Wechat windows dev database cache plus#

Remote caches: Remote caches are stored on dedicated servers and typically built upon key/value NoSQL stores such as Redis and Memcached.For most applications, the added network overhead associated with a remote cache is of little concern given that a request to it is generally fulfilled in sub-millisecond performance.

We won’t discuss this strategy in detail, but it is used typically used only when absolutely needed as it adds complexity. When network latency is of concern, a two-tier caching strategy can be applied that leverages a local and remote cache together. A remote cache (or “side cache”) is a separate instance (or multiple instances) dedicated for storing the cached data in-memory. The majority of these cons are mitigated with remote caches. In addition, when outages occur, the data in the local cache is lost and will need to be rehydrated effectively negating the cache. And since most applications utilize multiple app servers, if each server has its own cache, coordinating the values across these becomes a major challenge. This creates challenges in a distributed environment where information sharing is critical to support scalable dynamic environments. The information stored within an individual cache node, whether its database cached data, web sessions or user shopping carts cannot be shared with other local caches. A major disadvantage is that among your applications, each node has its own resident cache working in a disconnected manner. This not only speeds up your data retrieval but also removes network traffic associated with retrieving data, making data retrieval faster than other caching architectures. Local Caches: A local cache stores your frequently used data within your application.Integrated caches are typically limited to the available memory allocated to the cache by the database instance and cannot be leveraged for other purposes, such as sharing data with other instances. Where integrated caches fall short is in their size and capabilities. There is nothing within the application tier required to leverage this cache. When the underlying data changes on the database table, the database updates its cache automatically, which is great. Database Integrated Caches: Some databases such as Amazon Aurora offer an integrated cache that is managed within the database engine and has built-in write-through capabilities.The three most common types of database caches are the following: The cache itself can live in a number of areas including your database, application or as a standalone layer.

For example, say you are trying to lower the latency of your database query, doing this with reasonable expectations is a best practice, but trying to defy the laws of physics associated with retrieving data from disk is a waste of time.Ī database cache supplements your primary database by removing unnecessary pressure on it, typically in the form of frequently accessed read data. And while doing so with reasonable expectations is great, it’s counterproductive to try and solve a problem with the wrong tools. 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.