58 releases (16 breaking)

✓ Uses Rust 2018 edition

0.28.0 Sep 10, 2019
0.26.3 Aug 27, 2019
0.24.1 May 12, 2019
0.20.1 Mar 31, 2019
0.14.1 Sep 9, 2017

#1 in Database implementations

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Used in 22 crates (18 directly)


11K SLoC

sled - it's all downhill from here!!!

Build Status crates.io documentation chat

A (beta) modern embedded database. Doesn't your data deserve a (beta) beautiful new home?

use sled::Db;

let tree = Db::open(path)?;

// insert and get, similar to std's BTreeMap
tree.insert(k, v1);
assert_eq!(tree.get(&k), Ok(Some(v1)));

// range queries
let mut iter = tree.range(k..);
assert_eq!(iter.next(), Some(Ok((k, v2))));
assert_eq!(iter.next(), None);

// deletion

// compare and swap
tree.cas(k, Some(v1), Some(v2));

// block until all operations are stable on disk
// (flush_async also available to get a Future)

If your dataset resides entirely in cache (achievable at startup by setting the cache to a large enough value and performing a full iteration) then all reads and writes are non-blocking and async-friendly, without needing to use Futures or an async runtime. To asynchronously suspend on the durability of writes, we support the flush_async method, which returns a Future that your async tasks can await the completion of if they require high durability guarantees and you are willing to pay the latency costs of fsync. Note that sled automatically tries to sync all data to disk several times per second in the background without blocking user threads.


  • 2 million sustained writes per second with 8 threads, 1000 8 byte keys, 10 byte values, intel 9900k, nvme
  • 8.5 million sustained reads per second with 16 threads, 1000 8 byte keys, 10 byte values, intel 9900k, nvme

what's the trade-off? sled uses too much disk space sometimes. this will improve significantly before 1.0.



lock-free tree on a lock-free pagecache on a lock-free log. the pagecache scatters partial page fragments across the log, rather than rewriting entire pages at a time as B+ trees for spinning disks historically have. on page reads, we concurrently scatter-gather reads across the log to materialize the page from its fragments. check out the architectural outlook for a more detailed overview of where we're at and where we see things going!


  1. don't make the user think. the interface should be obvious.
  2. don't surprise users with performance traps.
  3. don't wake up operators. bring reliability techniques from academia into real-world practice.
  4. don't use so much electricity. our data structures should play to modern hardware's strengths.

known issues, warnings

  • if reliability is your primary constraint, use SQLite. sled is beta.
  • if storage price performance is your primary constraint, use RocksDB. sled uses too much space sometimes.
  • quite young, should be considered unstable for the time being.
  • the on-disk format is going to change in ways that require manual migrations before the 1.0.0 release!


  • Typed Trees that support working directly with serde-friendly types instead of raw bytes, and also allow the deserialized form to be stored in the shared cache for speedy access.
  • LSM tree-like write performance with traditional B+ tree-like read performance
  • MVCC and snapshots
  • forward-compatible binary format
  • concurrent snapshot delta generation and recovery
  • consensus protocol for PC/EC systems
  • pluggable conflict detection and resolution strategies for gossip + CRDT-based PA/EL systems
  • first-class programmatic access to replication stream

fund feature development and get commercial support

Want to support the project, prioritize a specific feature, or get commercial help with using sled in your project? Ferrous Systems provides commercial support for sled, and can work with you to solve a wide variety of storage problems across the latency-throughput, consistency, and price performance spectra. Get in touch!

Ferrous Systems

special thanks


Special thanks to Meili for providing engineering effort and other support to the sled project. They are building an event store backed by sled, and they offer a full-text search system which has been a valuable case study helping to focus the sled roadmap for the future.

Additional thanks to Arm, Works on Arm and Packet, who have generously donated a 96 core monster machine to assist with intensive concurrency testing of sled. Each second that sled does not crash while running your critical stateful workloads, you are encouraged to thank these wonderful organizations. Each time sled does crash and lose your data, blame Intel.

contribution welcome!

want to help advance the state of the art in open source embedded databases? check out CONTRIBUTING.md!



~70K SLoC