Python项目适用的轻量级和嵌入式数据库汇总

# 安装: pip install tinydb
from tinydb import TinyDB, Query

# 使用示例
db = TinyDB('db.json')
db.insert({'name': 'John', 'age': 30})
results = db.search(Query().age > 25)

# 安装: pip install pickledb
import pickledb

db = pickledb.load('example.db', False)
db.set('key', 'value')
value = db.get('key')

import shelve

with shelve.open('mydata') as db:
    db['key1'] = {'name': 'Alice', 'age': 25}
    data = db['key1']  # 读取数据

# 安装: pip install duckdb
import duckdb

# 内存数据库
conn = duckdb.connect(':memory:')
# 或文件数据库
conn = duckdb.connect('mydb.duckdb')

conn.execute("CREATE TABLE users (id INTEGER, name VARCHAR)")
conn.execute("INSERT INTO users VALUES (1, 'Alice')")
result = conn.execute("SELECT * FROM users").fetchall()

# 安装: pip install unqlite
import unqlite

db = unqlite.UnQLite('mydb.db')
db['key1'] = 'value1'  # 键值存储
db.store('users', {'name': 'John'})  # 文档存储

# 需要安装rqlite服务器
# pip install rqlite-python
import rqlite

conn = rqlite.connect('http://localhost:4001')
conn.execute('CREATE TABLE users (id INTEGER, name TEXT)')

# 需要PocketBase服务
# pip install pocketbase
from pocketbase import PocketBase

client = PocketBase('http://127.0.0.1:8090')
records = client.collection('posts').get_list()

# 安装: pip install questdb
from questdb.ingress import Sender, IngressError

sender = Sender('localhost', 9009)
sender.row(
    'weather',
    symbols={'city': 'London'},
    columns={'temperature': 23.5, 'humidity': 0.49}
)
sender.flush()

# 基于PostgreSQL，但针对时序优化
# pip install psycopg2
import psycopg2

conn = psycopg2.connect("dbname=tsdb user=postgres")

# 安装: pip install redis
import redis

r = redis.Redis(host='localhost', port=6379, db=0)
r.set('foo', 'bar')
value = r.get('foo')

# 安装: pip install python-memcached
import memcache

mc = memcache.Client(['127.0.0.1:11211'], debug=0)
mc.set("some_key", "Some value")
value = mc.get("some_key")

# 安装: pip install ZODB
from ZODB import DB
import transaction

db = DB('mydatabase.fs')
conn = db.open()
root = conn.root()

root['users'] = ['Alice', 'Bob']
transaction.commit()

# 安装可能较复杂，需编译
import metakit

db = metakit.storage('data.mk')
view = db.getas('users[name:S,age:I]')
view.append(name='John', age=30)

# 安装: pip install CodernityDB
from CodernityDB.database import Database

db = Database('/tmp/test')
db.create()

# 安装: pip install kyotocabinet
import kyotocabinet

db = kyotocabinet.DB()
db.open('test.kch', kyotocabinet.DB.OWRITER | kyotocabinet.DB.OCREATE)
db.set('key', 'value')

# 安装MySQL客户端
# pip install PyMySQL
import pymysql

conn = pymysql.connect(
    host='127.0.0.1',
    port=4000,
    user='root',
    database='test'
)

# 安装: pip install psycopg2
import psycopg2

conn = psycopg2.connect(
    host='localhost',
    port=26257,
    user='root',
    database='defaultdb'
)

"""
不同场景下的数据库选择示例
"""

def scenario_based_selection():
    print("根据使用场景选择数据库：")
    
    scenarios = {
        "移动应用/桌面应用": "SQLite",
        "Web应用缓存": "Redis",
        "小型项目原型": "TinyDB",
        "数据分析/OLAP": "DuckDB",
        "需要分布式": "rqlite 或 TiDB",
        "存储Python对象": "ZODB 或 Shelve",
        "时序数据": "QuestDB 或 TimescaleDB",
        "键值存储": "UnQLite 或 Kyoto Cabinet",
        "纯内存操作": "Python字典 + pickle",
        "需要完整SQL功能": "SQLite",
        "文档存储": "TinyDB",
        "需要事务支持": "SQLite",
        "零依赖部署": "SQLite 或 TinyDB",
        "高并发读写": "Redis",
        "持久化缓存": "Shelve",
        "配置存储": "JSON文件 或 TinyDB"
    }
    
    for scenario, recommendation in scenarios.items():
        print(f"  {scenario:25} → {recommendation}")

# 快速选择指南函数
def quick_selection_guide():
    """
    快速选择指南
    """
    questions = [
        ("需要完整SQL支持吗？", {"是": ["SQLite", "DuckDB"], "否": ["TinyDB", "Redis"]}),
        ("数据量有多大？", {"<100MB": ["SQLite", "TinyDB"], ">100MB": ["DuckDB", "外部数据库"]}),
        ("需要网络访问吗？", {"是": ["Redis", "rqlite"], "否": ["SQLite", "TinyDB"]}),
        ("性能要求高吗？", {"是": ["DuckDB", "Redis"], "否": ["SQLite", "TinyDB"]}),
        ("需要事务支持吗？", {"是": ["SQLite", "DuckDB"], "否": ["TinyDB", "Redis"]}),
        ("部署环境有限制吗？", {"是": ["SQLite", "TinyDB"], "否": "所有选择"}),
        ("主要做什么操作？", {
            "查询分析": ["DuckDB"],
            "缓存": ["Redis"],
            "简单存储": ["TinyDB", "Shelve"],
            "复杂关系": ["SQLite"]
        })
    ]
    
    return questions

if __name__ == "__main__":
    scenario_based_selection()
    
    print("\n推荐选择优先级：")
    print("1. 首选SQLite - 成熟稳定，功能完整")
    print("2. 考虑DuckDB - 数据分析场景")
    print("3. 选择TinyDB - 简单JSON存储需求")
    print("4. 使用Redis - 缓存和高并发场景")
    print("5. 尝试ZODB - 需要直接存储Python对象时")

# 方案1：SQLite（最通用）
import sqlite3
conn = sqlite3.connect('app.db')

# 方案2：TinyDB（更简单）
from tinydb import TinyDB
db = TinyDB('db.json')

# 方案3：DuckDB（数据分析）
import duckdb
conn = duckdb.connect('analytics.db')

# 主数据库：SQLite/PostgreSQL
# 缓存：Redis
# 会话存储：Redis或SQLite

# 首选DuckDB，次选SQLite
import duckdb
import pandas as pd

# 直接在Pandas DataFrame上查询
df = pd.read_csv('data.csv')
result = duckdb.query("SELECT * FROM df WHERE column > 100").df()

# 使用SQLite（资源占用小）
# 或TinyDB（无依赖）