Quick Start

Welcome to CedarDB! This quick start guide will help you get up and running with CedarDB within minutes. It covers the essential setup steps and walks you through simple examples to begin interacting with CedarDB.

Installation

To automatically download and decompress the appropriate CedarDB version, run:

curl https://get.cedardb.com | bash

Then start an interactive SQL shell connected to a temporary database:

./cedar/cedardb --interactive

Create Tables

Let’s define tables for a simple movie database using standard SQL DDL:

create table movies (
    id integer primary key,
    title text,
    year integer,
    length integer,
    genre text
);

create table stars (
    id integer primary key,
    name text,
    wikiLink text,
    gender char,
    birthdate date
);

create table starsIn (
    movieId integer references movies,
    starId integer references stars
);

Load data

Once the schema is in place, we can populate it using one of the following methods:

Plain Inserts

Use standard INSERT statements:

insert into movies values
(1, 'Oppenheimer', 2023, 180, 'Biopic'),
(2, 'Everything Everywhere All at Once', 2022, 139, 'Science Fiction'),
(3, 'Das Boot', 1981, 149, 'Drama');

Import from CSV

If your data is stored in a CSV file, like this:

stars.csv
1,Cilian Murphy,https://en.wikipedia.org/wiki/Cillian_Murphy,M,1976-05-25
2,Emily Blunt,https://en.wikipedia.org/wiki/Emily_Blunt,F,1983-02-23
3,Michelle Yeoh,https://en.wikipedia.org/wiki/Michelle_Yeoh,F,1962-08-06
4,Jürgen Prochnow,https://en.wikipedia.org/wiki/Jürgen_Prochnow,M,1941-06-10

You bulk import it:

copy stars from 'stars.csv' delimiter ',';

Importing an SQL dump

To import a SQL dump file:

dump.sql
insert into starsIn values (1, 1);
insert into starsIn values (1, 2);
insert into starsIn values (2, 3);
insert into starsIn values (3, 4);

Run it directly from the shell:

\i dump.sql

Query your dataset

CedarDB supports standard SQL queries.

Example: The following query returns the average movie length:

select avg(length) from movies;

avg
156.00

You can also perform more complex operations. For instance, compute the average age of actors by genre using joins and date functions:

select m.genre, avg(age(birthdate)) as age
from movies m 
    join starsIn si on m.id = si.movieId
    join stars s on s.id = si.starId
group by m.genre;

genre           age
Biopic          45 years 6 mons 27 days
Science Fiction 62 years 9 mons 1 day
Drama           83 years 10 mons 27 days

ℹ️

No tuning needed: If you’ve worked with database systems before, you’re probably familiar with techniques such as query decorrelation or schema denormalization to enhance query performance. However, with CedarDB, these practices are unnecessary. CedarDB automatically handles query decorrelation, even with complex queries containing hundreds of joins. This means you can focus on the essential aspect: the business logic driving your queries.

Modify data

Updates

Update existing rows using the UPDATE statement. For example:

update stars set name = '杨紫琼' where name = 'Michelle Yeoh';

Deletes

Delete rows using DELETE. For instance, to remove movies not linked to any stars:

delete from movies m
where not exists 
    (select * from starsIn si where si.movieId = m.id);

What’s next?

Want to go beyond basic examples? Explore the Data Cookbook to import your own datasets.
Curious about CedarDB’s performance in complex scenarios? Check out our sample datasets.
Looking for detailed documentation? Visit the reference section to dive deeper into CedarDB’s features.

Run with Docker