Package Overview¶
ArcadeDB Python provides a self-contained embedded package that runs the database directly in your Python process with a bundled JRE - no Java installation required!
The Package¶
| Package | Wheel Size | Installed Size | Java Required | Query Languages |
|---|---|---|---|---|
| arcadedb-embedded | ~62MB | ~87MB | ❌ No | SQL, OpenCypher |
Installation:
Requirements: Python 3.10–3.14 (packaged; primary testing on 3.12) - No Java installation needed!
What's Inside¶
The package includes everything you need:
- ArcadeDB JARs (~24MB, uncompressed): Core database with the embedded feature set
- Bundled JRE (~63MB, uncompressed): Platform-specific Java 25 runtime (via jlink)
Current Linux x86_64 package info: ~62MB compressed wheel, ~63MB JRE, ~24MB JARs, and ~87MB installed.
These numbers are measured from the built wheel file and the extracted
site-packages/arcadedb_embedded/ directory, and they vary by platform and version.
Platform Support¶
Pre-built platform-specific wheels are available for 4 platforms. Sizes stay in the same ballpark across platforms, but vary slightly by platform and version (see size breakdown below).
Key Features:
- ✅ All platforms use platform-specific wheels (not universal)
- ✅ pip automatically selects the correct wheel for your system
- ✅ Each platform has its own bundled JRE optimized for that architecture
- ✅ Full bindings suite passes on every platform build
- ✅ Built on native runners (no emulation) for optimal performance
What's Included¶
Core Features:
- ✅ No Java Installation Required: Platform-specific JRE bundled (~63MB uncompressed)
- ✅ Core Database: All models (Graph, Document, Key/Value, Vector, Time Series)
- ✅ Query Languages: SQL, OpenCypher (all included)
- ✅ Vector Search: Graph-based indexing for embeddings
- ✅ Data Import: CSV, XML, and ArcadeDB JSONL import
Optimized (embedded-only):
- The package is embedded-only: the HTTP server, Studio web UI, and wire protocols are not bundled. For client-server deployments, run the official ArcadeDB server alongside — see Access Methods.
- Additional components are excluded to optimize package size (e.g., gRPC
wire protocol). See
scripts/jar_exclusions.txtin the repository for the full list.
Use Cases¶
Perfect for:
- Production Python applications
- Cloud deployments (no Java setup needed!)
- Docker containers
- Desktop applications
- Multi-model database needs (Graph, Document, Vector, Time Series)
- Any scenario requiring SQL or OpenCypher queries
Import Statement¶
The import is always:
Simple and consistent across all platforms!
Size Breakdown¶
Current sizes are ballpark values and can move with ArcadeDB, the bundled JRE, the target platform, and filesystem overhead after installation:
- Wheel (compressed): ~62MB
- Installed package: ~87MB
Components (uncompressed):
- ArcadeDB JARs: ~24MB (51 JARs)
- Bundled JRE: ~63MB (platform-specific Java 25 runtime via jlink, 16 modules)
Optimizations:
- Embedded-only: server/Studio/wire-protocol JARs excluded
- See
scripts/jar_exclusions.txtin repository for details
Installation Tips¶
Check Installed Package¶
import arcadedb_embedded as arcadedb
print(f"Version: {arcadedb.__version__}")
# Verify database works
with arcadedb.create_database("./test") as db:
result = db.query("sql", "SELECT 1 as test")
print(f"Database working: {result.first().get('test') == 1}")
Platform Detection¶
pip automatically selects the correct platform-specific wheel:
# On Linux x64 for Python 3.12, installs: arcadedb_embedded-X.Y.Z-cp312-cp312-manylinux_2_34_x86_64.whl
# On macOS ARM64 for Python 3.12, installs: arcadedb_embedded-X.Y.Z-cp312-cp312-macosx_11_0_arm64.whl
# On Windows x86_64 for Python 3.12, installs: arcadedb_embedded-X.Y.Z-cp312-cp312-win_amd64.whl
# etc.
You can verify which platform you're on:
import platform
print(f"System: {platform.system()}")
print(f"Machine: {platform.machine()}")
print(f"Python: {platform.python_version()}")
Next Steps¶
- Installation Guide - Detailed install instructions
- Quick Start - Get started in 5 minutes
- Build Architecture - How platform-specific wheels are built
- Query Languages - SQL and OpenCypher examples