Fjall is an LSM-based embeddable key-value storage engine written in Rust. It features:
- Thread-safe BTreeMap-like API
- 100% safe & stable Rust
- Range & prefix searching with forward and reverse iteration
- Cross-partition snapshots (MVCC)
- Automatic background maintenance
- Single-writer transactions (optional)
Each Keyspace is a single logical database and is split into partitions (a.k.a. column families) - you should probably only use a single keyspace for your application. Each partition is physically a single LSM-tree and its own logical collection; however, write operations across partitions are atomic as they are persisted in a single keyspace-level journal, which will be recovered on restart.
It is not:
- a standalone server
- a relational database
- a wide-column database: it has no notion of columns
Keys are limited to 65536 bytes, values are limited to 65536 bytes. As is normal with any kind of storage engine, larger keys and values have a bigger performance impact.
Like any typical key-value store, keys are stored in lexicographic order. If you are storing integer keys (e.g. timeseries data), you should use the big endian form to adhere to locality.
cargo add fjalluse fjall::{Config, PersistMode, Keyspace, PartitionCreateOptions};
let keyspace = Config::new(folder).open()?;
// Each partition is its own physical LSM-tree
let items = keyspace.open_partition("my_items", PartitionCreateOptions::default())?;
// Write some data
items.insert("a", "hello")?;
// And retrieve it
let bytes = items.get("a")?;
// Or remove it again
items.remove("a")?;
// Search by prefix
for kv in items.prefix("prefix") {
// ...
}
// Search by range
for kv in items.range("a"..="z") {
// ...
}
// Iterators implement DoubleEndedIterator, so you can search backwards, too!
for kv in items.prefix("prefix").rev() {
// ...
}
// Sync the journal to disk to make sure data is definitely durable
// When the keyspace is dropped, it will try to persist
// Also, by default every second the keyspace will be persisted asynchronously
keyspace.persist(PersistMode::SyncAll)?;- Partitions (a.k.a. column families) with cross-partition atomic semantics (atomic write batches)
- Sharded journal for concurrent writes
- Cross-partition snapshots (MVCC)
- anything else implemented in
lsm-tree
For the underlying LSM-tree implementation, see: https://crates.io/crates/lsm-tree.
To support different kinds of workloads, Fjall is agnostic about the type of durability
your application needs. After writing data (insert, remove or committing a write batch), you can choose to call Keyspace::persist which takes a PersistMode parameter. By default every 1000ms data is fsynced asynchronously. Also when dropped, the keyspace will try to persist the journal synchronously.
Fjall is internally synchronized for multi-threaded access, so you can clone around the Keyspace and Partitions as needed, without needing to lock yourself. Common operations like inserting and reading are generally lock free.
For an async example, see the tokio example.
A single keyspace may not be loaded in parallel from separate processes however.
Uses bloom filters to reduce disk I/O for non-existing keys. Improves point read performance, but increases memory usage.
Disabled by default.
Allows opening a transactional Keyspace for single-writer (serialized) transactions, allowing RYOW (read-your-own-write), fetch-and-update and other atomic operations.
Enabled by default.
The disk format is stable as of 1.0.0. Future breaking changes will result in a major version bump and a migration path.
See here for practical examples.
And checkout Smoltable, a standalone Bigtable-inspired mini wide-column database using fjall as its storage engine.
How can you help?
- Ask a question
- or join the Discord server: https://discord.com/invite/HvYGp4NFFk
- Post benchmarks and things you created
- Open an issue (bug report, weirdness)
- Open a PR
All source code is licensed under MIT OR Apache-2.0.
All contributions are to be licensed as MIT OR Apache-2.0.
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