Distributed caching is an important aspect of cloud based applications, be it for on-premises, public or hybrid cloud environments. It facilitates incremental scaling, allowing the cache to grow and incorporate the data growth. In this blog we will explore distributed caching on cloud and why it is useful for environments with high data volume and load.
Traditional caching servers are usually deployed with limited storage and CPU speed. Often these caching infrastructures reside on data centers that are on-premises. I am referring to a non-distributed caching server. Traditional distributed caching comes with numerous challenges like:
Caching is a technique to store the state of data outside of the main storage and store it in high-speed memory to improve performance. In a microservices environment, all apps are deployed with their multiple instances across various servers/containers on the hybrid cloud. A single caching source is needed in a multicluster Kubernetes environment on cloud to persist data centrally and replicate it on its own caching cluster. It will serve as a single point of storage to cache data in a distributed environment.
Following are popular industry-recognized caching servers:
Redis: It’s one of the most popular distributed caching services. It supports different data structures. It’s an open source, in-memory data store used by millions of developers as a database, cache, streaming engine and message broker. It also has an enterprise version. It can be deployed in containers on private, public and hybrid clouds etc. it provides consistent and faster data synchronization between different data centers.
Memcached: It is an open source, high-performance, distributed memory object caching system. It is generic in nature, but intended for use in speeding up dynamic web applications by alleviating database load. Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls or page rendering. Memcached is simple yet powerful. Its simple design promotes easy, quick deployment and development. It solves many data-caching problems, and the API is available in various commonly used languages.
GemFire: It provides distributed in-memory data-grid cache, powered by Apache Geode open source. It scales data services on demand to support high performance. It’s a key-value store that performs read and write operations at fast speeds. It offers highly available parallel message queues, continuous availability and an event-driven architecture to scale dynamically with no downtime.
It provides multisite replication. As data size requirements increase to support high-performance, real-time apps, it can scale linearly with ease. Applications get low-latency responses to data-access requests, and always return fresh data. It maintains transaction integrity across distributed nodes and supports high-concurrency, low-latency data operations of the application. It also provides node failover and cross-data center or multi-data center replication to ensure applications are resilient, whether on-premises or in the cloud.
Hazelcast: Hazelcast is a distributed computation and storage platform for consistent low-latency querying, aggregation and stateful computation against event streams and traditional data sources. It allows you to quickly build resource-efficient, real-time applications. You can deploy it at any scale from small edge devices to a large cluster of cloud instances. A cluster of Hazelcast nodes share both the data storage and computational load, which can dynamically scale up and down. When you add new nodes to the cluster, the data is automatically rebalanced across the cluster. The computational tasks (jobs) that are currently in a running state, snapshot their state and scale with processing guarantee.
These are recommended ways to deploy and setup distributed caching, be it on public cloud or hybrid cloud:
Distributed caching is now a de-facto requirement for distributed microservices applications in a distributed deployment environment on hybrid cloud. It addresses concerns in important use cases like maintaining user sessions when cookies are disabled on the web browser, improving API query read performance, avoiding operational cost and database hits for the same type of requests, managing secret tokens for authentication and authorization, etc.
Distributed cache syncs data on hybrid clouds automatically without any manual operation and always gives the latest data. I would recommend industry-standard distributed caching solutions like Redis, Hazelcast and Memcached. We need to choose better distributed caching technology in the cloud, based on use cases.
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