nanasqlite 1.0.3rc4

A dict-like SQLite wrapper with APSW for instant persistence and memory caching

NanaSQLite

A dict-like SQLite wrapper with instant persistence and intelligent caching.

English | 日本語

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English

🚀 Features

• Dict-like Interface: Use familiar db["key"] = value syntax
• Instant Persistence: All writes are immediately saved to SQLite
• Smart Caching: Lazy load (on-access) or bulk load (all at once)
• Nested Structures: Full support for nested dicts and lists (up to 30+ levels)
• High Performance: WAL mode, mmap, and batch operations for maximum speed
• Zero Configuration: Works out of the box with sensible defaults

📦 Installation

pip install nanasqlite

⚡ Quick Start

from nanasqlite import NanaSQLite

# Create or open a database
db = NanaSQLite("mydata.db")

# Use it like a dict
db["user"] = {"name": "Nana", "age": 20, "tags": ["admin", "active"]}
print(db["user"])  # {'name': 'Nana', 'age': 20, 'tags': ['admin', 'active']}

# Data persists automatically
db.close()

# Reopen later - data is still there!
db = NanaSQLite("mydata.db")
print(db["user"]["name"])  # 'Nana'

🔧 Advanced Usage

# Bulk load for faster repeated access
db = NanaSQLite("mydata.db", bulk_load=True)

# Batch operations for high-speed writes
db.batch_update({
    "key1": "value1",
    "key2": "value2",
    "key3": {"nested": "data"}
})

# Context manager support
with NanaSQLite("mydata.db") as db:
    db["temp"] = "value"

📚 Documentation

• English Documentation
• API Reference

✨ New Features (v1.0.3rc3+)

Pydantic Support:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

db.set_model("user", User(name="Nana", age=20))
user = db.get_model("user", User)

Direct SQL Execution:

# Execute custom SQL
cursor = db.execute("SELECT * FROM data WHERE key LIKE ?", ("user%",))
rows = db.fetch_all("SELECT key, value FROM data")

SQLite Wrapper Functions:

# Create tables and indexes easily
db.create_table("users", {
    "id": "INTEGER PRIMARY KEY",
    "name": "TEXT NOT NULL",
    "email": "TEXT UNIQUE"
})
db.create_index("idx_users_email", "users", ["email"])

# Simple queries
results = db.query(table_name="users", where="age > ?", parameters=(20,))

✨ Additional Features (v1.0.3rc4+)

22 new wrapper functions for comprehensive SQLite operations:

# Data operations
rowid = db.sql_insert("users", {"name": "Alice", "age": 25})
db.sql_update("users", {"age": 26}, "name = ?", ("Alice",))
db.upsert("users", {"id": 1, "name": "Alice", "age": 25})
total = db.count("users", "age >= ?", (18,))

# Query extensions (pagination, grouping)
page2 = db.query_with_pagination("users", limit=10, offset=10)
stats = db.query_with_pagination("orders", 
    columns=["user_id", "COUNT(*) as count"], group_by="user_id")

# Schema management
db.alter_table_add_column("users", "phone", "TEXT")
schema = db.get_table_schema("users")
db.drop_table("old_table", if_exists=True)

# Utilities & transactions
db.vacuum()  # Optimize database
with db.transaction():
    db.sql_insert("logs", {"message": "Event"})

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日本語

🚀 特徴

• dict風インターフェース: おなじみの db["key"] = value 構文で操作
• 即時永続化: 書き込みは即座にSQLiteに保存
• スマートキャッシュ: 遅延ロード（アクセス時）または一括ロード（起動時）
• ネスト構造対応: 30階層以上のネストしたdict/listをサポート
• 高性能: WALモード、mmap、バッチ操作で最高速度を実現
• 設定不要: 合理的なデフォルト設定でそのまま動作

📦 インストール

pip install nanasqlite

⚡ クイックスタート

from nanasqlite import NanaSQLite

# データベースを作成または開く
db = NanaSQLite("mydata.db")

# dictのように使う
db["user"] = {"name": "Nana", "age": 20, "tags": ["admin", "active"]}
print(db["user"])  # {'name': 'Nana', 'age': 20, 'tags': ['admin', 'active']}

# データは自動的に永続化
db.close()

# 後で再度開いても、データはそのまま！
db = NanaSQLite("mydata.db")
print(db["user"]["name"])  # 'Nana'

🔧 高度な使い方

# 一括ロードで繰り返しアクセスを高速化
db = NanaSQLite("mydata.db", bulk_load=True)

# バッチ操作で高速書き込み
db.batch_update({
    "key1": "value1",
    "key2": "value2",
    "key3": {"nested": "data"}
})

# コンテキストマネージャ対応
with NanaSQLite("mydata.db") as db:
    db["temp"] = "value"

📚 ドキュメント

• 日本語ドキュメント
• APIリファレンス

✨ 新機能 (v1.0.3rc3+)

Pydantic互換性:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

db.set_model("user", User(name="Nana", age=20))
user = db.get_model("user", User)

直接SQL実行:

# カスタムSQLの実行
cursor = db.execute("SELECT * FROM data WHERE key LIKE ?", ("user%",))
rows = db.fetch_all("SELECT key, value FROM data")

SQLiteラッパー関数:

# テーブルとインデックスを簡単に作成
db.create_table("users", {
    "id": "INTEGER PRIMARY KEY",
    "name": "TEXT NOT NULL",
    "email": "TEXT UNIQUE"
})
db.create_index("idx_users_email", "users", ["email"])

# シンプルなクエリ
results = db.query(table_name="users", where="age > ?", parameters=(20,))

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License

MIT License - see LICENSE for details.