To implement a cache policy in Python web applications, you need to choose the right cache type, set the cache time reasonably, and use it in combination with the scenario. 1. Select the cache type: Small applications can be cached with memory (such as Flask SimpleCache), temporary large amounts of data can be cached with file, and production environments are recommended for Redis or Memcached to support distributed deployment. 2. Cache response results: Use the decorator provided by the framework such as @cache.cached() of Flask or @cache_page() of Django to cache highly accessed pages or interfaces. 3. Key cache dynamic data: manually set cache keys to achieve more flexible data cache, avoiding waste and conflicts. 4. Set a reasonable expiration time and actively update: Static content can be set to have a longer cache time, user-related data should be set to have a shorter time and cache should be automatically cleared when data is updated. Rationally designing cache structures and strategies can improve performance and avoid cache penetration, avalanches and other problems.

Implementing caching strategies in Python web applications is a key means to improve performance and response speed. Caching can reduce the pressure on the database by repeated requests, while speeding up page loading and improving user experience. The key is to choose the right cache type, set a reasonable cache time, and use it flexibly in combination with specific scenarios.

Use the appropriate cache type

Common caching methods in Python web applications include memory cache, file cache and distributed cache (such as Redis, Memcached). Which method to choose depends on your application size and deployment environment.

• Memory Cache : Suitable for small applications or development environments, such as Flask's SimpleCache or Django's local memory cache. Fast speed, but data will be lost after restart.
• File Caching : It is suitable for temporarily cached large amounts of data, but the disadvantage is that the read and write speed is not as good as memory.
• Redis / Memcached : Suitable for production environments and distributed deployments, supports persistence, cross-node shared cache, and stable performance.

If it is an application deployed on multiple servers, it is recommended to use Redis as the cache backend first.

Cache response results in view functions

Many web frameworks provide decorators to simplify cache operations. For example, in Flask, @cache.cached() can be used to cache the output of the entire view:

 @app.route('/data')
@cache.cached(timeout=300) # Cache for 5 minutes def get_data():
    return fetch_expensive_data()

There is also a similar decorator @cache_page() in Django, which is also very convenient to use:

 from django.views.decorators.cache import cache_page

@cache_page(60 * 15) # Cache for 15 minutes def my_view(request):
    ...

This method is suitable for caching pages or interfaces that do not change frequently and access frequently.

Keypad dynamic data

Sometimes we don’t want to cache the entire page, but cache some data with high computational cost, such as database query results or complex calculations. At this time, you can manually use the cache key to control it.

For example:

 user_profile = cache.get(f'user_profile_{user_id}')
if not user_profile:
    user_profile = fetch_user_profile_from_db(user_id)
    cache.set(f'user_profile_{user_id}', user_profile, timeout=3600)

The advantage of this is that it is more flexible, and it can dynamically generate cache keys based on users, parameters, etc. to avoid cache waste or conflicts.

Set a reasonable expiration time and update mechanism

Cache is not a one-time thing. Setting an expiration time too short will cause frequent rebuilding of caches and lose the meaning of caches; setting too long may cause data inconsistency.

• For static content (such as configuration, public data), a longer cache time (such as 24 hours).
• For user-related data or frequently updated data, it is recommended to set a short time (such as within 1 hour) and actively clear the cache when the data is updated.

An example of actively clearing cache:

 cache.delete(f'user_profile_{user_id}')

This ensures that users will not see expired cached content after updating the information.

Basically that's it. The cache strategy does not seem complicated, but it is easy to ignore details in actual applications, such as cache penetration, cache avalanche and other issues. Only by rationally designing the cache structure, setting expired strategies, and using it flexibly according to business needs can the value of cache be truly exerted.

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