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Redis – What is in-memory cache and how does it work?

I was reading about Redis and Apache ignite, both of them are in-memory cache and also act as distributed cache. I was wondering what is in-memory cache? Where is the data stored ? In the memory of local system on which an application is being used or in the memory of server where the application is hosted? How does in-memory caching works?
Example:
An application with ignite cache is running on x IP address and I am using the application on y IP address so cache will stored in memory of x IP address system or y IP address system?

Also What does it mean when we say distributed cache?

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Answers


  1. 0

    A distributed cache partitions/shards your data across multiple cluster nodes.This allows utilizing memory and CPU resources of the entire cluster, and load balance requests. A node is a process that can be running on your physical server, virtual machine, or just be a Kubernetes pod. This article might be helpful to understand the basics.

    Usually, an application needs to know the IP address of at least one cluster node to open a connection. Once the connection is opened, you would work with the cluster in a way similar to relational databases – just issue your SQL requests, compute tasks and perform other operations.

    Also, watch the In-Memory Computing Essentials for Software Engineers recording that covers most of your questions and introduces you to the essential capabilities of Ignite. There is free instructor-led training that is scheduled from time to time on this topic.

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  2. 0

    The in-memory cache can be thought of as a cache that has performance critical information/data of a database that is shared across requests in an application. There is direct access to data/memory rather than through other mechanisms that enables database related operations to operate with high efficiency inturn increasing the throughput, responsiveness of the system.

    In general, in the case of distributed cache based on deployment model, the cache memory can be between database and application in a distributed manner. This cache memory can be distributed between the nodes and shall operate based on the distributed hash table and the type of data. The access of data from cache in respective nodes can in turn shall apply the in-memory cache logic to bring in performance optimization.

    Here is an example of how it is achieved with Amazon Elastic Cache

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    As you can see, the Amazaon Elastic Cache solution has a Cache Engine running in each node which implements the caching protocol/algorithm and the Amazon Elastic Cache can support cache sizes from 6 to 67 GB in a particular node. A DNS name is assigned to each Cache Node while it is created and you need to configure the DNS names of the nodes into the client library that is being used. Once your application invokes the Put or Get requests to the cluster, the library shall algorithmically choose a particular node using the hash function that shall spread the data out across the nodes and also help in fetching the same from the nodes.

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