rhizome

A distributed, semantically sharded graph database for trillion-edge workloads.

What is rhizome?

rhizome is a horizontally scalable graph database designed for very large property graphs (billions of vertices, trillions of edges) that need to behave like a single logical database. It combines:

External consistency — Spanner-style commit-wait over a TrueTime-bounded clock, layered on top of Multi-Raft for per-shard replication;
Serializable Snapshot Isolation — optimistic MVCC with a Cahill–Fekete–Röhm conflict tracker and a Percolator-style two-phase commit for cross-shard writes;
Semantic sharding — instead of hashing IDs, the orchestrator continuously partitions the graph along communities discovered by the Leiden algorithm, so vertices that are frequently traversed together physically live together;
A native vector index — HNSW + Product Quantization sits inside the same storage engine that holds the graph, with one query language and one transaction model;
A GraphQL query surface — every CRUD, traversal, aggregation, KNN, and transaction lifecycle operation is exposed through one schema, with a cost-based optimiser pushing predicates down to the right shard;
Autonomous operation — a Kotlin control plane closes the loop over placement, schema evolution, deadlock detection, and load forecasting without asking an SRE to pick a shard count.

✨ Highlights

Capability	What we provide
Storage	Custom LSM on top of RocksDB with graph-aware compaction, SuRF/Xor presence filters, an ARC cache with a neighbour prefetcher, and AES-GCM-SIV field encryption.
Consensus	Multi-Raft with pipeline batching, joint consensus, witness nodes for geo-replication, pre-vote, lease-based reads, and credit-based flow control.
Transactions	Optimistic MVCC + SSI for read-write conflicts, Percolator-style 2PC for cross-shard writes, hybrid global deadlock detector, epoch-based GC.
Time	TrueTime-style oracle with explicit uncertainty; commit-wait guarantees external consistency; PTP-mode drops $\epsilon$ below 1 ms.
Vector search	HNSW + Product Quantization living inside the same SSTables as the graph; hot vectors in RAM, cold tiers on disk; one transaction covers both.
Query	GraphQL gateway with a cost-based optimiser, gRPC for low-overhead clients, BSP traversal engine with push/pull dispatch, native cursor pagination over MVCC scans.
Schema evolution	Git-like DAG of schema versions, expand–contract online migration, DBSCAN-driven type inference suggestions.
Autonomy	Leiden-based community detection drives semantic placement; an NSGA-II optimiser balances load vs. locality vs. migration cost; TFT load forecasting.
Observability	Prometheus metrics on every layer, pre-built Grafana dashboards, OpenTelemetry traces end-to-end (gateway → orchestrator → node → raft).
Security	Mandatory mTLS between every internal hop, attribute-based access control at the transaction layer, cryptographic shredding for GDPR right-to-be-forgotten.

🆚 Compared with the rest of the landscape

There is no shortage of graph databases, and we sit in good company. The tables below place rhizome next to the systems we get compared to most often. They are split into three thematic blocks so each fits on a screen.

Roster legend: LPG = property graph, RDF = triple/quad store, SQL = distributed SQL system included for the transaction-protocol comparison only.

Click to expand the full comparison — 17 systems × 3 thematic tables.

1. Engineering provenance & licensing

System	Type	License	Lang	Native graph store?	Embeddable?
Neo4j	LPG	GPLv3 (Community) + commercial	Java	yes	both
TigerGraph	LPG	commercial (free Developer Ed.)	C++	yes (GSE)	standalone
Dgraph	LPG	Apache 2.0	Go	yes (badger)	standalone
NebulaGraph	LPG	Apache 2.0	C++	yes (RocksDB)	standalone
JanusGraph	LPG	Apache 2.0	Java	no — over Cassandra/HBase/BerkeleyDB	both
Memgraph	LPG	BSL 1.1 / Apache 2.0 (MAGE)	C++	yes (in-memory)	standalone
ArangoDB	LPG	Apache 2.0 / commercial	C++	yes (RocksDB)	standalone
FalkorDB	LPG	SSPL (ex-RedisGraph)	C	yes (sparse matrix)	standalone (Redis module)
KuzuDB	LPG	MIT	C++	yes	embedded
Stardog	RDF	commercial	Java	yes	standalone
Apache Jena	RDF	Apache 2.0	Java	yes (TDB)	embeddable
Virtuoso	RDF	GPLv2 / commercial	C	yes	standalone
AllegroGraph	RDF	commercial	Common Lisp	yes	standalone
YugabyteDB	SQL	Apache 2.0	C++	n/a	standalone
CockroachDB	SQL	BSL 1.1 (CCL bits)	Go	n/a	standalone
Spanner	SQL	proprietary (Google Cloud)	C++	n/a	standalone
🌱 rhizome	LPG	MIT	Rust + Kotlin	yes (bedrock)	standalone

