# Case Study: Landing Page Caching¶

DiskCache version 4 added recipes for cache stampede mitigation. Cache stampedes are a type of system overload that can occur when parallel computing systems using memoization come under heavy load. This behaviour is sometimes also called dog-piling, cache miss storm, cache choking, or the thundering herd problem. Let’s look at how that applies to landing page caching.

import time

def generate_landing_page():
time.sleep(0.2)  # Work really hard.
# Return HTML response.


Imagine a website under heavy load with a function used to generate the landing page. There are five processes each with two threads for a total of ten concurrent workers. The landing page is loaded constantly and takes about two hundred milliseconds to generate.

When we look at the number of concurrent workers and the latency with no caching at all, the graph looks as above. Notice each worker constantly regenerates the page with a consistently slow latency.

import diskcache as dc

cache = dc.Cache()

@cache.memoize(expire=1)
def generate_landing_page():
time.sleep(0.2)


Assume the result of generating the landing page can be memoized for one second. Memoization supports a traditional caching strategy. After each second, the cached HTML expires and all ten workers rush to regenerate the result.

There is a huge improvement in average latency now but some requests experience worse latency than before due to the added overhead of caching. The cache stampede is visible too as the spikes in the concurrency graph. If generating the landing page requires significant resources then the spikes may be prohibitive.

To reduce the number of concurrent workers, a barrier can be used to synchronize generating the landing page.

@cache.memoize(expire=0)
@dc.barrier(cache, dc.Lock)
@cache.memoize(expire=1)
def generate_landing_page():
time.sleep(0.2)


The double-checked locking uses two memoization decorators to optimistically look up the cached result before locking. With expire set to zero, the cache’s get-operation is performed but the set-operation is skipped. Only the inner-nested memoize decorator will update the cache.

The number of concurrent workers is now greatly improved. Rather than having ten workers all attempt to generate the same result, a single worker generates the result and the other ten benefit. The maximum latency has increased however as three layers of caching and locking wrap the function.

Ideally, the system would anticipate the pending expiration of the cached item and would recompute the result in a separate thread of execution. Coordinating recomputation would be a function of the number of workers, the expiration time, and the duration of computation. Fortunately, Vattani, et al. published the solution in “Optimal Probabilistic Cache Stampede Prevention” in 2015.

@dc.memoize_stampede(cache, expire=1)
def generate_landing_page():
time.sleep(0.2)


Early probabilistic recomputation uses a random number generator to simulate a cache miss prior to expiration. The new result is then computed in a separate thread while the cached result is returned to the caller. When the cache item is missing, the result is computed and cached synchronously.

The latency is now its theoretical best. An initial warmup execution takes two hundred milliseconds and the remaining calls all return immediately from the cache. Behind the scenes, separate threads of execution are recomputing the result of workers and updating the cache. The concurrency graph shows a nearly constant stream of workers recomputing the function’s result.

@dc.memoize_stampede(cache, expire=1, beta=0.5)
def generate_landing_page():
time.sleep(0.2)


Vattani described an additional parameter, $$\beta$$, which could be used to tune the eagerness of recomputation. As the number and frequency of concurrent worker calls increases, eagerness can be lessened by decreasing the $$\beta$$ parameter. The default value of $$\beta$$ is one, and above it is set to half.

Latency is now still its theoretical best while the worker load has decreased significantly. The likelihood of simulated cache misses is now half what it was before. The value was determined through experimentation.

@dc.memoize_stampede(cache, expire=1, beta=0.3)
def generate_landing_page():
time.sleep(0.2)


Lets see what happens when $$\beta$$ is set too low.

When set too low, the cache item expires before a new value is recomputed. The real cache miss then causes the workers to synchronously recompute the landing page and cache the result. With no barrier in place, eleven workers cause a cache stampede. The eleven workers are composed of ten synchronous workers and one in a background thread. The best way to customize $$\beta$$ is through experimentation, otherwise the default is reasonable.

DiskCache provides data types and recipes for memoization and mitigation of cache stampedes. The decorators provided are composable for a variety of scenarios. The best way to get started is with the DiskCache Tutorial.