charybdis 0.4.0

High-Performance ORM for ScyllaDB
Documentation

Rust ORM for ScyllaDB

Use monstrous tandem of scylla and charybdis for your next project

⚠️ WIP: This project is currently in an experimental stage. It's not recommended to use it in production yet.

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

Announcements:

  • Queries are now configurable

    With 0.4.0 release we have provided users with ability to configure each query before execution

  • Breaking changes

    1. Operations: find, insert, update, delete now return CharybdisQuery that can be configured before execution.

      let mut user = user.find_by_primary_key().consistency(Consistency::One).execute(session);

    2. Callbacks: We now have only single Callbacks trait that is used for all operation that can accept extension. In case extension is not needed, we can use () or Option<()> and provide None as extension argument.

    3. Batch Operations: Batch is now coupled with Model and it's created by calling Model::batch() method. It can also be configured before execution.

      let batch = User::batch().consistency(Consistency::One).chunked_insert(&session, users, 100).await?;

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

Declare model as a struct within src/models dir:

// src/models/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 = [username],
    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

src/models/udts

// 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

src/models/materialized_views

// 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)

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
  • Change field types (drop and recreate column --drop-and-replace flag)
  • 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.

Model 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> --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

#[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.

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;

  • 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: Text,
    id: Uuid,
    ...
}

    We have macro generated functions for up to 3 fields from primary key. Note that if complete primary key is provided, we get single typed result. So in case of our User model, we would get:

    Post::find_by_date(date: Date).execute(session) -> Result<CharybdisModelStream<Post>, CharybdisError>
Post::find_by_date_and_category_id(date: Date, category_id: Uuid).execute(session) ->  Result<CharybdisModelStream<Post>, CharybdisError>
Post::find_by_date_and_category_id_and_title(date: Date, category_id: Uuid, title: Text).execute(session) -> Result<Post, CharybdisError>

    And for our user model we would have

    User::find_by_id(id: Uuid).execute(session) -> Result<User, CharybdisError>

  • 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?;

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?;
    .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);

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().execute(&session).await;

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

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

  • Partial Model Considerations:

    1. partial_<model> requires #[derive(Default)] on original model
    2. partial_<model> require complete primary key in definition
    3. All derives that are defined bellow #charybdis_model macro will be automatically added to partial model.
    4. 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.

  • 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().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:

  1. 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,
}
  2. 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();
        self.updated_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(())
    }
}

  • 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). 
     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).execute(&session).await;

Collections

  • For each collection field that is defined as List<T> or Set`, we get following collection queries:

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

  • Define Model:

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

  • Generated Collection Queries:

     User::PUSH_TAGS_QUERY;
 User::PULL_TAGS_QUERY;
 
 User::PUSH_POST_IDS_QUERY;
 User::PULL_POST_IDS_QUERY;

    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 PULL_TAGS_QUERY: &'static str = "UPDATE users SET tags = tags - ? WHERE id = ?";
  
  const PUSH_POST_IDS_QUERY: &'static str = "UPDATE users SET post_ids = post_ids + ? WHERE id = ?";
  const PULL_POST_IDS_QUERY: &'static str = "UPDATE users SET post_ids = post_ids - ? WHERE id = ?";
}

    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:

    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;

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