Gives independence from physical data storage Biol Practical Biocomputing 7

8 SQL Putting information into a relational database INSERT mysql> INSERT INTO employee VALUES ("Jane Doe", "127 Elm St.", " "); Query OK, 1 row affected (0.00 sec) mysql> INSERT INTO employee VALUES ("Richard Roe", "915 W. State St.", " "); Query OK, 1 row affected (0.00 sec) mysql> INSERT INTO employee VALUES ("Edgar Poe", "666 Usher House", " "); Query OK, 1 row affected (0.00 sec) How do you define the columns? RDBMS can be efficient because they have precise information on the type of information in the columns Makes it easy to index and look up Biol Practical Biocomputing 8

9 SQL Retrieving information from a relational database SELECT * is a wildcard select everything mysql> select * from employee; name address phone Jane Doe 127 Elm St Richard Roe 915 W. State St Edgar Poe 666 Usher House rows in set (0.00 sec) Biol Practical Biocomputing 9

10 SQL - selecting specific records SELECT column FROM table mysql> select name from employee; name Jane Doe Richard Roe Edgar Poe rows in set (0.00 sec) Biol Practical Biocomputing 10

11 SQL Selecting specific records SELECT columns FROM table mysql> select name,phone from employee; name phone Jane Doe Richard Roe Edgar Poe rows in set (0.00 sec) Biol Practical Biocomputing 11

12 SQL selecting specific records SELECT columns FROM table WHERE condition Condition is a logical condition that acts on a column (attribute) > (greater than) >= (greater or equal) < (less than) <= (less or equal) = (equal to) <> (not equal to) like (string comparison) mysql> select * from employee; name address phone age department Jane Doe 127 Elm St sales Richard Roe 915 W. State St sales Edgar Poe 666 Usher House sales Elmer Fudd 27 Pigsty Rd admin Bugs Bunny 1 Rabbit Hole Dr research Daffy Duck 43 Pond St sales Road Runner 177 Mesa Canyon Rd admin rows in set (0.00 sec) Biol Practical Biocomputing 12

13 SQL selecting specific records SELECT columns FROM table WHERE condition mysql> SELECT name FROM employee WHERE age >35; name Edgar Poe Elmer Fudd rows in set (0.00 sec) mysql> SELECT name,address FROM employee WHERE department="sales"; name address Jane Doe 127 Elm St. Richard Roe 915 W. State St. Edgar Poe 666 Usher House Daffy Duck 43 Pond St rows in set (0.00 sec) Biol Practical Biocomputing 13

14 SQL selecting specific records SELECT columns FROM table WHERE condition Conditions can contain logical operators AND OR NOT logical operators work just like in Perl - use parentheses, if needed, to group mysql> SELECT name,address FROM employee WHERE department="sales" AND age>30; name address Edgar Poe 666 Usher House Daffy Duck 43 Pond St Biol Practical Biocomputing 14

15 Counting SELECT COUNT(column) FROM table count counts the number of rows in the result mysql> SELECT COUNT(name) FROM employee; COUNT(name) row in set (0.00 sec) mysql> SELECT COUNT(*) FROM employee WHERE age>30; COUNT(*) row in set (0.01 sec) Biol Practical Biocomputing 15

16 Getting only unique things SELECT DISTINCT column FROM table counts the number of unique results mysql> SELECT department FROM employee; department sales sales sales admin research sales admin rows in set (0.01 sec) mysql> SELECT DISTINCT department FROM employee; department sales admin research rows in set (0.00 sec) Biol Practical Biocomputing 16

17 Limiting results Especially important during testing SELECT_STATEMENT LIMIT integer; mysql> SELECT * FROM employee LIMIT 3; name address phone age department Jane Doe 127 Elm St sales Richard Roe 915 W. State St sales Edgar Poe 666 Usher House sales rows in set (0.00 sec) Limit only affects the output, not the select! Biol Practical Biocomputing 17

