Tag: cloud

Demystified Service Mesh Capabilities for Developers

Service Meshes have been gaining a lot of popularity lately, more so amongst Spring and Java developers who wish to address cross-cutting concerns. But, are you wondering what exactly are Service Meshes? What are some of the popular types out there? And most importantly, what kind of problems do they actually solve? Well, look no further! This blog is here to provide you with the answers you seek.

What is a Service Mesh?

A service mesh is a dedicated infrastructure layer that helps manage communication between the various microservices within a distributed application. It acts as a transparent and decentralized network of proxies that are deployed alongside the application services. These proxies, often referred to as sidecars, handle service-to-service communication, providing essential features such as service discovery, load balancing, traffic routing, authentication, and observability.

By abstracting away the complexity of network communication, a service mesh enables developers to focus on application logic rather than dealing with the intricacies of networking code. It provides a consistent and flexible way to handle cross-service communication and allows for the implementation of advanced traffic management strategies, security policies, and observability mechanisms.

They provide a standardized approach to managing microservices communication, making it easier to monitor, secure, and control traffic within complex distributed systems.

Components of a Service Mesh

Service mesh architecture typically involves the following components and their interactions:

Data Plane: The data plane refers to a network of sidecar proxies deployed along with each service instance, so that it can communicate with the other services in the system. It acts as an intermediary between the service and the rest of the network. Sidecar proxies handle inbound and outbound traffic, intercepting communication and providing additional features.

  1. Sidecar: It’s based on Envoy proxy. It’s another container which runs in the same Kubernetes POD and takes care of all cross-cutting concerns. It’s based on the sidecar container design pattern.
  2. Application Traffic: Microservices connect through other microservices using sidecar containers. Application traffic is basically communication between Envoy sidecar proxy containers.
  3. Namespace: It’s an isolated space on a Kubernetes POD where the both containers (sidecar and microservices app) run in parallel.

Control Plane: The control plane is the centralized management and configuration layer of the service mesh. It is responsible for controlling and coordinating the behavior of the sidecar proxies. It provides a control plane API that allows administrators to configure policies, rules, and settings for traffic management, security, and observability.

  1. API Endpoints: API endpoints are the entry points through which services within the mesh can communicate with each other
  2. Controllers: A controller is a component responsible for managing and controlling the behavior of the mesh. It is typically a software component that monitors the state and health of services, configures traffic routing and load balancing rules, enforces security policies, and handles other aspects of service-to-service communication within the mesh.
  3. Service Discovery: Service discovery is an essential component in service mesh architecture. It enables services to dynamically locate and connect with each other without hard-coded addresses.
  4. Certificate Authority: It provides and manages root and intermediate certificates and performs certificate signing operations. 

Application Microservices: These are the individual services or microservices that make up the application. They are responsible for handling specific functions or tasks.

Use Case: E-commerce Application

Consider an e-commerce application use case, a service mesh would help manage the complex network of microservices responsible for different functions, such as inventory management, order processing, payment processing, and shipping. 

  • The sidecar proxies would handle load balancing, ensuring that traffic is distributed efficiently across multiple instances of each service.
  • Additionally, the service mesh would provide secure communication between services by enforcing encryption and authentication using TLS. This would help protect sensitive customer information during transmission and prevent unauthorized access to critical services.
  • Traffic management features would allow operators to control and monitor the flow of requests, enabling them to perform tasks like routing certain requests to a newer version of a service for testing purposes or limiting the rate of incoming requests to prevent overloading.
  • The observability and monitoring capabilities of the service mesh would provide operators with real-time insights into the application’s performance, enabling them to identify and resolve issues promptly.
  • They could analyze metrics, logs, and traces to optimize the application’s performance, troubleshoot problems, and ensure a smooth customer experience.

Overall, a service mesh simplifies the management and enhances the resilience, security, and observability of a distributed application, making it an essential component in modern microservices architectures.

What problems do Service Meshes solve?

