SQL CMSC 461 Michael Wilson

Finally, some code  This is where the theory and practice actually come together  Basically taking the relational algebra and mapping it all to a query language  The concepts are very close to what we’ve already seen, but there are some nuances

One true SQL?  There are ISO/ANSI SQL standards  In my experience, different DBMSes have different/proprietary extensions that can make raw SQL queries pretty DBMS specific  The extensions aren’t exactly frequent  Invariably, it affects whatever thing you’re working on

DBMS to focus on?  I firmly believe that PostgreSQL is going to be a big deal  MySQL is getting mired in some drama relating to being owned by Oracle  Forked off into MariaDB  Therefore, I’m going to focus on Postgres  Also, AWS RDS supports Postgres now  You are welcome to use whatever you wish for projects  RDS supports MySQL and PostgreSQL, but you’re not limited to AWS

Before we get started  PostgreSQL’s SQL language documentation  ml ml  Use it!  PostgreSQL is case insensitive and insensitive to whitespace between commands  Can spread a command across multiple lines or type it all on one line

Getting started  Most DBMSes can have multiple databases  Each database can consist of many tables  If we configure PostgreSQL using AWS RDS, then we don’t have to create the database ourselves  This can be a little complex in Postgres  We’re going to assume the database is created

Connecting to your database using psql  psql –h -d -U  Command line PostgreSQL client  On success, this will drop you to a prompt

Connecting to your database using psql

Table creation  Before you can do anything, you need to create a table  Basic syntax:  CREATE TABLE table_name ( column_name data_type, column_name data_type, …. );

Table creation  This is effectively creating a new relation  We’ll be referencing this a lot  We’ll also be building on it, so expect to learn new features about table creation as we go  This syntax will create a basic table with columns

Data types  Some basic ones to know  Integer (signed 4 byte integer)  Numeric (numbers with high precision)  Kind of like a double, but has EXACT calculations  Boolean (true/false)  Varchar (character strings of variable lengths)  Text (unlimited length strings)  Not part of SQL standard

Data types  Databases support TONS of data types  Numbers, dates, booleans, money, geometric shapes, JSON, etc.  Some are part of the SQL standard, some aren’t  Best to refer to the PostgreSQL documentation for guidance on data types  Lots of nuances, recommendations, etc.

Table creation example address daysSinceContact contactPhoneNumber numberTypecontactName 111 Great Street CellPhil 8 Get Out of Here Way WorkBob 7 RUN! Drive CellOctavio

Table creation example  CREATE TABLE AddressBook (address varchar(256), daysSinceContact integer, contactPhoneNumber varchar(64), numberType varchar(16), contactName varchar(256));

Table insertion  Figure it might be handy to actually add data to our table  This is like adding a tuple to the relation  Basic syntax:  INSERT INTO (column_name1, column_name2, … column_name_n) values (value_1, value_2, …, value_n)

Table insertion  You can specify the columns in any order you want  As long as you specify the values in the corresponding order

Table insertion  A note  If you recall, tuples are explicitly unique in databases  This is not the case in tables  They can be made this way, but this is not the default behavior

Table insertion example  INSERT INTO AddressBook (address, daysSinceContact, contactPhoneNumber, numberType, contactName) VALUES (’21 Jump Street’, 40, ‘ ’, ‘Cell’, ‘Johnny’)

Table insertion example address daysSinceContact contactPhoneNumber numberTypecontactName 111 Great Street CellPhil 8 Get Out of Here Way WorkBob 7 RUN! Drive CellOctavio 21 Jump Street CellJohnny

Enough for today  See you next class!