High-Performance ORM for ScyllaDB in Rust

Use monstrous tandem of scylla and charybdis for your next project

⚠️ WIP: This project is currently in an experimental stage; It uses async trait support from rust 1.75.0 beta release

Charybdis is a ORM layer on top of scylla_rust_driver focused on easy of use and performance

Usage considerations:

• Provide and expressive API for CRUD & Complex Query operations on model as a whole
• Provide easy way to work with subset of model fields by using automatically generated partial_<model>! macro
• Provide easy way to run complex queries by using automatically generated find_<model>! macro
• Automatic migration tool that analyzes the src/model/*.rs files and runs migrations according to differences between the model definition and database

Performance consideration:

• It's build by beta release, so it uses builtin support for async/await in traits that will be stabilized in Rust 1.75
• It uses prepared statements (shard/token aware) -> bind values
• It expects CachingSession as a session arg for operations
• Queries are macro generated str constants (no concatenation at runtime)
• By using find_<model>! macro we can run complex queries that are generated at compile time as &'static str
• Although it has expressive API it's thin layer on top of scylla_rust_driver, and it does not introduce any significant overhead

Table of Contents

• Charybdis Models
  • Define Tables
  • Define UDTs
  • Define Materialized Views
• Automatic migration with charybdis-migrate
• Basic Operations
  • Create
  • Find
    • Custom filtering
  • Update
  • Delete
• Partial Model Operations
  • Considerations
• View Operations
• Callbacks
• Batch Operations
• As Native
• Collection queries
• Ignored fields
• Roadmap

Charybdis Models

Define Tables

Declare model as a struct within src/models dir:

// src/modles/user.rs
use charybdis_macros::charybdis_model;
use charybdis::types::{Text, Timestamp, Uuid};

#[charybdis_model(
    table_name = users,
    partition_keys = [id],
    clustering_keys = [],
    global_secondary_indexes = [],
    local_secondary_indexes = [],
)]
pub struct User {
    pub id: Uuid,
    pub username: Text,
    pub email: Text,
    pub created_at: Timestamp,
    pub updated_at: Timestamp,
    pub address: Address,
}

(Note we use src/models as automatic migration tool expects that dir)

Define UDT

Declare udt model as a struct within src/models/udts dir:

// src/models/udts/address.rs
use charybdis_macros::charybdis_udt_model;
use charybdis::types::Text;

#[charybdis_udt_model(type_name = address)]
pub struct Address {
    pub street: Text,
    pub city: Text,
    pub state: Option<Text>,
    pub zip: Text,
    pub country: Text,
}

Define Materialized Views

Declare view model as a struct within src/models/materialized_views dir:

// src/models/materialized_views/users_by_username.rs
use charybdis_macros::charybdis_view_model;
use charybdis::types::{Text, Timestamp, Uuid};

#[charybdis_view_model(
    table_name=users_by_username,
    base_table=users,
    partition_keys=[username],
    clustering_keys=[id]
)]
pub struct UsersByUsername {
    pub username: Text,
    pub id: Uuid,
    pub email: Text,
    pub created_at: Timestamp,
    pub updated_at: Timestamp,
}

Resulting auto-generated migration query will be:

CREATE MATERIALIZED VIEW IF NOT EXISTS users_by_email
AS SELECT created_at, updated_at, username, email, id
FROM users
WHERE email IS NOT NULL AND id IS NOT NULL
PRIMARY KEY (email, id)

📝 Primary key fields should not be wrapped in Option<> as they are mandatory.

Automatic migration

charybdis-migrate tool that enables automatic migration to database without need to write migrations by hand. It expects src/models files and generates migrations based on differences between model definitions and database.

It supports following operations:

• Create new tables
• Create new columns
• Drop columns
• Create secondary indexes
• Drop secondary indexes
• Create UDTs (src/models/udts)
• Create materialized views (src/models/materialized_views)
• Table options

    #[charybdis_model(
        table_name = commits,
        partition_keys = [object_id],
        clustering_keys = [created_at, id],
        global_secondary_indexes = [],
        local_secondary_indexes = [],
        table_options = #r"
            WITH CLUSTERING ORDER BY (created_at DESC) 
            AND gc_grace_seconds = 86400
        ";
    )]
    #[derive(Serialize, Deserialize, Default)]
    pub struct Commit {...}
  

  ⚠️ If table exists, table options will result in alter table query that without CLUSTERING ORDER and COMPACT STORAGE options.

🟢 Tables, Types and UDT dropping is not added. If you don't define model within src/model dir it will leave db structure as it is.

cargo install charybdis-migrate

migrate --hosts <host> --keyspace <your_keyspace>

⚠️ If you are working with existing datasets, before running migration you need to make sure that your model definitions structure matches the database in respect to table names, column names, column types, partition keys, clustering keys and secondary indexes so you don't alter structure accidentally. If structure is matched, it will not run any migrations. As mentioned above, in case there is no model definition for table, it will not drop it. In future, we will add modelize command that will generate src/models files from existing data source.