18 Ordering results Like sorting in Perl SELECT_STATEMENT ORDER BY column ASC (ascending) SELECT_STATEMENT ORDER BY column DESC (descending) mysql> SELECT * FROM employee ORDER BY age ASC; name address phone age department Richard Roe 915 W. State St sales Road Runner 177 Mesa Canyon Rd admin Jane Doe 127 Elm St sales Bugs Bunny 1 Rabbit Hole Dr research Daffy Duck 43 Pond St sales Edgar Poe 666 Usher House sales Elmer Fudd 27 Pigsty Rd admin rows in set (0.00 sec) Biol Practical Biocomputing 18

19 Combine ORDER BY with LIMIT SQL clauses like LIMIT and ORDER BY must be in a specific order. This is where the reference manual is useful In MariaDB, just type in search box mysql> SELECT * FROM employee ORDER BY age DESC LIMIT 5; name address phone age department Elmer Fudd 27 Pigsty Rd admin Edgar Poe 666 Usher House sales Bugs Bunny 1 Rabbit Hole Dr research Daffy Duck 43 Pond St sales Jane Doe 127 Elm St sales rows in set (0.00 sec) Biol Practical Biocomputing 19

20 SELECT syntax (from help SELECT) SELECT [ALL DISTINCT DISTINCTROW ] select_expr,... [INTO OUTFILE 'file_name'] [FROM table_reference [WHERE where_definition] [GROUP BY {col_name [ASC DESC] ] [ORDER BY col_name [ASC DESC] ] [LIMIT row_count] ] SQL expressions [] optional {} choice options, choose 1 Biol Practical Biocomputing 20

21 Pooling things together GROUP BY SELECT_STATEMENT GROUP BY column mysql> SELECT name,phone,department FROM employee GROUP BY department; name phone department Elmer Fudd admin Bugs Bunny research Jane Doe sales rows in set (0.00 sec) Note the difference between ORDER BY and GROUP BY GROUP BY only reports the first record in the group Biol Practical Biocomputing 21

22 Counting types of things Combine GROUP BY with COUNT mysql> SELECT department,count(*) FROM employee GROUP BY department; department count(*) admin 2 research 1 sales rows in set (0.01 sec) Biol Practical Biocomputing 22

23 Sending output to a file SELECT_STATEMENT INTO OUTFILE filename Default location for output file is the mysql data directory. If you do not have privilege to write there, you must supply a complete path mysql> SELECT * FROM employee INTO OUTFILE "employee.txt"; Query OK, 7 rows affected (0.11 sec) mysql> SELECT * FROM employee INTO OUTFILE "/home/gribskov/employee.txt"; Biol Practical Biocomputing 23

24 Company Example Employee table has one-to-one relationships each employee has only one name, address, phone, etc. Employee Name Address Phone Age Biol Practical Biocomputing 24

25 Multiple tables SQL is mostly used for gathering information from multiple tables Say we want to add information about the department head and mailcode to our little example Employee Name Address Phone Age Department Employee Name Address Phone Age Department Manager Mailcode Since there are only three departments, admin, sales, and research much of this information is redundant Redundancy is bad because it wastes space it makes updating hard (stale information) error prone Biol Practical Biocomputing 25

26 Multiple tables More efficient: Break the information into two tables Employee and Department In the Department table the Department is the "primary key" In the Employee table, the Department is a foreign key There is a one-to-many relationship between department and employee (each department has many employees; each employee has only one department) Employee Name Address Phone Age Department Department Department Manager Mailcode Biol Practical Biocomputing 26

27 Relational Database Department & Employee tables mysql> SELECT * FROM department; department manager mailcode sales Bob Brown 156 admin Susan Sepia 162 research Roger Rabbit rows in set (0.00 sec) mysql> SELECT * FROM employee; name address phone age department Jane Doe 127 Elm St sales Richard Roe 915 W. State St sales Edgar Poe 666 Usher House sales Elmer Fudd 27 Pigsty Rd admin Bugs Bunny 1 Rabbit Hole Dr research Daffy Duck 43 Pond St sales Road Runner 177 Mesa Canyon Rd admin rows in set (0.00 sec) Biol Practical Biocomputing 27

