The first time you run ZipponDB, it will create a new ZipponDB directory and start the Zippon CLI.
From here, you can create a new engine by running schema build. It will use the file schema.zipponschema and build a custom binary using zig, that the CLI will then use to manipulate data. You then interact with the engine by using run "My query go here" or by directly using the engine binary.

Why Zippon ?

Open-source and written 100% in Zig with 0 dependency
Relational database
Small, fast and implementable everywhere

Declare a schema

ZipponDB need a schema to work. A schema is a way to define how your data will be store.

Compared to SQL, you can see it as a file where you declare all table name, columns name, data type and relationship.

But here you declare struct. A struct have a name and members. A member is one data or link and have a type associated. Here a simple example for a user:

User (
    name: str,
    email: str,
    best_friend: User,
)

In this example each user have a name and email as a string. But also one best friend as a link.

Here a more advance example with multiple struct:

User {
    name: str,
    email: str,
    friends: []User,
    posts: []Post,
    liked_posts: []Post,
    comments: []Comment,
    liked_coms: []Comment,
}

Post {
    title: str,
    image: str,
    at: date,
    from: User,
    like_by: []User,
    comments: []Comment,
}

Comment {
    content: str,
    at: date,
    from: User,
    like_by: []User,
    of: Post,
}

Can be simplify to take less space but can require more complexe query:

User {
    name: str,
    email: str,
    friends: []User,
    posts: []Post,
    comments: []Comment,
}

Post {
    title: str,
    image: str,
    at: date,
    like_by: []User,
    comments: []Comment,
}

Comment {
    content: str,
    at: date,
    like_by: []User,
}

Note: [] are list of value.

ZipponQL

Zippon have it's own query language. Here the keys point to remember:

{} Are filters
[] Are how much; what data
() Are new or updated data (Not already in file); Or to link condition between {}
|| Are additional options
By default all member that are not link are return
To return link or just some member, specify them between []

Examples

Command	Description
GRAB User	Get all users
GRAB User { name = 'Adrien' }	Get all users named Adrien
GRAB User [1; email]	Get one user's email
GRAB User \| ASCENDING name \|	Get all users ordered by name
GRAB User [name] { age > 10 AND name != 'Adrien' } \| DECENDING age \|	Get just the name of all users that are more than 10 years old and not named Adrien
GRAB User [1] { bestfriend = { name = 'Adrien' } }	Get one user that has a best friend named Adrien
GRAB User [10; friends [1]] { age > 10 }	Get one friend of the 10th user above 10 years old

Not yet implemented

Command	Description
GRAB Message [100; comments [ date ] ] { .writter = { name = 'Adrien' }.bestfriend }	Get the date of 100 comments written by the best friend of a user named Adrien
GRAB User { IN Message { date > '12-01-2014' }.writter }	Get all users that sent a message after the 12th January 2014
GRAB User { !IN Comment { }.writter }	Get all users that didn't write a comment
GRAB User { IN User { name = 'Adrien' }.friends }	Get all users that are friends with an Adrien
UPDATE User [1] { name = 'Adrien' } => ( email = 'new@email.com' )	Update a user's email
REMOVE User { id = '000-000' }	Remove a user by ID
ADD User ( name = 'Adrien', email = 'email', age = 40 )	Add a new user

Integration

For now there is only a python intregration, but because it is just 2-3 command, it is easy to implement with other language.

Python

import zippondb as zdb

client = zdb.newClient('path/to/binary')
client.exe('schema build')
print(client.exe('schema describe'))

# Return named tuple of all users
users = client.run('GRAB User {}')
for user in users:
    print(user.name)

Roadmap

v 0.1 - Base
[X] UUID
[X] CLI
[X] Tokenizers
[ ] File management [ ] Base Parser

v 0.2 - Usable
[ ] B-Tree [ ] Relationships
[ ] Date
[ ] Docker

v 0.3 - QoL
[ ] Schema migration
[ ] Dump/Bump data
[ ] Recovery

v 0.4 - Usability
[ ] Server
[ ] Python interface
[ ] Go interface

v 0.5 - In memory
[ ] In memory option
[ ] Cache

v 0.6 - Performance
[ ] Transaction
[ ] Lock manager [ ] Multi threading

v 0.7 - Safety
[ ] Auth [ ] Metrics

v 0.6 - Advanced
[ ] Query optimizer

v 1.0 - Web interface [ ] Query builder [ ] Tables [ ] Schema visualization [ ] Dashboard metrics