sop4py 2.0.21

Scalable Objects Persistence (SOP) V2 for Python. General Public Availability (GPA) Release

SOP for Python (sop4py)

Scalable Objects Persistence (SOP) is a high-performance, transactional storage engine for Python, powered by a robust Go backend. It combines the raw speed of direct disk I/O with the reliability of ACID transactions and the flexibility of modern AI data management.

Key Features

• Unified Database: Single entry point for managing Vector, Model, and Key-Value stores.
• Transactional B-Tree Store: Unlimited, persistent B-Tree storage for key-value data.
• Vector Database: Built-in vector search (k-NN) for AI embeddings and similarity search.
• AI Model Store: Versioned storage for machine learning models (B-Tree backed).
• ACID Compliance: Full transaction support (Begin, Commit, Rollback) with isolation.
• High Performance: Written in Go with a lightweight Python wrapper (ctypes).
• Caching: Integrated Redis-backed L1/L2 caching for speed.
• Replication: Optional Erasure Coding (EC) for fault-tolerant storage across drives.
• Flexible Deployment: Supports both Standalone (local) and Clustered (distributed) modes.

Documentation

• API Cookbook: Common recipes and patterns (Key-Value, Transactions, AI).
• Examples: Complete runnable scripts.

Prerequisites

• Redis: Required for caching and transaction coordination (especially in Clustered mode). Note: Redis is NOT used for data storage, just for coordination & to offer built-in caching.
• Storage: Local disk space (supports multiple drives/folders).
• OS: macOS (Darwin), Linux, or Windows (AMD64).

Installation

1. Build the Go Bridge:

   cd jsondb
   go build -o jsondb.so -buildmode=c-shared main/*.go
   

2. Install Python Dependencies:

   pip install -r jsondb/python/requirements.txt
   

3. Set PYTHONPATH:

   export PYTHONPATH=$PYTHONPATH:$(pwd)/jsondb/python
   

Quick Start Guide

SOP uses a unified Database object to manage all types of stores (Vector, Model, and B-Tree). All operations are performed within a Transaction.

1. Initialize Database & Context

First, create a Context and open a Database connection.

from sop import Context, TransactionMode, TransactionOptions, Btree, BtreeOptions, Item
from sop.ai import Database, DBType, Item as VectorItem

# Initialize Context
ctx = Context()

# Open Database (Standalone Mode)
# This creates/opens a database at the specified path.
db = Database(ctx, storage_path="data/my_db", db_type=DBType.Standalone)

2. Start a Transaction

All data operations (Create, Read, Update, Delete) must happen within a transaction.

# Begin a transaction (Read-Write by default)
# You can use 'with' block for auto-commit/rollback, or manage manually.
with db.begin_transaction(ctx) as tx:
    
    # --- 3. Vector Store (AI) ---
    # Open a Vector Store named "products"
    vector_store = db.open_vector_store(ctx, tx, "products")
    
    # Upsert a Vector Item
    vector_store.upsert(ctx, VectorItem(
        id="prod_101",
        vector=[0.1, 0.5, 0.9],
        payload={"name": "Laptop", "price": 999}
    ))

    # --- 4. Model Store (AI) ---
    # Open a Model Store named "classifiers"
    model_store = db.open_model_store(ctx, tx, "classifiers")
    
    # Save a Model
    model_store.save(ctx, "churn", "v1.0", {
        "algorithm": "random_forest",
        "trees": 100
    })

    # --- 5. General Purpose B-Tree ---
    # Create a new B-Tree store.
    bo = BtreeOptions(name="user_store", is_unique=True)
    user_store = db.new_btree(ctx, "user_store", tx, options=bo)
    
    # Add an item.
    user_store.add(ctx, Item(key="user1", value="John Doe"))

# Transaction commits automatically here.
# If an exception occurs, it rolls back.

6. Querying Data

You can perform queries in a separate transaction (e.g., Read-Only).

# Begin a Read-Only transaction (optional optimization)
with db.begin_transaction(ctx) as tx:
    
    # --- Vector Search ---
    vs = db.open_vector_store(ctx, tx, "products")
    hits = vs.query(ctx, vector=[0.1, 0.5, 0.8], k=5)
    for hit in hits:
        print(f"Vector Match: {hit.id}, Score: {hit.score}")

    # --- Model Retrieval ---
    ms = db.open_model_store(ctx, tx, "classifiers")
    model = ms.get(ctx, "churn", "v1.0")
    print(f"Loaded Model: {model['algorithm']}")

    # --- B-Tree Lookup ---
    us = db.open_btree(ctx, "user_store", tx)
    if us.find(ctx, "user1"):
        # Fetch the current item
        item = us.get_current_item(ctx)
        print(f"User Found: {item.value}")

Advanced Configuration

Transaction Options

You can configure timeouts, isolation levels, and more.

from sop import TransactionOptions, TransactionMode

opts = TransactionOptions(
    mode=TransactionMode.ForWriting.value,
    max_time=15,  # 15 minutes timeout
)

tx = db.begin_transaction(ctx, options=opts)

Clustered Mode

For distributed deployments, switch to DBType.Clustered. This requires Redis for coordination.

from sop.ai import DBType

db = Database(
    ctx, 
    storage_path="/mnt/shared_data", 
    db_type=DBType.Clustered
)

Architecture

SOP uses a split architecture:

1. Core Engine (Go): Handles disk I/O, B-Tree algorithms, caching, and transactions. Compiled as a shared library (.dylib, .so, .dll).
2. Python Wrapper: Uses ctypes to interface with the Go engine, providing a Pythonic API (sop package).

Project Links

• Source Code: GitHub - sharedcode/sop
• PyPI: sop4py

Contributing

Contributions are welcome! Please check the CONTRIBUTING.md file in the repository for guidelines.