28 Multiple tables The lookup of information across two tables requires the tables to be joined based on a common field The common field is the foreign key / primary key field Columns in tables may have the same names When multiple tables are involved the you must provide the table name for clarity - table_name.column_name Join finds every matching row in table 1, and concatenates it to every matching row in table 2 If you do this without specifying a column to join on this is called a cartesian join it generates the number of rows in table 1 times the number of rows in table 2 DANGER! What if each table contains 10,000 records? Biol Practical Biocomputing 28

29 Cartesian join Employee x Department mysql> SELECT * FROM employee,department; name address phone age department department manager mailcode Jane Doe 127 Elm St sales sales Bob Brown 156 Richard Roe 915 W. State St sales sales Bob Brown 156 Edgar Poe 666 Usher House sales sales Bob Brown 156 Elmer Fudd 27 Pigsty Rd admin sales Bob Brown 156 Bugs Bunny 1 Rabbit Hole Dr research sales Bob Brown 156 Daffy Duck 43 Pond St sales sales Bob Brown 156 Road Runner 177 Mesa Canyon Rd admin sales Bob Brown 156 Jane Doe 127 Elm St sales admin Susan Sepia 162 Richard Roe 915 W. State St sales admin Susan Sepia 162 Edgar Poe 666 Usher House sales admin Susan Sepia 162 Elmer Fudd 27 Pigsty Rd admin admin Susan Sepia 162 Bugs Bunny 1 Rabbit Hole Dr research admin Susan Sepia 162 Daffy Duck 43 Pond St sales admin Susan Sepia 162 Road Runner 177 Mesa Canyon Rd admin admin Susan Sepia 162 Jane Doe 127 Elm St sales research Roger Rabbit 102 Richard Roe 915 W. State St sales research Roger Rabbit 102 Edgar Poe 666 Usher House sales research Roger Rabbit 102 Elmer Fudd 27 Pigsty Rd admin research Roger Rabbit 102 Bugs Bunny 1 Rabbit Hole Dr research research Roger Rabbit 102 Daffy Duck 43 Pond St sales research Roger Rabbit 102 Road Runner 177 Mesa Canyon Rd admin research Roger Rabbit rows in set (0.00 sec) Biol Practical Biocomputing 29

30 Selecting certain columns In a cartesian join we don't get the correct manager because we haven't insisted that employee.department and department.department match mysql> SELECT employee.name,employee.department,department.manager FROM employee,department; name department manager Jane Doe sales Bob Brown Richard Roe sales Bob Brown Edgar Poe sales Bob Brown Elmer Fudd admin Bob Brown Bugs Bunny research Bob Brown Daffy Duck sales Bob Brown Road Runner admin Bob Brown Jane Doe sales Susan Sepia Richard Roe sales Susan Sepia Edgar Poe sales Susan Sepia Elmer Fudd admin Susan Sepia Bugs Bunny research Susan Sepia Daffy Duck sales Susan Sepia Road Runner admin Susan Sepia Jane Doe sales Roger Rabbit Richard Roe sales Roger Rabbit Edgar Poe sales Roger Rabbit Elmer Fudd admin Roger Rabbit Bugs Bunny research Roger Rabbit Daffy Duck sales Roger Rabbit Road Runner admin Roger Rabbit rows in set (0.00 sec) Biol Practical Biocomputing 30

31 Joining on a column that is equal (equijoin) Avoids Cartesian join mysql> SELECT employee.name,employee.department,department.manager -> FROM employee,department -> WHERE employee.department=department.department; name department manager Jane Doe sales Bob Brown Richard Roe sales Bob Brown Edgar Poe sales Bob Brown Daffy Duck sales Bob Brown Elmer Fudd admin Susan Sepia Road Runner admin Susan Sepia Bugs Bunny research Roger Rabbit rows in set (0.00 sec) Biol Practical Biocomputing 31

32 Joining on a column that is equal (equijoin) with conditions mysql> SELECT employee.name,employee.department,department.manager,department.mailcode -> FROM employee,department -> WHERE employee.department=department.department AND employee.age>40; name department manager mailcode Edgar Poe sales Bob Brown 156 Elmer Fudd admin Susan Sepia rows in set (0.00 sec) Biol Practical Biocomputing 32

