Cache Stampede or Dogpile Problem in System Design

The Cache Stempede or Dogpile Problem is defined as a situation where the system receives multiple requests for a cached resource simultaneously for which the cache has already expired or has become invalid.

Cache Stampede or Dogpile Problem in System Design is a phenomenon that can occur in systems that rely on caching to improve performance. As a result, the system experiences a sudden surge in demand, often overwhelming the backend resources and causing a performance degradation.

Example of Cache Stampede or Dogpile Problem

To demonstrate the cache stampede problem, let’s look at a straightforward example:

Scenario:

• Consider that you have a website application that shows the current weather in various cities, in which you put the weather data into caching to enhance performance, such that each response is cached for a specific amount of time after the weather data is fetched from a distant API.

Arrival of multiple requests:

• Suppose the cached data of a particular city has expired just now. Now, let us imagine that a large number of user requests arrive asking for the weather of that particular city immediately after its expiration.
• Since the data is no longer present in the cache, these requests will then result in cache misses.

How Cache stampede problem comes into picture?

• Now each of these user requests will concurrently try to fetch the weather data from the remote API because they all found the cache to be empty.
• This sudden surge in requests overwhelms the backend resources, causing increased response times, potential service degradation, and additional load on the API server.
• This situation is known as cache stampede.

How does Cache Stampede affect the System in this scenario?

• The cache stampede problem can be particularly impactful when the backend resource or API is slow or resource-intensive. It leads to redundant work being performed, as multiple requests attempt to retrieve the same data simultaneously, instead of leveraging the benefits of caching.

What can be done in this Cache Stampede scenario?

• To mitigate this problem, the following mentioned strategies can be applied:
  • implement cache population techniques,
  • cache locks, or
  • asynchronous caching mechanisms
• You can implement any of the above strategies to handle cache misses more effectively and reduce the impact of simultaneous requests on the backend resources.

Causes of Cache Stampede or Dogpile Problem

The cache stampede or dogpile problem can occur due to various causes. The following are some typical causes of this problem:

1. Cache expiration:

A cached resource must be refreshed when it becomes invalid after expiration. If multiple requests arrive simultaneously after the cache expiration, they all find the cache empty and trigger cache misses, leading to a surge in backend requests.

2. High contention:

In scenarios where multiple concurrent requests are accessing the same resource, such as a frequently accessed cache entry, the likelihood of cache stampede increases. As each request detects a cache miss, they all try to regenerate or populate the cache entry simultaneously, causing contention and potentially overwhelming the backend.

3. Synchronized cache invalidation:

If cache invalidation is performed in a synchronized manner, where multiple requests trying to invalidate the same cache entry block each other, it can lead to a cache stampede. As soon as the cache entry is invalidated, all the pending requests might simultaneously try to regenerate the cache, causing a surge in backend requests.

4. Time-based cache expiration:

When using a time-based expiration policy, such as setting a fixed cache duration for a resource, it increases the likelihood of cache stampede. If the cache duration is short, multiple requests are more likely to coincide with the expiration time and trigger simultaneous cache misses.

5. Event-based cache invalidation:

In systems where cache invalidation is triggered by specific events, such as data updates or changes, a high frequency of events can cause cache stampedes. If multiple events occur simultaneously or in rapid succession, they can lead to concurrent cache misses and subsequent resource regeneration.

6. Caching hotspots:

Certain cache entries or resources might experience higher traffic or popularity compared to others. If a popular cache entry expires or becomes invalidated, the subsequent cache misses can result in a cache stampede, especially if there is no effective mechanism to handle the sudden surge in demand.

It’s important to consider these causes when designing and implementing caching strategies. Employing appropriate cache management techniques, such as intelligent expiration policies, asynchronous caching, and concurrency control mechanisms, can help mitigate the cache stampede problem and ensure the smooth operation of the system.

Strategies to mitigate the Cache Stampede or Dogpile Problem

To mitigate the cache stampede problem, various strategies can be employed in system design:

1. Cache population:

When a cache miss occurs, instead of directly querying the backend resource, a single request can be made to retrieve the resource. Subsequent requests for the same resource during this period can wait and use the result from the first request. This prevents multiple requests from overwhelming the backend and minimizes the impact of the cache expiration.

2. Cache locks:

Another approach is to use cache locks or cache entry invalidation flags. When the cached resource is about to expire, the system can set a flag indicating that the resource is being regenerated or updated. Any subsequent requests that encounter the flag can wait until the new version of the resource is available, rather than attempting to regenerate it concurrently.

3. Exponential backoff:

In scenarios where cache locks are used, it can be beneficial to introduce a randomized exponential backoff mechanism. This means that if a request encounters a cache lock, it waits for a random period of time before retrying. If it encounters another lock, the waiting time increases exponentially. This helps stagger the requests and reduces the likelihood of multiple requests hitting the backend simultaneously.

4. Asynchronous caching:

Instead of performing cache population or regeneration synchronously during a cache miss, it can be beneficial to use asynchronous techniques. When a cache miss occurs, a background task or worker can be responsible for updating the cache while the main thread or process returns a cached version or a placeholder to the client. This approach decouples the cache regeneration process from the client request, reducing the chances of cache stampede.

5. Cache invalidation strategies:

Careful consideration should be given to cache expiration policies and invalidation strategies. Setting appropriate expiration times or using mechanisms like time-based invalidation or event-driven invalidation can help minimize the occurrence of cache stampedes. Additionally, proactive cache refreshing techniques, such as background updates before expiration, can ensure that the cache remains fresh and reduces the likelihood of simultaneous cache misses.

It is significant to note that the specific strategies to mitigate cache stampede may differ depending on the system architecture, caching mechanisms employed, and the type of application. The trade-offs between performance, consistency, and complexity must be taken into account by designers when using these tactics.