164 lines
4.5 KiB
Markdown
164 lines
4.5 KiB
Markdown
# ZipponDB
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Open-source database written 100% in zig.
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# Introduction
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ZipponDB is a relational database written entirely in Zig from stractch.
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It use a custom query language named ZipponQL or ZiQL for short.
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The first time you run ZipponDB, it will create a new ZipponDB directory and start the Zippon CLI.
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From here, you can create a new engine by running `schema build`. It will use the file `schema.zipponschema` and build a custom binary
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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
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by directly using the engine binary.
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### Why Zippon ?
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- Open-source and written 100% in Zig with 0 dependency
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- Relational database
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- Small, fast and implementable everywhere
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# Declare a schema
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ZipponDB need a schema to work. A schema is a way to define how your data will be store.
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Compared to SQL, you can see it as a file where you declare all table name, columns name, data type and relationship.
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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:
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```
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User (
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name: str,
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email: str,
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best_friend: User,
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)
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```
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In this example each user have a name and email as a string. But also one best friend as a link.
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Here a more advance example with multiple struct:
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```
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User {
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name: str,
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email: str,
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friends: []User,
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posts: []Post,
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liked_posts: []Post,
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comments: []Comment,
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liked_coms: []Comment,
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}
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Post {
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title: str,
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image: str,
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at: date,
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from: User,
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like_by: []User,
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comments: []Comment,
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}
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Comment {
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content: str,
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at: date,
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from: User,
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like_by: []User,
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of: Post,
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}
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```
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Can be simplify to take less space but can require more complexe query:
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```
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User {
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name: str,
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email: str,
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friends: []User,
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posts: []Post,
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comments: []Comment,
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}
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Post {
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title: str,
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image: str,
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at: date,
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like_by: []User,
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comments: []Comment,
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}
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Comment {
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content: str,
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at: date,
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like_by: []User,
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}
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```
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Note: [] are list of value.
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# ZipponQL
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Zippon have it's own query language. Here the keys point to remember:
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- {} Are filters
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- [] Are how much; what data
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- () Are new or updated data (Not already in file); Or to link condition between {}
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- || Are additional options
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- By default all member that are not link are return
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- To return link or just some member, specify them between []
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## Examples
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| Command | Description |
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| --- | --- |
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| GRAB User | Get all users |
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| GRAB User { name = 'Adrien' } | Get all users named Adrien |
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| GRAB User [1; email] | Get one user's email |
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| GRAB User \| ASCENDING name \| | Get all users ordered by name |
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| 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 |
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| GRAB User [1] { bestfriend = { name = 'Adrien' } } | Get one user that has a best friend named Adrien |
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| GRAB User [10; friends [1]] { age > 10 } | ASC name | | Get one friend of the 10th user above 10 years old in ascending name |
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### Not yet implemented
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| Command | Description |
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| --- | --- |
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| 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 |
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| GRAB User { IN Message { date > '12-01-2014' }.writter } | Get all users that sent a message after the 12th January 2014 |
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| GRAB User { !IN Comment { }.writter } | Get all users that didn't write a comment |
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| GRAB User { IN User { name = 'Adrien' }.friends } | Get all users that are friends with an Adrien |
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| UPDATE User [1] { name = 'Adrien' } => ( email = 'new@email.com' ) | Update a user's email |
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| REMOVE User { id = '000-000' } | Remove a user by ID |
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| ADD User ( name = 'Adrien', email = 'email', age = 40 ) | Add a new user |
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# Integration
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For now there is only a python intregration, but because it is just 2-3 command, it is easy to implement with other language.
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### Python
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```python
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import zippondb as zdb
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client = zdb.newClient('path/to/binary')
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client.exe('schema build')
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print(client.exe('schema describe'))
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# Return named tuple of all users
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users = client.run('GRAB User {}')
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for user in users:
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print(user.name)
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```
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# Roadmap
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[X] CLI
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[ ] Parser basic
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[ ] Relationships/links
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[ ] Multi threading
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[ ] Transaction
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[ ] Docker image
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[ ] Migration of schema
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[ ] Dump/Bump data
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[ ] In memory option
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[ ] Archives
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[ ] Date value type
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