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如何在 Flask-Restful 中使用 Flask-Cache

[英]How to use Flask-Cache with Flask-Restful

How do I use Flask-Cache @cache.cached() decorator with Flask-Restful?如何将 Flask-Cache @cache.cached() 装饰器与 Flask-Restful 一起使用? For example, I have a class Foo inherited from Resource, and Foo has get, post, put, and delete methods.比如我有一个class的Foo继承自Resource,Foo有get、post、put、delete方法。

How can I can invalidate cached results after a POST ?如何在POST后使缓存的结果无效?

@api.resource('/whatever')
class Foo(Resource):
    @cache.cached(timeout=10)
    def get(self):
        return expensive_db_operation()

    def post(self):
        update_db_here()

        ## How do I invalidate the value cached in get()?
        return something_useful()

Yes, you can use like that. 是的,你可以这样使用。

Maybe you will still need to read: flask-cache memoize URL query string parameters as well 也许您仍然需要阅读: flask-cache memoize URL查询字符串参数

As Flask-Cache implementation doesn't give you access to the underlying cache object, you'll have to explicitly instantiate a Redis client and use it's keys method (list all cache keys). 由于Flask-Cache实现不允许您访问底层cache对象,因此您必须显式实例化Redis客户端并使用它的keys方法(列出所有缓存键)。

  • The cache_key method is used to override the default key generation in your cache.cached decorator. cache_key方法用于覆盖cache.cached装饰器中的默认密钥生成。
  • The clear_cache method will clear only the portion of the cache corresponding to the current resource. clear_cache方法将仅清除与当前资源相对应的缓存部分。

This is a solution that was tested only for Redis and the implementation will probably differ a little when using a different cache engine. 这是一个仅针对Redis进行测试的解决方案,使用不同的缓存引擎时,实现可能略有不同。

from app import cache # The Flask-Cache object
from config import CACHE_REDIS_HOST, CACHE_REDIS_PORT # The Flask-Cache config
from redis import Redis
from flask import request
import urllib

redis_client = Redis(CACHE_REDIS_HOST, CACHE_REDIS_PORT)

def cache_key():
   args = request.args
   key = request.path + '?' + urllib.urlencode([
     (k, v) for k in sorted(args) for v in sorted(args.getlist(k))
   ])
   return key

@api.resource('/whatever')
class Foo(Resource):

    @cache.cached(timeout=10, key_prefix=cache_key)
    def get(self):
        return expensive_db_operation()

    def post(self):
        update_db_here()
        self.clear_cache()
        return something_useful()

    def clear_cache(self):
        # Note: we have to use the Redis client to delete key by prefix,
        # so we can't use the 'cache' Flask extension for this one.
        key_prefix = request.path
        keys = [key for key in redis_client.keys() if key.startswith(key_prefix)]
        nkeys = len(keys)
        for key in keys:
            redis_client.delete(key)
        if nkeys > 0:
            log.info("Cleared %s cache keys" % nkeys)
            log.info(keys)

You can invalidate cache using cache.clear() method. 您可以使用cache.clear()方法使缓存无效。
For more detials see: https://pythonhosted.org/Flask-Cache/#flask.ext.cache.Cache.clear and Clearing Cache section in https://pythonhosted.org/Flask-Cache/ 欲了解更多detials看到: https://pythonhosted.org/Flask-Cache/#flask.ext.cache.Cache.clear和清除缓存部分https://pythonhosted.org/Flask-Cache/

Answer from @JahMyst didn't work for me. @JahMyst 的回答对我不起作用。 Flask-Cache doesn't work with Flask restful framework. Flask-Cache 不适用于 Flask restful 框架。 @cache.Cached & @cache.memoize can't handle mutable objects per their documentation. @cache.Cached 和@cache.memoize 无法根据其文档处理可变对象。

Using mutable objects (classes, etc) as part of the cache key can become tricky. It is suggested to not pass in an object instance into a memoized function. However, the memoize does perform a repr() on the passed in arguments so that if the object has a __repr__ function that returns a uniquely identifying string for that object, that will be used as part of the cache key.