33 Joins with conditions Conditions can be on any attribute (column) Think of conditions as working on the cartesian product Who is the manager of each employee whose name ends "oe" or works in the admin department? mysql> SELECT employee.name,employee.department,department.manager -> FROM employee,department -> WHERE employee.department=department.department -> AND employee.name like "%oe%" -> OR employee.department="admin"; Note: % is the "match any number of characters" symbol in SQL for LIKE, not * as in Perl? Biol Practical Biocomputing 33

34 Joins with conditions Conditions can be on any attribute (column) Think of conditions as working on the cartesian product mysql> SELECT employee.name,employee.department,department.manager -> FROM employee,department -> WHERE employee.department=department.department -> AND employee.name like "%oe%" -> OR employee.department="admin"; name department manager Jane Doe sales Bob Brown Richard Roe sales Bob Brown Edgar Poe sales Bob Brown Elmer Fudd admin Bob Brown Road Runner admin Bob Brown Elmer Fudd admin Susan Sepia Road Runner admin Susan Sepia Elmer Fudd admin Roger Rabbit Road Runner admin Roger Rabbit rows in set (0.00 sec) Biol Practical Biocomputing 34

35 Joins with conditions Conditions can be on any attribute (column) Think of conditions as working on the cartesian product SQL has operator precedence (similar to perl) mysql> SELECT employee.name,employee.department,department.manager -> FROM employee,department -> WHERE employee.department=department.department -> AND employee.name like "%oe%" -> OR employee.department="admin"; name department manager Jane Doe sales Bob Brown Richard Roe sales Bob Brown employee.name like %oe% Edgar Poe sales Bob Brown Elmer Fudd admin Bob Brown Road Runner admin Bob Brown Elmer Fudd admin Susan Sepia Road Runner admin Susan Sepia employee.department = admin Elmer Fudd admin Roger Rabbit Road Runner admin Roger Rabbit rows in set (0.00 sec) SQL interpretation: (where employee,department=department.department AND employee.name like "%oe%" ) OR ( where employee.department = "admin") Biol Practical Biocomputing 35

36 -> WHERE employee.department=department.department -> AND employee.name like "%oe%" -> OR employee.department="admin" name address phone age department department manager mailcode Jane Doe 127 Elm St sales sales Bob Brown 156 Richard Roe 915 W. State St sales sales Bob Brown 156 Edgar Poe 666 Usher House sales sales Bob Brown 156 Elmer Fudd 27 Pigsty Rd admin sales Bob Brown 156 Bugs Bunny 1 Rabbit Hole Dr research sales Bob Brown 156 Daffy Duck 43 Pond St sales sales Bob Brown 156 Road Runner 177 Mesa Canyon Rd admin sales Bob Brown 156 Jane Doe 127 Elm St sales admin Susan Sepia 162 Richard Roe 915 W. State St sales admin Susan Sepia 162 Edgar Poe 666 Usher House sales admin Susan Sepia 162 Elmer Fudd 27 Pigsty Rd admin admin Susan Sepia 162 Bugs Bunny 1 Rabbit Hole Dr research admin Susan Sepia 162 Daffy Duck 43 Pond St sales admin Susan Sepia 162 Road Runner 177 Mesa Canyon Rd admin admin Susan Sepia 162 Jane Doe 127 Elm St sales research Roger Rabbit 102 Richard Roe 915 W. State St sales research Roger Rabbit 102 Edgar Poe 666 Usher House sales research Roger Rabbit 102 Elmer Fudd 27 Pigsty Rd admin research Roger Rabbit 102 Bugs Bunny 1 Rabbit Hole Dr research research Roger Rabbit 102 Daffy Duck 43 Pond St sales research Roger Rabbit 102 Road Runner 177 Mesa Canyon Rd admin research Roger Rabbit rows in set (0.00 sec) Biol Practical Biocomputing 36

37 Joins with conditions mysql> SELECT employee.name,employee.department,department.manager -> FROM employee,department -> WHERE employee.department=department.department -> AND (employee.name like "%oe%" -> OR employee.department="admin"); name department manager Jane Doe sales Bob Brown Richard Roe sales Bob Brown Edgar Poe sales Bob Brown Elmer Fudd admin Susan Sepia Road Runner admin Susan Sepia rows in set (0.00 sec) Biol Practical Biocomputing 37

