Caching

CACHE_BACKEND=memory # in-memory: single process, resets on restart # or CACHE_BACKEND=redis # shared across processes, persistent CACHE_URL=redis://localhost:6379/0 CACHE_DEFAULT_TTL=300 # seconds

Caching function results

Results are cached by the function’s arguments, so each distinct call gets its own entry.

from feather import cached

@cached(ttl=60)
def get_user_stats(user_id):
    return calculate_stats(user_id)

stats = get_user_stats(123)   # first call: executes
stats = get_user_stats(123)   # second call: cached

get_user_stats.invalidate(user_id=123)   # invalidate when data changes

Caching route responses

from feather import cache_response

@api.get('/products')
@cache_response(ttl=300)
def list_products():
    return {'products': Product.query.all()}

# custom cache key from URL params
@api.get('/users/<user_id>')
@cache_response(ttl=60, key='user:{user_id}')
def get_user(user_id):
    return {'user': User.query.get(user_id)}

# skip the cache conditionally
@api.get('/dashboard')
@cache_response(ttl=300, unless=lambda: current_user.is_admin)
def dashboard():
    return {'stats': get_stats()}

cache_response varies on the current user by default. On a genuinely public page pass vary_on_user=False, or every visitor gets their own copy of an identical response.

Direct access

from feather import get_cache

cache = get_cache()
cache.set('key', {'data': 'value'}, ttl=60)
value = cache.get('key')      # None if expired or missing
cache.delete('key')

The memory backend is per-process. Under gunicorn --workers 4 each worker keeps its own cache, so a hit rate looks worse than it is and invalidation reaches only one worker. Use redis whenever you run more than one process.

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Caching function results

Caching route responses

Direct access

​Caching function results

​Caching route responses

​Direct access

Caching function results

Caching route responses

Direct access