Benchmark: cache vs hard - MeasureThat.net

Script Preparation code:

var Cache = /** @class */ (function () {
        function Cache() {
            this.internalCache = new Map();
        }
        Cache.prototype.get = function (key, cb) {
            if (this.internalCache.has(key)) {
                return this.internalCache.get(key);
            }
            var value = cb();
            this.internalCache.set(key, value);
            return value;
        };
        return Cache;
    }());

window.cacheInstance = new Cache();

Tests:

With cache
window.cacheInstance.get('isRootDomain', () => { const origin = window.location.origin // Lazy way to count number of "." characters const matches = origin.match(/\./g); return !!(matches && matches.length === 1); });
without cache
(function() { const origin = window.location.origin // Lazy way to count number of "." characters const matches = origin.match(/\./g); return !!(matches && matches.length === 1); })();

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
With cache
without cache

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

The provided benchmark tests the performance difference between using a cache ( Memoization) and not using it, in terms of JavaScript execution speed.

**Cache Approach:**
In this approach, a custom `Cache` class is defined to store the results of expensive function calls. The `get` method checks if the result is already cached; if so, it returns the cached value; otherwise, it executes the callback function and caches its result.

Pros:

*   Reduces repeated calculations by storing intermediate results.
*   Can improve performance for functions with expensive computations.

Cons:

*   Requires extra memory to store cached values.
*   May lead to stale data if not properly invalidated.

**No Cache Approach:**
In this approach, the `get` method does not check for caching; instead, it always executes the callback function, and the result is stored in a variable. This approach relies on laziness (i.e., only calculating the value when needed).

Pros:

*   No extra memory required.
*   Can be simpler to implement.

Cons:

*   Repeated calculations can lead to performance issues for functions with expensive computations.

**Library:**
The `Cache` class used in this benchmark is a custom implementation. It's not a standard library like LRU Cache (used by Node.js or modern browsers) or memoization libraries like `memoize` (JavaScript).

**Special JS Feature/Syntax:**
This benchmark does not use any special JavaScript features or syntax.

**Other Considerations:**

*   The `match` method is used to count the number of `\` characters in the origin string. This approach can be slow for large strings due to regular expression overhead.
*   The `get` method uses a `Map` to store cached values, which provides fast lookup times.

**Alternatives:**
Other approaches to improve performance include:

1.  **Laziness with memoization**: Implementing a more advanced caching mechanism that invalidates entries based on time or conditions.
2.  **Parallel processing**: Dividing the calculation into smaller tasks and executing them in parallel using threads or worker threads.
3.  **Just-In-Time (JIT) compilation**: Using JavaScript engines like V8 to compile the code into optimized machine code, reducing execution time.

These alternatives may be more suitable for specific use cases and performance requirements, but they introduce additional complexity and overhead.

Related benchmarks:

cache vs hard (version: 1)

Comparing performance of: With cache vs without cache

Created: 8 years ago by: Registered User

Jump to the latest result

With cache

without cache

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):