Service mesh solves several problems in the context of modern application architectures. Here are some of the key problems that service mesh addresses:

  1. Service-to-service communication: In a microservices architecture, applications are composed of multiple independent services that need to communicate with each other. Service mesh provides a dedicated infrastructure layer to handle service-to-service communication, making it easier to manage and secure these interactions.
  2. Service discovery and load balancing: As the number of services increases, it becomes challenging to keep track of their locations and distribute traffic efficiently. Service mesh offers service discovery and load balancing capabilities, allowing services to discover and connect to each other dynamically while automatically distributing the traffic load across multiple instances.
  3. Traffic management and routing: Service mesh enables sophisticated traffic management and routing features, such as request routing based on service version, path, headers, or other attributes. It allows for traffic shifting, canary deployments, and A/B testing, empowering teams to implement complex deployment strategies with ease.
  4. Resilience and fault tolerance: Service mesh provides mechanisms for implementing resilience and fault tolerance patterns, such as retries, timeouts, circuit breaking, and load shedding. These features help services handle failures gracefully, isolate issues, and prevent cascading failures across the system.
  5. Observability and Debugging: Service mesh provides developers with powerful observability features such as distributed tracing, metrics collection, and logging. These capabilities help developers gain insights into the behavior and performance of their services, allowing them to debug issues, trace requests across service boundaries, and optimize the performance of their applications.
  6. Security and authentication: Service mesh strengthens the security of microservices architectures by providing features like transport-level encryption (TLS), mutual authentication, and authorization policies. It allows for fine-grained access control and identity management, enhancing the overall security posture of the system.
  7. Tight coupling of source code: Cloud configuration always comes with tight coupling with business logic source code, which makes it code-heavy to manage and debug for any code issues. This can make the process of adding new business features, inserting additional code, and resolving issues a cumbersome task. However, adopting a service mesh architecture allows for the segregation of cross-cutting concerns from the business logic source code. By employing this approach, the service mesh effectively handles all application configurations independently through the collaboration of DevOps platform/infrastructure teams.
  8. Testing overhead of cross-cutting configuration concerns: Testing new features, during integration, regression, and load testing for feature releases, necessitates additional testing effort. It is crucial to test the entire codebase, including the cross-cutting configuration code, even for minor changes in the business logic. By adopting a service mesh approach, the business logic code becomes more concise and streamlined, resulting in easier and faster testing. Furthermore, developers find it simpler to write fewer JUnit and integration test cases.
  9. Application performance issue: When business logic and cross-cutting configuration are combined, they need extra time to load, deploy, and run on app containers. It consumes extra CPU and RAM for even business-specific API calls, which can cause performance issues. In contrast, a service mesh utilizes a separate side-car container dedicated to running the cross-cutting concerns configuration code. This alleviates the load on the main application container, resulting in improved app performance. By running only the streamlined application business logic, the performance is enhanced.

What key features should you look for when selecting a Service Mesh?

  • Connect Kubernetes clusters: It provides connectivity between two or more Kubernetes clusters if it’s used with hybrid cloud technologies like Google Anthos, Azure Arc, AWS Outpost, VMware Tanzu Mission Control (TMC), etc. It could spread across on-premises, private, and public cloud providers.
  • Service discovery with the Ingress Controller and Ingress resources: It provides dynamic service discovery and routing to distributed microservice REST APIs across K8s clusters on multiple clouds with different dynamic IP addresses. It exposes the service by its service name through the Ingress Controller and Ingress resources, which can be used by any client or consumer. The ingress resource provides routing details to various services, and the ingress controller routes incoming requests to the API using the ingress resource.
  • Circuit breaker resiliency: A circuit breaker provides a retry mechanism if dependent services are not responding to the first attempt. A service mesh provides a powerful feature of the circuit breaker when a dependent service does not respond within a given ETA. Because of this, microservices are more resilient to downtime since a service mesh can reroute requests away from failed services using this mechanism.
  • API Tracing between microservices: It provides the API Tracing (API to API interactions) feature of microservices, which traces request and response interaction logs. This tracing helps improve the performance of API and SLA. It helps developers debug and diagnose bugs.
  • Observability: It provides a powerful mechanism to check application health and infra resources like CPU and memory usage. Also, it collects application performance matrices and visualizes them on the web dashboard. Performance metrics can suggest ways to optimize communication in the runtime environment. Also, monitor infrastructure and application monitoring.
  • Data Payload Security: It provides data encryption in transit between microservice API communications by applying two-way strong mTLS security encryption technology.
  • API Rate Limiting: It provides a mechanism to restrict the number of backend API calls and prevent distributed denial-of-service (DOS/DDOS) attackers where thousands or even millions of requests hit backend APIs randomly and crash the entire backend software system and infrastructure.‍
  • Load balancing: It provides load balancing by using its in-built ingress controller mechanism to expose microservices on Kubernetes clusters as external services exposed through the ingress controller load balancer. Ingress control can map and route client requests to distributed microservices based on ingress resources.