Global secondary indexes

They are simply defined as array of strings:

#[charybdis_model(
    table_name = users,
    partition_keys = [id],
    clustering_keys = [],
    global_secondary_indexes = ["username"]
)]

Local secondary Indexes

They are defined as array of tuples

• first element is array of partition keys
• second element is array of clustering keys

#[charybdis_model(
    table_name = menus,
    partition_keys = [location],
    clustering_keys = [name, price, dish_type],
    global_secondary_indexes = [],
    local_secondary_indexes = [
        ([location], [dish_type])
    ]
)]

resulting query will be: CREATE INDEX ON menus((location), dish_type);

Basic Operations:

For each operation you need to bring respective trait into scope. They are defined in charybdis::operations module.

Create

use charybdis::{CachingSession, Insert};

#[tokio::main]
async fn main() {
  let session: &CachingSession; // init session
  
  // init user
  let user: User = User {
    id,
    email: "charybdis@nodecosmos.com".to_string(),
    username: "charybdis".to_string(),
    created_at: Utc::now(),
    updated_at: Utc::now(),
    address: Some(
        Address {
            street: "street".to_string(),
            state: "state".to_string(),
            zip: "zip".to_string(),
            country: "country".to_string(),
            city: "city".to_string(),
        }
    ),
  };

  // create
  user.insert(&session).await;
}

Find

Find by primary key

This is preferred way to query data rather then find_by_primary_key_val associated fun, as it will automatically provide correct order based on primary key definition.

  let user = User {id, ..Default::default()};
  let user = user.find_by_primary_key(&session).await?;

Find by partition key

  let users =  User {id, ..Default::default()}.find_by_partition_key(&session).await;

Macro generated find helpers

Lets say we have model:

#[charybdis_model(
    table_name = posts,
    partition_keys = [date],
    clustering_keys = [categogry_id, title],
    global_secondary_indexes = [])
]
pub struct Post {
    date: Date,
    category_id: Uuid,
    title: String,
    id: Uuid,
    ...
}

We have macro generated functions for up to 3 fields from primary key.

Post::find_by_date(session: &Session, date: Date) -> Result<CharybdisModelStream<Post>, CharybdisError>
Post::find_by_date_and_category_id(session: &Session, date: Date, category_id: Uuid) ->  Result<CharybdisModelStream<Post>, CharybdisError>
Post::find_by_date_and_category_id_and_title(session: &Session, date: Date, category_id: Uuid, title: String) -> Result<Post, CharybdisError>

🟢 Note that if complete primary key is provided, we get single typed result. So for our user model we get find_by_id function that returns Result<User, CharybdisError>.

Custom filtering:

Let's say we have a model:

#[charybdis_model(
    table_name = posts, 
    partition_keys = [category_id], 
    clustering_keys = [date, title],
    global_secondary_indexes = []
)]
pub struct Post {...}

We get automatically generated find_post! macro that follows convention find_<struct_name>!. It can be used to create custom queries.

Following will return stream of Post models, and query will be constructed at compile time as &'static str.

// automatically generated macro rule
let res = find_post!(
    session,
    "category_id in ? AND date > ?",
    (categor_vec, date])
).await?;

We can also use find_first_post! macro to get single result:

let post = find_first_post!(
    session,
    "category_id in ? AND date > ? LIMIT 1",
    (date, categor_vec]
).await?;

If we just need the Query and not the result, we can use find_post_query! macro:

let query = find_post_query!(
    "date = ? AND category_id in ?",
    (date, categor_vec])

Update

let user = User::from_json(json);

user.username = "scylla".to_string();
user.email = "some@email.com";

user.update(&session).await;

Delete

  let user = User::from_json(json);

  user.delete(&session).await;

Macro generated delete helpers

Lets say we have model:

#[charybdis_model(
    table_name = posts,
    partition_keys = [date],
    clustering_keys = [categogry_id, title],
    global_secondary_indexes = [])
]
pub struct Post {
    date: Date,
    category_id: Uuid,
    title: String,
    id: Uuid,
    ...
}

We have macro generated functions for up to 3 fields from primary key.

Post::delete_by_date(session: &Session, date: Date);
Post::delete_by_date_and_category_id(session: &Session, date: Date, category_id: Uuid);
Post::delete_by_date_and_category_id_and_title(session: &Session, date: Date, category_id: Uuid, title: String);

Partial Model Operations:

Use auto generated partial_<model>! macro to run operations on subset of the model fields. This macro generates a new struct with same structure as the original model, but only with provided fields. Macro is automatically generated by #[charybdis_model]. It follows convention partial_<struct_name>!.

// auto-generated macro - available in crate::models::user
partial_user!(UpdateUsernameUser, id, username);

let id = Uuid::new_v4();
let user = UpdateUsernameUser { id, username: "scylla".to_string() };

// we can have same operations as on base model
// INSERT into users (id, username) VALUES (?, ?)
user.insert(&session).await;

// UPDATE users SET username = ? WHERE id = ?
user.update(&session).await;

// DELETE FROM users WHERE id = ?
user.delete(&session).await;

// get partial PartUser
let partial_user = user.find_by_primary_key(&:session).await?;

// get native user model by primary key
let user = user.as_native().find_by_primary_key(&session).await?;

Partial Model Considerations:

1. partial_<model> require complete primary key in definition

2. All derives that are defined bellow #charybdis_model macro will be automatically added to partial model.

3. partial_<model> struct implements same field attributes as original model, so if we have #[serde(rename = "rootId")] on original model field, it will be present on partial model field.

