actvalue.mysql-redis-cache 0.4.0

Async wrapper for MySQL queries with Redis caching

actvalue.mysql-redis-cache

Async wrapper for MySQL queries with Redis caching for Python 3.13+.

Installation

pip install actvalue.mysql-redis-cache

Using uv:

uv add actvalue.mysql-redis-cache

Quick Start

Client Usage

from mysql_redis_cache import MRCClient

mysql_config = {
    'host': 'localhost',
    'port': 3306,
    'user': 'myuser',
    'password': 'mypassword',
    'db': 'mydatabase',
}

redis_config = {
    'host': 'localhost',
    'port': 6379,
    'password': 'mypassword',
}

# Using context manager (recommended)
async with MRCClient(mysql_config, redis_config) as client:
    # Execute query with caching
    query = 'SELECT * FROM users WHERE id = ?'
    params = [123]
    param_names = ['UserId']
    ttl = 3600  # 1 hour
    
    result = await client.query_with_cache(query, params, param_names, ttl)

# Or manual connection management
client = MRCClient(mysql_config, redis_config)
result = await client.query_with_cache(query, params, param_names, ttl)
await client.close_redis_connection()

Server Usage (Cache Invalidation)

from mysql_redis_cache import MRCServer

redis_config = {
    'host': 'localhost',
    'port': 6379,
    'password': 'mypassword',
}

async with MRCServer(redis_config) as server:
    # Delete all cached queries with StoreId = 6 (default: OR logic)
    deleted_count = await server.drop_outdated_cache(['StoreId'], [6])
    print(f"Deleted {deleted_count} cache entries")
    
    # Delete with multiple parameters (OR logic - matches either)
    await server.drop_outdated_cache(['StoreId', 'UserId'], [6, 123])
    # Deletes cache with StoreId=6 OR UserId=123
    
    # Delete requiring ALL parameters to match (AND logic)
    await server.drop_outdated_cache(['StoreId', 'UserId'], [6, 123], require_all_match=True)
    # Deletes cache with BOTH StoreId=6 AND UserId=123

Caching Arbitrary Functions

import asyncio

async def expensive_computation(x: int, y: int) -> int:
    # Some expensive operation
    await asyncio.sleep(2)
    return x * y

# Cache the function result
result = await client.with_cache(
    fn=lambda: expensive_computation(10, 20),
    query='expensive_computation_v1',  # Signature for cache key
    params=[10, 20],
    param_names=['x', 'y'],
    ttl=3600
)

Direct MySQL Pool Access

# Get the MySQL pool for direct access
pool = client.get_mysql_pool()

async with pool.acquire() as conn:
    async with conn.cursor() as cursor:
        await cursor.execute("SELECT * FROM users")
        result = await cursor.fetchall()

Error Handling

from mysql_redis_cache import MRCClient
import asyncio

async def main():
    try:
        async with MRCClient(mysql_config, redis_config) as client:
            result = await client.query_with_cache(
                'SELECT * FROM users WHERE id = ?',
                [123],
                ['UserId']
            )
            print(result)
    except ConnectionError as e:
        print(f"Connection failed: {e}")
    except Exception as e:
        print(f"Query failed: {e}")

asyncio.run(main())

Manual Connection Management

# Without context manager - requires manual cleanup
client = MRCClient(mysql_config, redis_config)

try:
    result = await client.query_with_cache(query, params, param_names)
    # Process result
finally:
    # Always clean up connections
    await client.close_redis_connection()
    if client.mysql_pool:
        client.mysql_pool.close()
        await client.mysql_pool.wait_closed()

Configuration

MySQL Configuration

Option 1: Dictionary

mysql_config = {
    'host': 'localhost',
    'port': 3306,
    'user': 'myuser',
    'password': 'mypassword',
    'db': 'mydatabase',
    'minsize': 1,    # Connection pool minimum size
    'maxsize': 10,   # Connection pool maximum size
}

Option 2: Connection String

mysql_config = 'mysql://user:password@localhost:3306/database'

Redis Configuration

redis_config = {
    'host': 'localhost',
    'port': 6379,
    'password': 'mypassword',
    'username': 'default',
    'decode_responses': False,  # Important: keep binary for JSON storage
    'socket_connect_timeout': 30,
}

Features

TTL with Jitter

The library automatically adds ±10% jitter to TTL values to prevent cache stampede (thundering herd problem). When many cache entries with the same TTL expire simultaneously, it can cause a spike in database load. Jitter spreads out the expiration times.

