The graph database that queried 1 billion edges for $2.50
We loaded the entire PubMed corpus — every article published since 1966 — plus ClinicalTrials.gov, Reactome pathways, and DrugBank into one graph. Then we asked:
"What drugs are most tested in cancer clinical trials?"
MATCH (m:MeSHTerm)<-[:ANNOTATED_WITH]-(a:Article)
-[:REFERENCED_IN]->(t:ClinicalTrial)-[:TESTS]->(i:Intervention)
WHERE m.name = 'Neoplasms'
RETURN i.name, count(DISTINCT t) AS trials
ORDER BY trials DESC LIMIT 5| Drug | Trials |
|---|---|
| Placebo | 521 |
| Pembrolizumab | 137 |
| Carboplatin | 106 |
| Paclitaxel | 106 |
| Cyclophosphamide | 98 |
5.2 seconds. One query. Four databases. 74 million nodes. 1 billion edges. A single machine.
See all 100 benchmark queries →
A graph-vector database written in Rust. OpenCypher queries, Redis protocol, vector search, graph algorithms — one binary, no JVM, no GC pauses.
# Install and run (30 seconds)
git clone https://github.com/samyama-ai/samyama-graph && cd samyama-graph
cargo build --release
./target/release/samyama # RESP on :6379, HTTP on :8080# Connect with any Redis client
redis-cli -p 6379
GRAPH.QUERY mydb "CREATE (a:Person {name: 'Alice'})-[:KNOWS]->(b:Person {name: 'Bob'})"
GRAPH.QUERY mydb "MATCH (a)-[:KNOWS]->(b) RETURN a.name, b.name"If your data has relationships, you need a graph database. If your graph database can't handle a billion edges on a single machine, you need Samyama.
| What | How |
|---|---|
| 74M nodes, 1B edges | Loaded PubMed + ClinicalTrials.gov + Reactome + DrugBank on one r6a.8xlarge ($2.50 spot) |
| 96/100 queries pass | Point lookups, multi-hop traversals, cross-KG aggregations — all verified |
| Parallel everything | Rayon: PageRank 3.1x, LCC 9.1x, Triangle Count 6x. Parallel scan, filter, compaction |
| 975 QPS concurrent | 16-client read workload, p99 < 25ms, zero errors across 67K queries |
| LDBC certified | SNB Interactive 21/21, FinBench 40/40, Graphalytics 12/12 |
Cypher queries — ~90% OpenCypher. MATCH, CREATE, MERGE, aggregations, path finding, 30+ functions.
MATCH (a:Person)-[:KNOWS*1..3]->(b:Person)
WHERE a.name = 'Alice'
RETURN b.name, length(shortestPath(a, b))Graph algorithms — PageRank, WCC, SCC, BFS, Dijkstra, LCC, CDLP, Triangle Count. All rayon-parallelized.
CALL pagerank('social') YIELD nodeId, score
RETURN nodeId, score ORDER BY score DESC LIMIT 10Vector search — HNSW indexing for semantic search and Graph RAG.
CREATE VECTOR INDEX ON :Paper(embedding) OPTIONS {dimensions: 384, similarity: 'cosine'}
CALL vector.search('Paper', 'embedding', [0.1, 0.2, ...], 10) YIELD node, scoreNatural language — Ask questions in English. The LLM translates to Cypher.
NLQ "Who are Alice's friends of friends that work at Google?"
→ MATCH (a:Person {name:'Alice'})-[:KNOWS]->()-[:KNOWS]->(fof)-[:WORKS_AT]->(c:Company {name:'Google'}) RETURN fof.name
AI agents — Auto-generated MCP servers from your graph schema.
