# RomM Redis Caching Strategies: Sessions, API Responses, and Job Queues

> Discover RomM's Redis caching strategies for sessions, API responses, and job queues. Learn how RomM unifies Redis for efficient asynchronous and synchronous data management.

- Repository: [The RomM Project/romm](https://github.com/rommapp/romm)
- Tags: deep-dive
- Published: 2026-07-06

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**RomM uses a unified Redis instance configured via `REDIS_URL` to handle asynchronous session storage, synchronous API response caching, and priority-based background job queues.**

The open-source RomM application leverages Redis as a centralized caching layer to manage user sessions, cache expensive API computations, and queue background tasks. According to the rommapp/romm source code, the platform employs distinct Redis clients and data structures optimized for each use case, ensuring high performance and scalability across the retro game management workflow.

## Session Storage with Async Redis

RomM persists user sessions using an asynchronous Redis client (`async_cache`) that provides non-blocking I/O for the FastAPI application layer.

### RedisSessionMiddleware Implementation

In [`backend/handler/auth/middleware/redis_session_middleware.py`](https://github.com/rommapp/romm/blob/main/backend/handler/auth/middleware/redis_session_middleware.py), the `RedisSessionMiddleware` class manages session lifecycle using Redis hash data structures. Each session stores data under the key pattern `session:<session_id>`, enabling distributed stateless authentication across multiple application instances.

The middleware automatically handles session expiration using Redis's native `EXPIRE` command:

```python
class RedisSessionMiddleware:
    async def load_session(self, request):
        session_id = request.cookies.get("session_id")
        if session_id:
            data = await async_cache.hgetall(f"session:{session_id}")
            return Session(data)
        return Session()

    async def save_session(self, response, session):
        await async_cache.hmset_dict(f"session:{session.id}", session.data)
        await async_cache.expire(f"session:{session.id}", SESSION_TTL)

```

This implementation ensures that session data remains available for the duration of the `SESSION_TTL` while automatically cleaning up expired entries without requiring additional database queries.

## API Response Caching with Sync Redis

For data-intensive operations like ROM filtering and device authentication, RomM utilizes a synchronous Redis client (`sync_cache`) defined in [`backend/handler/redis_handler.py`](https://github.com/rommapp/romm/blob/main/backend/handler/redis_handler.py). This client runs with `decode_responses=True` to handle string serialization automatically.

### Cache Implementation Details

The synchronous cache stores frequently accessed data with Time-To-Live (TTL) values to prevent stale data accumulation. Helper functions in [`backend/utils/cache.py`](https://github.com/rommapp/romm/blob/main/backend/utils/cache.py) wrap the raw Redis operations, while direct calls use `sync_cache.get`, `sync_cache.setex`, and `sync_cache.sadd` for atomic operations.

The following pattern demonstrates how RomM caches filtered ROM lists with version tracking:

```python
def get_filtered_roms(cache_key, version):
    redis_key = f"filter_values:probe:{cache_key}:v{version}"
    cached = sync_cache.get(redis_key)
    if cached:
        return json.loads(cached)

    result = compute_filters(...)
    sync_cache.setex(redis_key, ROM_FILTERS_CACHE_TTL, json.dumps(result))
    sync_cache.sadd(f"filter_versions:{cache_key}", redis_key)
    return result

```

This approach reduces database load by serving cached filter results from memory while maintaining cache coherence through Redis sets that track related keys.

## Background Job Queues with RQ

RomM implements a priority-based task queue system using the Python RQ (Redis Queue) library, sharing the same Redis server instance but operating on distinct queue objects.

