Rust ORM for ScyllaDB and Apache Cassandra

⚠️ This project is currently in an early stage of development. Feedback and contributions are welcomed!

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 analyzes the project files and runs migrations according to differences between the model definition and database

Performance consideration:

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

Charybdis Models
Automatic migration with charybdis-migrate
Basic Operations
Configuration Options
Batch Operations
- Chunked Batch Operations
- Batch Configuration
Partial Model
- Considerations
- As Native
Callbacks
- Implementation
- Triggering Callbacks
Collection
- Generated Collection Queries
- Generated Collection Methods
Ignored fields
Roadmap

Charybdis Models

Define Tables

use charybdis::macros::charybdis_model;
use charybdis::types::{Text, Timestamp, Uuid};

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

Define UDT

 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,
 }

🚨 UDT fields must be in the same order as they are in the database.

Note that in order for migration to correctly detect changes on each migration, type_name has to match struct name. So if we have struct ReorderData we have to use #[charybdis_udt_model(type_name = reorderdata)] - without underscores.

Define Materialized Views

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)

Automatic migration

charybdis-migrate enables automatic migration to database without need to write migrations by hand. It iterates over project files and generates migrations based on differences between model definitions and database. It supports following operations:
- Create new tables
- Create new columns
- Drop columns
- Change field types (drop and recreate column --drop-and-replace flag)
- Create secondary indexes
- Drop secondary indexes
- Create UDTs
- Create 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#"
          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.
Model dropping is not added. If you removed model, you need to drop table manually.
Running migration
```
cargo install charybdis-migrate

migrate --hosts <host> --keyspace <your_keyspace> --drop-and-replace (optional)
```
⚠️ 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

If we have model:

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

resulting query will be: CREATE INDEX ON users (username);

Local secondary Indexes

Indexes that are scoped to the partition key

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

Insert

use charybdis::{CachingSession, Insert};

#[tokio::main]
async fn main() {
  let session: &CachingSession; // init sylla 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().execute(&session).await;
}

Find

Find by primary key

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

Find by partition key

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

Find by primary key associated

let users = User::find_by_primary_key_value(val: User::PrimaryKey).execute(&session).await;

Available find functions

use scylla::CachingSession;
use charybdis::errors::CharybdisError;
use charybdis::macros::charybdis_model;
use charybdis::stream::CharybdisModelStream;
use charybdis::types::{Date, Text, Uuid};

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

impl Post {
    async fn find_various(db_session: &CachingSession) -> Result<(), CharybdisError> {
       let date = Date::default();
       let category_id = Uuid::new_v4();
       let title = Text::default();
    
       let posts: CharybdisModelStream<Post> = Post::find_by_date(date).execute(db_session).await?;
       let posts: CharybdisModelStream<Post> = Post::find_by_date_and_category_id(date, category_id).execute(db_session).await?;
       let posts: Post = Post::find_by_date_and_category_id_and_title(date, category_id, title.clone()).execute(db_session).await?;
    
       let post: Post = Post::find_first_by_date(date).execute(db_session).await?;
       let post: Post = Post::find_first_by_date_and_category_id(date, category_id).execute(db_session).await?;
    
       let post: Option<Post> = Post::maybe_find_first_by_date(date).execute(db_session).await?;
       let post: Option<Post> = Post::maybe_find_first_by_date_and_category_id(date, category_id).execute(db_session).await?;
       let post: Option<Post> = Post::maybe_find_first_by_date_and_category_id_and_title(date, category_id, title.clone()).execute(db_session).await?;
    
       // find by local secondary index
       let posts: CharybdisModelStream<Post> = Post::find_by_date_and_title(date, title.clone()).execute(db_session).await?;
       let post: Post = Post::find_first_by_date_and_title(date, title.clone()).execute(db_session).await?;
       let post: Option<Post> = Post::maybe_find_first_by_date_and_title(date, title.clone()).execute(db_session).await?;

      // find by global secondary index
      let posts: CharybdisModelStream<Post> = Post::find_by_category_id(category_id).execute(db_session).await?;
      let post: Post = Post::find_first_by_category_id(category_id).execute(db_session).await?;
      let post: Option<Post> = Post::maybe_find_first_by_category_id(category_id).execute(db_session).await?;
    
      Ok(())
    }
}

Custom filtering:

Lets use our Post model as an example:

#[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 posts = find_post!("category_id in ? AND date > ?", (categor_vec, date))
    .execute(session)
    .await?;

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

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

Collection:

Let's use our User model as an example:

#[charybdis_model(
    table_name = users,
    partition_keys = [id],
    clustering_keys = [],
)]
pub struct User {
    id: Uuid,
    tags: Set<Text>,
    post_ids: List<Uuid>,
}

push_to_<field_name> and pull_from_<field_name> methods are generated for each collection field.