pip install samyama[mcp]
samyama-mcp-serve --demo cricket # Instant AI agent tools for any graph| KG | Source | Nodes | Edges |
|---|---|---|---|
| PubMed/MEDLINE | NLM | 66.2M | 1.04B |
| Clinical Trials | ClinicalTrials.gov | 7.8M | 27M |
| Pathways | Reactome | 119K | 835K |
| Drug Interactions | DrugBank + ChEMBL + SIDER | 245K | 388K |
Loaded in 31 minutes from snapshots. 96 of 100 queries return real data across all four KGs. Full results →
| Query | Time | Result |
|---|---|---|
| Cancer → Trial interventions | 5.2s | Pembrolizumab #1 (137 trials) |
| Diabetes → Trial interventions | 2.4s | Metformin #1 (70 trials) |
| Metformin → Trial adverse events | 2.1s | Diarrhoea (185 trials) — known side effect confirmed |
| Cancer trial sites by country | 3.8s | US 4,062 · China 1,170 · France 827 |
| NCI-funded → Trial drugs | 19.4s | Cyclophosphamide (517) · Radiation (362) |
| Aspirin articles → Trials | 1.5s | NCT00000491 "Aspirin MI study" |
| Benchmark | Pass Rate | Dataset |
|---|---|---|
| SNB Interactive | 21/21 (100%) | SF1: 3.18M nodes, 17.26M edges |
| SNB BI | 16/16 (100%) | SF1 |
| Graphalytics | 12/12 (100%) | XS reference graphs |
| FinBench | 40/40 (100%) | 7.7K nodes, 42.2K edges |
| Workload | 1 client | 16 clients | Scaling |
|---|---|---|---|
| Pure read | 145 QPS | 975 QPS | 6.7x |
| Mixed 80/20 | 181 QPS | 722 QPS | 4.0x |
| Write-heavy | 279 QPS | 482 QPS | 1.7x |
Cricket KG — 36K nodes, 1.4M edges, live graph simulation
Click for full demo (1:56)
| Domain | Command | Nodes | Edges |
|---|---|---|---|
| Banking & Fraud | cargo run --example banking_demo |
— | Fraud patterns, money laundering, OFAC |
| Clinical Trials | cargo run --example clinical_trials_demo |
— | Patient-trial matching, drug interactions |
| Supply Chain | cargo run --example supply_chain_demo |
— | Disruption analysis, port optimization |
| Manufacturing | cargo run --example smart_manufacturing_demo |
— | Digital twin, failure cascades |
| Social Network | cargo run --example social_network_demo |
— | Influence, communities, recommendations |
| Enterprise SOC | cargo run --example enterprise_soc_demo |
— | MITRE ATT&CK, attack paths, threat intel |
| Dataset | Command | Scale |
|---|---|---|
| LDBC SNB SF1 | cargo run --example ldbc_loader |
3.2M nodes, 17.3M edges |
| Clinical Trials | cargo run --release --example aact_loader |
7.8M nodes, 27M edges |
| Drug Interactions | cargo run --release --example druginteractions_loader |
245K nodes, 388K edges |
| Cricket | cargo run --release --example cricket_loader |
36K nodes, 1.4M edges |
| FinBench | cargo run --example finbench_loader |
7.7K nodes, 42K edges |
samyama-graph is the engine. Per-domain KGs and companion projects live separately and can be loaded into it:
- KGs: pubmed-kg (66M / 1B), clinicaltrials-kg (7.8M / 27M), druginteractions-kg (245K / 388K), pathways-kg (119K / 835K), cricket-kg (36K / 1.4M), assetops-kg (13K / 13K)
- Benchmarks: biomedqa — 40-question pharmacology benchmark across three KGs
- Companions: graphrag-rs — doc-to-KG + MCP server; optimization_algorithms — PyPI
rao-algorithmspackage (PyO3 bindings overcrates/samyama-optimization/)
samyama
├── graph/ Property graph model (Node, Edge, GraphStore, CSR adjacency)
├── query/ OpenCypher engine
│ ├── cypher.pest PEG grammar
│ ├── executor/ Volcano iterator + WCO LeapFrog TrieJoin
│ └── planner.rs Cost-based graph-native query planner
├── protocol/ RESP3 server (Redis-compatible, Tokio async)
├── persistence/ RocksDB + WAL + multi-tenancy
├── vector/ HNSW vector index
├── snapshot/ Portable .sgsnap v2 (CSR + ColumnStore)
├── raft/ Distributed consensus (openraft)
└── nlq/ Natural language → Cypher (OpenAI, Gemini, Ollama, Claude)
Companion crates:
- samyama-graph-algorithms — PageRank, BFS, Dijkstra, WCC, SCC, LCC, CDLP, Triangle Count (all rayon-parallelized)
- samyama-optimization — 15+ metaheuristic solvers (Jaya, Rao, GWO, NSGA-II, TLBO)
- samyama-sdk — Rust SDK with embedded and remote clients
| Resource | Link |
|---|---|
| The Book | graph.samyama.cloud/book |
| Biomedical Benchmark | 100 queries, 96 pass |
| Cypher Compatibility | docs/CYPHER_COMPATIBILITY.md |
| LDBC Results | docs/ldbc/ |
| Architecture Decisions | docs/ADR/ |
| API Spec | api/openapi.yaml |
Everything above is open source (Apache 2.0). Samyama Enterprise adds:
- GPU acceleration (wgpu + CUDA)
- OpenTelemetry OTLP metrics
- Prometheus + Grafana monitoring
- Backup & disaster recovery
- ADMIN commands + audit trail
- Ed25519 signed license tokens
Apache License 2.0 — use it in production, contribute back if you'd like.
Samyama (Sanskrit: संयम) — the union of focused query, sustained analysis, and unified insight.