Using Kubernetes for Continuous Integration and Continuous Delivery. Java2daysCarlos Sanchez
Learn how to scale your Continuous Integration and Continuous Delivery environment using containers. The Kubernetes project provides a container orchestration solution that greatly simplifies app deployments in large clusters and you can use Jenkins and Kubernetes together to run jobs on-demand.
Building and testing is a great use case for containers, both due to the dynamic and isolation aspects, but it increases complexity when scaling to multiple nodes and clusters.
Jenkins is an example of an application that can take advantage of Kubernetes technology to run Continuous Integration and Continuous Delivery workloads. Jenkins and Kubernetes can be integrated to transparently use on demand containers to run build agents and jobs, and isolate job execution. It also supports CI/CD-as-code using Jenkins Pipelines and automated deployments to Kubernetes clusters. The presentation will allow a better understanding of how to use Jenkins on Kubernetes for container based, totally dynamic, large scale CI and CD.
Join us to learn the concepts and terminology of Kubernetes such as Nodes, Labels, Pods, Replication Controllers, Services. After taking a closer look at the Kubernetes master and the nodes, we will walk you through the process of building, deploying, and scaling microservices applications. Each attendee gets $100 credit to start using Google Container Engine. The source code is available at https://github.com/janakiramm/kubernetes-101
On Friday 5 June 2015 I gave a talk called Cluster Management with Kubernetes to a general audience at the University of Edinburgh. The talk includes an example of a music store system with a Kibana front end UI and an Elasticsearch based back end which helps to make concrete concepts like pods, replication controllers and services.
Scaling Jenkins with Docker: Swarm, Kubernetes or Mesos?Carlos Sanchez
The Jenkins platform can be dynamically scaled by using several Docker cluster and orchestration platforms, using containers to run slaves and jobs and also isolating job execution. But which cluster technology should be used? Docker Swarm? Apache Mesos? Kubernetes? How do they compare? All of them can be used to dynamically run jobs inside containers. This talk will cover these main container clusters, outlining the pros and cons of each, the current state of the art of the technologies and Jenkins support.
A brief study on Kubernetes and its componentsRamit Surana
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. Using the concepts of "labels" and "pods", it groups the containers which make up an application into logical units for easy management and discovery.
XP Days Ukraine 2015 Talk http://xpdays.com.ua/programs/scaling-docker-with-kubernetes/
Kubernetes is an open source project to manage a cluster of Linux containers as a single system, managing and running Docker containers across multiple Docker hosts, offering co-location of containers, service discovery and replication control. It was started by Google and now it is supported by Microsoft, RedHat, IBM and Docker Inc amongst others.
Once you are using Docker containers the next question is how to scale and start containers across multiple Docker hosts, balancing the containers across them. Kubernetes also adds a higher level API to define how containers are logically grouped, allowing to define pools of containers, load balancing and affinity.
Using Kubernetes for Continuous Integration and Continuous Delivery. Java2daysCarlos Sanchez
Learn how to scale your Continuous Integration and Continuous Delivery environment using containers. The Kubernetes project provides a container orchestration solution that greatly simplifies app deployments in large clusters and you can use Jenkins and Kubernetes together to run jobs on-demand.
Building and testing is a great use case for containers, both due to the dynamic and isolation aspects, but it increases complexity when scaling to multiple nodes and clusters.
Jenkins is an example of an application that can take advantage of Kubernetes technology to run Continuous Integration and Continuous Delivery workloads. Jenkins and Kubernetes can be integrated to transparently use on demand containers to run build agents and jobs, and isolate job execution. It also supports CI/CD-as-code using Jenkins Pipelines and automated deployments to Kubernetes clusters. The presentation will allow a better understanding of how to use Jenkins on Kubernetes for container based, totally dynamic, large scale CI and CD.
Join us to learn the concepts and terminology of Kubernetes such as Nodes, Labels, Pods, Replication Controllers, Services. After taking a closer look at the Kubernetes master and the nodes, we will walk you through the process of building, deploying, and scaling microservices applications. Each attendee gets $100 credit to start using Google Container Engine. The source code is available at https://github.com/janakiramm/kubernetes-101
On Friday 5 June 2015 I gave a talk called Cluster Management with Kubernetes to a general audience at the University of Edinburgh. The talk includes an example of a music store system with a Kibana front end UI and an Elasticsearch based back end which helps to make concrete concepts like pods, replication controllers and services.
