There are instances where your applications may want to access the data in a particular structure or view to simplify data retrieval and increase application performance.īefore implementing database caching, many architects and engineers spend great effort in squeezing as much performance as they can out of their database. The need to simplify data access: While relational databases provide excellent means to data model relationships, they aren’t optimal for data access.Cost to scale: Whether the data is distributed in a disk-based NoSQL database or vertically scaled up in a relational database, scaling for extremely high reads can be costly and may require a number of database read-replicas to match what a single in-memory cache node can deliver in terms of requests per second. This assumes you have a steady load and your database is performing optimally. #WECHAT WINDOWS DEV DATABASE CACHE PLUS#Slow processing queries: While there are a number of query optimization techniques and schema designs that can help boost query performance, the data retrieval speed from disk plus the added query processing times generally will put your query response times in double-digit millisecond speeds, at best.When building distributed applications that require low latency and scalability, there are a number of challenges that disk-based databases can pose to your applications.
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