How to use Python cache to improve data access speed

Python uses caching

When developing web applications or distributed systems, caching is one of the common solutions, which can greatly improve system performance. In Python, we can use memory caching (for example, using functools.lru_cache) or external storage (for example, using Redis) to implement caching functions.

Django project is connected to Redis

Django is a very popular Python Web framework with many built-in functional modules, including caching. The default cache backend of the Django framework is memory cache. However, in actual applications, memory cache can easily cause OOM (Out of Memory) errors, so we need to connect the Django project to an external cache service, such as Redis.

In order to access Redis, we can use the django-redis Django plug-in. First, in the settings.py file of the project, we need to configure the Redis connection information, for example:

CACHES = {
    "default": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": "redis://127.0.0.1:6379/1",
        "OPTIONS": {
            "CLIENT_CLASS": "django_redis.client.DefaultClient",
        }
    }
}

Here we use the default django-redisAfter caching end. The LOCATION parameter specifies the Redis connection address and port, and the CLIENT_CLASS parameter in the OPTIONS parameter specifies the class name of the Redis connection client.

Next we can use the cache object in the code to perform caching operations, for example:

from django.core.cache import cache
...
data = cache.get(key)
if not data:
    data = db.query(...)
    cache.set(key, data, timeout=60)

Here we use cache.get Get the cached data. If there is no data in the cache, use the database query operation to obtain the data, and write the data into the cache through cache.set. The timeout parameter specifies the expiration time of cached data, in seconds.

Provide caching services for views

In Django, we can provide caching services for views to improve the response speed of the view. In order to provide caching services, we can use the decorators provided in the django.views.decorators.cache module.

Declarative cache

cache_pageThe decorator can cache the response results of the view into Redis, for example:

from django.views.decorators.cache import cache_page
...
@cache_page(60)
def my_view(request):
    ...

Here we use cache_pageDecorator caches the response results of the view into Redis, with an expiration time of 60 seconds.

It should be noted that cache_pageThe decorator can only be used for function views, not class views. This is because it is a decorator that decorates functions, and class view methods cannot be decorated directly. Therefore, the Django framework provides method_decorator to solve this problem. method_decorator is a decorator for decorating classes. For example:

from django.utils.decorators import method_decorator
from django.views.decorators.cache import cache_page
@method_decorator(cache_page(timeout=60), name='get')
class MyView(View):
    ...

Here we use method_decorator to apply the cache_page decorator to the get method of the class view.

Programmatic caching

In addition to declarative caching, we can also use programmatic caching to implement cache control of views. For example:

def my_view(request):
    # 先尝试从缓存中获取数据
    data = cache.get(key)
    if not data:
        # 如果缓存中没有数据，则查询数据库
        data = db.query(...)
        # 将查询结果缓存到Redis中
        cache.set(key, data, timeout=60)
    return HttpResponse(data)

Here we use cache.get to try to get the data from Redis. If it is not obtained, perform a database query operation and write the query results to Redis. .

It should be noted that the Django framework provides two ready-made variables cache and caches to support caching operations. Reading and writing operations on the cache can be achieved by sending get and set messages to the cache object, but the operations that can be done in this way are limited. If we need to operate the cache more flexibly, we can use caches['default'] to obtain the specified cache service and then operate it. For example:

from django.core.cache import caches
...
redis_cli = caches['default'].client

Cache-related issues

Cache is a very effective means of performance optimization, but in actual applications, we need to pay attention to some cache-related issues to avoid unexpected errors.

Cache avalanche

Cache avalanche is a phenomenon in which a large amount of data in the cache expires at the same time or the cache server is down, causing the cache to become invalid, causing an instantaneous increase in pressure on the database, or even a collapse. In order to avoid cache avalanche, we can use the following methods:

• Set the cache expiration time randomly to avoid a large number of caches from invalidating at the same time.

• Use distributed locks to ensure cache consistency.

• Use multi-level cache, for example, put hot data in the memory cache and cold data in Redis to avoid instantaneous pressure increase caused by cache failure.

Cache breakdown

Cache breakdown refers to the phenomenon that after a certain cache fails, a large number of requests flood into the database at the same time, causing the database to instantly increase pressure or even collapse. . In order to avoid cache breakdown, we can use the following methods:

• Use mutex locks to avoid a large number of requests from flooding into the database at the same time.

• Preload the cache, that is, refresh the cache in advance before the cache expires to avoid a large number of requests when the cache expires.

• Use hotspot data cache to place frequently requested data in the memory cache to avoid a large number of requests when the cache fails.

Cache Penetration

Cache Penetration refers to the phenomenon that there is no required data in the cache, causing requests to directly access the database, causing increased pressure on the database or even a crash. . In order to avoid cache penetration, we can use the following methods:

• For data that is not in the cache, you can set a default value to avoid requesting direct access to the database.

• Use Bloom filters to record which data does not exist in the cache to avoid direct access to the database.

• Verify the request parameters to avoid illegal requests to access the database.

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