https://github.com/vndee/redis-data-structures
A Python package providing Redis-backed data structures for building scalable and resilient applications.
https://github.com/vndee/redis-data-structures
Last synced: about 1 month ago
JSON representation
A Python package providing Redis-backed data structures for building scalable and resilient applications.
- Host: GitHub
- URL: https://github.com/vndee/redis-data-structures
- Owner: vndee
- License: mit
- Created: 2024-12-23T08:48:35.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-31T11:02:26.000Z (9 months ago)
- Last Synced: 2025-04-11T21:41:16.563Z (6 months ago)
- Language: Python
- Homepage: https://blog.duy.dev/redis-data-structures-a-pythonic-interface-for-distributed-data-structures/
- Size: 5.81 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🌟 Redis Data Structures
[](https://badge.fury.io/py/redis-data-structures)
[](https://www.python.org/downloads/)
[](https://codecov.io/gh/vndee/redis-data-structures)
[](https://github.com/vndee/redis-data-structures/actions/workflows/pylint.yaml)
[](https://github.com/vndee/redis-data-structures/actions/workflows/pytest.yaml)
[](https://opensource.org/licenses/MIT)
A Python library providing high-level, Redis-backed data structures with a clean, Pythonic interface. Perfect for distributed systems, microservices, and any application requiring persistent, thread-safe data structures, especially in environments where multiple workers share the same data structure.
.png)
💡 **[Examples](examples/)**
### 📋 Table of Contents
- [✨ Features](#-features)
- [📦 Installation](#-installation)
- [🚀 Quick Start](#-quick-start)
- [📊 Data Structures](#-data-structures)
- [💻 Usage Examples](#-usage-examples)
- [🔗 Connection Management](#-connection-management)
- [🔍 Complex Types](#-complex-types)
- [🤝 Contributing](#-contributing)
- [📝 License](#-license)
### ✨ Features
- **Thread-safe** data structures backed by Redis
- Clean, **Pythonic interface**
- Connection pooling, **automatic retries**, and **circuit breaker** pattern for fault tolerance
- **Type preservation** for complex types
- **Automatic compression** of large data using zlib (configurable)
- **>90%** test coverage
- **Async support** (coming soon)
### 📦 Installation
```bash
pip install redis-data-structures
```
> **Note:** Ensure that Redis is running for the library to function properly.
### 🚀 Quick Start
```python
from redis_data_structures import Queue, Stack, Set, ConnectionManager
# Initialize connection
conn = ConnectionManager(host='localhost', port=6379, db=0)
# Create and use data structures
queue = Queue("tasks", connection_manager=conn)
queue.push({'id': 1, 'action': 'process'})
stack = Stack("commands", connection_manager=conn)
stack.push({'action': 'create'})
set_ds = Set("users", connection_manager=conn)
set_ds.add({'id': 'user1', 'name': 'Alice'})
```
You can also skip using `ConnectionManager` if the following environment variables are set:
- `REDIS_HOST`
- `REDIS_PORT`
- `REDIS_DB`
- `REDIS_USERNAME`
- `REDIS_PASSWORD`
```python
from redis_data_structures import Queue, Stack, Set
queue = Queue("test_queue")
stack = Stack("test_stack")
set_ds = Set("test_set")
```
Refer to **[initialization](docs/initialization.md)** for more information.
### 📊 Data Structures
| Structure | Description | Use Case |
|------------------|----------------------------|-----------------------------------|
| [Queue](docs/queue.md) | FIFO queue | Job processing, message passing |
| [Stack](docs/stack.md) | LIFO stack | Undo systems, execution contexts |
| [Set](docs/set.md) | Unique collection | Membership testing, deduplication |
| [Dict](docs/dict.md) | Python-like dictionary (key-value store) | Caching, metadata storage |
| [HashMap](docs/hash_map.md) | Key-value store | Caching, metadata storage |
| [PriorityQueue](docs/priority_queue.md) | Priority-based queue | Task scheduling |
| [RingBuffer](docs/ring_buffer.md) | Fixed-size circular buffer | Logs, metrics |
| [Graph](docs/graph.md) | Graph with adjacency list | Relationships, networks |
| [Trie](docs/trie.md) | Prefix tree | Autocomplete, spell checking |
| [BloomFilter](docs/bloom_filter.md) | Probabilistic set | Membership testing |
| [Deque](docs/deque.md) | Double-ended queue | Sliding windows |
### 💻 Usage Examples
.png)
```python
from redis_data_structures import Queue
queue = Queue("tasks")
# Basic operations
queue.push({'id': 1, 'action': 'process'})
task = queue.pop()
size = queue.size()
stack = Stack("commands")
stack.push({'action': 'create'})
command = stack.pop()
size = stack.size()
set_ds = Set("users")
set_ds.add({'id': 'user1'})
exists = set_ds.contains({'id': 'user1'})
members = set_ds.members()
hash_map = HashMap("users")
hash_map.set('user:1', {'name': 'Alice', 'age': 30})
user = hash_map.get('user:1')
exists = hash_map.exists('user:1')
priority_queue = PriorityQueue("tasks")
priority_queue.push({'id': 1, 'priority': 1})
task = priority_queue.pop()
peek = priority_queue.peek()
...