Popular Service Meshes

Istio (OSS)

Istio is an open-source service mesh platform that provides a set of tools and capabilities for managing and securing microservices-based applications. It aims to address common challenges associated with service-to-service communication, observability, security, and traffic management in complex distributed systems. At its core, Istio deploys a sidecar proxy, called Envoy, alongside each microservice in the application. This sidecar proxy intercepts and manages all inbound and outbound traffic for the service, allowing Istio to control and monitor the communication between services.

Advantages:

  • Istio boasts one of the largest communities for online service mesh and is highly acclaimed and discussed on the internet. Its GitHub contributors far outnumber those of Linkerd by a significant margin. 
  • Furthermore, it offers support for both Kubernetes and VM modes.

Drawbacks:

  • Istio comes with a cost as it is not available for free. It demands a considerable time investment in terms of reading the documentation, setting it up, ensuring proper functionality, and ongoing maintenance. 
  • The implementation and integration of Istio into production can range from several weeks to several months, depending on the complexity of the infrastructure.
  • Using Istio requires a significant amount of resource overhead. 
  • Unlike Linkerd, it lacks a built-in administrative dashboard. 
  • Additionally, Istio mandates the use of its own ingress gateway. 
  • The Istio control plane is exclusively supported within Kubernetes containers, meaning there is no VM mode available for the Istio data plane.

Linkerd

Linkerd is an open-source service mesh platform designed to provide observability, reliability, and security to microservices architectures. It is developed by the Cloud Native Computing Foundation (CNCF) and focuses on simplicity, performance, and ease of use.

Advantages

  • Linkerd leverages the expertise of its creators, who are former Twitter engineers with experience in developing the internal tool, Finagle. They gained valuable insights from working on Linkerd v1, which contributes to the refinement of the service mesh. 
  • Being one of the pioneering service meshes, Linkerd enjoys an active and vibrant community, boasting more than 5,000 users on Slack, along with an engaged mailing list and Discord server. 
  • The availability of comprehensive documentation and tutorials further enhances its appeal.
  • Linkerd has reached a level of maturity with the release of version 2.9, which is evident from its adoption by prominent corporations such as Nordstrom, eBay, Strava, Expedia, and Subspace. 
  • Additionally, Linkerd offers paid enterprise-grade support through Buoyant, ensuring professional assistance is readily available.

Drawbacks

  • Using Linkerd service meshes to their full potential requires a significant learning curve. It is important to note that Linkerd is exclusively supported within Kubernetes containers and does not offer a VM-based or “universal” mode. 
  • Unlike Envoy, the Linkerd sidecar proxy differs, providing Buoyant the flexibility to optimize it according to their requirements. However, this customization comes at the expense of losing the inherent extensibility offered by Envoy. 
  • Consequently, Linkerd lacks support for essential features such as circuit breaking, delay injection, and rate limiting. Additionally, there is no straightforward API exposed for easy control of the Linkerd control plane, although a gRPC API binding can be found.

