$ cat services.json

Caching Strategy

Design and implement caching to dramatically improve application performance.

Deliverables:

Cache key design
Invalidation strategies
TTL optimization
Cache warming
Performance benchmarking

Real-Time Systems

Build real-time features with Redis Pub/Sub and Streams.

Deliverables:

Pub/Sub implementation
Redis Streams processing
Real-time notifications
Event broadcasting
Consumer groups

Session & State Management

Implement fast, reliable session and state management.

Deliverables:

Session store implementation
Distributed locks
Rate limiting
Feature flags
Leaderboards and counters

$ man redis

Redis Data Structures I Use

Strings - Simple caching, counters Hashes - Object storage, session data Lists - Queues, activity feeds Sets - Unique collections, tags Sorted Sets - Leaderboards, time-series Streams - Event sourcing, message queues HyperLogLog - Unique visitor counting

Production Redis Patterns

Caching Patterns

Cache-aside with graceful fallback
Write-through for consistency
Cache warming on deployment

Reliability

Redis Sentinel for HA
Redis Cluster for scaling
Connection pooling
Circuit breakers

$ cat README.md

Redis Architecture Patterns

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import redis.asyncio as redis
from functools import wraps
import hashlib
import json

class CacheService:
    """Production-grade Redis caching service"""
    
    def __init__(self, redis_url: str):
        self.redis = redis.from_url(redis_url, decode_responses=True)
    
    async def get_or_compute(
        self, 
        key: str, 
        compute_fn, 
        ttl: int = 3600
    ):
        """Cache-aside pattern with graceful degradation"""
        try:
            cached = await self.redis.get(key)
            if cached:
                return json.loads(cached)
        except redis.RedisError:
            pass  # Fallback to compute
        
        result = await compute_fn()
        
        try:
            await self.redis.setex(
                key, 
                ttl, 
                json.dumps(result)
            )
        except redis.RedisError:
            pass  # Non-blocking cache write
        
        return result
    
    async def rate_limit(
        self, 
        key: str, 
        limit: int, 
        window: int
    ) -> bool:
        """Sliding window rate limiter"""
        now = time.time()
        pipe = self.redis.pipeline()
        
        pipe.zremrangebyscore(key, 0, now - window)
        pipe.zadd(key, {str(now): now})
        pipe.zcard(key)
        pipe.expire(key, window)
        
        _, _, count, _ = await pipe.execute()
        return count <= limit

Redis Use Cases

Use Case	Data Structure	Example
Caching	String, Hash	API responses, user profiles
Sessions	Hash	User authentication data
Rate Limiting	Sorted Set	API request limits
Leaderboards	Sorted Set	Game scores, rankings
Real-Time	Pub/Sub	Notifications, live updates
Queues	List, Stream	Job processing, events
Counting	HyperLogLog	Unique visitors

Redis Deployment Options

Option	Use Case	Pros
Standalone	Dev, small apps	Simple
Sentinel	HA without sharding	Automatic failover
Cluster	Large scale	Sharding, linear scaling
AWS ElastiCache	Managed AWS	Easy ops
Redis Cloud	Fully managed	Multi-cloud

Experience:

Case Studies: Cannabis E-commerce Platform | LLM Email Assistant | Real-time EdTech Platform

Related Technologies: Python, PostgreSQL, Celery, FastAPI

$ ls -la projects/

LLM Response Caching

@ Flowrite

Challenge:

Reduce LLM API costs and latency for common email patterns.

Solution:

Redis caching with semantic similarity keys—similar prompts return cached responses. TTL-based invalidation.

Result:

Significant cost reduction and faster response times.

Real-Time Dispatch System

@ OPERR Technologies

Challenge:

Track vehicle locations and driver status in real-time for dispatch.

Solution:

Redis for location caching, Pub/Sub for status updates, sorted sets for proximity queries.

Result:

Sub-second dispatch updates for NYC NEMT operations.

Session Management

@ The Virtulab

Challenge:

Manage user sessions across microservices with low latency.

Solution:

Redis as centralized session store with hashes for session data and automatic TTL expiration.

Result:

Consistent authentication across services, sub-millisecond session lookups.

⚡ Redis

$ cat services.json

Caching Strategy

Real-Time Systems

Session & State Management

$ man redis

Redis Data Structures I Use

Production Redis Patterns

$ cat README.md

Redis Architecture Patterns

Redis Use Cases

Redis Deployment Options

$ ls -la projects/

LLM Response Caching

Real-Time Dispatch System

Session Management

$ diff me competitors/

Optimize Your Application