Scaling Jenkins with Docker: Swarm, Kubernetes or Mesos?Carlos Sanchez
The Jenkins platform can be dynamically scaled by using several Docker cluster and orchestration platforms, using containers to run slaves and jobs and also isolating job execution. But which cluster technology should be used? Docker Swarm? Apache Mesos? Kubernetes? How do they compare? All of them can be used to dynamically run jobs inside containers. This talk will cover these main container clusters, outlining the pros and cons of each, the current state of the art of the technologies and Jenkins support.
A brief study on Kubernetes and its componentsRamit Surana
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. Using the concepts of "labels" and "pods", it groups the containers which make up an application into logical units for easy management and discovery.
XP Days Ukraine 2015 Talk http://xpdays.com.ua/programs/scaling-docker-with-kubernetes/
Kubernetes is an open source project to manage a cluster of Linux containers as a single system, managing and running Docker containers across multiple Docker hosts, offering co-location of containers, service discovery and replication control. It was started by Google and now it is supported by Microsoft, RedHat, IBM and Docker Inc amongst others.
Once you are using Docker containers the next question is how to scale and start containers across multiple Docker hosts, balancing the containers across them. Kubernetes also adds a higher level API to define how containers are logically grouped, allowing to define pools of containers, load balancing and affinity.
Continuous Deployment with Jenkins on KubernetesMatt Baldwin
Google Senior Software Engineer Evan Brown's presentation from the March 18, 2016 Seattle Kubernetes meetup hosted by StackPointCloud. Evan shows how you deploy Jenkins into Kubernetes, then takes us through CD and canary deployments. Join us in Seattle: http://www.meetup.com/Seattle-Kubernetes-Meetup/
Orchestrating Docker Containers with Google Kubernetes on OpenStackTrevor Roberts Jr.
Kubernetes, Docker, CoreOS, and OpenStack for container workload management.
No audio, but there are annotations to follow along with the workload.
A video accompanies a Microservices Meetup talk that I presented on February 18, 2015 at https://www.youtube.com/watch?v=RfyIYhOzyPY
Acknowledgements to Kelsey Hightower for the workflow that I used, and Google for the example application shown.
Using Containers for Building and Testing: Docker, Kubernetes and Mesos. FOSD...Carlos Sanchez
Building and testing is a great use case for containers, both due to the dynamic and isolation aspects, but running in just one machine is not enough and quickly needs to scale to a clustered setup. But which cluster technology should be used? Docker Swarm? Apache Mesos? Kubernetes? how do they compare? All of them can be used to dynamically run a cluster of containers.
Building and testing is a great use case for containers, both due to the dynamic and isolation aspects, but running in just one machine is not enough and quickly needs to scale to a clustered setup. But which cluster technology should be used? Docker Swarm? Apache Mesos? Kubernetes? how do they compare? All of them can be used to dynamically run a cluster of containers.
The Jenkins platform is an example of dynamically scaling by using several Docker cluster and orchestration platforms, using containers to run build agents and jobs, and also isolate job execution.
This talk will cover these main container clusters, outlining the pros and cons, the current state of the art of the technologies and Jenkins support.
The presentation will allow a better understanding of using Docker in the main Docker cluster/orchestration platforms out there (Docker Swarm, Apache Mesos, Kubernetes), sharing my experience and helping people decide which one to use, going through Jenkins examples and current support.
Overview of kubernetes and its use as a DevOps cluster management framework.
Problems with deployment via kube-up.sh and improving kubernetes on AWS via custom cloud formation template.
WSO2Con US 2015 Kubernetes: a platform for automating deployment, scaling, an...Brian Grant
Kubernetes can run application containers on clusters of physical or virtual machines.
It can also do much more than that.
Kubernetes satisfies a number of common needs of applications running in production, such as co-locating helper processes, mounting storage systems, distributing secrets, application health checking, replicating application instances, horizontal auto-scaling, load balancing, rolling updates, and resource monitoring.
However, even though Kubernetes provides a lot of functionality, there are always new scenarios that would benefit from new features. Ad hoc orchestration that is acceptable initially often requires robust automation at scale. Application-specific workflows can be streamlined to accelerate developer velocity.
This is why Kubernetes was also designed to serve as a platform for building an ecosystem of components and tools to make it easier to deploy, scale, and manage applications. The Kubernetes control plane is built upon the same APIs that are available to developers and users, implementing resilient control loops that continuously drive the current state towards the desired state. This design has enabled Apache Stratos and a number of other Platform as a Service and Continuous Integration and Deployment systems to build atop Kubernetes.
This presentation introduces Kubernetes’s core primitives, shows how some of its better known features are built on them, and introduces some of the new capabilities that are being added.