38 Project table Add a table with the projects on which each employee works mysql> select * from project; number title name database Jane Doe 2 space shuttle Richard Roe 3 database Edgar Poe 4 space shuttle Bugs Bunny 5 space shuttle Road Runner 6 database Elmer Fudd 7 nation building Daffy Duck 8 nation building Bugs Bunny Neither project.name nor project.title are unique Use autoincrementing number as primary key many-to-many relationship: each project has many employees, each employee has many projects Biol Practical Biocomputing 38

39 What Projects does department manager X have? Department and Project have no common attribute but can be joined via Employee Project Number Title Name Employee Name Address Phone Age Department Department Department Manager Mailcode Biol Practical Biocomputing 39

40 What Projects does department manager X have? mysql> SELECT department.department,department.manager,project.title -> FROM department,employee,project -> WHERE department.department=employee.department -> AND employee.name=project.name -> AND department.department="sales"; department manager title sales Bob Brown database sales Bob Brown space shuttle sales Bob Brown database sales Bob Brown nation building rows in set (0.00 sec) database appears twice Biol Practical Biocomputing 40

41 What Projects does department manager X have? Use DISTINCT to get unique list mysql> SELECT DISTINCT department.department,department.manager, project.title -> FROM department,employee,project -> WHERE department.department=employee.department -> AND employee.name=project.name -> AND department.department="sales"; department manager title sales Bob Brown database sales Bob Brown space shuttle sales Bob Brown nation building Biol Practical Biocomputing 41

42 Shorthand for table names I'm getting tired of typing table names, let's abbreviate them mysql> SELECT DISTINCT d.department,d.manager, p.title -> FROM department d, employee e, project p -> WHERE d.department=e.department -> AND e.name=p.name -> AND d.department="sales"; department manager title sales Bob Brown database sales Bob Brown space shuttle sales Bob Brown nation building Biol Practical Biocomputing 42

43 MySQL Creating tables Data in RDBMS are carefully defined so that they can be efficiently stored and accessed CREATE table_name ( column_spec ); column_spec = column_name column_type [, column_spec] Column types are often vendor-specific Types numeric date and time string Biol Practical Biocomputing 43

44 MySQL Creating tables Column Types (Numeric) UNSIGNED, if specified, disallows negative values, useful for unique IDs TINYINT[(M)] [UNSIGNED] [ZEROFILL] A very small integer. The signed range is -128 to 127. The unsigned range is 0 to 255. Can also use BIT, BOOL, BOOLEAN SMALLINT[(M)] [UNSIGNED] [ZEROFILL] A small integer. The signed range is to The unsigned range is 0 to MEDIUMINT[(M)] [UNSIGNED] [ZEROFILL] A medium-sized integer. The signed range is to The unsigned range is 0 to INT[(M)] [UNSIGNED] [ZEROFILL] A normal-size integer. The signed range is to The unsigned range is 0 to can also use INTEGER[(M)] [UNSIGNED] [ZEROFILL] BIGINT[(M)] [UNSIGNED] [ZEROFILL] A large integer. The signed range is to The unsigned range is 0 to FLOAT[(M,D)] [UNSIGNED] [ZEROFILL] A small (single-precision) floating-point number. Allowable values are E+38 to E-38, 0, and E-38 to E+38. These are the theoretical limits, based on the IEEE standard. The actual range might be slightly smaller depending on your hardware or operating system. M is the total number of decimal digits and D is the number of digits following the decimal point. If M and D are omitted, values are stored to the limits allowed by the hardware. A single-precision floating-point number is accurate to approximately 7 decimal places. DOUBLE[(M,D)] [UNSIGNED] [ZEROFILL] A normal-size (double-precision) floating-point number. Allowable values are E+308 to E-308, 0, and E-308 to E+308. These are the theoretical limits, based on the IEEE standard. The actual range might be slightly smaller depending on your hardware or operating system. can also use DOUBLE PRECISION[(M,D)] [UNSIGNED] [ZEROFILL], REAL[(M,D)] [UNSIGNED] [ZEROFILL] DECIMAL[(M[,D])] [UNSIGNED] [ZEROFILL] An unpacked fixed-point number. Behaves like a CHAR column; unpacked means the number is stored as a string, using one character for each digit of the value. M is the total number of digits and D is the number of digits after the decimal point. The decimal point and (for negative numbers) the - sign are not counted in M, although space for them is reserved. If D is 0, values have no decimal point or fractional part. The maximum range of DECIMAL values is the same as for DOUBLE, but the actual range for a given DECIMAL column may be constrained by the choice of M and D. If D is omitted, the default is 0. If M is omitted, the default is 10. can also use DEC[(M[,D])] [UNSIGNED] [ZEROFILL], NUMERIC[(M[,D])] [UNSIGNED] [ZEROFILL], FIXED[(M[,D])] [UNSIGNED] [ZEROFILL] Biol Practical Biocomputing 44