2. Distribution, replication and consistency

System	Distribution	Replication	Default consistency	Cross-shard tx	External consistency	Multi-region	Built-in mTLS
Neo4j	replicated (Causal Cluster); sharding via Fabric	raft	causal	no (read-only Fabric joins)	no	enterprise	yes
TigerGraph	sharded + replicated	proprietary sync	tunable	yes (ACID)	no	enterprise	yes
Dgraph	sharded + replicated	raft (per group)	linearizable reads	yes	no	yes	yes
NebulaGraph	sharded + replicated	raft	strong (leader reads)	yes (SI)	no	yes	yes
JanusGraph	inherits backend	backend-dependent	eventual (Cassandra default)	backend-dependent	no	backend-dependent	partial
Memgraph	single primary + replicas	sync / async	ACID single-node	n/a	no	no	yes
ArangoDB	sharded + replicated	raft (agency) + async	tunable	yes (community limited; cluster ACID is Enterprise)	no	enterprise	enterprise
FalkorDB	single primary	Redis async replication	ACID single-node	n/a	no	no	Redis TLS
KuzuDB	single process	none	ACID single-process	n/a	no	no	n/a
Stardog	replicated (cluster)	raft	ACID	yes (within cluster)	no	enterprise	yes
Apache Jena	single process (Fuseki)	none built-in	ACID single-node	n/a	no	no	configurable
Virtuoso	replicated / clustered (Enterprise)	proprietary	ACID	yes	no	enterprise	yes
AllegroGraph	replicated + FedShard	proprietary spread	ACID	yes (federated)	no	yes	yes
YugabyteDB	sharded + replicated	raft	snapshot (tunable → serializable)	yes	clock-skew bounded	yes	yes
CockroachDB	sharded + replicated	raft	serializable	yes	HLC with optional commit-wait	yes	yes
Spanner	sharded + replicated	paxos	external consistency	yes	TrueTime + commit-wait	yes (designed-in)	yes
🌱 rhizome	sharded + replicated	Multi-Raft	SSI	yes (Percolator 2PC)	TrueTime + commit-wait	yes (witness voters)	yes (mandatory)

3. Data model, query language, vector & autonomy

System	Query language(s)	Schema model	Online schema migration	Native vector index	Sharding strategy	Graph analytics in-DB
Neo4j	Cypher / GQL	schemaless + constraints	yes	yes (HNSW, since 5.x)	manual (Fabric)	yes (GDS: PageRank, Louvain, Leiden…)
TigerGraph	GSQL	typed strict	yes	limited add-on	hash + workload-aware	yes (GSQL is Turing-complete)
Dgraph	DQL + GraphQL	typed strict	yes	no	predicate-based	limited
NebulaGraph	nGQL / openCypher	typed strict	yes	external (ES)	hash	yes (Nebula Algorithm)
JanusGraph	Gremlin (TinkerPop)	typed (managed)	yes	external (ES/Solr)	backend-dependent	yes (TinkerPop OLAP)
Memgraph	Cypher / openCypher	schemaless	yes	yes (HNSW)	n/a	yes (MAGE)
ArangoDB	AQL	schemaless / JSON-schema	yes	experimental	hash (SmartGraphs = community-aware, Enterprise)	yes (Pregel)
FalkorDB	Cypher	schemaless	yes	no	n/a	limited
KuzuDB	Cypher	typed strict	limited	no	n/a	limited
Stardog	SPARQL + GraphQL	OWL ontology	yes	no	replication only	SPARQL only
Apache Jena	SPARQL	OWL ontology	n/a (schemaless triples)	no	n/a	SPARQL only
Virtuoso	SPARQL + SQL	OWL + relational	yes	no	hash	limited
AllegroGraph	SPARQL + Prolog	OWL ontology	yes	yes (since 8.x)	FedShard (manual)	SPARQL + reasoning
YugabyteDB	SQL (Postgres) + CQL	relational	yes	pgvector (HNSW)	hash + range	SQL only
CockroachDB	SQL (Postgres)	relational	yes	no	range (auto-split)	SQL only
Spanner	SQL	relational	yes	yes (recent GA)	range (auto-split)	SQL only
🌱 rhizome	GraphQL	typed, versioned DAG	yes (expand–contract)	yes (HNSW + PQ, in-engine)	semantic (Leiden + NSGA-II)	yes (BSP, KNN, community detection)

