![]() ![]() The reference application platform is designed to generate gRPC-based, synchronous service-to-service IPC (inter-process communication), asynchronous TCP-based service-to-queue-to-service communications, and TCP-based service-to-database communications. Reference Application Platform’s Angular-based UI The platform comprises a backend of eight Go-based microservices labeled generically as Service A - Service H, one Angular 12 TypeScript-based frontend UI, one Go-based gRPC Gateway reverse proxy, four MongoDB databases, and one RabbitMQ message queue. The application platform was developed to demonstrate different Kubernetes platforms, such as EKS, GKE, AKS, and concepts such as service meshes, API management, observability, CI/CD, DevOps, and Chaos Engineering. To demonstrate the use of the observability tools, we will deploy a reference application platform to Amazon EKS on AWS. You can read more about the binary wire format of Protobuf on Google’s Developers Portal. For this post, we have compiled our protobufs for Go. With the proto3 language version, protocol buffers currently support generated code in Java, Python, Objective-C, C++, Dart, Go, Ruby, and C#, with more languages to come. proto file to generate data access classes. Once you have defined your messages, you run the protocol buffer compiler for your application’s language on your. Google’s previous documentation claimed protocol buffers were “ 3 to 10 times smaller and 20 to 100 times faster than XML.” According to Google, Protocol buffers are Google’s language-neutral, platform-neutral, extensible mechanism for serializing structured data - think XML, but smaller, faster, and simpler. With gRPC, you define your service using Protocol Buffers (aka Protobuf), a powerful binary serialization toolset and language. This is mainly due to the tight packing of the Protocol Buffers and the use of HTTP/2 by gRPC.” Protocol Buffers ![]() gRPC currently supports over ten languages, including C#, C++, Dart, Go, Java, Kotlin, Node, Objective-C, PHP, Python, and Ruby.Īccording to widely-cited 2019 tests published by Ruwan Fernando, “gRPC is roughly 7 times faster than REST when receiving data & roughly 10 times faster than REST when sending data for this specific payload. gRPC is now used in many organizations outside of Google, including Square, Netflix, CoreOS, Docker, CockroachDB, Cisco, and Juniper Networks. In March 2015, Google decided to build the next version of Stubby and make it open source. GRPC was initially created by Google, which has used a single general-purpose RPC infrastructure called Stubby to connect the large number of microservices running within and across its data centers for over a decade. gRPC is also applicable in the last mile of distributed computing to connect devices, mobile applications, and browsers to backend services. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking, and authentication. ![]() ![]() Kiali Management Console showing gRPC-based reference application platform Technologies gRPCĪccording to the gRPC project, gRPC is a modern open source high-performance Remote Procedure Call (RPC) framework that can run in any environment. We will learn how Kubernetes, Istio, and the observability tools work seamlessly with gRPC, just as they do with JSON over HTTP on Amazon EKS. In this post, we will examine those same observability tools to monitor an alternate set of Go-based microservices that use Protocol Buffers (aka Protobuf) over gRPC (gRPC Remote Procedure Calls) and HTTP/2 for client-server communications as opposed to the more common RESTful JSON over HTTP. The application platform, running on EKS, used Amazon DocumentDB as a persistent data store and Amazon MQ to exchange messages. We used these tools to observe a distributed, microservices-based, RESTful application deployed to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. We rounded out the toolset with the addition of Fluent Bit for log processing and aggregation to Amazon CloudWatch Container Insights. Tools included Jaeger and Zipkin for distributed transaction monitoring, Prometheus for metrics collection and alerting, Grafana for metrics querying, visualization, and alerting, and Kiali for overall observability and management of Istio. In the previous two-part post, Kubernetes-based Microservice Observability with Istio Service Mesh, we explored a set of popular open source observability tools easily integrated with the Istio service mesh. Observing a gRPC-based Kubernetes application using Jaeger, Zipkin, Prometheus, Grafana, and Kiali on Amazon EKS running Istio service mesh Introduction ![]()
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