Distributed tracing using open tracing & jaeger 2
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This presentation talks about Open Tracing, Jaeger, OpenCensus and their use in the world of Microservices Architecture.
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Netflix Open Source: Building a Distributed and Automated Open Source Programaspyker Netflix has been using and contributing to open source for several years. Over the years, Netflix has released over one hundred Netflix Open Source (aka NetflixOSS) libraries, servers, and technologies. Netflix engineers benefit by accepting contributions and gathering feedback with key collaborators around the world. Users of NetflixOSS from many industries benefit from our solutions including Big Data, Build and Delivery Tools, Runtime Services and Libraries, Data Persistence, Insight, Reliability and Performance, Security and User Interface. With such a large and mature open source program, Netflix has worked on approaches and tools that help manage and improve the NetflixOSS source offerings and communities. Netflix has taken a different approach to building support for open source as compared to other Internet scale companies. Come to this session to learn about the unique approaches Netflix has taken to both distribute and automate the responsibilities of building a world-class open source program.
Portfolio
PortfolioShrey Sangal I'm an enthusiastic, DevOps Evangelist with more than six years of experience, comprising of DevTools, Build & Release and Integration Administration.
Passionate about DevOps, Possess Rich experience in various DevTools used for Source Control, Build Automation, Continuous Integration, Continuous Deployment, Provisioning, Scripting and Monitoring.
Proven track record in Delivering best DevOps practice by automating the build, test and deployment in agile development life cycle, maximizing the velocity of development teams by automating release workflows (pipelines) from development.
Continuous Integration & Deployment of applications using Git, Maven, Nexus, Tuxedo, Docker, shell and Jenkins pipelines.
An individual contributor, well organized with strong problem solving and good communication skills and a team player.
Distributed Tracing
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SCaLE 16x - Application Monitoring And Tracing In Kubernetes
SCaLE 16x - Application Monitoring And Tracing In KubernetesDavid vonThenen SCaLE 16x - Application Monitoring And Tracing In Kubernetes
Session Info:
https://www.socallinuxexpo.org/scale/16x/presentations/application-monitoring-and-tracing-kubernetes-avoiding-microservice-hell
Creating and deploying microservices is easy. The real problem is how to manage and support these services out in the wild and in production. What happens when these services stop working or worse yet when they are running but running slowly? Which service instance is the culprit? This session talks about how you can leverage metrics and tracing tools in order to give better visibility into the distributed nature of a microservice architecture in a Kubernetes environment.
This presentation will discuss key concepts of metrics and tracing and highlight Open Source Projects available that address: 1) the value of instrumentation and how Prometheus can be used to monitor and measure a Kubernetes cluster, and 2) how Jaeger can provide visibility into your applications and microservices.
Will provide a demo of Microservices leveraging Prometheus and Jaeger deployed to Kubernetes cluster.
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As the Pulsar communities grows, more and more connectors will be added. To enhance the availability of sources and sinks and to make use of the greater Apache Streaming community, joining forces between Apache NiFi and Apache Pulsar is a perfect fit. Apache NiFi also adds the benefits of ELT, ETL, data crunching, transformation, validation and batch data processing. Once data is ready to be an event, NiFi can launch it into Pulsar at light speed.
I will walk through how to get started, some use cases and demos and answer questions.
https://www.devfest-uki.com/schedule
https://linktr.ee/tspannhw
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Introduction to gRPC
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Observability in the world of microservices
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Production ready tooling for microservices on kubernetes
Production ready tooling for microservices on kubernetesChandresh Pancholi This document provides an overview of various production-ready tools for building and managing microservices on Kubernetes, including tools for service discovery, request routing and load balancing, monitoring and visualization, configuration management, security, centralized logging, package management, and more. It discusses common tools like Prometheus, Kubernetes Ingress, ConfigMaps, and Istio, and also introduces tools like Kubeless, Kubewatch, and Kube-monkey.
Monitoring on Kubernetes using prometheus
Monitoring on Kubernetes using prometheusChandresh Pancholi This document discusses using Prometheus to monitor Kubernetes clusters. It provides background on Kubernetes and Prometheus architectures. It then describes challenges with the previous monitoring setup and proposes using the Prometheus operator to more easily monitor Kubernetes and application metrics. The Prometheus operator allows automatically generating target configurations based on Kubernetes labels and provides Custom Resource Definitions for Prometheus and Service Monitors.
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Instruments to play microserviceChandresh Pancholi This document discusses microservices architecture and the various tools needed to build microservices. It begins by explaining the pros and cons of monolith applications versus microservices. It then covers requirements for building microservices like security, asynchronous communication, monitoring, logging, tracing, service discovery, configuration management, and circuit breakers. Specific open source tools are mentioned for each requirement area, including RabbitMQ, Kafka, Prometheus, Zipkin, Elasticsearch, Eureka, Spring Cloud Config, and Kubernetes. The document concludes by asking if the audience has any additional questions.
Microservices on kubernetes
Microservices on kubernetesChandresh Pancholi This document summarizes microservices on Kubernetes and the benefits of using Istio as a service mesh. It discusses some of the issues with earlier microservices architectures and how Istio addresses them. The key points are:
- Istio is an open platform that provides a service mesh for microservices on Kubernetes to enable security, observability, and traffic management between services.
- It addresses issues with earlier architectures that used an API gateway for routing by deploying Envoy proxies as sidecars, which provide features like discovery, load balancing and failure handling.
- The Istio control plane manages the proxies using components like Pilot and Mixer to configure routing and enforce policies.
Istio a service mesh
Istio a service meshChandresh Pancholi Istio is an open platform for providing a service mesh on Kubernetes clusters. It consists of three main components: Envoy proxies that mediate service-to-service communication, Pilot that configures the proxies, and Mixer that enforces policies and collects telemetry data. Istio injects Envoy sidecar proxies into applications so they can provide features like load balancing, authentication, failure recovery, and observability without requiring code changes. This provides a way to manage microservices that is more robust and flexible than using an API gateway alone.
Prometheus kubernetes tech talk
Prometheus kubernetes tech talkChandresh Pancholi This document discusses using Prometheus for application monitoring on Kubernetes. It describes the current monitoring systems in use and their limitations. Prometheus is introduced as an open-source monitoring system developed by SoundCloud. Two approaches are presented for using Prometheus on Kubernetes - running Prometheus on EC2 instances and pointing it at Kubernetes, or using the Prometheus Operator which automates Prometheus configuration based on Kubernetes resources. The Prometheus Operator approach is recommended for its simplified configuration.
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Distributed tracing using open tracing & jaeger 2
- 1. Distributed tracing using Open Tracing & Jaeger Chandresh Pancholi
- 2. Co-ordinates Linkedin : https://www.linkedin.com/in/chandresh-pancholi-467a8015/ Github: http://github.com/chandresh-pancholi?tab=repositories
- 9. Why Tracing? ● Distributed Context Processing ● Distributed transaction monitoring ● Root Cause Analysis ● Service dependency analysis ● Performance and latency optimization
- 11. Terminology ● Traces ● Span ● Tags ● Baggages
- 13. Jaeger ● A product built at Uber ● Inspired by Dapper from Google ● Donated to CNCF ● Supported libraries in Go, Java, Node.js, Python, C++, C# ● Accepts span in Zipkin format for backward compatibility ● Emit prometheus metrics
- 16. Observability 1. Monitoring 2. Logging 3. Tracing
- 18. Demo
- 19. Questions?