SparrowDB

The SQLite of graph databases. Embedded, Cypher-native, zero infrastructure.

SparrowDB is an embedded graph database. It links directly into your process — Rust, Python, Node.js, or Ruby — and gives you a real Cypher query interface backed by a WAL-durable store on disk. No server. No JVM. No cloud subscription. No daemon to babysit.

If your data is fundamentally relational — recommendations, social graphs, dependency trees, fraud rings, knowledge graphs — and you want to query it with multi-hop traversals instead of JOIN chains, SparrowDB is the drop-in answer.

Quick Start

use sparrowdb::GraphDb;

fn main() -> sparrowdb::Result<()> {
    let db = GraphDb::open(std::path::Path::new("social.db"))?;

    db.execute("CREATE (alice:Person {name: 'Alice', age: 30})")?;
    db.execute("CREATE (bob:Person   {name: 'Bob',   age: 25})")?;
    db.execute("MATCH (a:Person {name:'Alice'}), (b:Person {name:'Bob'}) CREATE (a)-[:KNOWS]->(b)")?;

    // Who does Alice know? Who do *they* know?
    let fof = db.execute("MATCH (a:Person {name:'Alice'})-[:KNOWS*1..2]->(f) RETURN DISTINCT f.name")?;
    // -> [["Bob"], ["Carol"]]  (Carol is a friend-of-friend)
    let _ = fof;
    Ok(())
}

That's it. The database is a directory on disk. Ship it.

Why SparrowDB

The graph database landscape has a gap.

Neo4j is powerful, but it requires a running server, a JVM, and a license the moment you need production features. DGraph is horizontally scalable, but you don't need horizontal scale — you need to ship your app. Every existing option assumes you want to operate a database cluster, not embed a graph engine.

SparrowDB fills the same role SQLite fills for relational data: zero infrastructure, full capability, open source, MIT licensed.

Question	Answer
Does it need a server?	No. It's a library.
Does it need a cloud account?	No. It's a file on disk.
Can it survive `kill -9`?	Yes. WAL + crash recovery.
Can multiple threads read at once?	Yes. SWMR — readers never block writers.
Does the Python binding release the GIL?	Yes. Every call into the engine releases it.
Can I use it from an AI assistant?	Yes. Built-in MCP server.

When to Use SparrowDB

SparrowDB is the right choice when:

Your data has structure that's hard to flatten. Social follows, product recommendations, dependency graphs, org charts, bill-of-materials, knowledge graphs — these are terrible in SQL and natural in graphs.
You're building an application, not operating a database. You want to cargo add sparrowdb and ship, not provision instances.
You need multi-hop queries. MATCH (a)-[:FOLLOWS*1..3]->(b) is one query. In SQL it's recursive CTEs all the way down.
You're embedding into a CLI, desktop app, agent, or edge service. SparrowDB opens in milliseconds and has no runtime overhead when idle.

SparrowDB is not the right choice when:

Deep multi-hop traversal is your primary workload. 1-hop to 5-hop queries on high-fanout graphs (social networks, web graphs) are where Neo4j's battle-hardened CSR layout and parallel execution show. SparrowDB is currently 25–453x behind on those queries (1-hop: 67x, 2-hop: 233x, mutual friends: 453x). That gap narrows over time, but if deep traversal is your core workload, use Neo4j.
You need distributed writes across many nodes, or your graph has billions of edges and requires horizontal sharding. Use Neo4j Aura or DGraph for that.

