ZipponDB goal is to be ACID, light, simple and high performance. It aim 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

In ZipponDB you use structures, or struct for short, and not tables to organize how your data is store and manipulate. A struct have a name like User and members like name and age.

For that you create a file with inside the schema of the database that describe all structs. Compared to SQL, you can see it as a file where you declare all table name, columns name, data type and relationship.

Here an example of a schema.zipponschema file:

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,
    comments: []Comment,
)

Post (
    title: str,
    image: str,
    at: date,
    like_by: []User,
    comments: []Comment,
)

Comment (
    content: str,
    at: date,
    like_by: []User,
)

Note: [] before the type mean a list/array of this type.

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 only some members, specify them between []

Disclaimer: Lot of stuff are still missing and the language may change over time.

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|

Use multiple condition:

GRAB User {name = 'Bob' AND (age > 30 OR age < 10)}

Not yet implemented

You can specify how much and what to return even for link inside struct. In this example I get 1 friend name for 10 User:

GRAB User [10; friends [1; name]]

Using IN

You can use the IN operator to check if something is in an array:

GRAB User { age > 10 AND name IN ['Adrien' 'Bob']}

This also work by using other filter. Here I get User that have a best friend named Adrien:

GRAB User { bestfriend IN { name = 'Adrien' } }

You can use IN on itself. Here I get all User that liked a Comment that is from 2024. Both queries return the same thing:

GRAB User { IN Comment {at > '2024/01/01'}.like_by}
GRAB Comment.like_by { at > '2024/01/01'}

Return relationship

You can also return a relationship only. The filter will be done on User but will return Comment:

GRAB User.comments {name = 'Bob'}

You can do it as much as you like. This will return all User that liked comments from Bob:

GRAB User.comments.like_by {name = 'Bob'}

This can also be use inside filter. Note that we need to specify User because it is a different struct that Post. Here I get all Post that have a comment from Bob:

GRAB Post {comments IN User{name = 'Bob'}.comments}

Can also do the same but only for the first Bob found:

GRAB Post {comments IN User [1] {name = 'Bob'}.comments}

Be carefull, this will return all User that liked a comment from 10 User named Bob:

GRAB User.comments.like_by [10] {name = 'Bob'}

To get 10 User that liked a comment from any User named Bob, you need to use:

GRAB User.comments.like_by [comments [like_by [10]]] {name = 'Bob'}

Using !

You can use ! to return the opposite. Use with IN, it check if it is NOT is the list. Use it with filters, it return entities that do not respect the filter.

This will return all User that didn't like a Comment in 2024:

GRAB User { !IN Comment {at > '2024/01/01'}.like_by}

Be carefull because this do not return the same, it return all User that liked a Comment not in 2024:

GRAB Comment.like_by !{ at > '2024/01/01'}

Which is the same as:

GRAB Comment.like_by { at < '2024/01/01'}

ADD

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

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).

Not yet implemented

And you can also add them in batch

ADD User (name = 'Bob', age = 30, email = 'bob@email.com', scores = [1 100 44 82]) (name = 'Bob2', age = 33, email = 'bob2@email.com', scores = [])

You don't need to specify the member in the second entity as long as the order is respected.

ADD User (name = 'Bob', age = 30, email = 'bob@email.com', scores = [1 100 44 82]) ('Bob2', 33, 'bob2@email.com', [])

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'} TO (name = 'Adrien')

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

Not yet implemented

You can use operations on itself too when updating:

UPDATE User {name = 'Bob'} TO (age += 1)

You can also manipulate array, like adding or removing values.

UPDATE User {name='Bob'} TO (scores APPEND 45)
UPDATE User {name='Bob'} TO (scores REMOVEAT [0 1 2])

For now there is 4 keywords to manipulate list:

APPEND: Add value at the end of the list.
REMOVE: Check the list and if the same value is found, delete it.
REMOVEAT: Delete the value at a specific index.
CLEAR: Remove all value in the array.

Except CLEAR that take no value, each can use one value or an array of value, if chose an array it will perform the operation on all value in the array.

For relationship, you can use filter on it:

UPDATE User {name='Bob'} TO (comments APPEND {id = '000'})
UPDATE User {name='Bob'} TO (comments REMOVE { at < '2023/12/31'})

I may include more options later.

Link query - Not yet implemented

You an also link query. Each query return a list of UUID of a specific struct. You can use it in the next query. Here an example where I create a new Comment that I then append to the list of comment of one specific User.

ADD Comment (content='Hello world', at=NOW, like_by=[]) => added_comment => UPDATE User {id = '000'} TO (comments APPEND added_comment)

The name between => is the variable name of the list of UUID used for the next queries, you can have multiple one if the link have more than 2 queries. You can also just use one => but the list of UUID is discard in that case.

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

v0.2 - Usable

B-Tree
Relationships
Date
Link query
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 ;)