When designing an API, it’s important to consider the expected load on the API, the authorization to it, the effects of version changes on the API consumers and most importantly the API’s ease of use, among other considerations. An API generally is a RESTful web service that can be accessed over HTTP and exposes resources that allow the user to interact with the application. Today, most web applications are built upon APIs. It integrates with other Amazon Web Services products to give developers and businesses an easy way to accelerate content to end users with no minimum usage commitments. For dynamic data, many CDNs can be configured to retrieve data from the origin servers.Īmazon CloudFront is a global CDN service that accelerates delivery of your websites, APIs, video content or other web assets. Throughput is dramatically increased given that the web assets are delivered from cache. To reduce response time, the CDN utilizes the nearest edge location to the customer or originating request location in order to reduce the response time. A CDN provides you the ability to utilize its global network of edge locations to deliver a cached copy of web content such as videos, webpages, images and so on to your customers. When your web traffic is geo-dispersed, it’s not always feasible and certainly not cost effective to replicate your entire infrastructure across the globe. Design strategies and characteristics of different In-Memory engines can be applied to meet most RTO and RPO requirements. In this scenario, it’s important to define an appropriate RTO (Recovery Time Objective-the time it takes to recover from an outage) and RPO (Recovery Point Objective-the last point or transaction captured in the recovery) on the data resident in the In-Memory engine to determine whether or not this is suitable. In some cases, an In-Memory layer can be used as a standalone data storage layer in contrast to caching data from a primary location. Another consideration may be whether or not the cache environment needs to be Highly Available, which can be satisfied by In-Memory engines such as Redis. Controls such as TTLs (Time to live) can be applied to expire the data accordingly. A cache miss occurs when the data fetched was not present in the cache. A successful cache results in a high hit rate which means the data was present when fetched. In a distributed caching environment, the data can span multiple cache servers and be stored in a central location for the benefit of all the consumers of that data.Ĭaching Best Practices: When implementing a cache layer, it’s important to understand the validity of the data being cached. In addition, when local caches are used, they only benefit the local application consuming the data.

If the cache is resident on the same node as the application or systems utilizing it, scaling may affect the integrity of the cache. This is especially relevant in a system where application nodes can be dynamically scaled in and out.

The cache serves as a central layer that can be accessed from disparate systems with its own lifecycle and architectural topology. Due to the speed of the underlying hardware, manipulating this data in a disk-based store is a significant bottleneck for these applications.ĭesign Patterns: In a distributed computing environment, a dedicated caching layer enables systems and applications to run independently from the cache with their own lifecycles without the risk of affecting the cache.

In these applications, very large data sets must be accessed in real-time across clusters of machines that can span hundreds of nodes. Compute-intensive workloads that manipulate data sets, such as recommendation engines and high-performance computing simulations also benefit from an In-Memory data layer acting as a cache. Cached information can include the results of database queries, computationally intensive calculations, API requests/responses and web artifacts such as HTML, JavaScript, and image files. You can use caching to significantly reduce latency and improve IOPS for many read-heavy application workloads, such as Q&A portals, gaming, media sharing, and social networking. These additional resources drive up cost and still fail to achieve the low latency performance provided by an In-Memory cache.Īpplications: Caches can be applied and leveraged throughout various layers of technology including Operating Systems, Networking layers including Content Delivery Networks (CDN) and DNS, web applications, and Databases. To support the same scale with traditional databases and disk-based hardware, additional resources would be required. RAM and In-Memory Engines: Due to the high request rates or IOPS (Input/Output operations per second) supported by RAM and In-Memory engines, caching results in improved data retrieval performance and reduces cost at scale.

Local private cache write through