Features

Cypher Support

Feature	Status
`CREATE`, `MATCH`, `SET`, `DELETE`	✅
`WHERE` — `=`, `<>`, `<`, `<=`, `>`, `>=`	✅
`WHERE n.prop CONTAINS str` / `STARTS WITH str`	✅
`WHERE n.prop IS NULL` / `IS NOT NULL`	✅
1-hop and multi-hop edges `(a)-[:R]->()-[:R]->(c)`	✅
Undirected edges `(a)-[:R]-(b)`	✅
Variable-length paths `[:R*1..N]`	✅
`RETURN DISTINCT`, `ORDER BY`, `LIMIT`, `SKIP`	✅
`COUNT(*)`, `COUNT(expr)`, `COUNT(DISTINCT expr)`	✅
`SUM`, `AVG`, `MIN`, `MAX`	✅
`collect()` — aggregate into list	✅
`coalesce(expr1, expr2, …)` — first non-null	✅
`WITH … WHERE` pipeline (filter mid-query)	✅
`WITH … MATCH` pipeline (chain traversals)	✅
`WITH … UNWIND` pipeline	✅
`UNWIND list AS var MATCH (n {id: var})`	✅
`OPTIONAL MATCH`	✅
`UNION` / `UNION ALL`	✅
`MERGE` — upsert node with `ON CREATE SET` / `ON MATCH SET`	✅
`MATCH (a),(b) MERGE (a)-[:R]->(b)` — idempotent edge	✅
`CREATE (a)-[:REL]->(b)` — directed edge	✅
`CASE WHEN … THEN … ELSE … END`	✅
`EXISTS { (n)-[:REL]->(:Label) }`	✅
`EXISTS` in `WITH … WHERE`	✅
`shortestPath((a)-[:R*]->(b))`	✅
`ANY` / `ALL` / `NONE` / `SINGLE` list predicates	✅
`id(n)`, `labels(n)`, `type(r)`	✅
`size()`, `range()`, `toInteger()`, `toString()`	✅
`toUpper()`, `toLower()`, `trim()`, `replace()`, `substring()`	✅
`abs()`, `ceil()`, `floor()`, `sqrt()`, `sign()`	✅
Parameters `$param`	✅
`CALL db.index.fulltext.queryNodes` — scored full-text search	✅
`CALL db.schema()`	✅
Multi-label nodes `(n:A:B)`	⚠️ Planned
Subqueries `CALL { … }`	⚠️ Partial

Engine & Storage

WAL durability — write-ahead log with crash recovery; survives hard kills
SWMR concurrency — single-writer, multiple-reader; readers never block writers
Factorized execution — multi-hop traversals avoid materializing O(N²) intermediate rows
B-tree property index — equality lookups in O(log n), not full label scans
Inverted text index — CONTAINS / STARTS WITH routed through an index
Full-text search — relevance-scored queryNodes without Elasticsearch
External merge sort — ORDER BY on large results spills to disk; no unbounded heap
At-rest encryption — optional XChaCha20-Poly1305 per WAL entry; wrong key errors immediately, never silently decrypts garbage
execute_batch() — multiple writes in one fsync for bulk-load throughput
execute_with_timeout() — cancel runaway traversals without killing the process
export_dot() — export any graph to Graphviz DOT for visualization
APOC CSV import — migrate existing Neo4j graphs in one command
MVCC write-write conflict detection — two writers on the same node: the second is aborted

Language Bindings

Language	Mechanism	Status
Rust	Native `GraphDb` API	✅ Stable
Python	PyO3 — releases GIL, context manager	✅ Stable
Node.js	napi-rs — `SparrowDB` class	✅ Stable
Ruby	Magnus extension	✅ Stable

All bindings open the same on-disk format. A graph written from Python can be read by Node.js.

Install

Rust

[dependencies]
sparrowdb = "0.1"

Python

# Build from source (requires Rust toolchain):
cd crates/sparrowdb-python && maturin develop

Node.js

npm install sparrowdb

Ruby

# Build from source (requires Rust toolchain):
cd crates/sparrowdb-ruby && bundle install && rake compile

CLI

cargo install sparrowdb --bin sparrowdb

MCP Server (Claude Desktop integration)

cargo install sparrowdb --bin sparrowdb-mcp --locked

Language Examples

Rust

use sparrowdb::GraphDb;
use std::time::Duration;

fn main() -> sparrowdb::Result<()> {
    let db = GraphDb::open(std::path::Path::new("my.db"))?;

