We saw severe outages impacting our tech giants such as Google and AWS in the last few months. These players are amongst the inventors of highly available cloud environments. After all, their services were down for up to several hours in November and October. As a result, millions of customers and companies could not buy or sell goods and wondered what was going on.
What is the secret behind fast and reliable IT services? Is a world-class observability concept sufficient to avoid such blackouts, or do you need something beyond that?
A modern monitoring concept helps to detect and resolve such outages before many end-users are impacted. But, keep in mind that faulty services can keep your entire DevOps team extremely busy and block them from developing new capabilities. So, if you built your IT services on shaky ground, you should look to the root cause and improve the core structure of these elements.
We can't risk crashing down when building a house because humans are at risk, and there is no way to restart it. Therefore, we ensure that all elements such as the bricks, basement, walls, and roof fulfill the highest standards. When building business applications, we should follow a similar philosophy and cut out all weak structural elements.
According to Merriam Webster, a structure is a way that something is built, arranged, or organized. Following this definition, structural performance patterns are the building blocks for fast and reliable services.
From my perspective, structural performance patterns require the most significant attention because a weak building block could result in a severe outage or a terrible user experience.
The list of structural performance patterns is long-spun, so we are working on a detailed paper about this topic. Nevertheless, I am sharing insights about the caching pattern with you below. For more insights, please check out our upcoming article.
# Structural Performance Pattern - Caching
Intend: Speeding up performance and reducing costs by effectively reusing previously computed or retrieved data
Motivation: Accessing data stored in RAM or random-access memory is always faster than data stored in the disk. Whether you have an internal or customer-facing service, you should consider a caching strategy if you request the same or similar data sets on and on again.
A cache stores a subset of data to enable the best performance of frequently used data. In contrast, a database holds the complete data, but the access in highly distributed environments can be slow.
Structure: Caching is entirely useless if you cache the wrong data. Therefore, we focus on cache hit rates in performance optimization activities, which means how much data was present when fetched.
Application Cache: The main goal is to reduce latency and improve IOPS throughput. We can implement such caches in many layers from the client through networks and databases. Accessing data stored in a database is always time-consuming. In time-sensitive environments, highly frequented data is stored in application caches to speed up latency and allow better throughput.
RAM or in Memory Cache: High throughput and low latency are essential. We know that reading data from a disk can be slow, and to avoid that, advanced concepts store data sets in the much faster memory layer and improve response times significantly.
Caching Layer: In complex environments, application caches are not the best choice because they are not robust against outages. If you scale nodes up and down, the availability of central caching layers is more important. They follow their lifecycle, and everyone benefits from this independence.
Example:
Benefits of caching
Improve performance
Increase read throughput
Reduce load on the backend
Avoid database hotspots
Decrease database costs
Foreseen performance
I am hoping that you've learned why caching is one of the most important elements for high speed applications. Follow us on our website and download our paper about performance patterns soon.
Keep up the great work! Happy Performance Engineering!
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