Manish Kumar Paneri

In the computing world, when we are looking at performance improvement for quick wins, we first look into caching. Caching stores frequently accessed data in a fast and convenient way, reducing the load on your database. It is helpful to access more frequent data quickly and to avoid any additional computation that was done to fetch and store previous data. Caching stores the data for a small duration of time and turns slower operations into faster performance. Caching is largely divided into two types: a local cache and external cache. A local cache uses the JVM heap for storage, and a remote (or cluster) cache uses in-memory stores such as Redis or Memcached. Types of Caching Strategies and Their Challenges Local Cache: A local cache is easier to implement by having some sort of storage within the service (say, a Hashmap) but leads to a cache coherence problem. That is, being a local cache, it will be different per server and lead to inconsistent data. External Caches: External cache

😎 Imagine in few minutes you can launch your serverless microservice with the help of using AWS lambda, API gateway to offer fastest path to production to help engineers efficiently build and scale applications.  One of the biggest advantage of using AWS Lambda cloud service is that it can be deployed independently. Also, offers the opportunity to work many other different technologies and last but not the least but using this can quickly increase scalability and high availability of system.  AWS Lambda as a Microservice AWS Lambda is a service that allows you to run your functions in the cloud completely Serverless and eliminates the operational complexity. You upload your code to Lambda, and it takes care of everything needed to run and scale its execution and fulfils conditions and high availability requirements. Its easy to deploy your builds and service related configurations in the lambda function.  It integrates with the API gateway, allows you to invoke functions with the API