ZipponDB goal is to be ACID, light, simple and high performance. It is aim for small to medium application that don't need fancy features but a simple and reliable database.

Why Zippon ?

Open-source and written 100% in Zig with 0 dependency
Relational database
Simple and minimal query language
Small, light, 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,
)

Note that the best friend is a link to another User.

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

ZipponDB use it's own query language, ZipponQL or ZiQL for short. Here the keys point to remember:

4 actions available: GRAB ADD UPDATE DELETE
All query start with an action then a struct name
{} Are filters
[] Are how much; what data
() Are new or updated data (Not already in file)
|| Are additional options
By default all member that are not link are return
To return link or just some member, specify them between []

GRAB

The main action is GRAB, this will parse files and return data. Here how it's work:

GRAB StructName [number_of_entity_max; member_name1, member_name2] { member_name1 = value1}

Note that [] and {} are both optional.
So this will work and return all User without any filtering:

GRAB User

Here a simple example where to get all User above 18 years old:

GRAB User {age > 18}

To just return the name of User:

GRAB User [name] {age > 18}

To return the 10 first User:

GRAB User [10] {age > 18}

You can use both:

GRAB User [10; name] {age > 18}

To order it using the name:

GRAB User [10; name] {age > 10} |ASC name|

ADD

The ADD action will add one entity into the database (batch are comming).
The synthax is similare but use (), this mean that the data is not yet in the database if between ().

Here an example:

ADD User (name = 'Bob', age = 30, email = 'bob@email.com', scores = [1 100 44 82])

You need to specify all member when adding an entity (default value are comming).

DELETE

Similare to GRAB but delete all entity found using the filter and return the list of UUID deleted.

DELETE User {name = 'Bob'}

UPDATE

A mix of GRAB and ADD. This take a filter first, then the new data.
Here we update the 5 first User named adrien to add a capital and become Adrien.

UPDATE User [5] {name='adrien'} => (name = 'Adrien')

Note that compared to ADD, you don't need to specify all member between (). Only the one specify will be updated.

Examples list

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 \| ASC name \|	Get all users ordered by name
GRAB User [name] { age > 10 AND name != 'Adrien' }	Get users' name if more than 10 years old and not named Adrien
GRAB User { age > 10 AND (name = 'Adrien' OR name = 'Bob'}	Use multiple condition
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

Not yet implemented

Command	Description
GRAB User { age > 10 AND name IN ['Adrien' 'Bob']}	In comparison
GRAB User [1] { bestfriend IN { 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
GRAB Message [100; comments [ date ] ] { writter IN { 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

Data types

Their is 5 data type for the moment:

int: 64 bit integer
float: 64 bit float. Need to have a dot, 1. is a float 1 is an integer.
bool: Boolean, can be true or false
string: Character array between ''
uuid: Id in the UUID format, used for relationship, ect. All struct have an id member.

Comming soon:

date: A date in yyyy/mm/dd
datetime: A date time in yyyy/mm/dd/hh/mm/ss
time: A time in hh/mm/ss

All data type can be an array of those type using [] in front of it. So []int is an array of integer.

All data type can also be null. Expect array that can only be empty.

Lexique

Struct: A struct of how to store data. E.g. User
Entity: An entity is one instance of a struct.
Member: A member is one data saved in a struct. E.g. name in User

How does it work ?

TODO: Create a tech doc of what is happening inside.

Roadmap

v0.1 - Base

UUID
CLI
Tokenizers
ZiQL parser
Schema engine
File engine
Loging

v0.2 - Usable

B-Tree
Relationships
Date
Docker

v0.3 - QoL

Schema migration
Dump/Bump data
Recovery
Better CLI

v0.4 - Usability

Server
Python interface
Go interface

v0.5 - In memory

In memory option
Cache

v0.6 - Performance

Transaction
Multi threading
Lock manager

v0.7 - Safety

Auth
Metrics
Durability

v0.8 - Advanced

Query optimizer

v0.9 - Docs

ZiQL tuto
Deployment tuto
Code docs
CLI help

v1.0 - Web interface

Query builder
Tables
Schema visualization
Dashboard metrics

Let's see where it (or my brain) start explode ;)