let user: User;

user.push_tags(vec![tag]).execute(&session).await;
user.pull_tags(vec![tag]).execute(&session).await;

user.push_post_ids(vec![tag]).execute(&session).await;
user.pull_post_ids(vec![tag]).execute(&session).await;

Counter

Let's define post_counter model:

#[charybdis_model(
    table_name = post_counters,
    partition_keys = [id],
    clustering_keys = [],
)]
pub struct PostCounter {
    id: Uuid,
    likes: Counter,
    comments: Counter,
}

We can use increment_<field_name> and decrement_<field_name> methods to update counter fields.

let post_counter: PostCounter;
post_counter.increment_likes(1).execute(&session).await;
post_counter.decrement_likes(1).execute(&session).await;

post_counter.increment_comments(1).execute(&session).await;
post_counter.decrement_comments(1).execute(&session).await;

Delete

let user = User::from_json(json);

user.delete().execute(&session).await;

Macro generated delete helpers

Lets use our Post model as an example:

#[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: Text,
    id: Uuid,
    ...
}

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

Post::delete_by_date(date: Date).execute(&session).await?;
Post::delete_by_date_and_category_id(date: Date, category_id: Uuid).execute(&session).await?;
Post::delete_by_date_and_category_id_and_title(date: Date, category_id: Uuid, title: Text).execute(&session).await?;

Custom delete queries

We can use delete_post! macro to create custom delete queries.

delete_post!("date = ? AND category_id in ?", (date, category_vec)).execute(&session).await?

Configuration

Every operation returns CharybdisQuery that can be configured before execution with method chaining.

let user: User = User::find_by_id(id)
    .consistency(Consistency::One)
    .timeout(Some(Duration::from_secs(5)))
    .execute(&app.session)
    .await?;
    
let result: QueryResult = user.update().consistency(Consistency::One).execute(&session).await?;

Supported configuration options:

consistency
serial_consistency
timestamp
timeout
page_size
timestamp

Batch

CharybdisModelBatch operations are used to perform multiple operations in a single batch.

Batch Operations

let users: Vec<User>;
let batch = User::batch();

// inserts
batch.append_inserts(users);

// or updates
batch.append_updates(users);

// or deletes
batch.append_deletes(users);

batch.execute(&session).await?;

Chunked Batch Operations

Chunked batch operations are used to operate on large amount of data in chunks.

  let users: Vec<User>;
  let chunk_size = 100;

  User::batch().chunked_inserts(&session, users, chunk_size).await?;
  User::batch().chunked_updates(&session, users, chunk_size).await?;
  User::batch().chunked_deletes(&session, users, chunk_size).await?;

Batch Configuration

Batch operations can be configured before execution with method chaining.

let batch = User::batch()
    .consistency(Consistency::One)
    .retry_policy(Some(Arc::new(DefaultRetryPolicy::new())))
    .chunked_inserts(&session, users, 100)
    .await?;

We could also use method chaining to append operations to batch:

let batch = User::batch()
    .consistency(Consistency::One)
    .retry_policy(Some(Arc::new(DefaultRetryPolicy::new())))
    .append_update(&user_1)
    .append_update(&user_2)
    .execute(data.db_session())
    .await?;

Statements Batch

We can use batch statements to perform collection operations in batch:

let batch = User::batch();
let users: Vec<User>;

for user in users {
    batch.append_statement(User::PUSH_TAGS_QUERY, (vec![tag], user.id));
}

batch.execute(&session).await;

Partial Model:

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);
```
Now we have new struct UpdateUsernameUser that is equivalent to User model, but only with id and username fields.
```
let mut update_user_username = UpdateUsernameUser {
    id,
    username: "updated_username".to_string(),
};

update_user_username.update().execute(&session).await?;
```
Partial Model Considerations:
- partial_<model> requires #[derive(Default)] on native model
- partial_<model> require complete primary key in definition
- All derives that are defined bellow #charybdis_model macro will be automatically added to partial model.
- partial_<model> struct implements same field attributes as native model, so if we have #[serde(rename = "rootId")] on native model field, it will be present on partial model field.
- partial_<model> should be defined in same file as native model, so it can reuse imports required by native model
As Native

In case we need to run operations on native model, we can use as_native method:
```
let native_user: User = update_user_username.as_native().find_by_primary_key().execute(&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.
Recommended naming convention is Purpose + Original Struct Name. E.g: UpdateAdresssUser, UpdateDescriptionPost.

Callbacks

Callbacks are convenient way to run additional logic on model before or after certain operations. E.g.

we can use before_insert to set default values and/or validate model before insert.
we can use after_update to update other data sources, e.g. elastic search.