A few honest caveats:

Spanner owns external consistency through TrueTime + commit-wait; rhizome adopts the same recipe but ships it on commodity NTP/PTP rather than purpose-built atomic clocks. CockroachDB approximates the property with HLC and optional commit-wait. We sit closer to Spanner in semantics but to CockroachDB in deployment cost.
Neo4j remains the most polished graph product on the market and its GDS algorithm catalogue is unmatched. rhizome's differentiator is horizontal scale-out: Neo4j shards only through the Fabric federation layer, while rhizome partitions a single logical graph along community boundaries.
Dgraph is structurally the closest to rhizome: distributed, raft, GraphQL-first. The split is in placement (predicate-based vs. semantic) and in transaction guarantees (linearizable reads vs. SSI + external consistency).
RDF stores (Stardog, Jena, Virtuoso, AllegroGraph) live in an adjacent space — strong reasoning and SPARQL ergonomics, weaker multi-region transactions. We treat them as a different design point, not a head-to-head competitor.
YugabyteDB, CockroachDB, Spanner are not graph databases. They appear in the tables because they share the distribution and transaction recipe (sharded raft/paxos, MVCC, external/serializable consistency) and let readers calibrate where rhizome sits on that axis.

🏗 Architecture

rhizome separates data plane (Rust) from control plane (Kotlin). Cluster metadata (membership, shard map, leases, replicated wait-graph) lives in a 3-voter raft group called the metastore, which is co-located inside the same rhizome-node binary as the shard raft groups. Clients talk to a stateless gateway, which compiles GraphQL queries into gRPC calls against the right shard's leader.

flowchart TB
  client["Application<br/>(GraphQL · gRPC)"]

  subgraph cp["Control plane · Kotlin"]
    gw["Gateway<br/><i>GraphQL · CBO · sticky tx router</i>"]
    orch["Orchestrator<br/><i>Placement · Schema · Deadlock slow path</i>"]
  end

  subgraph dp["Data plane · rhizome-node × N · Rust"]
    direction TB
    subgraph shards["Shard raft groups (per shard)"]
      direction LR
      n1["node 1<br/>shards {0..k}"]
      n2["node 2<br/>shards {k+1..2k}"]
      n3["node N<br/>shards {...}"]
      n1 <-->|Multi-Raft| n2
      n2 <--> n3
      n3 <--> n1
    end
    subgraph meta["Metastore raft group (× 3 voters, co-located in rhizome-node)"]
      direction LR
      m1[("on node 1")]
      m2[("on node 2")]
      m3[("on node 3")]
      m1 <-.-> m2
      m2 <-.-> m3
      m3 <-.-> m1
    end
  end

  obs[("Prometheus + Grafana")]

  client ==>|HTTPS · mTLS| gw
  gw ==>|gRPC · sticky route| shards
  gw -->|shard-map lookup| meta
  orch <-->|membership · leases · wait-graph| meta
  orch -->|migrate · abort · drain| shards

  dp -.->|metrics + traces| obs
  cp -.-> obs

Hot path is the thick line: client → gateway → data plane, with a side lookup into the metastore for shard map. The metastore is not a separate process — its 3-voter raft group runs alongside the shard raft groups inside the same rhizome-node binary, scheduled by a shared MultiRaftManager. The orchestrator never sits in the request path: it talks to the metastore raft group for membership / leases / the replicated wait-graph, and to the data plane only for rare management operations (shard migration, deadlock abort, graceful drain).

