Good-ormning is an ORM, probably? In a nutshell:
build.rsLike other Rust ORMs, Good-ormning doesn't abstract away from actual database workflows, but instead aims to enhance type checking with normal SQL.
See Comparisons, below, for information on how Good-ormning differs from other Rust ORMs.
Alpha:
You'll need the following runtime dependencies:
tokio-postgres for PostgreSQLrusqlite for Sqlitehex_literal if you use byte array literals in any queriesAnd build.rs dependencies:
good-ormningCreate a build.rs and define your initial schema version and queries
goodormning::generate() to output the generated codemigrategoodormning::generate(), which will generate the new migration statements.migrate call will make sure the database is updated to the new schema version.This build.rs file
rust
fn main() {
println!("cargo:rerun-if-changed=build.rs");
let mut latest_version = Version::default();
let users = latest_version.table("zQLEK3CT0", "users");
let id = users.rowid_field(&mut latest_version, None);
let name = users.field(&mut latest_version, "zLQI9HQUQ", "name", field_str().build());
let points = users.field(&mut latest_version, "zLAPH3H29", "points", field_i64().build());
goodormning::sqlite::generate(&root.join("tests/sqlite_gen_hello_world.rs"), vec![
// Versions
(0usize, latest_version)
], vec![
// Queries
new_insert(&users, vec![(name.clone(), Expr::Param {
name: "name".into(),
type_: name.type_.type_.clone(),
}), (points.clone(), Expr::Param {
name: "points".into(),
type_: points.type_.type_.clone(),
})]).build_query("create_user", QueryResCount::None),
new_select(&users).where_(Expr::BinOp {
left: Box::new(Expr::Field(id.clone())),
op: BinOp::Equals,
right: Box::new(Expr::Param {
name: "id".into(),
type_: id.type_.type_.clone(),
}),
}).return_fields(&[&name, &points]).build_query("get_user", QueryResCount::One),
new_select(&users).return_field(&id).build_query("list_users", QueryResCount::Many)
]).unwrap();
}
Generates this code
```rust
pub struct GoodError(pub String);
impl std::fmt::Display for GoodError { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { self.0.fmt(f) } }
impl std::error::Error for GoodError { }
impl From
pub fn migrate(db: &mut rusqlite::Connection) -> Result<(), GoodError> {
db.execute(
"create table if not exists _goodversion (rid int primary key, version bigint not null, lock int not null);",
(),
)?;
db.execute("insert into _goodversion (rid, version, lock) values (0, -1, 0) on conflict do nothing;", ())?;
loop {
let txn = db.transaction()?;
match (|| {
let mut stmt =
txn.prepare("update _goodversion set lock = 1 where rid = 0 and lock = 0 returning version")?;
let mut rows = stmt.query(())?;
let version = match rows.next()? {
Some(r) => {
let ver: i64 = r.get(0usize)?;
ver
},
None => return Ok(false),
};
drop(rows);
stmt.finalize()?;
if version > 0i64 {
return Err(
GoodError(
format!(
"The latest known version is {}, but the schema is at unknown version {}",
0i64,
version
),
),
);
}
if version < 0i64 {
txn.execute("create table \"users\" ( \"name\" text not null , \"points\" integer not null )", ())?;
}
txn.execute("update _goodversion set version = $1, lock = 0", rusqlite::params![0i64])?;
let out: Result
pub fn createuser(db: &mut rusqlite::Connection, name: &str, points: i64) -> Result<(), GoodError> { db .execute("insert into \"users\" ( \"name\" , \"points\" ) values ( $1 , $2 )", rusqlite::params![name, points]) .maperr(|e| GoodError(e.to_string()))?; Ok(()) }
pub struct DbRes1 { pub name: String, pub points: i64, }
pub fn getuser(db: &mut rusqlite::Connection, id: i64) -> Result
pub fn listusers(db: &mut rusqlite::Connection) -> Result
```
And can be used like
```rust fn main() { use sqlitegenhello_world as queries;
let mut db = rusqlite::Connection::open_in_memory().unwrap();
queries::migrate(&mut db).unwrap();
queries::create_user(&mut db, "rust human", 0).unwrap();
for user_id in queries::list_users(&mut db).unwrap() {
let user = queries::get_user(&mut db, user_id).unwrap();
println!("User {}: {}", user_id, user.name);
}
Ok(())
} ```
User 1: rust human
IDs are used both in SQL and Rust, so must be valid in both (however, some munging is applied to ids in Rust if they clash with keywords). Depending on the database, you can change IDs arbitrarily between schema versions but swapping IDs in consecutive versions isn't currently supported - if you need to do swaps do it over three different versions (like v0: A and B, v1: A_ and B, v2: B and A).
Schema IDs are internal ids used for matching fields across versions, to identify renames, deletes, etc. Schema IDs must not change once used in a version. I recommend using randomly generated IDs, via a macro.
Use type_* field_* functions to get expression/field type builders. Use new_insert/select/update/delete to get a query builder for the associated query type.
When defining a field in the schema, call .custom("mycrate::MyString", type_str().build()) on the field type builder (or pass it in as Some("mycreate::MyType".to_string()) if creating the type structure directly).
Custom types need to implement functions like this:
```rust pub struct MyString(pub String);
impl MyString { pub fn to_sql(&self) -> &str { &self.0 }
pub fn from_sql(s: String) -> Result<Self, MyErr> {
Ok(Self(s))
}
} ```
Any std::err::Error can be used for the error. The to_sql result and from_sql arguments should correspond to the base type you specified. If you're not sure what type that is, guess, and when you compile you'll get an compiler error saying which type you need.
Good-ormning is functionally most similar to Diesel.
build.rs filebuild.rsSeaORM focuses on runtime checks rather than compile time checks, so the focus is quite different.
Obviously writing an SQL VM isn't great. The ideal solution would be for popular databases to expose their type checking routines as libraries so they could be imported into external programs, like how Go publishes reusable ast-parsing and type-checking libraries.
It would be great to provider more flexibility in migrations, but for downtime-less migrations with complex migrations the code also needs to be adjusted significantly. Common advice appears to be to make smaller, incremental, backward-compatible migrations and make larger changes over multiple versions and deploys, which seems a reasonable solution.