discussionpros and consthere are two benefits of using redis as a key-value database, which is the usage of scripting in the server-side and the abundant supporting infrastructure around redis.one of the advantages of using redis is server-side scripting. according to zhang, tudor, chen, and ooi (2014), server-side scripting is supported by redis and they pointed out that redis server could directly handle some processing using lua scripts. they called this procedure “redis server-side data analytics”.another advantage of redis is the ecosystem of tools around it. just as anthony and rao (n.d.) stated, there is great user-friendliness with redis, which is the result of the appearance of its capabilities of monitoring and various built-in commands. they also introduced that redis has not only a number of users but also a great policy of documentation, which reduces people’s workload. both the installation and configuration is pretty easy and the default configuration may be the most suitable configuration for the majority of users. different use scenarios with sample codes are also available in official documents. in considering different languages, there are various adaptable libraries provided. all the components within the ecosystem make us use redis efficiently without any difficulties.although there are many advantages, there are also disadvantages of using redis.the first drawback is the memory fragmentation problem. liu and yuan (2019) explained that the allocation of memory in linux should begin with an address which is defined by the paging scheme of the memory management unit. as a result, the allocated memory can only be inflexible size pages leading to the extra memory created. this extra memory space is internal memory fragmentation. they also pointed out the reason why redis has the issue of memory fragmentation. the default memory management component in the most recent version of redis is jemalloc. in order to reduce the outside fragmentation, jemalloc classifies the request of memory distribution. despite that, this design pattern has not made any optimization for the internal fragmentation issue, and the common use case of redis is cache. for the purpose of processing the expired data with various length and writing these data, there are significant high frequencies of applying and clearing memory in redis. consequently, there are obvious increases in occupied memory and fragmentation ratio.the second problem of using redis is scalability. kago and yamashita (2014) discussed that the pattern that redis works is a singular process and there is no support for the cluster system. what’s more, redis provides a basic master-slave reproduction function without a failover system. this means that there is no horizontal scalability with redis.
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