Introduction

dbeel is an attempt to learn modern database architecture.

The best one-liner to describe the db is: A distributed thread-per-core document database written in rust.

So basically it has a document API like in MongoDB with leaderless replication like in Cassandra and thread-per-core architecture like in ScyllaDB.

It's not production ready at all, but that doesn't mean there is no value in the project. If you ever wanted to read database code without getting overwhelmed by massive amounts of code, dbeel is for you.

You can try it out by running cargo install dbeel.

I've also posted a blog post as a summary of what I've learned working on this project.

Traits

• Documents + API in msgpack format
• LSM Tree
  • Memtable is a red black tree
• Thread per core (thanks glommio)
• io_uring (thanks again glommio)
• Direct I/O
  • Page cache implemented using WTiny-LFU eviction algorithm
• Load balanced via consistent hashing
  • Each shard (core) is placed on the ring
• Metadata events sent using gossip dissemination
• Leaderless replication with tunable consistency
  • replication_factor (parameter in create_collection command) - Number of nodes that will store a copy of data
  • Write consistency (parameter in set command) - Number of nodes that will acknowledge a write for it to succeed
  • Read consistency (parameter in get command) - Number of nodes that have to respond to a read operation for it to succeed
  • Max timestamp conflict resolution

Performance

Running the benchmark on my machine (System76 lemp11) with no fdatasync results in the following output:

Set:
total: 54.424290449s, min: 80.219µs, p50: 446.851µs, p90: 905.422µs, p99: 1.806261ms, p999: 7.463916ms, max: 35.385961ms

Get:
total: 29.281556369s, min: 36.577µs, p50: 231.464µs, p90: 479.929µs, p99: 1.222589ms, p999: 3.269881ms, max: 6.242454ms

Running with --wal-sync (calls fdatasync after each write to the WAL file) results in the following output for Set (note that fdatasync on my machine takes 6-10ms):

Set:
total: 1253.611595658s, min: 6.625024ms, p50: 12.57609ms, p90: 12.858347ms, p99: 13.4931ms, p999: 19.062725ms, max: 31.880792ms

You can always configure --wal-sync to achieve better throughput, with worse tail latencies, by setting --wal-sync-delay (try setting half the time it takes to fdatasync a file on average in your setup).

How to use

The only implemented client is in async rust, and can work on either glommio or tokio (select which using cargo features).

Documents are formatted in msgpack and the best crate I found for it is rmpv, so the client makes heavy use of it.

Example (mostly copied from tokio_example/):

// When connecting to a cluster, you provide nodes to request cluster metadata from.
let seed_nodes = [("127.0.0.1", 10000)];
let client = DbeelClient::from_seed_nodes(&seed_nodes).await?;

// Create a collection with replication of 3 (meaning 3 copies for each document).
let collection = client.create_collection_with_replication(COLLECTION_NAME, 3).await?;

// Create key and document using rmpv.
let key = Value::String("key".into());
let document = Value::Map(vec![
    (Value::String("is_best_db".into()), Value::Boolean(true)),
    (Value::String("owner".into()), Value::String("tontinton".into())),
]);

// Write document using quorum consistency.
collection.set_consistent(key.clone(), value.clone(), Consistency::Quorum).await?;

// Read document using quorum consistency.
let response = collection.get_consistent(key, Consistency::Quorum).await?;
assert_eq!(response, value);

// Drop collection.
collection.drop().await?;

Try out the benchmarks yourself

To compile the DB (you can skip building the db by running cargo install dbeel):

cargo build --release
./target/release/dbeel --help

To compile the blackbox benchmarks:

cd blackbox_bench
cargo build --release

To run the benchmarks:

# If you installed using cargo instead of building, dbeel should be in your PATH.
./target/release/dbeel               # On first terminal
./target/release/blackbox-bench      # On second terminal