Python bindings for ArcadeDB - A multi-model database supporting Graph, Document, Key/Value, Vector, and Time Series models with extreme performance.

📖 Documentation | 🐛 Issues

# Temporarily install from GitHub Pages (awaiting PyPI size limit approval)
pip install arcadedb-embedded \
  --index-url https://humemai.github.io/arcadedb-embedded-python/simple/ \
  --extra-index-url https://pypi.org/simple/

# Once PyPI approves our size limit request, installation will be simpler:
# pip install arcadedb-embedded

Note: We're temporarily using GitHub Pages while awaiting PyPI size limit approval (~160MB wheels, default limit 100MB). The --extra-index-url ensures dependencies like JPype1 are installed from PyPI.

Requirements:

• Python 3.8+ only - No Java installation required! (JRE is bundled)

🌍 Platform Support: Prebuilt wheels for 6 platforms:

• ✅ Linux (x86_64, ARM64)
• ✅ macOS (Intel, Apple Silicon)
• ✅ Windows (x86_64, ARM64)

💡 Note: Package includes a platform-specific JRE (~47-63MB compressed) optimized for ArcadeDB. See "JVMCI is not enabled" warnings? Install GraalVM for optimal performance.

Technology: Uses JPype to bridge Python and Java, providing direct access to ArcadeDB's embedded engine with minimal overhead.

import arcadedb_embedded as arcadedb

# Create database
with arcadedb.create_database("/tmp/mydb") as db:

    # CREATE: Define schema
    db.command("sql", "CREATE DOCUMENT TYPE Person")

    # CREATE: Insert data (requires transaction)
    with db.transaction():
        db.command("sql", """
            INSERT INTO Person SET name = 'Alice', age = 30, city = 'NYC'
        """)
        db.command("sql", """
            INSERT INTO Person SET name = 'Bob', age = 25, city = 'LA'
        """)

    # READ: Query data
    result = db.query("sql", "SELECT FROM Person WHERE age > 20")
    for record in result:
        print(f"{record.get_property('name')} - {record.get_property('age')}")

    # UPDATE: Modify records
    with db.transaction():
        db.command("sql", "UPDATE Person SET age = 31 WHERE name = 'Alice'")

    # DELETE: Remove records
    with db.transaction():
        db.command("sql", "DELETE FROM Person WHERE name = 'Bob'")

# Create graph schema
db.command("sql", "CREATE VERTEX TYPE User")
db.command("sql", "CREATE EDGE TYPE Follows")

# Insert vertices and edges
with db.transaction():
    db.command("sql", "CREATE VERTEX User SET name = 'Alice'")
    db.command("sql", "CREATE VERTEX User SET name = 'Bob'")
    db.command("sql", """
        CREATE EDGE Follows
        FROM (SELECT FROM User WHERE name = 'Alice')
        TO (SELECT FROM User WHERE name = 'Bob')
    """)

# Query graph
result = db.query("sql", """
    SELECT expand(out('Follows')) FROM User WHERE name = 'Alice'
""")

# Create vector index
db.command("sql", "CREATE VERTEX TYPE Document")
db.command("sql", """
    CREATE PROPERTY Document.embedding ARRAY OF DOUBLE
""")
db.command("sql", """
    CREATE VECTOR INDEX Document_embedding
    ON Document(embedding)
    HNSW M 16, EF_CONSTRUCTION 128
""")

# Insert vectors
with db.transaction():
    db.command("sql", """
        CREATE VERTEX Document
        SET text = 'Hello world', embedding = [0.1, 0.2, 0.3]
    """)

# Search by vector similarity
result = db.query("sql", """
    SELECT FROM Document
    ORDER BY embedding <-> [0.1, 0.2, 0.4]
    LIMIT 5
""")

📚 More Examples | Full Tutorial

✅ No Java Installation Required - JRE bundled in package ✅ 6 Platforms Supported - Linux, macOS, Windows (Intel + ARM) ✅ Embedded Mode - Runs in your Python process (no server needed) ✅ Multi-Model - Graph, Document, Key/Value, Vector, Time Series ✅ Query Languages - SQL, Cypher, Gremlin, MongoDB (all included) ✅ ACID Transactions - Full transactional support ✅ Vector Search - HNSW indexing for embeddings ✅ Studio UI - Web-based database browser (included) ✅ Self-Contained - Single package (~155-161MB wheel, ~215-230MB installed) ✅ Production Ready - 205/205 tests passing on all platforms

Package Contents (platform-specific):

• JARs: 167.4MB (identical across all platforms, gRPC excluded)
• JRE: 47-63MB (platform-specific Java 21 runtime via jlink)
• Wheel: 155-161MB compressed (varies by platform)
• Installed: ~215-230MB uncompressed (varies by platform)

Platform Details:

ArcadeDB is a high-performance multi-model database that supports:

• Data Models: Graph, Document, Key/Value, Vector, Time Series
• Query Languages: SQL, Cypher, Gremlin, MongoDB, GraphQL
• Architecture: ACID transactions, native graph engine, full-text search
• Performance: Millions of records/second with minimal resources

This fork provides embedded Python bindings using JPype to bridge Python and the Java-based ArcadeDB engine.

Main ArcadeDB Resources:

• 📖 Official Documentation
• 🐳 Docker Images
• 💬 Community Discord
• 🔧 Upstream Repository

Python Bindings: Issues and PRs welcome at this repository Main ArcadeDB: Contribute to the upstream project

Apache License 2.0 - See LICENSE

Built on ArcadeDB | Python bindings by HumemAI