RedisClient | RESP3 Protocol supported Redis Client library | Key Value Database library

What Is Cache ?. Cache is an extremely common software term which has a lot of areas of usage in our lives and a vital role that makes systems work properly. Its examples also can be encountered often out of software field. For instance, preparing the ingredients for a meal or acquiring the materials supposed to be used in repair serves the same purpose with creating a cache system for a specific job. The essential point here is that whatever you keep in order to fulfill whatever you planned must be available at the closest place to the its point of use. If you store the ingredients in a cellar or refrigerator instead of the bench that you prepare the food on, it does not provide convenience and sufficient speed to you. We are using these mechanisms in our software career tons of times and it is quite probable that there are many other fields we are using it but we did not notice yet. When the cases of using the variables defined in an ordinary method more than once in their run-time is thought, they are stored in CPU's memory(L3 Cache) because of not applying to RAM frequently. You can see the CPU's are designed as apt to storing and hold standby the things that they need to carry on a task at their nearest slot although access speed of RAM is fast enough like an HTTP request as we disregard its toil. However, the actual data need to be processed in some cases while the mutual data of multiple transactions are disscussed. Despite the perfomance loss occurs, cache mechanism is deactivated in this situation because of the setup of operations. It is enough to use the "volatile" keyword while defining the variables in C# programming language so that caching usages of variables become prevented. Thus, the data which have been obtained from every variable access gets brought through RAM. As developers, most of the systems we have developed connect each other through internet. We execute our operations with the data that collected from many resources on the internet consonant to our profession principles. If there are some that do not get updated often at their resources within these data or there is not such obligation to use current data in the meantime, writing these data to the RAM and using them through the RAM when the next neccesity occurs make the daha approach our bench and provide high performance gain we access them next time. Caching operations that we have mentioned by this point are basic and already used technologies without making a great effort. The next need of cache after this point starts to become a little bit more complex where we try to access the common data via different applications on the internet because there is a group of applications in our system and all of them need to work on the same data. This leads to assuring those applications work with same data but none of them cannot directly store the data at their local variables. If they do, then they are not able to ensure that they are using the same data with other ones. Here are the two ways to proceed;. Maybe direct access makes sense if the data does not pass through any transaction and distance of data source is close as any common spot. However, there is a requirement of a close spot to locate data and that spot needs to include rapid technologies for the access if adjustment of writing speed needs an operation at the data source and the data source is not close enough. This type of systems are called as "Distributed Cache Systems". Redis and Memcached are some example technologies of the distributed cache systems. They are designed and developed to achieve high performance for storing and collecting data and at the transfer protocols. To supply with the high traffic occured due to increase of the importance of internet nowadays, an available application's amount should be able to increment when required or sources of server that the application involves in can be enhanced. Requirement of horizontal scaling increases the application numbers and its caching transactions need to be managed on multiple applications. In this case, caching transactions should be executed on distributed cache systems because data consistency is quite important. Distributed cache systems are never the closest method to the place where their own transactions are being executed and therefore their perfomance must be managed certainly. For example, reaching or updating the data that is stored on a Redis server substantially consume the bandwidth when accessing a data is needed thousands of time in a loop because it is provided through a network transfer. On the other hand, also data recency would need to be guaranteed if the data was directly written on the RAM which is one of the nearest place to execution area for each application. Some "Distributed In-Memory Cache" solutions are produced and used on horizontal scalable systems in consideration of these problems. The requested data at the most common solutions should be included together with the close memory of applications(RAM) and distributed cache at the mutual spot. Besides, applications listen to a message channel in order to find out the situations of data recency changes in their close memories. Other applications that update these related data on the distributed cache leave the update messages to this message channel. Furthermore, the applications that receive the update messages remove the updated data from their close memories and when needed again, they store those messages as they acquire through the distributed cache in common spot to reuse for once in their close memories. Hence, the applications access the data that its update messages have not been received yet after they had been collected and stored in applications' close memories with a faster way instead of consuming the bandwidth with continously applying for the common place on the network.