### Queue Priorities and Worker Management

In [`backend/handler/redis_handler.py`](https://github.com/rommapp/romm/blob/main/backend/handler/redis_handler.py), RomM defines three distinct queues with different priority levels:

- **high_prio_queue**: Immediate execution tasks like library refreshes
- **default_queue**: Standard metadata operations
- **low_prio_queue**: Background maintenance and bulk updates

Task enqueueing occurs throughout [`backend/tasks/tasks.py`](https://github.com/rommapp/romm/blob/main/backend/tasks/tasks.py) using the following pattern:

```python

# Enqueue a low-priority job

low_prio_queue.enqueue("backend.tasks.tasks.update_metadata", rom_id, retry=False)

# Enqueue a high-priority job

high_prio_queue.enqueue("backend.tasks.tasks.refresh_library", force=True)

```

Worker processes defined in [`backend/watcher.py`](https://github.com/rommapp/romm/blob/main/backend/watcher.py) consume jobs from these queues using the RQ `Worker` class. The `get_job_func_name` helper function in [`backend/handler/redis_handler.py`](https://github.com/rommapp/romm/blob/main/backend/handler/redis_handler.py) safely extracts job function names for logging and error tracking:

```python
def get_job_func_name(job):
    """Safely extract function name from RQ job for monitoring."""
    if hasattr(job, 'func_name'):
        return job.func_name
    return str(job.func)

```

This architecture ensures that critical user-facing operations (like library scans) process immediately while relegating lengthy metadata fetches to lower-priority queues.

## Summary

- **Unified Redis Architecture**: RomM configures a single Redis instance via `REDIS_URL` in [`backend/config/config_manager.py`](https://github.com/rommapp/romm/blob/main/backend/config/config_manager.py), sharing connection pools across sessions, cache, and queues.
- **Async Session Management**: The `RedisSessionMiddleware` uses `async_cache` (aioredis-compatible) to store session hashes with automatic expiration, eliminating the need for server-side session state.
- **Synchronous API Caching**: The `sync_cache` client handles ROM filter results and authentication tokens with TTL-based expiration, implemented in [`backend/utils/cache.py`](https://github.com/rommapp/romm/blob/main/backend/utils/cache.py) and [`backend/handler/redis_handler.py`](https://github.com/rommapp/romm/blob/main/backend/handler/redis_handler.py).
- **Priority Job Processing**: Three RQ queues (`high_prio_queue`, `default_queue`, `low_prio_queue`) manage background tasks with dedicated workers in [`backend/watcher.py`](https://github.com/rommapp/romm/blob/main/backend/watcher.py), ensuring responsive UI performance during intensive operations.

## Frequently Asked Questions

### How does RomM handle session expiration in Redis?

RomM's `RedisSessionMiddleware` automatically sets expiration timestamps on session keys using Redis's `EXPIRE` command. When `save_session` is called, it executes `await async_cache.expire(f"session:{session.id}", SESSION_TTL)`, ensuring that inactive sessions automatically purge from Redis after the configured timeout period without requiring manual cleanup.

### What is the difference between async_cache and sync_cache in RomM?

`async_cache` is an `aioredis`-compatible client (`redis.asyncio.Redis`) used exclusively for session management in the async request cycle, while `sync_cache` is a synchronous `redis.Redis` client used for API response caching and data retrieval. The separation prevents blocking the event loop during cache operations while allowing synchronous code paths in background tasks to access cached data efficiently.

### How are job priorities managed in RomM's Redis queue system?

RomM creates three distinct RQ Queue instances in [`backend/handler/redis_handler.py`](https://github.com/rommapp/romm/blob/main/backend/handler/redis_handler.py): `high_prio_queue`, `default_queue`, and `low_prio_queue`. Tasks enqueue to specific queues based on urgency—library scans use high priority, while metadata updates use low priority. Workers process jobs from these queues according to RQ's priority semantics, ensuring critical operations complete before background maintenance tasks.

### Where is the Redis connection configured in RomM?

The Redis connection URL loads from the environment variable `REDIS_URL` in [`backend/config/config_manager.py`](https://github.com/rommapp/romm/blob/main/backend/config/config_manager.py). This configuration initializes the shared Redis client used by `async_cache`, `sync_cache`, and the RQ queue system, ensuring all caching layers connect to the same Redis instance for consistency and simplified deployment.