45 MySQL Creating tables Date and time columns DATE A date. The supported range is ' ' to ' '. MySQL displays DATE values in 'YYYY-MM-DD' format, but allows you to assign values to DATE columns using either strings or numbers. DATETIME A date and time combination. The supported range is ' :00:00' to ' :59:59'. MySQL displays DATETIME values in 'YYYY-MM-DD HH:MM:SS' format, but allows you to assign values to DATETIME columns using either strings or numbers. TIMESTAMP[(M)] A timestamp. The range is ' :00:00' to partway through the year A TIMESTAMP column is useful for recording the date and time of an INSERT or UPDATE operation. By default, the first TIMESTAMP column in a table is automatically set to the date and time of the most recent operation if you do not assign it a value yourself. You can also set any TIMESTAMP column to the current date and time by assigning it a NULL value. TIME A time. The range is '-838:59:59' to '838:59:59'. MySQL displays TIME values in 'HH:MM:SS' format, but allows you to assign values to TIME columns using either strings or numbers. YEAR[(2 4)] A year in two-digit or four-digit format. The default is four-digit format. In four-digit format, the allowable values are 1901 to 2155, and In two-digit format, the allowable values are 70 to 69, representing years from 1970 to MySQL displays YEAR values in YYYY format, but allows you to assign values to YEAR columns using either strings or numbers. The YEAR type is unavailable prior to MySQL Biol Practical Biocomputing 45

46 MySQL Creating tables String column types CHAR and VARCHAR Types Character strings of up to a specified length BINARY and VARBINARY Types Byte strings rather than character strings. This means that they have no character set, and sorting and comparison are based on the numeric values of the bytes in the values. BLOB and TEXT Types Binary large object - large char and binary ENUM Type A single string object that must be one of a list of values specified when the table is created For example, a column specified as ENUM('one', 'two', 'three') can have values NULL, "", "one","two","three" An enumeration can have a maximum of 65,535 elements. SET Type A SET is a string object that can have zero or more values, each of which must be chosen from a list of allowed values specified when the table is created. Biol Practical Biocomputing 46

47 MySQL Creating tables String columns CHAR This type is a synonym for CHAR(1). [NATIONAL] VARCHAR(M) [BINARY] A variable-length string. M represents the maximum column length. The range of M is 0 to 255 Note: Trailing spaces are removed when VARCHAR values are stored. This differs from the standard SQL specification. VARCHAR is shorthand for CHARACTER VARYING. BINARY(M) (MySQL 4.1.2) The BINARY type is similar to the CHAR type, but stores binary byte strings rather than non-binary character strings. VARBINARY(M) (MySQL 4.1.2) The VARBINARY type is similar to the VARCHAR type, but stores binary byte strings rather than non-binary character strings. ENUM('value1','value2',...) An enumeration. A string object that can have only one value, chosen from the list of values 'value1', 'value2',..., NULL or the special '' error value. An ENUM column can have a maximum of 65,535 distinct values. ENUM values are represented internally as integers. SET('value1','value2',...) A set. A string object that can have zero or more values, each of which must be chosen from the list of values 'value1', 'value2',... A SET column can have a maximum of 64 members. SET values are represented internally as integers. TEXT[(M)] A TEXT column with a maximum length of 65,535 (216 1) characters. an optional length M can be given for this type. MySQL creates the column as the smallest TEXT type large enough to hold values M characters long. TINYTEXT - A TEXT column with a maximum length of 255 (28 1) characters. MEDIUMTEXT - A TEXT column with a maximum length of 16,777,215 (224 1) characters. LONGTEXT - A TEXT column with a maximum length of 4,294,967,295 or 4GB (232 1) characters. BLOB[(M)] Binary Large Objects (e.g., images) come in the same sizes as TEXT. TINYBLOB, BLOB, MEDIUM BLOB, AND LONGBLOB Biol Practical Biocomputing 47

