In the world of web development, performance optimization is a critical aspect that directly influences user experience and overall system efficiency. Caching is a technique that helps to mitigate the load on backend services, reducing response times and enhancing the user experience. One particular approach, the System Cached List, offers a smart and efficient way to cache data and deliver it seamlessly to users.
The Challenge: Sub-Second Response Times for a Weather API
Performance Requirements
You're running a Web API that provides weather forecasts to a large number of users. Each forecast request takes approximately 10 seconds to complete. Your consumers expect near-instantaneous responses and your API is handling thousands of hits every minute.
Caching Strategy
Data Retrieval
- Check cache for existing data
- Serve cached data directly
Cache Miss
- Wait for data if cache empty
- Serve stale data if past refresh time
Background Refresh
- Asynchronous cache refresh
- Non-blocking main thread
- Single refresh process control
Understanding the System Cached List
The System Cached List is a caching mechanism that maintains a cached dataset, periodically updating it while ensuring minimal impact on the application's performance. This technique employs the power of the MemoryCache class and a custom CachedData class to manage cached data efficiently.
Sample Implementation
I used a minimal Api sample project to start this solution. I then added a generic implementation of caching a list of T using system memory as the caching mechanism. You can view the source for this article in my a.text-decoration-none(href='https://github.com/markhazleton/sandbox/tree/main/WebApiCache' target='_blank' rel='noopener noreferrer') sandbox repository on GitHub | .
Advantages of the System Cached List
Improved Performance
Significantly improves performance by reducing backend load. Cached data provides faster response times and smoother user experience.
Reduced Latency
With cached data readily available, there's notable latency reduction. Users experience quicker load times and higher satisfaction levels.
Efficient Resource Usage
Memory caching optimizes resource usage and reduces database queries, leading to lower resource consumption.
Customizability
Allows customization of cache expiration times based on data volatility, ensuring freshness while avoiding unnecessary updates.
Concurrency Handling
Synchronization locks prevent concurrency issues during cache updates, ensuring data integrity with single-threaded updates.
Graceful Updates
Smart asynchronous update process avoids blocking the application, maintaining responsive user interactions.
Disadvantages of the System Cached List
Stale Data Risk
Risk of serving outdated information if cache expiration settings are not properly configured or updates are delayed.
Increased Complexity
Requires careful consideration of thread safety, cache management, and update scheduling, potentially leading to maintenance challenges.
Memory Usage
Large datasets in memory can increase consumption, potentially affecting overall system performance.
Cold Start Overhead
Initial cache population creates slight overhead during application start or cache expiration, momentarily impacting user experience. // Implementation Section
Implementing the System Cached List
The implementation showcases two primary components that work together to provide efficient memory caching with smart update strategies.
CachedData Class
This class encapsulates the cached data along with metadata such as the last update time and the expected next update time, providing a complete picture of cache state.
class CachedData
{
public DateTime LastUpdated { get; set; }
public DateTime NextUpdate { get; set; }
public object Data { get; set; }
}
SystemValuesCache Class
This static class acts as the core of the caching mechanism. It initializes a MemoryCache instance and provides the GetCachedData method to fetch and manage cached data. It also uses a synchronization lock to ensure thread safety.
FetchDataFunction Delegate
The GetCachedData method's fetchDataFunction argument plays a crucial role in the functionality. It is a delegate that defines a function responsible for retrieving the data that will be cached.
This callback function performs the actual data retrieval from any data source (database, web API, file) and is asynchronous (Task<List<T>>), allowing non-blocking execution.
public static CachedData<T> GetCachedData<T>(
string cacheKey,
Func<Task<List<T>>> fetchDataFunction,
double cacheTimeInSeconds)
{
List<T> cachedValues = _cache.Get(cacheKey) as List<T> ?? new List<T>();
if (cachedValues.Count == 0 || DateTime.Now - _lastUpdateTime > TimeSpan.FromSeconds(cacheTimeInSeconds))
{
lock (LockObject)
{
Task.Run(async () =>
{
var data = await fetchDataFunction();
cachedValues.Clear();
cachedValues.AddRange(data);
var cachePolicy = new CacheItemPolicy
{
AbsoluteExpiration = DateTimeOffset.Now.AddSeconds(cacheTimeInSeconds)
};
_cache.Set(cacheKey, cachedValues, cachePolicy);
_lastUpdateTime = DateTime.Now;
}).Wait();
}
}
return new CachedData<T>()
{
Data = cachedValues,
LastUpdateTime = _lastUpdateTime,
NextUpdateTime = _lastUpdateTime.AddSeconds(cacheTimeInSeconds)
};
}
Conclusion
The System Cached List provides an effective approach to enhance web application performance by intelligently managing and serving cached data.
Performance Enhancement
Combines memory caching efficiency with smart update strategies for optimal performance.
Reduced Backend Load
Ensures users receive quick responses while significantly reducing backend resource usage.
Implementation Considerations
While offering numerous advantages, it's crucial to carefully consider cache expiration times and handle potential complexities to reap the full benefits of this caching technique. Proper configuration and monitoring are essential for optimal results.