A short tour of the modules:

Module	Language	Purpose
`bedrock`	Rust	Graph-native storage engine over RocksDB. Adjacency-block SSTables, HNSW vectors, ARC cache, encryption, MVCC retention.
`clockwork-ferryman`	Rust	Consensus core. Wraps `raft-rs`, owns the log + state machine, talks Multi-Raft over a tonic transport with mTLS.
`transaxxxtion`	Rust	Transaction manager. MVCC, SSI conflict graph, TrueTime oracle, Percolator 2PC, local wait-for graph for deadlock probes.
`pathfinder-pete`	Rust	Graph compute. BSP traversal, weighted shortest path (Dijkstra and A* with heuristics), KNN over HNSW, cursor pagination, push/pull strategy switch.
`chrysalis`	Rust	Schema registry client + validator. Mirrors the Kotlin authority store and enforces shape on every write.
`rhizome-metastore`	Rust	Raft group for cluster metadata (membership, shard map, leases, replicated wait-graph). 3 voters by default, co-located inside `rhizome-node` and scheduled by the shared `MultiRaftManager`.
`rhizome-node`	Rust	The shipping binary. Hosts the gRPC services and wires every Rust crate above into one process.
`control-plane/gateway`	Kotlin	Stateless edge service: GraphQL endpoint, query planning, sticky transaction routing, mTLS termination.
`control-plane/orchestrator`	Kotlin	Placement, schema authority, deadlock slow path, load forecasting, autoscaling decisions.
`control-plane/common`	Kotlin	Shared DTOs, metastore client, gRPC stubs, retry policies, TLS material loading.

See docs/architecture.md for the full breakdown and docs/adr/ for the architectural decision records.

🚀 Getting Started

Pre-reqs

A POSIX host (Linux is the supported target; macOS works for development).
git and docker with docker compose v2 for the recommended path.
For building from source:
- rustup — the pinned toolchain is in rust-toolchain.toml;
- JDK 21 + Gradle (the wrapper in control-plane/ will fetch the right version);
- Python 3.11+ with pip for the end-to-end suite.

Quick start with Docker Compose

The fastest way to see rhizome alive is the bundled stack — one etcd, three metastore voters, three storage nodes, two orchestrator replicas, one gateway, plus Prometheus and Grafana:

git clone https://github.com/maxbarsukov/rhizome.git
cd rhizome

# 1. Mint a development PKI for mTLS (CA + per-service leaf certs).
./deploy/scripts/gen-test-pki.sh

# 2. Build images and bring the cluster up.
docker compose -f deploy/docker-compose.yml up --build --wait

# 3. Verify everything is healthy.
curl -fsS http://localhost:8080/ready    # orchestrator
curl -fsS http://localhost:7003/ready    # node #1
curl -fsS http://localhost:8081/graphql  # gateway (GraphQL playground)

Open Grafana at http://localhost:3000 (default admin / admin) — the rhizome dashboards are pre-provisioned in deploy/grafana/.

Tear it down with docker compose -f deploy/docker-compose.yml down -v when you're done.

From source

# Rust workspace — engine, transactions, traversal, all of which link
# into the single `rhizome-node` binary.
cargo build --workspace --release

# Kotlin control plane — gateway + orchestrator + common.
( cd control-plane && ./gradlew build )

# Run a single-node rhizome-node for smoke tests:
./target/release/rhizome-node \
    --id 1 \
    --listen 0.0.0.0:7001 \
    --data /tmp/rhizome-data \
    --single-node

# Run the GraphQL gateway pointed at it:
( cd control-plane && \
  ./gradlew :gateway:run \
    --args="--node-addr=127.0.0.1:7001 --listen=0.0.0.0:8081" )

🔎 Querying rhizome

GraphQL gateway

The GraphQL endpoint at http://<gateway>:8081/graphql exposes vertices, edges, traversals, aggregations, and the full transaction lifecycle:

mutation {
  beginTx(timeoutMs: 5000) { id, nodeId }
}

mutation Insert($tx: TxHandleInput!) {
  createVertex(tx: $tx, input: {
    type: "Person"
    properties: [
      { key: "name", value: "Ada" }
      { key: "country", value: "UK" }
    ]
  }) { id }
}

mutation Commit($tx: TxHandleInput!) {
  commitTx(tx: $tx) { ok, commitTs }
}

query Friends($tx: TxHandleInput) {
  traverse(tx: $tx, from: 42, depth: 2, edgeTypes: ["KNOWS"]) {
    vertex { id, type, properties }
  }
}

