Distributed caching is a very important aspect of cloud-based applications, be it for on-prem, 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 the cloud and why it is useful for environments with high data volume and load. This blog will cover,
- Challenges with Traditional Caching
- What is Distributed Caching
- Benefits of distributed caching on the cloud
- Recommended Distributed Caching Database Tools
- Ways to Deploy Distributed Caching on the cloud
Traditional Distributed Caching Challenges
Traditional distributed caching servers are usually deployed with limited storage and CPU speed on a few limited dedicated servers or Virtual Machines (VMs). Often these caching infrastructures reside on data centers (DCs) that are on-prem or the cloud on VMs which are not resilient, not highly available, and fault-torent. . This kind of traditional caching comes with numerous challenges:
- Traditional caching is called in-process caching which is at the instance server level. In-process caching stores data at the application level locally like storing in EhCache etc. It doesn’t provide accurate data consistency.
- In-process cache creates performance issues, because they occupy extra memory, and due to garbage collection overhead.
- It’s not reliable, because it uses the same heap memory which is used by the application. If an application got crashed due to memory or some other issues, cached data will be also wiped out.
- Hard to scale cache storage and CPU speed on fewer servers because often these servers are not auto-scalable.
- High operational cost to manage infrastructure and unutilized hardware resources. These servers are managed manually on traditional DevOps infrastructure.
- Traditional distributed caching is not containerized (not deployed on Kubernetes/Docker containers). That’s why it is not easily scalable, resilient, and self-managed. Also, more possibilities of these fewer servers crashing if the client load is higher than the actual.
What is Distributed Caching
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 multi-cluster Kubernetes environment on the 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.
Benefits of Distributed Caching on cloud
These are a few benefits of distributed caching:
- Periodic caching of frequently used read REST API’s response ensures faster API read performance.
- Reduced database network calls by accessing cached data directly from distributed caching databases.
- Resilience and fault tolerance by maintaining multiple copies of data at various caching databases in a cluster.
- High availability by auto-scaling the cache databases, based on load or client requests.
- Storage of session secret tokens like JSON Web Token (ID/JWT) for authentication & authorization purposes for microservices apps containers.
- Provide faster read and write access in-memory if it’s used as a dedicated database solution for high-load mission-critical applications.
- Avoid unnecessary roundtrip data calls to persistent databases.
- Auto-scalable cloud infrastructure deployment.
- Containerization of Distributed caching libraries/solutions.
- Provide consistent read data from any synchronized connected caching data centers (DC).
- Minimal to no outage, high availability of caching data.
- Faster data synchronization between caching data servers.
Recommended Distributed Caching Databases Tools
Following are popular industry-recognized caching servers:
- GemFire and
- HazelCast databases
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 (DC).
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 a processing guarantee.
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 the 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 multi-site replication. As data size requirements increase to support high-performance, real-time apps, they can scale linearly with ease. Applications get low-latency responses to data access requests, and always return fresh data. Maintain transaction integrity across distributed nodes. It supports high-concurrency, low-latency data operations of applications. It also provides node failover and multi Geo (Cross Data Center or Multi Data Center) replication to ensure applications are resilient, whether on-premises or in the cloud.
Ways to Deploy Distributed Caching on Hybrid cloud
These are recommended ways to deploy and setup distributed caching be it public cloud or hybrid cloud:
- Open source distributed caching on traditional VM instances.
- Open source distributed caching on Kubernetes container. I would recommend deploying on a Kubernetes container for high availability, resiliency, scalable and faster performance.
- Enterprise COTS distributed caching deployment on VM and Container. I would recommend the enterprise version because it will provide additional features and support.
- The public cloud offers managed services of distributed caching open and enterprise sources like Redis, Hazelcast and Memcache, etc.
- Caching servers can be deployed on multiple sources like on-prem and public cloud together, public servers, or only one public server in different availability zones.
Distributed caching is now a de-facto requirement for distributed microservices apps in a distributed deployment environment on a hybrid cloud. It addresses concerns in important use cases like maintaining user sessions when a cookie is disabled at the web browser side, improving API query read performance, avoiding operational cost and database hit for the same type of requests, managing secret tokens for authentication and authorization, etc.
Distributed cache syncs data on the hybrid cloud automatically without any manual operation and always gives the latest data. I would recommend industry-standard distributed caching solutions – Redis, Hazelcast, and Memcache. We need to choose a better distributed caching technology in the cloud based on use cases.