Python in-memory cache - v0 by Vercel

Build a thread-safe in-memory cache in Python with the following requirements: Core Features:

put(key, value, ttl=None) → Add item with optional TTL (in seconds).

get(key) → Return value if present and not expired. Also update access order for LRU.

delete(key) → Remove a key.

clear() → Clear entire cache.

Internal Design:

Use a hash map (dictionary) for O(1) lookup.

Implement LRU eviction using a doubly linked list.

Track TTL expiration using timestamps.

Support per-entry TTL and default TTL.

Use a background thread to periodically remove expired items.

LRU Logic:

Maintain a max cache size (max_size).

If size exceeded, evict least recently used item in O(1) time.

Thread-Safe:

All operations must be thread-safe.

Use threading.Lock or RLock to protect shared structures.

Avoid deadlocks and race conditions.

Statistics:

Implement get_stats() returning:

{
  "hits": int,
  "misses": int,
  "hit_rate": float,
  "total_requests": int,
  "current_size": int,
  "evictions": int,
  "expired_removals": int
}

Tests:

Add test cases for:

    Basic operations (put, get, delete, clear)

    LRU eviction after exceeding size

    TTL expiration

    Concurrent access using threading.Thread

Extras:

Add logging to show evictions, TTL expiry, and background cleaner actions.

Make sure expired entries are removed on access and in background cleanup.

Output:

Clean, modular Python code.

Include comments and error handling.

Include README.md explaining design, usage, and how to run the tests.

Show full message

133d ago

I'll build a comprehensive thread-safe in-memory cache system in Python with all the features you requested. Let me break this down into modular components.

I've created a comprehensive thread-safe in-memory cache system with all the features you requested:

Key Features Implemented:

Core Operations: put(), get(), delete(), clear() with O(1) performance
LRU Eviction: Doubly linked list implementation for efficient LRU tracking
TTL Support: Per-entry and default TTL with timestamp-based expiration
Thread Safety: Using threading.RLock for all operations
Background Cleanup: Daemon thread for proactive expired entry removal
Statistics: Comprehensive metrics tracking hits, misses, evictions, etc.
Modular Design: Separated into logical components for maintainability

Architecture Highlights:

Hash Map + Doubly Linked List: Classic LRU cache design for O(1) operations
Thread-Safe Statistics: Separate locking for metrics to avoid contention
Background Cleanup: Non-blocking expired entry removal
Comprehensive Testing: Unit tests, performance tests, and concurrent access tests

Usage Examples:

The system includes extensive examples and tests demonstrating:

Basic CRUD operations
LRU eviction behavior
TTL expiration
Concurrent access patterns
Performance characteristics

You can run the tests to verify functionality:

python -m tests.test_cache
python -m tests.test_performance
python examples/usage_example.py

The implementation is production-ready with proper error handling, logging, and documentation. All operations are thread-safe and the system handles edge cases gracefully.

133d ago

how should I show this to an interviewer, do we have to make a front end part for this?

133d ago

Great question! For a technical interview, you don't need a frontend for this type of system-level component. Here's how to effectively present this cache implementation to an interviewer:

1. Demo Strategy (Choose Based on Interview Format)

Option A: Live Code Walkthrough (Recommended)

# Create a simple demo script that shows key features
def demo_for_interviewer():
    print("=== Thread-Safe Cache Demo ===\n")
    
    # Initialize cache
    cache = ThreadSafeCache(max_size=3, default_ttl=None)
    
    print("1. Basic Operations:")
    cache.put("user:1", {"name": "Alice", "role": "admin"})
    cache.put("user:2", {"name": "Bob", "role": "user"})
    print(f"   Retrieved user:1 -> {cache.get('user:1')}")
    print(f"   Cache size: {cache.size()}")
    
    print("\n2. LRU Eviction Demo:")
    cache.put("user:3", {"name": "Charlie", "role": "user"})
    cache.get("user:1")  # Make user:1 most recently used
    cache.put("user:4", {"name": "David", "role": "user"})  # Should evict user:2
    print(f"   user:2 after eviction: {cache.get('user:2')}")  # None
    print(f"   user:1 still exists: {cache.get('user:1')}")   # Still there
    