Google has been running everything in containers for the past 15 years, but how do we orchestrate and manage all those containers? We've built and released the open source Kubernetes (http://kubernetes.io), which is based on years of running containers internally at Google. Join us for an introduction to containers and Kubernetes, followed by a hands-on workshop building and deploying your own Kubernetes cluster with multiple front end, database and caching instances.
Docker containers help solve the issue of process-level reproducibility by packaging up your apps and execution environments into a number of containers. But once you have a lot of containers running, you'll need to coordinate them across a cluster of machines while keeping them healthy and making sure they can find each other. This can quickly turn into an unmanageable mess! Wouldn't it be helpful if you could declare what wanted, and then have the cluster assign the resources to get it done and to recover from failures and scale on demand? Kubernetes is here to help!
Key takeaways
- Gentle introduction into containers: why and how
- Learn how Google manages applications using containers
- Intro to Kubernetes: managing applications and services
- Build and deploy your own multi-tier application using Kubernetes
- Archeology: before and without Kubernetes
- Deployment: kube-up, DCOS, GKE
- Core Architecture: the apiserver, the kubelet and the scheduler
- Compute Model: the pod, the service and the controller
Configuration Management and Transforming Legacy Applications in the Enterpri...Docker, Inc.
Share the continuity of Société Générale's journey with Docker Enterprise from different points of view, from executives to devops, with CD platform as an enabler. Creating a Dockerfile that runs a container on a developer's laptop is pretty straightforward. But extending that to stacks of containers running on a dozen environments (development, integration, testing, staging, production, etc.) with different configuration and topologies can be a challenge. This talk will cover aspects of our journey to Docker Enterprise:
What configuration should go in an image?
Where to put different types of configuration? Images, environment variables, entrypoint, ...?
How to store assets for building images and configuration for deployment in version control.
We will discuss how Société Générale has implemented these, and what we plan next for Docker Enterprise deployment.
Kubernetes Architecture - beyond a black box - Part 2Hao H. Zhang
This continues the Kubernetes architecture deep dive series. (Part 1 see https://www.slideshare.net/harryzhang735/kubernetes-beyond-a-black-box-part-1)
In Part 2 I'm going to cover the following:
- Kubernetes's 3 most import design choices: Micro-service Choreography, Level-Triggered Control, Generalized Workload and Centralized Controller
- Default scheduler limitation and community's next step
- Interface to production environment
- Workload abstraction: strength and limitations
This concludes my work and knowledge sharing about Kubernetes.
2016 - Continuously Delivering Microservices in Kubernetes using Jenkinsdevopsdaysaustin
Presentation by Sandeep Parikh
In this talk, we will cover the basics of Kubernetes and show how to set up continuous delivery pipelines using Jenkins and Jenkins Workflow to go from code to deployment, without developers having to interact with the production deployment infrastructure. The goal is an end-to-end set of steps to automate deployment and delivery of an application composed of several microservices.
Docker Kubernetes Istio
Understanding Docker and creating containers.
Container Orchestration based on Kubernetes
Blue Green Deployment, AB Testing, Canary Deployment, Traffic Rules based on Istio
Continuous Deployment with Jenkins on KubernetesMatt Baldwin
Google Senior Software Engineer Evan Brown's presentation from the March 18, 2016 Seattle Kubernetes meetup hosted by StackPointCloud. Evan shows how you deploy Jenkins into Kubernetes, then takes us through CD and canary deployments. Join us in Seattle: http://www.meetup.com/Seattle-Kubernetes-Meetup/
Orchestrating Docker Containers with Google Kubernetes on OpenStackTrevor Roberts Jr.
Kubernetes, Docker, CoreOS, and OpenStack for container workload management.
No audio, but there are annotations to follow along with the workload.
A video accompanies a Microservices Meetup talk that I presented on February 18, 2015 at https://www.youtube.com/watch?v=RfyIYhOzyPY
Acknowledgements to Kelsey Hightower for the workflow that I used, and Google for the example application shown.
Using Containers for Building and Testing: Docker, Kubernetes and Mesos. FOSD...Carlos Sanchez
Building and testing is a great use case for containers, both due to the dynamic and isolation aspects, but running in just one machine is not enough and quickly needs to scale to a clustered setup. But which cluster technology should be used? Docker Swarm? Apache Mesos? Kubernetes? how do they compare? All of them can be used to dynamically run a cluster of containers.
Building and testing is a great use case for containers, both due to the dynamic and isolation aspects, but running in just one machine is not enough and quickly needs to scale to a clustered setup. But which cluster technology should be used? Docker Swarm? Apache Mesos? Kubernetes? how do they compare? All of them can be used to dynamically run a cluster of containers.
