Differences between Relational and Non-Relational

  1. Adding a new field to data: 1. Relational: requires a new column 2. Non-Relational: just add the field to one single document, thus can be easily decentralized.

Basics and Background

  1. SQL: Structured Query Language
  2. Relational Database:
    1. usually in tables
    2. rows are called records
    3. columns are certain types of data. Data type of rows are specified: 1. INTEGER 2. TEXT 3. DATE 4. REAL, real numbers 5. NULL 6. …
  3. RDBMS: Relational Database Management System, most RDBMS use SQL as the query language. SQLite is one of the RDBMS. 1. SQLite: open source and minimal 2. MySQL: powerful and popular, also open source, controlled by Oracle, not really scalable. 3. PostgreSQL: open source, even slower than MySQL. 4. OracleDB: not open source 5. SQL Server: from Microsoft, not open source, and only on windows.



Semicolon in SQL is a statement terminator. Just use it.

Capitalization Strategy

For readablilty

  1. Capitalize clauses
  2. Capitalize table names etc

On stackoverflow: stackoverflow

Statements for Manipulation

  1. Create table

    CREATE TABLE table_name (
        column_1 data_type,
        column_2 data_type,
        column_3 data_type

    Constraints can be included when creating tables. I am using the example from CodeAcademy.

    CREATE TABLE awards (
    --id should be integer, and is the primary key
    --recipient should be text and can not be null
       recipient TEXT NOT NULL,
    --default values for a column
       award_name TEXT DEFAULT "Grammy"

    As mentioned in the basics section, primary keys should be unique to identify the specific row.

  2. Insert new row

    INSERT INTO table_name (column_1, column_2, column_3)
    VALUES (some_value_1, some_value_2, some_value_3);
  3. Update some values

    -- Specify the table
    UPDATE table_name
    -- choose column to be updated
    SET column_1 = some_other_value_1
    -- specify row location
    WHERE column_2 = some_specific_value_to_locate_the_row;
  4. Add new columns

    -- speficy table
    ALTER table_name
    -- add column and specify data type, here I use TEXT
    ADD COLUMN column_4 TEXT
  5. Delete rows

    DELETE FROM celebs
    -- I use column_4 as an example
    -- Delete every row if column_4 has NULL values
    WHERE column_4 IS NULL;

Statements for Queries

  1. Select from table; select returns result set which is a new table.

    -- Select out everything from table
    SELECT * FROM table_name;
    -- Select out the values of a specific column
    SELECT column_1 FROM table_name;
  2. Select specific columns

    SELECT column_1, column_2
    FROM table_name;
  3. AS keyword: allows you to select the column and return it as the specified new name of the column; the database is NOT modified.

    SELECT column_1 AS 'A NEW NAME'
    FROM table_name;
  4. Select and show only the distinct values of the column

    SELECT DISTINCT column_1
    FROM table_name;
  5. WHERE key: using operators such as =, !=, >, <, >=, <= to filter results

    SELECT * FROM table_name
    WHERE column_1 = 0;
  6. LIKE key: patern specified like AA_B where _ is for a single character.

    SELECT * FROM table_name
    WHERE column_1 LIKE `AA_B`

    Wildcards: _, % for 0 or more characters.


    SELECT *
    FROM movies
    WHERE name BETWEEN 'D%' AND 'G%';
  8. SORT BY: Can be either DESC or ASC and goes after where

    SELECT * FROM movies
    WHERE year > 2014
    ORDER BY name DESC;
  9. LIMIT

    SELECT *
    FROM movies
    ORDER BY imdb_rating DESC
    LIMIT 3;
  10. CASE:

    SELECT name,
       WHEN genre = 'romance' THEN 'fun'
       WHEN genre = 'comedy' THEN 'fun'
       ELSE 'serious'
    FROM movies;


  1. COUNT:

    FROM tabe_name;
  2. SUM:

    SELECT SUM(column_1)
    FROM table_name;
  3. MAX and MIN:

    SELECT MAX(column_1)
    FROM table_name;
  4. AVG: average

    SELECT AVG(column_1)
    FROM table_name;
  5. ROUND: round to specified decimals

    --round the price to integers
    SELECT name, ROUND(price,0)
    FROM fake_apps;
    --round the price to integers
    --even with other keys as arguments
    SELECT name, ROUND(AVG(price),0)
    FROM fake_apps;
  6. GROUP BY: group by column values

    SELECT price, COUNT(*)
    FROM fake_apps
    WHERE downloads > 20000
    GROUP BY price;


    SELECT category, SUM(downloads)
    FROM fake_apps
    GROUP BY category;

    References can be used in GROUP BY

    SELECT category, SUM(downloads)
    FROM fake_apps
    GROUP BY 1;
    --1 here is identical to category
  7. HAVING: The problem with WHERE is that it goes before GROUP BY. What if we need to filter the groups?

    SELECT price, ROUND(AVG(downloads))
    FROM fake_apps
    GROUP BY price
    HAVING COUNT(price) > 9;

Multiple Tables

The normalization is explained in Normalization.

  1. JOIN: Join tables with specified column

    SELECT *
    FROM orders
    JOIN subscriptions
    ON orders.subscription_id = subscriptions.subscription_id
    WHERE description = 'Fashion Magazine';
  2. Inner Join: only join the rows that have common values on the specified join columns.

    FROM newspaper;
    --Output 60
    FROM online;
    --Output 65
    FROM newspaper
    JOIN online
    ON online.id = newspaper.id;
    --Output 50 <= 60 or 65
  3. Left Join: simply plug all the right table onto left tables, where the values of the specified column match. The number of rows will be the number of rows for the left table.

    SELECT *
    FROM newspaper
    LEFT JOIN online
    ON newspaper.id=online.id
    WHERE online.id IS NULL;
  4. Cross join: combine all the information

    SELECT month,
    COUNT(*) as subscribers
    FROM months
    CROSS JOIN newspaper
    WHERE months.month > newspaper.start_month AND months.month < newspaper.end_month
    GROUP BY months.month;
  5. UNION: stack tables

    SELECT *
    FROM newspaper
    SELECT *
    FROM online;
  6. WITH: create a result with alias

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