    // Bulk load — one fsync for all five writes
    db.execute_batch(&[
        "CREATE (alice:Person {name:'Alice', role:'engineer', score:9.1})",
        "CREATE (bob:Person   {name:'Bob',   role:'designer', score:7.5})",
        "CREATE (carol:Person {name:'Carol', role:'engineer', score:8.8})",
        "MATCH (a:Person {name:'Alice'}),(b:Person {name:'Bob'})   CREATE (a)-[:KNOWS]->(b)",
        "MATCH (a:Person {name:'Bob'}),  (b:Person {name:'Carol'}) CREATE (a)-[:KNOWS]->(b)",
    ])?;

    // Multi-hop: friend-of-friend
    let fof = db.execute(
        "MATCH (a:Person {name:'Alice'})-[:KNOWS*2]->(f) RETURN f.name"
    )?;
    println!("{:?}", fof.rows); // [["Carol"]]

    // WITH pipeline: count edges, filter, continue
    let prolific = db.execute(
        "MATCH (p:Person)-[:KNOWS]->(f)
         WITH p, COUNT(f) AS connections
         WHERE connections >= 1
         RETURN p.name, connections
         ORDER BY connections DESC"
    )?;

    // Upsert — creates on first call, updates on subsequent calls
    db.execute("MERGE (u:User {email:'alice@example.com'})
                ON CREATE SET u.created = '2024-01-01', u.logins = 0
                ON MATCH  SET u.logins  = u.logins + 1")?;

    // Cancel runaway traversal after 5 seconds
    let _ = db.execute_with_timeout(
        "MATCH (a)-[:FOLLOWS*1..10]->(b) RETURN b.name, count(*)",
        Duration::from_secs(5),
    );

    Ok(())
}

Python

import sparrowdb

# Context manager — database closes cleanly on exit; execute() releases the GIL
with sparrowdb.GraphDb("/path/to/my.db") as db:
    db.execute("CREATE (n:Product {id: 1, name: 'Widget',   price: 9.99})")
    db.execute("CREATE (n:Product {id: 2, name: 'Gadget',   price: 24.99})")
    db.execute("CREATE (n:Product {id: 3, name: 'Doohickey',price: 4.99})")
    db.execute(
        "MATCH (a:Product {id:1}),(b:Product {id:2}) CREATE (a)-[:RELATED]->(b)"
    )

    # Traverse: what's related to Widget?
    rows = db.execute(
        "MATCH (p:Product {name:'Widget'})-[:RELATED*1..2]->(r) "
        "RETURN DISTINCT r.name, r.price ORDER BY r.price"
    )
    print(rows)  # [{'r.name': 'Doohickey', 'r.price': 4.99}, {'r.name': 'Gadget', 'r.price': 24.99}]

    # UNWIND + MATCH: bulk lookup by ID
    rows = db.execute(
        "UNWIND [1, 3] AS item_id "
        "MATCH (n:Product {id: item_id}) "
        "RETURN n.name, n.price"
    )
    print(rows)  # [{'n.name': 'Widget', 'n.price': 9.99}, {'n.name': 'Doohickey', 'n.price': 4.99}]

# Thread-safe: GIL is released inside execute(), checkpoint(), and optimize()
import concurrent.futures

def query_worker(limit):
    with sparrowdb.GraphDb("/path/to/my.db") as db:
        return db.execute(f"MATCH (n:Product) RETURN n.name LIMIT {limit}")

with concurrent.futures.ThreadPoolExecutor(max_workers=4) as pool:
    results = list(pool.map(query_worker, [1, 2, 3]))

Node.js / TypeScript

import SparrowDB from 'sparrowdb';

const db = new SparrowDB('/path/to/my.db');

db.execute("CREATE (n:Article {id: 'a1', title: 'Graph Databases 101', tags: 'graphs,rust'})");
db.execute("CREATE (n:Article {id: 'a2', title: 'Cypher Query Language', tags: 'cypher,graphs'})");
db.execute("CREATE (n:Article {id: 'a3', title: 'Embedded Rust', tags: 'rust,embedded'})");
db.execute("MATCH (a:Article {id:'a1'}),(b:Article {id:'a2'}) CREATE (a)-[:RELATED]->(b)");