Sticky-routing keeps a long-lived transaction pinned to the node that owns its session — see docs/transactions.md for the routing contract.

gRPC for power users

Every gateway call is a thin wrapper around protobuf services defined in proto/rhizome/v1/. You can talk to a node directly when you want lower overhead or precise control:

grpcurl -plaintext -d '{"id": 42, "vertex_type": "Person"}' \
    localhost:7001 rhizome.v1.Rhizome/CreateVertex

See docs/api.md for the full surface (CRUD, traversal, KNN, aggregation, transactions, deadlock, metastore).

📡 Observability

rhizome exposes Prometheus metrics on every component:

Data plane — rhizome_commit_latency_seconds, rhizome_ssi_aborts_total, rhizome_raft_propose_total, rhizome_mvcc_retained_versions, lock-probe outcomes, raft snapshot age, RocksDB pebble metrics.
Control plane — deadlock_slow_*, placement_migrations_total, schema_versions_active, gateway query-plan cost histograms.
Metastore — rhizome_metastore_watch_lag_ms_max, raft membership changes, lease expirations.

Pre-built Grafana dashboards live in deploy/grafana/ and load automatically when you bring the docker-compose stack up. A walkthrough with screenshots is in docs/observability.md.

✅ Testing

# Rust unit + integration tests.
cargo test --workspace

# Kotlin tests.
( cd control-plane && ./gradlew test )

# End-to-end Python suite (spins up the docker-compose stack).
( cd tests/e2e && pip install -r requirements.txt && pytest -v )

# Chaos sub-suite (requires Docker, takes ≈ 10 minutes).
RHIZOME_E2E_CHAOS=1 pytest tests/e2e/test_chaos.py -v

CI runs all three suites on every PR — see .github/workflows/ci.yml.

🎨 Linting

# Rust — clippy with -D warnings + rustfmt.
cargo clippy --workspace --all-targets -- -D warnings
cargo fmt --all -- --check

# Kotlin — detekt + Kover coverage.
( cd control-plane && ./gradlew detekt koverHtmlReport )

Both sets of checks are required on every commit through GitHub Actions.

🗺 Roadmap

The next milestones beyond the current v0.3.0 release, in shipping order:

Security & compliance — full surface. ABAC policy engine with a Rego dialect, field-level encryption with AES-GCM-SIV (deterministic AEAD for searchable PII), cryptographic shredding for GDPR right-to-be-forgotten, WORM audit log with Merkle-tree chaining, per-tenant rate limiting, multi-tenancy isolation enforced at storage and policy layers.
Autonomy v2 — predictive and adaptive. Load forecasting via Temporal Fusion Transformers; anomaly detection (Isolation Forest + VAE ensemble) over the live metric stream; reinforcement-learning placement (PPO over a Digital Twin simulator) replacing the heuristic NSGA-II loop; federated learning for embedding training without exporting raw data.
Observability++, backup, and chaos. End-to-end OpenTelemetry tracing with span propagation through gRPC and raft, ClickHouse sink for high-cardinality telemetry, incremental backup with point-in-time restore from raft log + RocksDB checkpoint, an admin chaos-injection API for fault tolerance drills, and graceful drain on SIGTERM for zero-loss rolling deploys.
Developer experience & ecosystem. Native client SDKs for Python, Go, Java, and TypeScript; a rhizome-cli admin tool; a Kubernetes operator with CRDs and a Helm chart; migration importers from Neo4j Bolt, JanusGraph Gremlin, and Dgraph DQL.

Longer-horizon work — multi-region native deployments, hardware-accelerated analytics (CUDA / FPGA), federated cross-deployment queries, formal verification of the consensus layer, and confidential-computing integrations — lives in docs/roadmap.md.