In case you wish to read more about the above service meshes comparison and what more they have to offer, you can read all about it here.

That’s not it, there many many options in the market for you to choose from like:

Conclusion

Service mesh technology is a boon for developers. It increases developer productivity by delegating cross-cutting concerns from application source code to in-house DevSecOps. Service Mesh provides a ton of more features to solve developer challenges and increase developer productivity. It’s now a de facto standard for managing cross-cutting configuration code for cloud-native microservice apps on Kubernetes.

Cloud Distributed Caching for Microservices

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:  

  • Redis 
  • Memcache 
  • 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.

Conclusion

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.

My first book release!! Cloud Native Microservices with Spring and Kubernetes (453 pages)

I am happy to announce release of my first book “Cloud Native Microservices with Spring and Kubernetes” with BPB Publications!! It’s all about design, build and deploy scalable cloud native microservices on container using the Spring framework and Kubernetes. Need your support! Please buy and review this book on Amazon. Also, share book detail with your IT software engineers colleagues and friends.

The main objective of this book is to give an overview of cloud-native microservices, their architecture, design patterns, best practices, use cases, and practical coverage of modern applications. This book covers a strong understanding of microservices, API first approach, Testing, Observability, API Gateway, Service Mesh, and Kubernetes alternatives of Spring Cloud. This book covers the implementation of various design patterns of developing cloud native microservices using Spring framework, docker and Kubernetes. It also covers containerization concepts and hands-on code exercises.

After reading this book, the readers will have a holistic understanding of building, running, and managing cloud native microservices applications on Kubernetes containers.

It’s the first book on this subject in India by any Indian writer, which is also economical than foreign publications. This book is about learning of software application design and development using Microservices, Spring and Kubernetes based technologies. It’s useful for software developers, cloud engineers, DevOps and technical architects.

Available on:

This book is available in paperback and Kindle (eBook on free Kindle app on Android/iOS/Laptop/Desktop) editions on amazon and BPB in most of the countries of North America, Europe, the Middle East, Asia, and Africa.

Refer free preview and TOC/Index of this book:

https://drive.google.com/drive/folders/1Lq280d6hUcyh2xm8cFuyt1vADNzk61dg?usp=sharing

What you will learn:

  • Learn fundamentals of microservice and design patterns.
  • Perform end-to-end microservices testing using Cucumber.
  • Learn microservices development using Spring Boot and Kubernetes.
  • Learn to develop reactive, event-driven, and batch microservices.
  • Perform end-to-end microservices testing using Cucumber.
  • Implement API gateway, authentication & authorization, load balancing, caching, and rate limiting.
  • Learn observability and monitoring techniques of microservices

Who this book is for:
This book is for the Spring Developers, Microservice Developers, Cloud Engineers, DevOps Consultants, Technical Architect and Solution Architects.

Table of Contents (Chapters):
1. Overview of Cloud Native microservices
2. Microservice design patterns
3. API first approach
4. Build microservices using the Spring Framework
5. Batch microservices
6. Build reactive and event-driven microservices
7. The API gateway, security, and distributed caching with Redis
8. Microservices testing and API mocking
9. Microservices observability
10. Containers and Kubernetes overview and architecture
11. Run microservices on Kubernetes
12. Service Mesh and Kubernetes alternatives of Spring Cloud

KEY FEATURES:

  • Complete coverage on how to design, build, run, and deploy modern cloud native microservices.
  • Includes numerous sample code exercises on microservices, Spring and Kubernetes.
  • Develop a stronghold on Kubernetes, Spring, and the microservices architecture.
  • Complete guide of application containerization on Kubernetes containers.
  • Coverage on managing modern applications and infrastructure using observability tools.

Chapter 1: Overview of Cloud Native microservices, introduces cloud native modern applications, cloud first overview, benefits, types of clouds, classification, and the need for cloud native modern applications. It will cover detailed microservices (MSA ) overview, characteristics, motivations, benefits, best practices, architecture principles, challenges and solutions, application modernization spectrum, twelve-factor apps, and beyond twelve-factor apps.s

Chapter 2: Microservice design patterns, introduces various microservices design patterns with use cases, advantages, and disadvantages.