    print("\n3. TTL Demo:")
    cache.put("session:abc", "active", ttl=2)
    print(f"   Session immediately: {cache.get('session:abc')}")
    time.sleep(2.1)
    print(f"   Session after 2s: {cache.get('session:abc')}")  # None
    
    print("\n4. Statistics:")
    stats = cache.get_stats()
    print(f"   Hit rate: {stats['hit_rate']:.1%}")
    print(f"   Total requests: {stats['total_requests']}")
    print(f"   Evictions: {stats['evictions']}")
    
    cache.shutdown()

Option B: Interactive Python REPL Demo

Show the interviewer how to use it interactively:

>>> from cache import ThreadSafeCache
>>> cache = ThreadSafeCache(max_size=5)
>>> cache.put("key1", "value1")
>>> cache.get("key1")
'value1'
>>> cache.get_stats()
{'hits': 1, 'misses': 0, 'hit_rate': 1.0, ...}

2. Presentation Structure (15-20 minutes)

Phase 1: Problem & Design (5 mins)

"I built a thread-safe in-memory cache with these requirements:
- O(1) get/put operations
- LRU eviction when full
- TTL support with background cleanup
- Thread-safe for concurrent access
- Statistics tracking

Key design decisions:
- HashMap + Doubly Linked List for LRU
- RLock for thread safety
- Background daemon thread for cleanup"

Phase 2: Code Architecture (5 mins)

Show the file structure and explain:

cache/
├── __init__.py           # Public API
├── thread_safe_cache.py  # Main implementation
├── lru_list.py          # Doubly linked list
├── node.py              # Cache node structure
└── stats.py             # Statistics tracking

Phase 3: Live Demo (5-10 mins)

Run your demo script showing:

Basic operations
LRU eviction in action
TTL expiration
Thread safety (if time permits)
Statistics output

3. What Interviewers Want to See

Technical Depth:

Time Complexity: "All operations are O(1) because..."
Thread Safety: "I used RLock because it's reentrant and..."
Memory Management: "The doubly linked list allows O(1) eviction..."

Code Quality:

Clean, modular design
Comprehensive error handling
Good documentation
Extensive test coverage

System Thinking:

"I added background cleanup to prevent memory leaks"
"Statistics help with monitoring and tuning"
"The design scales to handle high concurrency"

4. Preparation Tips

Before the Interview:

# Test everything works
python examples/usage_example.py
python -m tests.test_cache
python -m tests.test_performance

Have Ready to Discuss:

Trade-offs: "I chose RLock over Lock because..."
Alternatives: "Could use Redis, but this is embedded..."
Scaling: "For distributed caching, we'd need..."
Monitoring: "The statistics help identify..."

5. Common Follow-up Questions & Answers

Q: "How would you make this distributed?" A: "Add consistent hashing, network protocol, replication..."

Q: "What about persistence?" A: "Could add write-through/write-behind to disk or database..."

Q: "Performance bottlenecks?" A: "Lock contention is main bottleneck. Could use lock-free structures or sharding..."

Q: "How to monitor in production?" A: "Export metrics to Prometheus, add health checks, log eviction patterns..."

6. No Frontend Needed Because:

This is infrastructure/backend code
Interviewers want to see system design and algorithms
A cache is a library component, not a user-facing application
Your tests and examples demonstrate functionality better than a UI
Focus should be on performance, correctness, and design

7. If They Ask for Visualization:

You could quickly create a simple monitoring script:

def monitor_cache():
    cache = ThreadSafeCache(max_size=10)
    
    while True:
        stats = cache.get_stats()
        print(f"Size: {stats['current_size']}/10 | "
              f"Hit Rate: {stats['hit_rate']:.1%} | "
              f"Evictions: {stats['evictions']}")
        time.sleep(1)

Bottom Line: Your comprehensive implementation with tests, examples, and documentation is exactly what technical interviewers want to see. No frontend required!