The Jenkins platform is an example of dynamically scaling by using several Docker cluster and orchestration platforms, using containers to run build agents and jobs, and also isolate job execution.
This talk will cover these main container clusters, outlining the pros and cons, the current state of the art of the technologies and Jenkins support.
The presentation will allow a better understanding of using Docker in the main Docker cluster/orchestration platforms out there (Docker Swarm, Apache Mesos, Kubernetes), sharing my experience and helping people decide which one to use, going through Jenkins examples and current support.
Overview of kubernetes and its use as a DevOps cluster management framework.
Problems with deployment via kube-up.sh and improving kubernetes on AWS via custom cloud formation template.
WSO2Con US 2015 Kubernetes: a platform for automating deployment, scaling, an...Brian Grant
Kubernetes can run application containers on clusters of physical or virtual machines.
It can also do much more than that.
Kubernetes satisfies a number of common needs of applications running in production, such as co-locating helper processes, mounting storage systems, distributing secrets, application health checking, replicating application instances, horizontal auto-scaling, load balancing, rolling updates, and resource monitoring.
However, even though Kubernetes provides a lot of functionality, there are always new scenarios that would benefit from new features. Ad hoc orchestration that is acceptable initially often requires robust automation at scale. Application-specific workflows can be streamlined to accelerate developer velocity.
This is why Kubernetes was also designed to serve as a platform for building an ecosystem of components and tools to make it easier to deploy, scale, and manage applications. The Kubernetes control plane is built upon the same APIs that are available to developers and users, implementing resilient control loops that continuously drive the current state towards the desired state. This design has enabled Apache Stratos and a number of other Platform as a Service and Continuous Integration and Deployment systems to build atop Kubernetes.
This presentation introduces Kubernetes’s core primitives, shows how some of its better known features are built on them, and introduces some of the new capabilities that are being added.
Google has been running everything in containers for the past 15 years, but how do we orchestrate and manage all those containers? We've built and released the open source Kubernetes (http://kubernetes.io), which is based on years of running containers internally at Google. Join us for an introduction to containers and Kubernetes, followed by a hands-on workshop building and deploying your own Kubernetes cluster with multiple front end, database and caching instances.
Docker containers help solve the issue of process-level reproducibility by packaging up your apps and execution environments into a number of containers. But once you have a lot of containers running, you'll need to coordinate them across a cluster of machines while keeping them healthy and making sure they can find each other. This can quickly turn into an unmanageable mess! Wouldn't it be helpful if you could declare what wanted, and then have the cluster assign the resources to get it done and to recover from failures and scale on demand? Kubernetes is here to help!
Key takeaways
- Gentle introduction into containers: why and how
- Learn how Google manages applications using containers
- Intro to Kubernetes: managing applications and services
- Build and deploy your own multi-tier application using Kubernetes
- Archeology: before and without Kubernetes
- Deployment: kube-up, DCOS, GKE
- Core Architecture: the apiserver, the kubelet and the scheduler
- Compute Model: the pod, the service and the controller
Configuration Management and Transforming Legacy Applications in the Enterpri...Docker, Inc.
Share the continuity of Société Générale's journey with Docker Enterprise from different points of view, from executives to devops, with CD platform as an enabler. Creating a Dockerfile that runs a container on a developer's laptop is pretty straightforward. But extending that to stacks of containers running on a dozen environments (development, integration, testing, staging, production, etc.) with different configuration and topologies can be a challenge. This talk will cover aspects of our journey to Docker Enterprise:
What configuration should go in an image?
Where to put different types of configuration? Images, environment variables, entrypoint, ...?
How to store assets for building images and configuration for deployment in version control.
We will discuss how Société Générale has implemented these, and what we plan next for Docker Enterprise deployment.
Kubernetes Architecture - beyond a black box - Part 2Hao H. Zhang
This continues the Kubernetes architecture deep dive series. (Part 1 see https://www.slideshare.net/harryzhang735/kubernetes-beyond-a-black-box-part-1)
In Part 2 I'm going to cover the following:
- Kubernetes's 3 most import design choices: Micro-service Choreography, Level-Triggered Control, Generalized Workload and Centralized Controller
- Default scheduler limitation and community's next step
- Interface to production environment
- Workload abstraction: strength and limitations
This concludes my work and knowledge sharing about Kubernetes.
2016 - Continuously Delivering Microservices in Kubernetes using Jenkinsdevopsdaysaustin
Presentation by Sandeep Parikh
In this talk, we will cover the basics of Kubernetes and show how to set up continuous delivery pipelines using Jenkins and Jenkins Workflow to go from code to deployment, without developers having to interact with the production deployment infrastructure. The goal is an end-to-end set of steps to automate deployment and delivery of an application composed of several microservices.