48 MySQL Creating tables mysql> CREATE TABLE employee ( mysql> name tinytext, address tinytext, phone tinytext, age int); Query OK, 0 rows affected (0.04 sec) mysql> DESCRIBE employee; Field Type Null Key Default Extra name tinytext YES NULL address tinytext YES NULL phone tinytext YES NULL age int(11) YES NULL rows in set (0.00 sec) Biol Practical Biocomputing 48

49 MySQL Creating tables PRIMARY KEY DEFAULT AUTO_INCREMENT NULL /NOT NULL column_definition: col_name type [NOT NULL NULL] [DEFAULT default_value] [AUTO_INCREMENT] [UNIQUE [KEY] [PRIMARY] KEY] type: TINYINT[(length)] [UNSIGNED] [ZEROFILL] SMALLINT[(length)] [UNSIGNED] [ZEROFILL] MEDIUMINT[(length)] [UNSIGNED] [ZEROFILL] INT[(length)] [UNSIGNED] [ZEROFILL] INTEGER[(length)] [UNSIGNED] [ZEROFILL] BIGINT[(length)] [UNSIGNED] [ZEROFILL] REAL[(length,decimals)] [UNSIGNED] [ZEROFILL] DOUBLE[(length,decimals)] [UNSIGNED] [ZEROFILL] FLOAT[(length,decimals)] [UNSIGNED] [ZEROFILL] DECIMAL(length,decimals) [UNSIGNED] [ZEROFILL] NUMERIC(length,decimals) [UNSIGNED] [ZEROFILL] DATE TIME TIMESTAMP DATETIME YEAR CHAR(length) [BINARY ASCII UNICODE] VARCHAR(length) [BINARY] BINARY(length) VARBINARY(length) TINYBLOB BLOB MEDIUMBLOB LONGBLOB TINYTEXT [BINARY] TEXT [BINARY] MEDIUMTEXT [BINARY] LONGTEXT [BINARY] ENUM(value1,value2,value3,...) SET(value1,value2,value3,...) Biol Practical Biocomputing 49

50 MySQL Null values Null values are values that are missing in a column similar to undefined value in Perl Null is different from 0 or "" 0 is the value zero "" is a zero length string NULL (null) is nothing at all, an undefined value NULL/NOT NULL indicates whether NULL values are allowed default is NULL Biol Practical Biocomputing 50

51 MySQL INSERT Putting data into tables mysql> CREATE TABLE project ( -> number INTEGER AUTO_INCREMENT PRIMARY KEY, -> title TINYTEXT, -> name TINYTEXT ); Query OK, 0 rows affected (0.02 sec) mysql> INSERT INTO project VALUES ( NULL, "database", "Jane Doe" ); Query OK, 1 row affected (0.01 sec) mysql> INSERT INTO project VALUES ( NULL, "space shuttle", "Richard Roe" ); Query OK, 1 row affected (0.00 sec) mysql> INSERT INTO project VALUES ( NULL, "nation building", "Bugs Bunny" ); Query OK, 1 row affected (0.00 sec) mysql> SELECT * FROM project; number title name database Jane Doe 2 space shuttle Richard Roe 3 nation building Bugs Bunny rows in set (0.00 sec) Biol Practical Biocomputing 51