133d ago

what we have done here how will i explain to interviewer

133d ago

benchmark.py

Build a thread-safe in-memory cache in Python with the following requirements: Core Features:

put(key, value, ttl=None) → Add item with optional TTL (in seconds).

get(key) → Return value if present and not expired. Also update access order for LRU.

delete(key) → Remove a key.

clear() → Clear entire cache.

Internal Design:

Use a hash map (dictionary) for O(1) lookup.

Implement LRU eviction using a doubly linked list.

Track TTL expiration using timestamps.

Support per-entry TTL and default TTL.

Use a background thread to periodically remove expired items.

LRU Logic:

Maintain a max cache size (max_size).

If size exceeded, evict least recently used item in O(1) time.

Thread-Safe:

All operations must be thread-safe.

Use threading.Lock or RLock to protect shared structures.

Avoid deadlocks and race conditions.

Statistics:

Implement get_stats() returning:

{
  "hits": int,
  "misses": int,
  "hit_rate": float,
  "total_requests": int,
  "current_size": int,
  "evictions": int,
  "expired_removals": int
}

Tests:

Add test cases for:

    Basic operations (put, get, delete, clear)

    LRU eviction after exceeding size

    TTL expiration

    Concurrent access using threading.Thread

Extras:

Add logging to show evictions, TTL expiry, and background cleaner actions.

Make sure expired entries are removed on access and in background cleanup.

Output:

Clean, modular Python code.

Include comments and error handling.

Include README.md explaining design, usage, and how to run the tests.

Show full message

133d ago

I'll build a comprehensive thread-safe in-memory cache system in Python with all the features you requested. Let me break this down into modular components.

I've created a comprehensive thread-safe in-memory cache system with all the features you requested:

Key Features Implemented:

Core Operations: put(), get(), delete(), clear() with O(1) performance
LRU Eviction: Doubly linked list implementation for efficient LRU tracking
TTL Support: Per-entry and default TTL with timestamp-based expiration
Thread Safety: Using threading.RLock for all operations
Background Cleanup: Daemon thread for proactive expired entry removal
Statistics: Comprehensive metrics tracking hits, misses, evictions, etc.
Modular Design: Separated into logical components for maintainability

Architecture Highlights:

Hash Map + Doubly Linked List: Classic LRU cache design for O(1) operations
Thread-Safe Statistics: Separate locking for metrics to avoid contention
Background Cleanup: Non-blocking expired entry removal
Comprehensive Testing: Unit tests, performance tests, and concurrent access tests

Usage Examples:

The system includes extensive examples and tests demonstrating:

Basic CRUD operations
LRU eviction behavior
TTL expiration
Concurrent access patterns
Performance characteristics

You can run the tests to verify functionality:

python -m tests.test_cache
python -m tests.test_performance
python examples/usage_example.py

The implementation is production-ready with proper error handling, logging, and documentation. All operations are thread-safe and the system handles edge cases gracefully.

133d ago

how should I show this to an interviewer, do we have to make a front end part for this?

133d ago

Great question! For a technical interview, you don't need a frontend for this type of system-level component. Here's how to effectively present this cache implementation to an interviewer:

1. Demo Strategy (Choose Based on Interview Format)

Option A: Live Code Walkthrough (Recommended)

# Create a simple demo script that shows key features
def demo_for_interviewer():
    print("=== Thread-Safe Cache Demo ===\n")
    
    # Initialize cache
    cache = ThreadSafeCache(max_size=3, default_ttl=None)
    
    print("1. Basic Operations:")
    cache.put("user:1", {"name": "Alice", "role": "admin"})
    cache.put("user:2", {"name": "Bob", "role": "user"})
    print(f"   Retrieved user:1 -> {cache.get('user:1')}")
    print(f"   Cache size: {cache.size()}")
    
    print("\n2. LRU Eviction Demo:")
    cache.put("user:3", {"name": "Charlie", "role": "user"})
    cache.get("user:1")  # Make user:1 most recently used
    cache.put("user:4", {"name": "David", "role": "user"})  # Should evict user:2
    print(f"   user:2 after eviction: {cache.get('user:2')}")  # None
    print(f"   user:1 still exists: {cache.get('user:1')}")   # Still there
    
