What is Distributed Caching

In this tutorial we are going to learn about what a cache is ? when we are going to use?, and How to use it? in a detailed manner.

So first of all,

What is a Cache?

Imagine that you have a system like this. Client Application request for some results from the server and the server asks those details form the Database. Then Database pullout the results to the Application server. Without pulling data from the Database all the time we can maintain another database/server to store data called Cache. Here there are 2 scenarios that you might want to use a cache.

  • When you requesting for a commonly used data, and every time we ask for those data we need to provide from the Database. Instead of this, you can save those commonly used data in a cache (in-memory cache). Here we can reduce network calls.
  • When you are doing a calculation by getting data from the database. You can reduce the number of calculations here. Store the result in cache and get the value from the cache without doing recomputations all the time. (Example: Assume you have a Student Management System and you need to calculate the average marks for a particular exam for a particular student. Store Average value in cache memory with key-value pair.)
  • We have all servers and they are hitting the database. It’s going to be a lot of loads. Instead of getting one cache, we can use more caches as a distributed system for Avoid load in the Database.

Can we store all the data in the cache?

No! We can’t store all the data in the cache because of multiple reasons.

  • The hardware that we use to make cache memories is much more expensive than a normal database.
  • If you store a ton of data on cache the search time will increase compared to the database.

So that now you know we can store infinite data on the database and we need to store the most valuable data in the cache.

When do you load data into the cache? When do you evict data from the cache?

Loading or Evicting data from the cache is called a Policy. So the cache performance depends on your cache policy. There are a number of policies you can have. The Most popular one is LRU(Least Recently Used).

**LRU **— you can add recently used entries to the bottom of the cache and least recently used entries go to the bottom. If you want to add new entries but the cache is almost full, then you can evict(kick) out those least recently used data.

Image for post

Some other Policies are,

  • Least Recently Used (LRU)
  • First In First Out (FIFO)
  • Random

#distributed-cache #caching-server #redis #caching

What is GEEK

Buddha Community

What is Distributed Caching

What is Distributed Caching

In this tutorial we are going to learn about what a cache is ? when we are going to use?, and How to use it? in a detailed manner.

So first of all,

What is a Cache?

Imagine that you have a system like this. Client Application request for some results from the server and the server asks those details form the Database. Then Database pullout the results to the Application server. Without pulling data from the Database all the time we can maintain another database/server to store data called Cache. Here there are 2 scenarios that you might want to use a cache.

  • When you requesting for a commonly used data, and every time we ask for those data we need to provide from the Database. Instead of this, you can save those commonly used data in a cache (in-memory cache). Here we can reduce network calls.
  • When you are doing a calculation by getting data from the database. You can reduce the number of calculations here. Store the result in cache and get the value from the cache without doing recomputations all the time. (Example: Assume you have a Student Management System and you need to calculate the average marks for a particular exam for a particular student. Store Average value in cache memory with key-value pair.)
  • We have all servers and they are hitting the database. It’s going to be a lot of loads. Instead of getting one cache, we can use more caches as a distributed system for Avoid load in the Database.

Can we store all the data in the cache?

No! We can’t store all the data in the cache because of multiple reasons.

  • The hardware that we use to make cache memories is much more expensive than a normal database.
  • If you store a ton of data on cache the search time will increase compared to the database.

So that now you know we can store infinite data on the database and we need to store the most valuable data in the cache.

When do you load data into the cache? When do you evict data from the cache?

Loading or Evicting data from the cache is called a Policy. So the cache performance depends on your cache policy. There are a number of policies you can have. The Most popular one is LRU(Least Recently Used).

**LRU **— you can add recently used entries to the bottom of the cache and least recently used entries go to the bottom. If you want to add new entries but the cache is almost full, then you can evict(kick) out those least recently used data.

Image for post

Some other Policies are,

  • Least Recently Used (LRU)
  • First In First Out (FIFO)
  • Random

#distributed-cache #caching-server #redis #caching

Redis Labs Partners with Microsoft to Deliver a New Redis Cache

In a recent blog post, Microsoft announced a new partnership with Redis Labs to deliver Redis Enterprise as newly, fully integrated tiers of Azure Cache for Redis. The enhanced service offering, currently in private preview, will provide customers with two new Enterprise tiers – which include Redis on Flash, modules, and the ability to create an active geo-redundant cache for hybrid-cloud architectures in the future.