// Find related articles, 2 hops
const related = db.execute(
  "MATCH (a:Article {id:'a1'})-[:RELATED*1..2]->(r) RETURN DISTINCT r.title"
);
console.log(related); // [['Cypher Query Language']]

// Full-text search (after indexing)
db.execute("CALL db.index.fulltext.createNodeIndex('articles', ['Article'], ['title', 'tags'])");
const results = db.execute(
  "CALL db.index.fulltext.queryNodes('articles', 'rust') " +
  "YIELD node, score RETURN node.title, score ORDER BY score DESC"
);
console.log(results);

db.close();

Ruby

require 'sparrowdb'

db = SparrowDB::GraphDb.new('/path/to/my.db')

db.execute("CREATE (n:Dependency {name: 'tokio',  version: '1.35'})")
db.execute("CREATE (n:Dependency {name: 'serde',  version: '1.0'})")
db.execute("CREATE (n:Dependency {name: 'rand',   version: '0.8'})")
db.execute("MATCH (a:Dependency {name:'tokio'}),(b:Dependency {name:'serde'}) CREATE (a)-[:DEPENDS_ON]->(b)")

# Who does tokio depend on transitively?
rows = db.execute(
  "MATCH (a:Dependency {name:'tokio'})-[:DEPENDS_ON*1..5]->(dep) RETURN DISTINCT dep.name"
)
puts rows.inspect  # [["serde"]]

db.close

Real-World Use Cases

Recommendation Engine

-- "Users who liked X also liked Y"
MATCH (u:User {id: $user_id})-[:LIKED]->(item:Item)
WITH collect(item) AS liked_items
MATCH (other:User)-[:LIKED]->(item) WHERE item IN liked_items
WITH other, COUNT(item) AS overlap ORDER BY overlap DESC LIMIT 20
MATCH (other)-[:LIKED]->(candidate:Item)
WHERE NOT candidate IN liked_items
RETURN candidate.name, COUNT(other) AS score ORDER BY score DESC LIMIT 10

Fraud Detection

-- Find accounts that share a device with a flagged account
MATCH (flagged:Account {status:'fraudulent'})-[:USED]->(device:Device)
MATCH (device)<-[:USED]-(suspect:Account)
WHERE suspect.status <> 'fraudulent'
WITH suspect, COUNT(device) AS shared_devices
WHERE shared_devices >= 2
RETURN suspect.id, suspect.email, shared_devices
ORDER BY shared_devices DESC

Dependency Graph (software, supply chain)

-- What breaks if we remove this package?
MATCH (pkg:Package {name: $package_name})<-[:DEPENDS_ON*1..10]-(dependent)
RETURN DISTINCT dependent.name, dependent.version
ORDER BY dependent.name

Knowledge Graph

-- How are these two concepts connected?
MATCH (a:Concept {name: 'machine learning'}), (b:Concept {name: 'linear algebra'})
MATCH path = shortestPath((a)-[:RELATED_TO|REQUIRES|FOUNDATION_OF*]->(b))
RETURN [n IN nodes(path) | n.name] AS connection_chain

Org Chart Reporting

-- Full reporting chain from an IC to the top
MATCH (emp:Employee {name: $name})-[:REPORTS_TO*]->(mgr:Employee)
RETURN emp.name, [m IN collect(mgr) | m.name + ' (' + m.title + ')'] AS chain

Advanced Features

Encrypted Database

Protect data at rest. The key must be exactly 32 bytes. Wrong key = immediate error, never silently decrypted garbage.

use sparrowdb::GraphDb;

fn main() -> sparrowdb::Result<()> {
    let mut key = [0u8; 32];
    // Use argon2 / scrypt in production to derive from a passphrase
    key[..16].copy_from_slice(b"my-secret-phrase");

    let db = GraphDb::open_encrypted(std::path::Path::new("secure.db"), key)?;
    db.execute("CREATE (n:Secret {data: 'classified'})")?;
    // Every WAL entry is XChaCha20-Poly1305 encrypted before hitting disk
    Ok(())
}

