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# Merkle Search Tree

This crate implements a Merkle Search Tree as described in the 2019 paper
[Merkle Search Trees: Efficient State-Based CRDTs in Open Networks][paper] by
Alex Auvolat & François Taïani.[^cite]

When paired with CRDT-based value types, a Merkle Search Tree can act as an
efficient anti-entropy primitive, allowing independent replicas to concurrently
modify data within the tree with deterministic and eventual convergence across
all peers.

See [`MerkleSearchTree`] documentation.

## Use It

```rust
use merkle_search_tree::{MerkleSearchTree, diff::diff};

// Initialise a tree using the default configuration, appropriate for most uses.
let mut node_a = MerkleSearchTree::default();

// Upsert values into the tree.
//
// For the MST construct to be a CRDT itself, the values stored into the tree
// must also be CRDTs (or at least, have deterministic conflict resolution).
// Here the MST is used as an add-only set (a trivial CRDT) by using () as the
// key values.
node_a.upsert("bananas", &());
node_a.upsert("plátanos", &());

// Another node has differing keys.
let mut node_b = MerkleSearchTree::default();
node_b.upsert("donkey", &());

// The MST root hash can be used as an efficient consistency check (comparison
// is O(1) in space and time).
//
// In this case, both trees are inconsistent w.r.t each other, which is
// indicated by their differing root hashes.
assert_ne!(node_a.root_hash(), node_b.root_hash());

// Generate compact summaries of the MST content, suitable for transmission over
// the network, and use it to compute the diff between two trees.
let diff_range = diff(
    node_b.serialise_page_ranges().unwrap().into_iter(),
    node_a.serialise_page_ranges().unwrap().into_iter(),
);

// In this case, node B can obtain the missing/differing keys in node A by
// requesting keys within the computed diff range (inclusive):
assert_matches::assert_matches!(diff_range.as_slice(), [range] => {
    assert_eq!(range.start(), &"bananas");
    assert_eq!(range.end(), &"plátanos");
});
```

## Performance

Operations against a Merkle Search Tree are _fast_, executing against
millions/billions of keys per second:

| Key Count    | Insert All Keys | Generate Root | Serialise | Diff (consistent) | Diff (inconsistent) |
| ------------ | --------------- | ------------- | --------- | ----------------- | ------------------- |
| 100 keys     | 6µs             | 3µs           | 98ns      | 130ns             | 261ns               |
| 1,000 keys   | 80µs            | 38µs          | 837ns     | 574ns             | 3µs                 |
| 10,000 keys  | 1100us          | 388us         | 10µs      | 4µs               | 28µs                |
| 100,000 keys | 12ms            | 3ms           | 112µs     | 36µs              | 225µs               |

The above measurements capture the single-threaded performance of operations
against a tree containing varying numbers of keys on a M1 MacBook Pro.

* _Insert All Keys_: insert the N keys listed for the row into an empty tree
* _Generate Root_: regenerate the root hash of a modified tree
* _Serialise_: encode the tree into a diff format for network communication
* _Diff (consistent)_: diff generation for identical trees (no differing ranges)
* _Diff (inconsistent)_: diff generation for a fully inconsistent tree

The benchmarks to generate these numbers are included in this repo (run `cargo
bench`).

## Testing

This crate is extensively tested using randomised fuzzing & property testing to
validate correctness, and ensure no panics occur in release builds.

[paper]: https://inria.hal.science/hal-02303490
[`MerkleSearchTree`]:
    https://docs.rs/merkle-search-tree/latest/merkle_search_tree/struct.MerkleSearchTree.html
[^cite]: Alex Auvolat, François Taïani. Merkle Search Trees: Efficient
    State-Based CRDTs in Open Networks. SRDS 2019 - 38th IEEE International
    Symposium on Reliable Distributed Systems, Oct 2019, Lyon, France. pp.1-10,
    ⟨10.1109/SRDS.2019.00032⟩. ⟨hal-02303490⟩