    print("\n3. TTL Demo:")
    cache.put("session:abc", "active", ttl=2)
    print(f"   Session immediately: {cache.get('session:abc')}")
    time.sleep(2.1)
    print(f"   Session after 2s: {cache.get('session:abc')}")  # None
    
    print("\n4. Statistics:")
    stats = cache.get_stats()
    print(f"   Hit rate: {stats['hit_rate']:.1%}")
    print(f"   Total requests: {stats['total_requests']}")
    print(f"   Evictions: {stats['evictions']}")
    
    cache.shutdown()

Option B: Interactive Python REPL Demo

Show the interviewer how to use it interactively:

>>> from cache import ThreadSafeCache
>>> cache = ThreadSafeCache(max_size=5)
>>> cache.put("key1", "value1")
>>> cache.get("key1")
'value1'
>>> cache.get_stats()
{'hits': 1, 'misses': 0, 'hit_rate': 1.0, ...}

2. Presentation Structure (15-20 minutes)

Phase 1: Problem & Design (5 mins)

"I built a thread-safe in-memory cache with these requirements:
- O(1) get/put operations
- LRU eviction when full
- TTL support with background cleanup
- Thread-safe for concurrent access
- Statistics tracking

Key design decisions:
- HashMap + Doubly Linked List for LRU
- RLock for thread safety
- Background daemon thread for cleanup"

Phase 2: Code Architecture (5 mins)

Show the file structure and explain:

cache/
├── __init__.py           # Public API
├── thread_safe_cache.py  # Main implementation
├── lru_list.py          # Doubly linked list
├── node.py              # Cache node structure
└── stats.py             # Statistics tracking

Phase 3: Live Demo (5-10 mins)

Run your demo script showing:

Basic operations
LRU eviction in action
TTL expiration
Thread safety (if time permits)
Statistics output

3. What Interviewers Want to See

Technical Depth:

Time Complexity: "All operations are O(1) because..."
Thread Safety: "I used RLock because it's reentrant and..."
Memory Management: "The doubly linked list allows O(1) eviction..."

Code Quality:

Clean, modular design
Comprehensive error handling
Good documentation
Extensive test coverage

System Thinking:

"I added background cleanup to prevent memory leaks"
"Statistics help with monitoring and tuning"
"The design scales to handle high concurrency"

4. Preparation Tips

Before the Interview:

# Test everything works
python examples/usage_example.py
python -m tests.test_cache
python -m tests.test_performance

Have Ready to Discuss:

Trade-offs: "I chose RLock over Lock because..."
Alternatives: "Could use Redis, but this is embedded..."
Scaling: "For distributed caching, we'd need..."
Monitoring: "The statistics help identify..."

5. Common Follow-up Questions & Answers

Q: "How would you make this distributed?" A: "Add consistent hashing, network protocol, replication..."

Q: "What about persistence?" A: "Could add write-through/write-behind to disk or database..."

Q: "Performance bottlenecks?" A: "Lock contention is main bottleneck. Could use lock-free structures or sharding..."

Q: "How to monitor in production?" A: "Export metrics to Prometheus, add health checks, log eviction patterns..."

6. No Frontend Needed Because:

This is infrastructure/backend code
Interviewers want to see system design and algorithms
A cache is a library component, not a user-facing application
Your tests and examples demonstrate functionality better than a UI
Focus should be on performance, correctness, and design

7. If They Ask for Visualization:

You could quickly create a simple monitoring script:

def monitor_cache():
    cache = ThreadSafeCache(max_size=10)
    
    while True:
        stats = cache.get_stats()
        print(f"Size: {stats['current_size']}/10 | "
              f"Hit Rate: {stats['hit_rate']:.1%} | "
              f"Evictions: {stats['evictions']}")
        time.sleep(1)

Bottom Line: Your comprehensive implementation with tests, examples, and documentation is exactly what technical interviewers want to see. No frontend required!

what we have done here how will i explain to interviewer

133d ago

thread_safe_cache.py