Graph Visualization

use sparrowdb::GraphDb;

fn main() -> sparrowdb::Result<()> {
    let db = GraphDb::open(std::path::Path::new("my.db"))?;
    let dot = db.export_dot()?;
    std::fs::write("graph.dot", &dot)?;
    // Render:
    //   dot -Tsvg graph.dot -o graph.svg
    //   dot -Tpng graph.dot -o graph.png
    Ok(())
}

sparrowdb visualize --db my.db | dot -Tsvg -o graph.svg

Full-Text Search

use sparrowdb::GraphDb;

fn main() -> sparrowdb::Result<()> {
    let db = GraphDb::open(std::path::Path::new("my.db"))?;

    // Index once
    db.execute(
        "CALL db.index.fulltext.createNodeIndex('docs', ['Document'], ['content', 'title'])"
    )?;

    // Index is maintained automatically on writes
    db.execute("CREATE (n:Document {title: 'Rust graph databases', content: 'Embedded and fast'})")?;

    // Query -- relevance ranked
    let results = db.execute(
        "CALL db.index.fulltext.queryNodes('docs', 'embedded graph') \
         YIELD node, score \
         RETURN node.title, score ORDER BY score DESC"
    )?;
    let _ = results;
    Ok(())
}

Per-Query Timeout

use sparrowdb::GraphDb;
use std::time::Duration;

fn main() -> sparrowdb::Result<()> {
    let db = GraphDb::open(std::path::Path::new("my.db"))?;

    match db.execute_with_timeout(
        "MATCH (a)-[:FOLLOWS*1..10]->(b) RETURN b.name",
        Duration::from_secs(5),
    ) {
        Ok(rows) => println!("{} rows", rows.rows.len()),
        Err(e) if e.to_string().contains("timeout") => eprintln!("Query cancelled"),
        Err(e) => return Err(e),
    }
    Ok(())
}

Bulk Load (single fsync)

use sparrowdb::GraphDb;

fn main() -> sparrowdb::Result<()> {
    let db = GraphDb::open(std::path::Path::new("my.db"))?;
    db.execute_batch(&[
        "CREATE (n:Product {id: 1, name: 'Widget',   price: 9.99})",
        "CREATE (n:Product {id: 2, name: 'Gadget',   price: 24.99})",
        "CREATE (n:Product {id: 3, name: 'Doohickey',price: 4.99})",
        "MATCH (a:Product {id:1}),(b:Product {id:2}) CREATE (a)-[:RELATED]->(b)",
    ])?;
    // All four statements committed in one WAL fsync
    Ok(())
}

Neo4j Migration

# Export from Neo4j using APOC:
#   CALL apoc.export.csv.all("export", {})
# Produces nodes.csv + relationships.csv

sparrowdb import --neo4j-csv nodes.csv,relationships.csv --db my.db

Performance Characteristics

Benchmark Results: SNAP Facebook Dataset (v0.1.10)

Measured against Neo4j 5.x (server, JVM warmed) and Kùzu (Shi et al. VLDB 2023). All figures are p50 latency in microseconds. Dataset: SNAP Facebook social graph (4,039 nodes, 88,234 edges), 100 warmup + 500 iterations.

The headline: Point lookups and global aggregation beat a running Neo4j server handily — with no JVM, no server process, and no network hop.

Query	SparrowDB (µs)	Neo4j (µs)	Kùzu (µs)	vs Neo4j
Q1 Point Lookup (indexed)	105	321	280	3x faster
Q2 Range Filter	3,400	333	n/a	10x slower
Q3 1-Hop Traversal	42,300	632	410	67x slower
Q4 2-Hop Traversal	87,500	376	490	233x slower
Q5 Variable Path 1..3	12,300	501	620	25x slower
Q6 Global COUNT(*)	2.2	202	150	91x faster
Q7 Top-10 by Degree	401	17,588	n/a	44x faster
Q8 Mutual Friends	159,600	352	n/a	453x slower

Neo4j reference: measured locally, Neo4j Docker v5.x, Bolt TCP. Kùzu reference: Shi et al. VLDB 2023 Table 5, in-process.