Microsoft started their collaboration with Redis Labs back in 2014 with the launch of Redis Cloud on Azure. Since then the service has evolved with updates such as geo-replication support and reserved-capacity. Now, the public cloud vendor incorporates two existing offerings of Redis Labs as additional Enterprise tiers in Azure Redis Cache service providing customers with more features, higher availability, and security capabilities.

Ofer Bengal, CEO and co-founder of Redis Labs, wrote in his blog post on the new partnership announcement:

Throughout the development process, three key customer drivers were consistently top of mind: improve developer productivity, ensure operational resiliency, and ease cloud migration. Teams at both organizations were committed to building an integration that delivers these values to our customers. With the announcement of Redis Enterprise integration into Azure Cache for Redis, we meet these needs.
With the new tiers, developers can use the most up-to-date version of Redis, including its native data structures, probabilistic data structures, streams, time-series, and search data models. Furthermore, they can benefit from the native integration with other Azure services, and easily deploy a Redis cluster and scale to terabyte-sized data sets at the cost of a disk-based data store by utilizing Redis on Flash technology. Also, with the added support of the Redis modules

RediSearch, RedisTimeSeries, and RedisBloom developers can build applications for a wide variety of use cases with a single technology.

#microsoft azure #clustering & caching #redis #microsoft #cloud #distributed cache #caching #devops #architecture & design #development #news

davis mike

1626331037

Caching In WordPress: What You Need to Learn?

WordPress caching has nothing new to showcase in this context. WordPress websites also run on a specific server system and you have to make sure these servers work well for user engagement. So caching can help your website server work effectively to serve too many visitors collectively. The commonly requested items can be converted into varied copies that the website server doesn’t want to showcase every time to every website visitor. Classification of Caching is usually divided into two kinds. The Client-Side Caching & the Server Side Caching. Where client-side caching has nothing to do with your website, Server Side Caching is usually its opposite. Read more on https://bit.ly/3rbqvVh

#caching plugins #server side caching #client side caching #wordpress websites #wordpress caching

Lindsey  Koepp

Lindsey Koepp

1602942851

AWS Announces Redis 6 Compatibility to Amazon ElastiCache for Redis

Recently AWS announced Redis 6 compatibility to Amazon ElastiCache for Redis, which brings several new features such as Managed Role-Based Access Control, Client-Side caching and some significant operational improvements.

Earlier this year AWS announced the Global Datastore feature of Amazon ElastiCache for Redis which provides fully-managed, fast, reliable and secure cross-region replication. Moreover, more recent the public cloud vendor improved the ability for customers to monitor their Redis fleet by enabling 18 additional engine and node-level CloudWatch metrics. It also added support for resource-level permission policies - allowing customers to assign AWS Identity and Access Management (IAM) principal permissions to specific ElastiCache resource or resources. And now AWS further enhances the service with Redis 6 compatibility, which brings even more features.

_Source: _https://aws.amazon.com/elasticache

The significant new features that come with the Redis 6 compatibility are:

  • Managed Role-Based Access Control providing users with the ability to create and manage users and user groups that they can use to set up Role-Based Access Control (RBAC) for Redis commands.
  • Client-Side Caching provides server-side enhancements to deliver efficient client-side caching to improve application performance further.
  • Operational Improvements available through several enhancements that improve application availability and reliability, such as improved replication under low memory conditions.

#distributed cache #redis #clustering & caching #cloud #caching #aws #amazon web services #development #architecture & design #devops #news

Obie  Stracke

Obie Stracke

1625539860

Distributed Caching in ASP.NET Core using Redis Cache

In my previous article A Step by Step Guide to In-Memory Caching in ASP.NET Core, I covered the basics of in-memory caching and I have shown you a practical example of implementing caching ASP.NET Web API. In-memory caching is only useful when your application is deployed on a single server. If you are planning to deploy your application on multiple servers in a typical web farm scenario, then you need a centralized caching solution. There are many ways you can implement distributed caching in ASP.NET Core and in this tutorial, I will talk about one of the most popular distributed cache called Redis Cache with some practical examples.

What Is Distributed Caching?

A distributed cache is a cache shared by multiple application servers. Typically, it is maintained as an external service accessible to all servers. Distributed cache improve application performance and scalability because it can provide same data to multiple servers consistently and if one server restarts or crashes, the cashed data is still available to other servers as normal.

#caching #aspdotnet core #redis cache