5 releases (3 breaking)
0.4.0 | Jun 11, 2020 |
---|---|
0.3.0 | Sep 6, 2019 |
0.2.0 | Oct 27, 2018 |
0.1.1 | Oct 1, 2018 |
0.1.0 | Sep 28, 2018 |
#1491 in Database interfaces
24 downloads per month
Used in 2 crates
210KB
5K
SLoC
Lightweight embedded database
The LEDB is an attempt to implement simple but efficient, lightweight but powerful document storage.
The abbreviation LEDB may be treated as an Lightweight Embedded DB, also Low End DB, also Literium Engine DB, also LitE DB, and so on.
Links
- ledb Crate on crates.io
- ledb API Docs on docs.rs
- ledb-types Crate on crates.io
- ledb-types API Docs on docs.rs
- ledb-derive Crate on crates.io
- ledb-derive API Docs on docs.rs
- ledb-actix Crate on crates.io
- ledb-actix API Docs on docs.rs
- ledb NodeJS addon on npmjs.com
Key features
- Processing documents which implements
Serialize
andDeserialize
traits from serde. - Identifying documents using auto-incrementing integer primary keys.
- Indexing any fields of documents using unique or duplicated keys.
- Searching and ordering documents using indexed fields or primary key.
- Selecting documents using complex filters with fields comparing and logical operations.
- Updating documents using rich set of modifiers.
- Storing documents into independent storages so called collections.
- Flexible
query!
macro which helps write clear and readable queries. - Using LMDB as backend for document storage and indexing engine.
Usage example
use serde::{Serialize, Deserialize};
use ledb::{Options, Storage, IndexKind, KeyType, Filter, Comp, Order, OrderKind, Primary};
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Document)]
struct MyDoc {
#[document(primary)]
id: Option<Primary>,
title: String,
#[document(index)]
tag: Vec<String>,
#[document(unique)]
timestamp: u32,
}
fn main() {
let db_path = ".test_dbs/my_temp_db";
let _ = std::fs::remove_dir_all(&db_path);
// Open storage
let storage = Storage::new(&db_path, Options::default()).unwrap();
// Get collection
let collection = storage.collection("my-docs").unwrap();
// Ensure indexes
query!(index for collection
title str unique,
tag str,
timestamp int unique,
).unwrap();
// Insert JSON document
let first_id = query!(insert into collection {
"title": "First title",
"tag": ["some tag", "other tag"],
"timestamp": 1234567890,
}).unwrap();
// Insert typed document
let second_id = collection.insert(&MyDoc {
title: "Second title".into(),
tag: vec![],
timestamp: 1234567657,
}).unwrap();
// Find documents
let found_docs = query!(
find MyDoc in collection
where title == "First title"
).unwrap().collect::<Result<Vec<_>, _>>().unwrap();
// Update documents
let n_affected = query!(
update in collection modify title = "Other title"
where title == "First title"
).unwrap();
// Find documents with descending ordering
let found_docs = query!(
find MyDoc in collection order desc
).unwrap().collect::<Result<Vec<_>, _>>().unwrap();
// Remove documents
let n_affected = query!(
remove from collection where title == "Other title"
).unwrap();
}
Dependencies
~5–7MB
~135K SLoC