prismiq 0.2.3

Open-source embedded analytics platform

Prismiq

Open-source embedded analytics engine for Python.

Installation

pip install prismiq

Quick Start

from prismiq import PrismiqEngine

# Connect to your database
engine = await PrismiqEngine.create(
    database_url="postgresql://user:pass@localhost/mydb",
    exposed_tables=["customers", "orders", "products"]
)

# Get schema
schema = await engine.get_schema()

# Execute a query
result = await engine.execute({
    "tables": [{"id": "t1", "name": "orders"}],
    "columns": [
        {"table_id": "t1", "name": "status"},
        {"table_id": "t1", "name": "total", "aggregation": "sum"}
    ],
    "group_by": [{"table_id": "t1", "column": "status"}]
})

Features

• Schema introspection - Discover tables, columns, and relationships
• Visual query building - Build SQL from JSON query definitions
• Async execution - Non-blocking database operations with asyncpg
• Type safe - Full type hints with Pydantic models
• Multi-tenant support - Schema-based isolation with Alembic integration

Multi-Tenant Integration

For applications with schema-based multi-tenancy, Prismiq provides SQLAlchemy declarative models and sync table creation:

from sqlalchemy import create_engine
from prismiq import ensure_tables_sync, PrismiqBase

engine = create_engine("postgresql://user:pass@localhost/db")

# Create tables in a tenant-specific schema
with engine.connect() as conn:
    ensure_tables_sync(conn, schema_name="tenant_123")
    conn.commit()

# For Alembic integration, access the metadata:
# PrismiqBase.metadata.tables contains all Prismiq table definitions

See Multi-Tenant Integration Guide for complete documentation.

Documentation

See the main repository for full documentation.

License

MIT