📚 Documentation

Document	What's inside
`docs/architecture.md`	The full architectural tour — every module, every protocol.
`docs/transactions.md`	SSI + 2PC + TrueTime + deadlock detection in one place.
`docs/observability.md`	Prometheus metrics catalogue + Grafana dashboard guide.
`docs/benchmarks.md`	Reproducible benchmark methodology and result tables.
`docs/deployment.md`	Production deployment: sizing, networking, PKI, backup/restore.
`docs/api.md`	GraphQL schema + gRPC service catalogue, with error codes.
`docs/adr/`	Architectural Decision Records (ADR-001 through ADR-008).
`CHANGELOG.md`	Release-by-release changes.

🤝 Contributing

Hey! We're glad you're thinking about contributing to rhizome! Pick an issue labeled good first issue, ask any question you need on the tracker, and we'll guide you through.

Bug reports and pull requests are welcome on GitHub at https://github.com/maxbarsukov/rhizome.

Before opening a PR, please read CONTRIBUTING.md — it covers the development workflow, the test matrix you're expected to run locally, and the commit-message conventions we follow.

⚖ Code of Conduct

This project is intended to be a safe, welcoming space for collaboration. Everyone interacting in the rhizome project's codebases, issue trackers, chat rooms and mailing lists is expected to adhere to the Code of Conduct.

📫 Get in touch!

🐛 Found a bug? Open an issue!
💬 Want to discuss design or ask questions? Start a thread in GitHub Discussions.

🛡 Security

rhizome takes the security of distributed data infrastructure seriously. If you believe you have found a security vulnerability in this repository, please report it privately according to our security policy — do not open a public issue.

📖 Citing rhizome

If you use rhizome in academic work, please cite it via the metadata in CITATION.cff. A short form:

@software{rhizome,
  author  = {Barsukov, Max},
  title   = {rhizome: a distributed graph database with semantic sharding},
  year    = {2026},
  url     = {https://github.com/maxbarsukov/rhizome},
  version = {0.3.0}
}

🌐 Useful Links

Link	Description
raft.github.io/raft.pdf	The original Raft paper — In Search of an Understandable Consensus Algorithm.
research.google/pubs/spanner/	Spanner — TrueTime and external consistency at planet scale.
research.google/pubs/percolator/	Percolator — the 2PC variant we extend for cross-shard writes.
vldb.org/pvldb/vol10/p781-Wu.pdf	Wu et al. (VLDB 2017) — An empirical evaluation of in-memory MVCC.
doi.org/10.1145/1376616.1376690	Cahill, Fekete, Röhm (SIGMOD 2008) — Serializable Isolation for Snapshot Databases.
github.com/CWTSLeiden/networkanalysis	Reference implementation of the Leiden community-detection algorithm.
arxiv.org/abs/1603.09320	Malkov & Yashunin — HNSW: efficient and robust ANN search.
tikv.org/blog/	TiKV engineering blog — our reference for production raft-rs operation.

🪪 License

rhizome is available as open source under the terms of the MIT License.

Leave a star ⭐ if you find this project useful — it helps a lot.

An @maxbarsukov project · graduate work at ITMO University · 2026.

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.github		.github
bench		bench
control-plane		control-plane
crates		crates
deploy		deploy
docs		docs
proto/rhizome/v1		proto/rhizome/v1
tests		tests
.dockerignore		.dockerignore
.editorconfig		.editorconfig
.gitattributes		.gitattributes
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
rust-toolchain.toml		rust-toolchain.toml
tarpaulin.toml		tarpaulin.toml

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rhizome

What is rhizome?

Table of contents

✨ Highlights

🆚 Compared with the rest of the landscape

1. Engineering provenance & licensing

2. Distribution, replication and consistency

3. Data model, query language, vector & autonomy

🏗 Architecture

🚀 Getting Started

Pre-reqs

Quick start with Docker Compose

From source

🔎 Querying rhizome

GraphQL gateway

gRPC for power users

📡 Observability

✅ Testing

🎨 Linting

🗺 Roadmap

📚 Documentation

🤝 Contributing

⚖ Code of Conduct

📫 Get in touch!

🛡 Security

📖 Citing rhizome

🌐 Useful Links

🪪 License

About

Topics

Resources

License

Code of conduct

Contributing

Security policy

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Stars

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Sponsor this project

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