Chapter 3: API first approach, discusses fundamentals of the API first approach. It discusses details of the REST overview, API model, best practices, design principles, components, security, communication protocols, and how to document dynamically with OpenAPI Swagger. It discusses API design planning, specifications, API management tools, and testing API with SwaggerHub inspector and PostMan REST client.

Chapter 4: Build microservices using the Spring Framework, is a key chapter of Spring Boot and Spring Cloud components with hands-on lab exercises. It will cover steps to build microservice using the REST API framework. It covers the Spring Cloud config server and resiliency of microservices practical aspects.

Chapter 5: Batch microservices, introduces batch microservices, use cases, Spring Cloud Task, and Spring Batch. It discusses hands-on lab exercises using Spring Cloud Data Flow (SCDF) and Kafka. It also discusses a few Spring batch practices, auto-scaling techniques, batch orchestration, and compositions methods for sequential or parallel batch processing. Last but not the least; it talks about alerts and monitoring of Spring Cloud Task and Spring Batch.

Chapter 6:  Build reactive and event-driven microservices, describes building of reactive microservices, non-blocking synchronous APIs, and event-driven asynchronous microservices. It covers steps to develop sample reactive microservices with Spring’s project Reactor, Spring WebFlux, and event-driven asynchronous microservices. It discusses Spring Cloud Stream, Zookeeper, SpringBoot, and overview of Kafka. It also covers hands-on lab exercises of event-driven asynchronous microservices using Spring Cloud Stream and Kafka.

Chapter 7:  API gateway, security, and distributed caching with Redis, introduces the API Gateway overview, features, advantages, and best practices. It covers hands-on lab exercises to expose REST APIs of microservices externally with the Spring Cloud Gateway. It covers distributed caching overview and hands-on lab exercises using Redis. It discusses API gateway rate limiting and Implementation of API gateway rate limiting with Redis and Spring Boot. Last but not the least, it covers best practices of API Security. Implementation of SSO using Spring Cloud Gateway, Spring Security, Oauth2, Keycloak, OpenId, and JWT tokens.

Chapter 8: Microservices testing and API mocking, describes important aspects of microservices testing practices, challenges, benefits, testing strategy, testing pyramid, and different types of microservices testing. It covers implementation of the integration testing framework using Behavioral Driven Development (BDD) with hands-on code examples. It also discusses microservices testing tools and best practices of microservices testing. It covers the role of testing in the microservices CI/CD pipeline. Last but not the least; it talks about API mocking and hands-on lab implementation with the WireMock framework.

Chapter 9: Microservices observability, covers detail observability and monitoring overview and techniques of microservices with the Spring actuator, micrometer health APIs, and Wavefront APM. It covers application logging overview, best practices, simple logging, and log aggregation of distributed microservices with implementation using Elasticsearch, Fluentd, and Kibana (EFK) on the Kubernetes container. It discusses the need of APM performance and telemetry monitoring tools for distributed microservices and how to trace multiple microservices in a distributed environment. It also covers hands-on lab implementation of monitoring microservices with Prometheus and Grafana.

Chapter 10: Containers and Kubernetes overview and architecture, is a key chapter which introduces containers, docker, docker engine containerization, Buildpacks, components of docker files, build docker files, run docker files, and inspect docker images. It covers docker image registry and how to persist docker images in image container registries. It covers an overview of Kubernetes, need, and architecture. Last but not the least; it covers a detailed introduction of Kubernetes resources.

Chapter 11: Run microservices on Kubernetes, discusses practical aspects of Kubernetes, installation, and configuration with monitoring and visualization tools with Octant and Proxy. It discusses how to create and manage Kubernetes clusters in detail. It discusses hands-on exercises of creating docker images of Java microservices, pushing it to the Docker hub container image registry, and deploying to Kubernetes clusters. It covers hands-on lab examples of exposing API endpoints of microservices outside the Kubernetes cluster by using the Nginx ingress controller. Last but not the least; it covers various popular and useful Kubernetes application deployment and configuration of management tools.