Implementation:

Let's say we define custom extension that will be used to update elastic document on every post update:
```
pub struct AppExtensions {
    pub elastic_client: ElasticClient,
}
```

Now we can implement Callback that will utilize this extension:

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

impl ExtCallbacks for Post {
    type Extention = AppExtensions;
    type Error = AppError; // From<CharybdisError>
    
   // use before_insert to set default values
    async fn before_insert(
        &mut self,
        _session: &CachingSession,
        extension: &AppExtensions,
    ) -> Result<(), CustomError> {
        self.id = Uuid::new_v4();
        self.created_at = Utc::now();
        
        Ok(())
    }
    
    // use before_update to set updated_at
    async fn before_update(
        &mut self,
        _session: &CachingSession,
        extension: &AppExtensions,
    ) -> Result<(), CustomError> {
        self.updated_at = Utc::now();
        
        Ok(())
    }

    // use after_update to update elastic document
    async fn after_update(
        &mut self,
        _session: &CachingSession,
        extension: &AppExtensions,
    ) -> Result<(), CustomError> {
        extension.elastic_client.update(...).await?;

        Ok(())
    }
    
    // use after_delete to delete elastic document
    async fn after_delete(
        &mut self,
        _session: &CachingSession,
        extension: &AppExtensions,
    ) -> Result<(), CustomError> {
        extension.elastic_client.delete(...).await?;

        Ok(())
    }
}

Possible Callbacks:
- before_insert
- before_update
- before_delete
- after_insert
- after_update
- after_delete

Triggering Callbacks

In order to trigger callback we use <operation>_cb. method: insert_cb, update_cb, delete_cb according traits. This enables us to have clear distinction between insert and insert with callbacks (insert_cb). Just as on main operation, we can configure callback operation query before execution.

 use charybdis::operations::{DeleteWithCallbacks, InsertWithCallbacks, UpdateWithCallbacks};

 post.insert_cb(app_extensions).execute(&session).await;
 post.update_cb(app_extensions).execute(&session).await;
 post.delete_cb(app_extensions).consistency(Consistency::All).execute(&session).await;

Collections

For each collection field, we get following:

PUSH_<field_name>_QUERY static str
PUSH_<field_name>_IF_EXISTS_QUERY static str'
PULL_<field_name>_QUERY static str
PULL_<field_name>_IF_EXISTS_QUERY static str
push_<field_name> method
push_<field_name>_if_exists method
pull_<field_name> method
pull_<field_name>_if_exists method

Model:

#[charybdis_model(
    table_name = users,
    partition_keys = [id],
    clustering_keys = []
)]
pub struct User {
    id: Uuid,
    tags: Set<Text>,
    post_ids: List<Uuid>,
    books_by_genre: Map<Text, Frozen<List<Text>>>,
}

Generated Collection Queries:

Generated query will expect value as first bind value and primary key fields as next bind values.

impl User {
    const PUSH_TAGS_QUERY: &'static str = "UPDATE users SET tags = tags + ? WHERE id = ?";
    const PUSH_TAGS_IF_EXISTS_QUERY: &'static str = "UPDATE users SET tags = tags + ? WHERE id = ? IF EXISTS";

    const PULL_TAGS_QUERY: &'static str = "UPDATE users SET tags = tags - ? WHERE id = ?";
    const PULL_TAGS_IF_EXISTS_QUERY: &'static str = "UPDATE users SET tags = tags - ? WHERE id = ? IF EXISTS";
     
    const PUSH_POST_IDS_QUERY: &'static str = "UPDATE users SET post_ids = post_ids + ? WHERE id = ?";
    const PUSH_POST_IDS_IF_EXISTS_QUERY: &'static str = "UPDATE users SET post_ids = post_ids + ? WHERE id = ? IF EXISTS";

    const PULL_POST_IDS_QUERY: &'static str = "UPDATE users SET post_ids = post_ids - ? WHERE id = ?";
    const PULL_POST_IDS_IF_EXISTS_QUERY: &'static str = "UPDATE users SET post_ids = post_ids - ? WHERE id = ? IF EXISTS";

    const PUSH_BOOKS_BY_GENRE_QUERY: &'static str = "UPDATE users SET books_by_genre = books_by_genre + ? WHERE id = ?";
    const PUSH_BOOKS_BY_GENRE_IF_EXISTS_QUERY: &'static str = "UPDATE users SET books_by_genre = books_by_genre + ? WHERE id = ? IF EXISTS";
    
    const PULL_BOOKS_BY_GENRE_QUERY: &'static str = "UPDATE users SET books_by_genre = books_by_genre - ? WHERE id = ?";
    const PULL_BOOKS_BY_GENRE_IF_EXISTS_QUERY: &'static str = "UPDATE users SET books_by_genre = books_by_genre - ? WHERE id = ? IF EXISTS";
}

Now we could use this constant within Batch operations.

let batch = User::batch();
let users: Vec<User>;

for user in users {
    batch.append_statement(User::PUSH_TAGS_QUERY, (vec![tag], user.id));
}

batch.execute(&session).await;

Generated Collection Methods: