Redis

Distributed in-memory storage for production deployments

Overview

For production environments where horizontal scaling is required, use Redis as your Arivu memory backend.

Pros ✅

• Distributed architecture - Share state across multiple workers
• High performance - In-memory data store with microsecond latency
• Scalable - Handles high concurrency efficiently
• Cluster support - Redis cluster for fault tolerance
• Session sharing - Multiple instances can access the same sessions

Cons ⚠️

• Requires external service - Must run a Redis server
• Data persistence overhead - Requires configuration for durability
• Memory constraints - Limited by available RAM

Configuration

Redis must be installed and running before Arivu can connect to it. Choose a setup method below:

Docker (Recommended)

Easiest way to get Redis running locally:

docker run -d -p 6379:6379 redis:latest

Local Installation

macOS

brew install redis
redis-server

Ubuntu/Debian

sudo apt-get install redis-server
redis-server

Windows

Use WSL2 or Docker (recommended)

1. Set Environment Variables

   Add these to your .env file:

   MEMORY_BACKEND=redis
   REDIS_URL=redis://localhost:6379/0
   
2. Verify Connection

   Test your Redis connection:

   redis-cli ping
   # Should respond with: PONG
   
3. Initialize Arivu

   Start using Redis in your Python code:

   from arivu.memory import RedisBackend, SessionStore
   
   backend = RedisBackend(redis_url="redis://localhost:6379/0")
   store = SessionStore(backend=backend)
   

Connection String Format

redis://[user:password@]host:port[/db-number]

Local Development

redis://localhost:6379/0

Performance Optimization

Connection Pooling

RedisBackend automatically handles connection pooling, so you don’t need to configure it manually.

Data Persistence

Configure RDB or AOF to ensure your data survives Redis restarts:

# In redis.conf
save 900 1          # Save if 1 key changed in 900 seconds
appendonly yes      # Enable AOF persistence

Memory Management

Redis stores everything in RAM. Monitor memory usage to prevent out-of-memory errors.

• Set a max memory policy: maxmemory-policy allkeys-lru
• Monitor with redis-cli INFO memory
• Clean up old sessions regularly

Clustering

For high availability, use Redis Cluster:

redis-cli cluster create node1:6379 node2:6379 node3:6379 \
  node4:6379 node5:6379 node6:6379

Production Deployment

Standard Setup

REDIS_URL=redis://redis-master:6379/0
MEMORY_BACKEND=redis

Clustered Setup

REDIS_URL=redis://redis-master:6379/0
REDIS_CLUSTER=true
REDIS_REPLICAS=2

Kubernetes

apiVersion: v1
kind: ConfigMap
metadata:
  name: arivu-config
data:
  MEMORY_BACKEND: redis
  REDIS_URL: redis://redis-service:6379/0

When to Use Redis

Use Redis when you:

• Need to scale horizontally (multiple workers)
• Require high performance and low latency
• Want to share sessions across services
• Are deploying to production
• Need fault tolerance and failover

Redis is ideal for microservices architectures where multiple Arivu instances need to share session state.

Monitoring & Troubleshooting

Health Checks

Test your Redis connection:

# Check if Redis is responding
redis-cli ping
# Should respond with: PONG

# Check connected clients
redis-cli INFO clients

# Monitor memory usage
redis-cli INFO memory

# Check database size
redis-cli DBSIZE

Troubleshooting

Connection Refused

Problem: Cannot connect to Redis

Solutions:

• Ensure Redis server is running: redis-cli ping
• Check connection string is correct
• Verify firewall rules allow the port
• Check credentials if using authentication

High Memory Usage

Problem: Redis consuming too much memory

Solutions:

• Review session retention policies
• Clear old sessions: redis-cli FLUSHALL (use with caution)
• Set max memory limit: maxmemory 2gb in config
• Monitor with redis-cli INFO memory

Performance Issues

Problem: Slow session operations

Solutions:

• Check Redis response times: redis-cli --latency
• Verify network connectivity
• Consider Redis clustering for high load
• Monitor with redis-cli --stat