```
For more examples, see **[examples](examples/)**.
### 🔗 Connection Management
```python
from redis_data_structures import ConnectionManager
from datetime import timedelta
conn = ConnectionManager(
host='localhost',
port=6379,
db=0,
max_connections=20,
retry_max_attempts=5,
circuit_breaker_threshold=10,
circuit_breaker_timeout=timedelta(minutes=5),
ssl=True
)
# Reuse for multiple queues
pq1 = PriorityQueue("tasks", connection_manager=conn)
pq2 = PriorityQueue("tasks", connection_manager=conn)
stack = Stack("commands", connection_manager=conn)
set_ds = Set("users", connection_manager=conn)
```
### 🔍 Complex Types
.png)
.png)
```python
from redis_data_structures import LRUCache, HashMap, SerializableType
from datetime import datetime, timezone
from pydantic import BaseModel
class User(SerializableType):
"""Example of a custom Redis data type using standard class."""
def __init__(self, name: str, joined: datetime):
"""Initialize the User object."""
self.name = name
self.joined = joined
def to_dict(self) -> dict:
"""Convert the User object to a dictionary."""
return {
"name": self.name,
"joined": self.joined.isoformat(), # Convert datetime to string
}
@classmethod
def from_dict(cls, data: dict) -> "User":
"""Create a User object from a dictionary."""
return cls(
name=data["name"],
joined=datetime.fromisoformat(data["joined"]), # Convert string back to datetime
)
def __eq__(self, other) -> bool:
"""Override __eq__ for proper equality comparison."""
return (
isinstance(other, User) and
self.name == other.name and
self.joined == other.joined
)
def __str__(self) -> str:
"""Return a string representation of the User object."""
return f"User(name='{self.name}', joined={self.joined.isoformat()})"
class Address(BaseModel):
"""Nested Pydantic model for demonstration."""
street: str
city: str
country: str
postal_code: Optional[str] = None
class UserModel(BaseModel):
"""Example of a Pydantic model - works automatically with Redis structures."""
name: str
email: str
age: int = Field(gt=0, lt=150)
joined: datetime
address: Optional[Address] = None
tags: Set[str] = set()
def __str__(self) -> str:
"""Return a string representation of the UserModel instance."""
return f"UserModel(name='{self.name}', email='{self.email}', age={self.age})"
# Initialize data structures
cache = LRUCache("test_cache", capacity=1000) # Using default connection settings
hash_map = HashMap("type_demo_hash") # Using default connection settings
# Example 1: Basic Python Types
data = {
"string": "hello",
"integer": 42,
"float": 3.14,
"boolean": True,
"none": None,
}
for key, value in data.items():
hash_map.set(key, value)
result = hash_map.get(key)
# Example 2: Collections
collections = {
"tuple": (1, "two", 3.0),
"list": [1, 2, 3, "four"],
"set": {1, 2, 3, 4},
"dict": {"a": 1, "b": 2},
}
for key, value in collections.items():
hash_map.set(key, value)
result = hash_map.get(key)
# Example 3: DateTime Types
now = datetime.now(timezone.utc)
hash_map.set("datetime", now)
result = hash_map.get("datetime")
# Example 4: Custom Type
user = User("John Doe", datetime.now(timezone.utc))
hash_map.set("custom_user", user)
result = hash_map.get("custom_user")
# Example 5: Pydantic Models
user_model = UserModel(
name="Jane Smith",
email="jane@example.com",
age=30,
joined=datetime.now(timezone.utc),
address=Address(
street="123 Main St",
city="New York",
country="USA",
postal_code="10001",
),
tags={"developer", "python"},
)
# Store in different data structures
cache.put("pydantic_user", user_model)
hash_map.set("pydantic_user", user_model)
# Retrieve and verify
cache_result = cache.get("pydantic_user")
hash_result = hash_map.get("pydantic_user")
# Example 6: Nested Structures
nested_data = {
"user": user,
"model": user_model,
"list": [1, user, user_model],
"tuple": (user, user_model),
"dict": {
"user": user,
"model": user_model,
"date": now,
},
}
hash_map.set("nested", nested_data)
result = hash_map.get("nested")
```
> **Important Note for Distributed Systems**: In scenarios where some processes only consume data (without storing any), you need to manually register types before deserializing since the type registering is only automatically done when storing data. This is common in worker processes, read-only replicas, or monitoring systems. Example:
> ```python
> from redis_data_structures import RedisDataStructure
>
> # In consumer processes, register types before reading data
> redis_structure = RedisDataStructure(key="my_key")
>
> # Register your custom types
> redis_structure.register_types(User) # For SerializableType classes
> redis_structure.register_types(UserModel) # For Pydantic models
>
> # Register multiple types at once
> redis_structure.register_types([User, UserModel]) # For multiple types
>
> # Now you can safely deserialize data
> user = hash_map.get("custom_user") # Will correctly deserialize as User instance
> model = hash_map.get("pydantic_user") # Will correctly deserialize as UserModel instance
> ```
See **[type preservation](docs/type_preservation.md)** for more information.
### 🤝 Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit changes (`git commit -m 'Add amazing feature'`)
4. Push to branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
### 📝 License
This project is licensed under the MIT License - see the **[LICENSE](LICENSE)** file for details.