Recommended naming convention is Purpose + Original Struct Name. E.g: UpdateAdresssUser, UpdateDescriptionPost.

Callbacks

We can define callbacks that will be executed before and after certain operations. Note that callbacks returns custom error class that implements From<CharybdisError>.

use charybdis::*;

#[charybdis_model(
    table_name = organizations, 
    partition_keys = [id], 
    clustering_keys = [],
    global_secondary_indexes = [name]
)]
pub struct Organization {
    ...
}

impl Organization {
  pub async fn find_by_name(&self, session: &CachingSession) -> Option<Organization> {
    find_first_organization!(session, "name = ?", (&self.name,)).await.ok()
  }
}

impl Callbacks for Organization {
  type Error = CustomError;

  async fn before_insert(&self, session: &CachingSession) -> Result<(), CustomError> {
    if self.find_by_name(session).await.is_some() {
      return Err(CustomError::ValidationError((
        "name".to_string(),
        "is taken".to_string(),
      )));
    }

    Ok(())
  }
}

Possible callbacks:

• before_insert
• after_insert
• before_update
• after_update
• before_delete
• after_delete

⚠️ In order to trigger callback, instead of calling insert method on model, we can call insert_cb. This enables us to have clear distinction between insert and insert with callbacks.

let post = Post::from_json(json);
let res = post.insert_cb(&session).await;
match res {
        Ok(_) => println!("success"),
        Err(e) => match e {
            CharybdisError::ValidationError((field, reason)) => {
                println!("validation error: {} {}", field, reason)
            }
            _ => println!("error: {:?}", e),
        },
    }

ExtensionCallbacks

We can also define callbacks that will be given custom extension if needed.

Let's say we define custom extension that will be used to update elastic document on every post update:

pub struct CustomExtension {
    pub elastic_client: ElasticClient,
}

We can define after_update callback on Post
that has custom extension as type:

#[charybdis_model(...)]
pub struct Post {}

impl ExtCallbacks for Post {
    type Extention = CustomExtension;
    type Error = CustomError;

    async fn after_update(
        &mut self,
        _db_session: &CachingSession,
        extension: &CustomExtension,
    ) -> Result<(), CustomError> {
        extension.elastic_client.update(...).await?;

        Ok(())
    }
}

So to trigger callback we use same update_cb method:

let post = Post::from_json(json);
let res = post.update_cb(&session, custom_extensions).await;

Note that CustomError has to implement From<CharybdisError>.

Batch Operations

For batched operations we can make use of CharybdisModelBatch.

let mut batch = CharybdisModelBatch::new();
let users: Vec<User> = Vec::from_json(json);

// inserts
batch.append_inserts(users);

// or updates
batch.append_updates(users);

// or deletes
batch.append_deletes(users);

batch.execute(&session).await;

It also supports chunked batch operations

chunk_size = 100;
CharybdisModelBatch::chunked_inserts(&session, users, chunk_size).await?;

As Native

In case we need to run operations on native model, we can use as_native method:

partial_user!(UpdateUser, id, username);

let mut update_user_username = UpdateUser {
    id,
    username: "updated_username".to_string(),
};

let native_user: User = update_user_username.as_native().find_by_primary_key(&session).await?;

// action that requires native model
authorize_user(&native_user);

as_native works by returning new instance of native model with fields from partial model. For other fields it uses default values.

Collections

For every field that is defined with List<T> type or Set<T>, we get following:

• PUSH_<field_name>_QUERY static str
• PULL_<field_name>_QUERY static str
• push_<field_name> method
• pull_<field_name> method

pub struct User {
    id: Uuid,
    tags: Set<String>,
    post_ids: List<Uuid>,
}

let query = User::PUSH_TAGS_QUERY;
execute(query, (vec![tag], &user.id)).await;

let query = User::PULL_POST_IDS_QUERY;
execute(query, (post_ids_vec, &user.id)).await;

Methods take session and value as arguments:

let user = User::from_json(json);
user.push_tags(&session, vec![tag]).await;
user.pull_post_ids(&session, post_ids_vec).await;

Ignored fields

We can ignore fields by using #[charybdis(ignore)] attribute:

#[charybdis_model(...)]
pub struct User {
    id: Uuid,
    #[charybdis(ignore)]
    organization: Option<Organization>,
}

So field organization will be ignored in all operations and default value will be used when deserializing from other data sources. It can be used to hold data that is not persisted in database.

Roadmap:

• Add tests
• Write modelize command to generate src/models/* structs from existing database
• Add --drop flag to migrate command to drop tables, types and UDTs if they are not defined in src/models