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Monitoring Java Microservices with Kubernetes | Use Case

Gursimran Singh | 06 Dec 2022

Introduction to Microservices

  • Microservices Architecture is a style of structuring applications as loosely coupled services. An Application based on itsrchitecture is small, messaging–enabled, bounded by contexts, autonomously developed, independently deployable, decentralized, language–agnostic, build and release with automated processes.
  • Microservices Architecture enables secure Continuous Integration and Continuous Delivery Pipeline for delivery, automation, self-service environment, and On-demand. Deploy the application to production by decreasing the time through the automation of entire delivery pipeline comprising build, deploy, test, and release.
  • Spring Boot is a framework for building web and enterprise applications designed to simplify the bootstrapping and development of Spring Applications. Spring framework provides flexibility to configure beans in multiple ways such as XML, Annotations, and JavaConfig.
  • Spring Cloud is a collection of tools that provide solutions to some of the commonly encountered patterns when building distributed systems. Spring Cloud offers tools for Configuration Management, Service Discovery, Circuit Breakers, Distributed Sessions.

What are the challenges for setting up services?

  • To create Microservices using Spring by setting up a new service using Spring Boot.
  • Work on code that analyses the meaning of metrics than a code that collects, moves, stores, and displays metrics.
  • Validation of a measurement system to achieve accuracy and precision. Collect histograms of response time.
  • Monitoring systems need to be more available and scalable than the systems (and services) monitored.
  • Optimization of monitoring distributed, ephemeral, containerized Microservices.

Solution offered for microservices monitoring platform

  Monitoring is a critical piece of the control systems of it, as more complex your software gets, the harder it is to understand its performance and troubleshoot problems.   For monitoring, we use tools to get Time Series Database, Metrics Collector, Visualize the data for each running containers -  
  • Prometheus - Prometheus is a system and service monitoring system.
  • InfluxDB - InfluxDB is an open source Time Series Database.
  • Elasticsearch - Elasticsearch is a highly scalable open-source full-text search and analytics engine.
  • Grafana - Grafana is an open source metric analytics & visualization suite.
  • Kibana - Kibana is an open source Data Visualization plugin for Elasticsearch.
  • CAdvisor - CAdvisor is an open source tool to monitor Kubernetes resource usage and performance.

Managing Applications using prometheus and grafana

Prometheus is best for open source Cloud Native Systems and service monitoring. The main component of Prometheus consists of Prometheus Server responsible for monitoring. It is a Time-Series database used to scrape data at regular intervals having container based infrastructure. It scrapes and stores data by HTTP pull and GET requests. Grafana models collected data into useful and excellent dashboards for visualization. Web UI used for graph expressions and Alert Manager to trigger alerts from Prometheus Server.

Visualization layer with Grafana

Grafana is an open source software for Time-Series Analytics. It sets alert rules based on metrics data. On alert state change, it notifies through various channels.

Prometheus and Grafana Real-Time Applications

  • Banking Sector for performance reporting.
  • Alert Noise management through Prometheus Server.
  • Containers CheckUp.
  • Kubernetes Integration.
  • Software as a Service.
  • Data Storage and Migration.

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