Chapter 12: Service Mesh and Kubernetes alternatives of Spring Cloud, covers a detailed overview and benefits of GitOps and Service Mesh. It covers the Istio Service Mesh architecture and deployment of microservices on Kubernetes with Argo CD. Last but not the least; it discusses various Kubernetes alternatives of Spring Cloud projects and popular cloud buzzwords!

About the Author
Rajiv Srivastava is the founder of cloudificationzone.com, which is a cloud native modern application tech blog site. He is a cloud solution architect and modern application specialist with 17+ years of work experience in software development and architectural design.

KEYWORDS 

1.      Spring framework

2.      API first approach

3.      Cloud Native

4.      Microservices observability

5.      API testing

6.      API gateway

7.      Microservices observability

8.      Service mesh

9.      API gateway

10.    Redis distributed caching

11.    Kafka

12.    Spring Cloud

13.    Service discovery

14.    Spring cloud data flow

15.    Ingress controller

Business Benefits of Cloud

I had been busy with my other work assignments. publishing this blog after a long interval. Hope you like it !

It’s important to understand benefits for the business who is planning to migrate to cloud and invest time and money. These are some generic and common benefits after adopting cloud technology using modern application approach:

  • Smoother and faster app user experience: Cloud provides faster, highly available app interfaces which improves rich user experience. For example, AWS stores static web pages and images at nearby CDN(Content Delivery Network) servers physically on cloud, which provides faster and smooth application response.
  • On demand scaling for infrastructure: Cloud provides on demand compute, memory and storage horizontal/vertical scaling. Organizations/customers should not bother about infra prediction for higher load and they also save money to use only required infrastructure resources.
  • No outage for users and clients: Cloud provides high availability, so whenever any app server is down, client load will be diverted to other app server or a new app server will be created. User and client sessions will also be managed automatically using internal load balancers.
  • Less operational cost (OPEX): Cloud manages most of the infra management operation automatically or by cloud providers. For example, PAAS (Platform as a service) automates entire platform automation with a smaller number of DevOps resources, which saves a lot operational cost.
  • Easy to manage: Cloud providers and PAAS platforms provides very easy and intuitive web, CLI and API based console or interface, which can be easily integrated with the CI/CD tools, IAAC (Infrastructure as a Code) and scripts. They can also be integrated with apps etc.
  • Release app features quickly to compete in market: Cloud provides a lot of ready-to use services on cloud, which takes lesser time to build and deploy apps to cloud quickly using microservices agile like development methodologies. It supports container-based orchestration services like Kubernetes, where smaller microservices can be deployed in quick time, which enables organizations to release new feature quickly.
  • Increased security: Cloud solutions provider out of the box intrinsic security features at various level of application, network, data and infra level. For example, AWS provides DDOS and OWASP security features with firewall etc.
  • Increase developer productivity: Cloud providers various tools and services to improve developer productivity like PAAS, Tanzu Build Service. Spring framework, AWS BeanStalk, GCP, OpenShift developer tools etc.
  • Modular Teams:  Cloud migration motivates to follow modern applications microservice framework for dev and test teams to work in agile on independent modules or microservices independently.
  • Public cloud’s “pay as you go” usage policy: Customer has to pay for pay as you go usage of infra, so that no extra infra resources wasted. These public service providers pricing model saves a lot of cost.
  • Easy disaster recovery handling: Cloud deployed on multiple data centers (DCs) or availability zones (AZs) for disaster recovery (DR), so that if any site (DC/AZ) is down then client or application load will be automatically routed to other site using load balancers.
  • Business continuity: It provides all necessary processes and tools to manages business continuity (BC) for smooth and resilient business operations. They. Provide faster site recovery in case of disaster and data backup. Cloud also provides enterprise compliances for various industries like HIPAA for health insurance etc.