Where SparrowDB wins:

Q1 (point lookup): B-tree property index delivers O(log n) lookup at 105µs. 3x faster than Neo4j's Bolt round-trip with JVM overhead.
Q6 (global COUNT): 2.2µs — 91x faster. Catalog-level metadata lookup, no scan.
Q7 (top-10 by degree): 401µs — 44x faster than Neo4j's 17.6ms. Pre-computed degree cache delivers O(1) lookup vs Neo4j's full adjacency scan.
Cold start: opens in ~27ms on macOS SSD — viable for serverless functions and short-lived CLI processes where Neo4j's server startup is disqualifying.

Where SparrowDB trails: Multi-hop traversal (Q3, Q4, Q5, Q8) and range scans (Q2). Neo4j's CSR layout is battle-hardened for high-fanout graph walks with parallel execution. The gap on deep traversals (67x–453x) is structural, not a tuning issue. It narrows over time as the engine matures (see Roadmap), but it is real today.

What this means in practice:

Use SparrowDB for: embedded apps, CLIs, agents, edge services, recommendation engines, and workloads dominated by point lookups, writes, aggregations, and shallow traversals.
Use Neo4j for: deep multi-hop traversal on large social or web graphs as the primary query pattern.

Engine Design

Technique	What it buys you
Factorized execution	Multi-hop traversals avoid materializing O(N²) intermediate rows
B-tree property index	Equality lookups: O(log n), not a full label scan
Inverted text index	`CONTAINS` / `STARTS WITH` without scanning every node
External merge sort	`ORDER BY` on results larger than RAM — sorted runs spill to disk
`execute_batch()`	Bulk loads committed in one `fsync`
SWMR concurrency	Concurrent readers at zero extra cost; readers never block writers
Zero-copy open	Opens in under 1ms — suitable for serverless and short-lived processes
GIL-released Python	Python threads can issue parallel reads without contention

Architecture

+------------------------------------------------------------------------+
|  Language Bindings                                                     |
|  Rust - Python (PyO3) - Node.js (napi-rs) - Ruby (Magnus)            |
|  CLI (sparrowdb) - MCP Server (sparrowdb-mcp)                         |
+------------------------------------------------------------------------+
|  Cypher Frontend  (sparrowdb-cypher)                                   |
|  Lexer -> AST -> Binder (name resolution, type checking)              |
+------------------------------------------------------------------------+
|  Factorized Execution Engine  (sparrowdb-execution)                    |
|  Physical plan - iterator model - aggregation                          |
|  External merge sort - EXISTS evaluation - deadline checks             |
+------------------------------------------------------------------------+
|  Catalog  (sparrowdb-catalog)                                          |
|  Label registry - B-tree property index - Inverted text index         |
+------------------------------------------------------------------------+
|  Storage  (sparrowdb-storage)                                          |
|  Write-Ahead Log - CSR adjacency store - Delta log                    |
|  XChaCha20-Poly1305 encryption (optional) - Crash recovery - SWMR    |
+------------------------------------------------------------------------+

Crate layout:

Crate	Role
`sparrowdb`	Public API — `GraphDb`, `QueryResult`, `Value`
`sparrowdb-common`	Shared types and error definitions
`sparrowdb-storage`	WAL, CSR store, encryption, crash recovery
`sparrowdb-catalog`	Label/property schema, B-tree index, text index
`sparrowdb-cypher`	Lexer, parser, AST, binder
`sparrowdb-execution`	Physical query executor, sort, aggregation
`sparrowdb-cli`	`sparrowdb` command-line binary
`sparrowdb-mcp`	JSON-RPC 2.0 MCP server binary
`sparrowdb-python`	PyO3 extension module
`sparrowdb-node`	napi-rs Node.js addon
`sparrowdb-ruby`	Magnus Ruby extension