Had to come-up with my own implementation.不得不想出我自己的实现。 Leaving this code snippet incase someone else gets stuck with the same issue.留下这个代码片段以防其他人遇到同样的问题。

cache_key function converts the user req into hash. cache_res_pickled function is being used to pickle or unpickle the data cache_key function 将用户请求转换为 hash。cache_res_pickled cache_res_pickled用于 pickle 或 unpickle 数据

|-flask-app
   |-app.py
   |-resource
      |--some_resource.py
import json
import logging
import pickle
import time
import urllib

from flask import Response, abort, request
from redis import Redis

redis_client = Redis("127.0.0.1", "6379")
exp_setting_s = 1500


def json_serial(obj):
    """
    JSON serializer for objects not serializable by default json code"
    Args:
            obj: JSON serialized object for dates

    Returns:
            serialized JSON data
    """
    if isinstance(obj, datetime.datetime):
        return obj.__str__()


def cache_key():
    """ ""
    Returns: Hashed string of request made by the user.

    """
    args = request.args
    key = (
        request.path
        + "?"
        + urllib.parse.urlencode(
            [(k, v) for k in sorted(args) for v in sorted(args.getlist(k))]
        )
    )
    key_hashed = hashlib.sha256(key.encode())
    return key_hashed.hexdigest()


def cache_res_pickled(data, encode):
    """
    Args:
        data (dict): Data in dict format
        encode (Boolean): Encode (true) or decode (false) the data

    Returns: Result after pickling
    """
    if encode:
        return pickle.dumps(data)
    else:
        data = pickle.loads(data)
        return data


class SomeResource(Resource):
    @auth.login_required
    def get(self):
        # Get the key for request in hashed format SHA256
        key = cache_key()
        result = redis_client.get(key)

        def generate():
            """
            A lagging generator to stream JSON so we don't have to hold everything in memory
            This is a little tricky, as we need to omit the last comma to make valid JSON,
            thus we use a lagging generator, similar to http://stackoverflow.com/questions/1630320/
            """
            releases = res.__iter__()
            try:
                prev_release = next(releases)  # get first result
                # We have some releases. First, yield the opening json
                yield '{"data": ['
                # Iterate over the releases
                for release in releases:
                    yield json.dumps(prev_release, default=json_serial) + ", "
                    prev_release = release
                logging.info(f"For {key} # records returned = {len(res)}")
                # Now yield the last iteration without comma but with the closing brackets
                yield json.dumps(prev_release, default=json_serial) + "]}"
            except StopIteration:
                # StopIteration here means the length was zero, so yield a valid releases doc and stop
                logging.info(f"For {key} # records returned = {len(res)}")
                yield '{"data": []}'

        if result is None:
            # Secure a key on Redis server.
            redis_client.set(key, cache_res_pickled({}, True), ex=exp_setting_s)

            try:
                # Do the querying to the DB or math here to get res. It should be in dict format as shown below
                res = {"A": 1, "B": 2, "C": 2}
                # Update the key on Redis server with the latest data
                redis_client.set(key, cache_res_pickled(res, True), ex=exp_setting_s)
                return Response(generate(), content_type="application/json")
            except Exception as e:
                logging.exception(e)
                abort(505, description="Resource not found. error - {}".format(e))
        else:
            res = cache_res_pickled(result, False)
            if res:
                logging.info(
                    f"The data already exists!😊 loading the data form Redis cache for Key - {key} "
                )
                return Response(generate(), content_type="application/json")
            else:
                logging.info(
                    f"There is already a request for this key. But there is no data in it. Key: {key}."
                )
                s = time.time()
                counter = 0
                # loops aimlessly till the data is available on the Redis
                while not any(res):
                    result = redis_client.get(key)
                    res = cache_res_pickled(result, False)
                    counter += 1
                logging.info(
                    f"The data was available after {time.time() - s} seconds. Had to loop {counter} times.🤦‍"
                )
                return Response(generate(), content_type="application/json")
##create a decarator
from werkzeug.contrib.cache import SimpleCache
CACHE_TIMEOUT = 300
cache = SimpleCache()
class cached(object):

 def __init__(self, timeout=None):
    self.timeout = timeout or CACHE_TIMEOUT

 def __call__(self, f):
    def decorator(*args, **kwargs):
        response = cache.get(request.path)
        if response is None:
            response = f(*args, **kwargs)
            cache.set(request.path, response, self.timeout)
        return response
    return decorator


#add this decarator to your views like below
@app.route('/buildingTotal',endpoint='buildingTotal') 
@cached()
def eventAlert():
  return 'something'

@app.route('/buildingTenants',endpoint='buildingTenants')
@cached()
def buildingTenants():
  return 'something'

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