ZipponDB

Open-source database written 100% in zig.

Introduction

ZipponDB is a relational database written entirely in Zig from stractch.
It use a custom query language named ZipponQL or ZiQL for short.

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,
}

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)
|| Are additional options
Link need to be specify between [] to be return, other are returned automatically
Data are in struct format and can have link

Some examples

GRAB User
Get all users

GRAB User { name = 'Adrien' }
Get all user named Adrien

GRAB User [1; email]
Get one user 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 have a best friend named Adrien

GRAB User [10; friends [1]] { age > 10 } | ASC name |
Get one friend of the 10 first user above 10 years old in ascending name.

Not yet implemented

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 sended a message after the 12 january 2014

GRAB User { !IN Comment { }.writter }
Get all user that didn't wrote a comment

GRAB User { IN User { name = 'Adrien' }.friends }
Get all user that are friends with an Adrien

UPDATE User [1] { name = 'Adrien' } => ( email = 'new@email.com' )

REMOVE User { id = '000-000' }

ADD User ( name = 'Adrien', email = 'email', age = 40 )

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

[X] CLI
[ ] Beta without link
[ ] Relationships/links
[ ] Multi threading
[ ] Transaction
[ ] Docker image
[ ] Migration of schema
[ ] Dump/Bump data
[ ] In memory option
[ ] Archives
[ ] Date value type

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