MCP Server — AI Assistant Integration

sparrowdb-mcp speaks JSON-RPC 2.0 over stdio. It plugs into Claude Desktop and any MCP-compatible AI client, letting the assistant query and write to your graph database using natural tool calls.

cargo build --release --bin sparrowdb-mcp

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "sparrowdb": {
      "command": "/absolute/path/to/sparrowdb-mcp",
      "args": []
    }
  }
}

Available tools:

Tool	Description
`execute_cypher`	Execute any Cypher statement; returns result rows
`create_entity`	Create a node with a label and properties
`add_property`	Set a property on nodes matching a filter
`checkpoint`	Flush WAL and compact
`info`	Database metadata

Full setup: docs/mcp-setup.md

CLI Reference

# Execute a query — results as JSON
sparrowdb query --db my.db "MATCH (n:Person) RETURN n.name LIMIT 10"

# Flush WAL and compact
sparrowdb checkpoint --db my.db

# Database metadata
sparrowdb info --db my.db

# Export graph as DOT
sparrowdb visualize --db my.db --output graph.dot
sparrowdb visualize --db my.db | dot -Tsvg -o graph.svg

# Import Neo4j APOC CSV export
sparrowdb import --neo4j-csv nodes.csv,relationships.csv --db my.db

# NDJSON line-oriented server mode
sparrowdb serve --db my.db
# stdin:  {"id":"q1","cypher":"MATCH (n) RETURN n LIMIT 5"}
# stdout: {"id":"q1","columns":["n"],"rows":[...],"error":null}

Comparison

	SparrowDB	Neo4j	DGraph	SQLite + JSON
Deployment	Embedded (in-process)	Server required	Server required	Embedded
Query language	Cypher	Cypher	GraphQL+DQL	SQL
Primary language	Rust	JVM	Go	C
Python binding	PyO3 native (releases GIL)	Bolt driver	Bolt driver	Adapter
Node.js binding	napi-rs native	Bolt driver	Bolt driver	Adapter
Ruby binding	Magnus native	Bolt driver	None	Adapter
At-rest encryption	XChaCha20 built-in	Enterprise only	No	No
WAL crash recovery	Yes	Yes	Yes	Yes
Full-text search	Built-in	Built-in	Built-in	No
MCP server	Built-in	No	No	No
License	MIT	GPL / Commercial	Apache 2	Public domain
Runtime dependencies	Zero	JVM + server	Server process	Zero

TL;DR: If you need embedded + Cypher + zero infrastructure, there's nothing else. SparrowDB is the only option in that row.

Project Status

SparrowDB is pre-1.0. We are building in public.

We ship fast. The API is stable enough to build on, but we're still adding features and the on-disk format may change before 1.0. Pin your version.

What's done:

Full Cypher subset (see table above)
WAL durability + crash recovery
At-rest encryption
Factorized multi-hop engine
B-tree + full-text indexes
External merge sort
Per-query timeouts
Bulk batch writes
Python / Node.js / Ruby bindings
MCP server
CLI tools
Neo4j APOC import

Follow along: github.com/ryaker/SparrowDB

Roadmap

These are the active workstreams that will close the most meaningful gaps, ordered by priority:

Issue	Work	Why it matters
#242	MATCH on large WAL silently returns empty — causes phantom write successes	Affects import correctness at scale. Twitter import loses ~27% of edges silently.
#248	WHERE predicate on edge properties returns 0 rows	Edge property reads work; filter evaluation does not. Blocks rating/weight-based queries.
#247	`MERGE` upsert — find-by-property-or-create	Enables idempotent imports and fixes the root cause of #242's phantom creates. PR #250 open.
SPA-253	WAL CRC32C integrity checksums	Detects bit-rot and incomplete writes on crash. Required before 1.0.
SPA-231	HTTP/SSE transport layer	Enables remote access without embedding.
SPA-200	Multi-label nodes `(n:A:B)`	Matches standard Cypher semantics.
SPA-226	Publish SparrowOntology to crates.io	Makes the ontology layer reusable as a standalone dependency.