52 MySQL LOAD inserting data from a file load is much faster than inserting line by line LOAD DATA INFILE 'file_name' INTO TABLE tbl_name [FIELDS [TERMINATED BY 'string'] [[OPTIONALLY] ENCLOSED BY 'char'] [ESCAPED BY 'char'] ] [LINES [STARTING BY 'string'] [TERMINATED BY 'string'] ] [IGNORE number LINES] [(col_name,...)] Biol Practical Biocomputing 52

53 MySQL LOAD DATA INFILE "filename" INTO TABLE tablename Remember, default file location is mysql directory, provide a path mysql> LOAD DATA INFILE "/home/gribskov/project.txt" INTO TABLE project; Query OK, 10 rows affected, 22 warnings (0.01 sec) Records: 10 Deleted: 0 Skipped: 0 Warnings: 22 mysql> SELECT * FROM project; number title name database Jane Doe 2 space shuttle Richard Roe 3 nation building Bugs Bunny 4 space shuttle Bugs Bunny 5 space shuttle Road Runner 6 database Elmer Fudd 7 nation building Daffy Duck 8 nation building Bugs Bunny 9 database Edgar Poe rows in set (0.00 sec) "project.txt" 10 lines, 216 characters NULL database "Jane Doe" NULL "space shuttle" "Richard Roe" NULL "space shuttle" "Bugs Bunny" NULL "space shuttle" "Road Runner" NULL database "Elmer Fudd" NULL "nation building" "Daffy Duck" NULL "nation building" "Bugs Bunny" NULL database "Edgar Poe" Biol Practical Biocomputing 53

54 MySQL INSERT Inserting values from one table into another INSERT [INTO] tbl_name [(col_name,...)] SELECT_STATEMENT Inserting only some values INSERT [INTO] tbl_name [(list_of_col_name)] VALUES (list_of_values); works for INSERT...SELECT and LOAD Biol Practical Biocomputing 54

55 MySQL DELETE Remove rows - be very careful, if you do not specify a condition it deletes the entire contents of the table Rule: always select first and confirm that you get the rows you want, then change SELECT to DELETE DELETE FROM table_name WHERE condition mysql> SELECT * FROM project WHERE number>21; number title name NULL NULL 23 NULL NULL rows in set (0.00 sec) mysql> SELECT * FROM project WHERE title IS NULL; number title name NULL NULL 23 NULL NULL rows in set (0.00 sec) Biol Practical Biocomputing 55

56 MySQL DELETE mysql> DELETE FROM project WHERE title IS NULL; Query OK, 2 rows affected (0.00 sec) mysql> SELECT * FROM project; number title name database Jane Doe 2 space shuttle Richard Roe 3 nation building Bugs Bunny 17 "space shuttle" "Bugs Bunny" 16 database "Edgar Poe" 15 "space shuttle" "Richard Roe" 14 database "Jane Doe" 18 "space shuttle" "Road Runner" 19 database "Elmer Fudd" 20 "nation building" "Daffy Duck" 21 "nation building" "Bugs Bunny" rows in set (0.00 sec) Biol Practical Biocomputing 56

57 MySQL DROP Removes tables and their contents DROP table_name; Removes databases, all tables, and contents DROP database_name; Be careful with DROP and DELETE When its gone... its gone for good Biol Practical Biocomputing 57

58 MySQL ALTER TABLE - Change tables after they are created Add columns delete columns change column definitions ALTER TABLE tbl_name alter_specification alter_specification: ADD [COLUMN] column_definition [FIRST AFTER col_name ] ADD [COLUMN] (column_definition,...) ADD INDEX [index_name] [index_type] (index_col_name,...) ALTER [COLUMN] col_name {SET DEFAULT literal DROP DEFAULT} CHANGE [COLUMN] old_col_name column_definition [FIRST AFTER col_name] MODIFY [COLUMN] column_definition [FIRST AFTER col_name] DROP [COLUMN] col_name DROP PRIMARY KEY DROP INDEX index_name RENAME [TO] new_tbl_name ORDER BY col_name Biol Practical Biocomputing 58

59 MariaDB Read the MariaDB documentation to find out User privileges Restricting access to databases Data dumps How data is stored in tables Biol Practical Biocomputing 59