# Example: TTL = 3600 seconds
# Actual TTL will be between 3240-3960 seconds (3600 ± 10%)
result = await client.query_with_cache(query, params, param_names, ttl=3600)

Cache Key Generation

Cache keys are automatically generated from the query and parameters:

# Format: {param_name1}={value1}_{param_name2}={value2}_{SHA1_hash}

# Example 1: No parameters
key = client.get_key_from_query('SELECT * FROM users')
# Result: "a1b2c3d4e5f6..." (just the SHA1 hash)

# Example 2: With parameters
key = client.get_key_from_query(
    'SELECT * FROM users WHERE id = ? AND store_id = ?',
    [123, 456],
    ['UserId', 'StoreId']
)
# Result: "UserId=123_StoreId=456_a1b2c3d4e5f6..."

Working Without Redis

If Redis is unavailable or not configured, the library falls back to direct MySQL queries:

# Redis config is optional
client = MRCClient(mysql_config)  # No Redis

# Queries will execute directly against MySQL
result = await client.query_with_cache(query, params)

API Reference

MRCClient

Methods

• async __init__(mysql_config, redis_config=None) - Initialize client
• async __aenter__() - Context manager entry - connect to services
• async __aexit__(exc_type, exc_val, exc_tb) - Context manager exit - cleanup
• async query_with_cache(query, params=None, param_names=[], ttl=86400) - Execute MySQL query with caching
• async with_cache(fn, query, params=None, param_names=[], ttl=86400) - Execute arbitrary async function with caching
• async read_from_cache(query, params=None, param_names=[]) - Read cached query result
• async write_to_cache(query, value, params=None, param_names=[], ttl=86400) - Write query result to cache
• get_key_from_query(query, params=None, param_names=[]) - Generate cache key
• async query_to_promise(query, params=None) - Execute MySQL query without caching
• get_mysql_pool() - Get MySQL connection pool for direct access
• async close_redis_connection() - Close Redis connection

MRCServer

Methods

• async __init__(redis_config) - Initialize server
• async __aenter__() - Context manager entry
• async __aexit__(exc_type, exc_val, exc_tb) - Context manager exit
• async drop_outdated_cache(key_names, key_values, require_all_match=False) - Delete cached queries matching key patterns
  • Default behavior (OR logic): Deletes cache entries matching at least one parameter
  • AND logic: Set require_all_match=True to delete only entries matching all parameters
  • Example OR: drop_outdated_cache(['StoreId', 'UserId'], [6, 123]) deletes entries with StoreId=6 OR UserId=123
  • Example AND: drop_outdated_cache(['StoreId', 'UserId'], [6, 123], require_all_match=True) deletes only entries with both
• async close_redis_connection() - Close Redis connection

Cache Key Format

Cache keys are generated using the format:

{param_name1}={value1}_{param_name2}={value2}_{SHA1_hash}

Examples:

• No params: {SHA1_hash}
• With params: UserId=123_StoreId=456_{SHA1_hash}

TTL Jitter

To prevent thundering herd problems, TTL values have a ±10% random jitter applied:

actual_ttl = ttl + round(ttl * random.uniform(-0.1, 0.1))

Cross-Platform Compatibility

This Python implementation is fully compatible with the TypeScript version:

• Cache keys generated by Python match TypeScript exactly
• JSON serialization format is identical
• Both implementations can read each other's cached data
• MySQL data types are handled consistently

Data Type Handling

The library automatically converts MySQL data types for cross-platform compatibility:

MySQL Type	Python Type	JSON Type	Notes
INT/BIGINT	int	number	Direct mapping
VARCHAR/TEXT	str	string	Direct mapping
DECIMAL/NUMERIC	Decimal	number	Auto-converted to float
DATETIME	datetime	string	ISO 8601 format
DATE	date	string	ISO 8601 format
NULL	None	null	Direct mapping
JSON	dict	object	Direct mapping
BOOLEAN	int	number	0/1 values

Example: Cross-Platform Cache Sharing

# Python writes to cache
async with MRCClient(mysql_config, redis_config) as py_client:
    await py_client.query_with_cache(
        'SELECT id, name, price FROM products WHERE category = ?',
        ['electronics'],
        ['Category']
    )

# TypeScript can read the same cache entry
// const client = new MRCClient(mysqlConfig, redisConfig);
// const result = await client.queryWithCache(
//     'SELECT id, name, price FROM products WHERE category = ?',
//     ['electronics'],
//     ['Category']
// );

Development Setup

# Clone repository
git clone <repo-url>
cd mysql-redis-cache/Python

# uv automatically creates and manages virtual environment
uv sync

# Run tests (uv handles venv activation automatically)
uv run pytest

# Run tests with coverage
uv run pytest --cov=mysql_redis_cache --cov-report=html

# Lint code
uv run ruff check src tests

# Format code
uv run ruff format src tests

# The virtual environment is created at .venv/ but you don't need to activate it manually
# All uv run commands automatically use the virtual environment

Best Practices

1. Use Context Managers

Always use async context managers to ensure proper cleanup:

async with MRCClient(mysql_config, redis_config) as client:
    # Your code here
    pass
# Connections are automatically closed

2. Choose Appropriate TTL

Set TTL based on data volatility:

# Frequently changing data (user sessions)
await client.query_with_cache(query, params, param_names, ttl=300)  # 5 minutes

# Moderately changing data (product catalog)
await client.query_with_cache(query, params, param_names, ttl=3600)  # 1 hour

# Rarely changing data (configuration)
await client.query_with_cache(query, params, param_names, ttl=86400)  # 24 hours

3. Use Meaningful Parameter Names

Parameter names are part of the cache key, so use descriptive names:

# Good - clear parameter names
await client.query_with_cache(
    'SELECT * FROM orders WHERE user_id = ? AND status = ?',
    [123, 'pending'],
    ['UserId', 'Status']  # Clear and descriptive
)

# Avoid - generic names make debugging harder
await client.query_with_cache(
    'SELECT * FROM orders WHERE user_id = ? AND status = ?',
    [123, 'pending'],
    ['param1', 'param2']  # Not descriptive
)

4. Cache Invalidation Strategy

Invalidate cache when data changes:

from mysql_redis_cache import MRCClient, MRCServer

# When updating data
async with MRCClient(mysql_config, redis_config) as client:
    await client.query_to_promise(
        'UPDATE products SET price = ? WHERE id = ?',
        [99.99, 123]
    )

# Invalidate related cache entries
async with MRCServer(redis_config) as server:
    await server.drop_outdated_cache(['ProductId'], [123])

5. Connection Pooling

Configure appropriate pool sizes for your workload:

mysql_config = {
    'host': 'localhost',
    'port': 3306,
    'user': 'myuser',
    'password': 'mypassword',
    'db': 'mydatabase',
    'minsize': 1,   # Minimum connections to maintain
    'maxsize': 10,  # Maximum concurrent connections
}

AWS Lambda Usage

This library is compatible with AWS Lambda:

import aiomysql
from mysql_redis_cache import MRCClient

# Initialize client outside handler for connection reuse
client = None

async def lambda_handler(event, context):
    global client
    
    # Reuse client across invocations
    if client is None:
        client = MRCClient(mysql_config, redis_config)
    
    result = await client.query_with_cache(
        'SELECT * FROM users WHERE id = ?',
        [event['userId']],
        ['UserId']
    )
    
    return {
        'statusCode': 200,
        'body': json.dumps(result)
    }

Lambda Considerations:

• Connection pools persist across warm invocations
• First invocation (cold start) will be slower
• Store credentials in environment variables
• Use VPC configuration for database access

Requirements

• Python 3.13+
• aiomysql
• redis[asyncio]

License

MIT