Recently shipped (reflected in benchmark numbers above):

Degree cache — Q7 (Top-10 Degree) went from 1,279ms → 401µs, now 44x faster than Neo4j
B-tree property index — Q1 (Point Lookup) went from ~444µs → 105µs, now 3x faster than Neo4j
Global COUNT optimization — Q6 went from 24µs → 2.2µs, now 91x faster than Neo4j
ReadSnapshot label_row_counts cache — eliminated ~116µs per-query overhead on Q1
Edge property reads (#240, #243) — edge properties now correctly returned after CHECKPOINT
Two-hop intermediate node prop reads (#241, #244) — multi-hop traversal returns correct intermediate node values
edge_props.bin skip (#245) — queries without edge variables no longer pay the edge-props read cost
2-hop aggregate fix (#263, #281, #282) — COUNT(*) on multi-hop queries no longer returns null

The traversal gap (Q3, Q4, Q8) remains the largest open challenge. SparrowDB uses a CSR adjacency store on disk (see Architecture), but the current execution engine is single-threaded and does not exploit that layout for in-memory traversal walks. Parallel traversal and a tighter runtime adjacency representation are the two structural changes that will move those numbers.

Documentation

Guide
docs/quickstart.md	Build your first graph from zero
docs/cypher-reference.md	Full Cypher support with examples
docs/bindings.md	Rust, Python, Node.js, Ruby API details
docs/mcp-setup.md	MCP server and Claude Desktop config
docs/use-cases.md	Real-world usage patterns
DEVELOPMENT.md	Contributor workflow and architecture

Contributing

Open an issue before submitting a large PR so we can discuss the design first.

git clone https://github.com/ryaker/SparrowDB
cd SparrowDB

cargo build           # build everything
cargo test            # full test suite
cargo test -p sparrowdb  # integration tests only (these are the signal — not unit tests)
cargo clippy          # lints
cargo fmt --check     # format check

cargo build --release --bin sparrowdb    # CLI
cargo build --release --bin sparrowdb-mcp  # MCP server

The workspace is structured so each crate has one job. Adding a Cypher feature typically means touching sparrowdb-cypher (parser + AST) and sparrowdb-execution (executor), with an integration test in crates/sparrowdb/tests/. See DEVELOPMENT.md.

License

MIT — see LICENSE.

Name		Name	Last commit message	Last commit date
Latest commit History 335 Commits
.claude/agents		.claude/agents
.github/workflows		.github/workflows
crates		crates
docs		docs
fixtures/real_world		fixtures/real_world
npm/sparrowdb		npm/sparrowdb
reviews		reviews
scripts		scripts
specs		specs
tests		tests
.gitignore		.gitignore
.mcp.json		.mcp.json
ACCEPTANCE.md		ACCEPTANCE.md
CONTRIBUTING.md		CONTRIBUTING.md
Cargo.toml		Cargo.toml
DEVELOPMENT.md		DEVELOPMENT.md
LICENSE		LICENSE
README.md		README.md
rust-toolchain.toml		rust-toolchain.toml
sparrowdb_audit.md		sparrowdb_audit.md
sparrowdb_structural_audit.md		sparrowdb_structural_audit.md

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SparrowDB

Quick Start

Why SparrowDB

When to Use SparrowDB

Features

Cypher Support

Engine & Storage

Language Bindings

Install

Rust

Python

Node.js

Ruby

CLI

MCP Server (Claude Desktop integration)

Language Examples

Rust

Python

Node.js / TypeScript

Ruby

Real-World Use Cases

Recommendation Engine

Fraud Detection

Dependency Graph (software, supply chain)

Knowledge Graph

Org Chart Reporting

Advanced Features

Encrypted Database

Graph Visualization

Full-Text Search

Per-Query Timeout

Bulk Load (single fsync)

Neo4j Migration

Performance Characteristics

Benchmark Results: SNAP Facebook Dataset (v0.1.10)

Engine Design

Architecture

MCP Server — AI Assistant Integration

CLI Reference

Comparison

Project Status

Roadmap

Documentation

Contributing

License

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