Docker Kubernetes Istio
Understanding Docker and creating containers.
Container Orchestration based on Kubernetes
Blue Green Deployment, AB Testing, Canary Deployment, Traffic Rules based on Istio
DCEU 18: Building Your Development PipelineDocker, Inc.
Oliver Pomeroy - Solution Engineer, Docker
Laura Frank Tacho - Director of Engineering, CloudBees
Enterprises often want to provide automation and standardisation on top of their container platform, using a pipeline to build and deploy their containerized applications. However this opens up new challenges… Do I have to build a new CI/CD Stack? Can I build my CI/CD pipeline with Kubernetes orchestration? What should my build agents look like? How do I integrate my pipeline into my enterprise container registry? In this session full of examples and “how-to”s, Olly and Laura will guide you through common situations and decisions related to your pipelines. We’ll cover building minimal images, scanning and signing images, and give examples on how to enforce compliance standards and best practices across your teams.
Docker introduction.
References : The Docker Book : Containerization is the new virtualization
http://www.amazon.in/Docker-Book-Containerization-new-virtualization-ebook/dp/B00LRROTI4/ref=sr_1_1?ie=UTF8&qid=1422003961&sr=8-1&keywords=docker+book
ExpoQA 2017 Using docker to build and test in your laptop and JenkinsElasTest Project
In this workshop the basics about container use in the development environment are presented. Then we go further by describing how to leverage containers in the CI server, using Jenkins and Pipelines.
What is this Docker and Microservice thing that everyone is talking about? A primer to Docker and Microservice and how the two concepts complement each other.
Originally Presented at WebSummit 2015. Find all the materials for the workshop here: https://github.com/emccode/training/tree/master/docker-workshop/websummit
5 Skills To Force Multiply Technical Talents.pdfArun Gupta
This talk explains what are non-technical skills, why they are relevant, and what are some of the most important skills to master to force multiply your technical talent.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
20. Underlying Technology
• Written in Go
• Uses several Linux features
• Namespaces to provide isolation
• Control groups to share/limit hardware resources
21. Underlying Technology
• Written in Go
• Uses several Linux features
• Namespaces to provide isolation
• Control groups to share/limit hardware resources
• Union File System makes it light and fast
22. Underlying Technology
• Written in Go
• Uses several Linux features
• Namespaces to provide isolation
• Control groups to share/limit hardware resources
• Union File System makes it light and fast
• libcontainer defines container format
58. Arquillian Cube
• Controls the lifecycle of Docker images as part of
test cycle - automatically or manually
• Uses Docker REST API to talk to container
• Talk using WildFly remote adapter (in container)
• Try it out
http://blog.arungupta.me/run-javaee-tests-wildfly-docker-arquillian-cube/
60. Docker: Pros and Cons
• PROS
• Extreme application portability
• Very easy to create and work with derivative
• Fast boot on containers
61. Docker: Pros and Cons
• PROS
• Extreme application portability
• Very easy to create and work with derivative
• Fast boot on containers
• CONS
• Host-centric solution
• No higher-level provisioning
• No usage tracking/reporting
65. Kubernetes
• Open source orchestration system for Docker
containers
• Provide declarative primitives for the “desired state”
• Self-healing
• Auto-restarting
• Schedule across hosts
• Replicating
68. Concepts
• Pods: collocated group of
Docker containers that
share an IP and storage
volume
Docker
Pod 1 Pod 2
C1 C2 C3
69. Concepts
• Pods: collocated group of
Docker containers that
share an IP and storage
volume
• Service: Single, stable
name for a set of pods, also
acts as LB
Docker
Pod 1 Pod 2
C1 C2 C3
Pod 1
JBoss
Pod 2
JBoss
Service “web”
port 8080 port 8080
70. Concepts
• Pods: collocated group of
Docker containers that
share an IP and storage
volume
• Service: Single, stable
name for a set of pods, also
acts as LB
• Replication Controller:
manages the lifecycle of
pods and ensures specified
number are running
Docker
Pod 1 Pod 2
C1 C2 C3
Pod 1
JBoss
Pod 2
JBoss
Service “web”
port 8080 port 8080
71. Concepts
• Pods: collocated group of
Docker containers that
share an IP and storage
volume
• Service: Single, stable
name for a set of pods, also
acts as LB
• Replication Controller:
manages the lifecycle of
pods and ensures specified
number are running
• Label: used to organize
and select group of objects
Docker
Pod 1 Pod 2
C1 C2 C3
Pod 1
JBoss
Pod 2
JBoss
Service “web”
port 8080 port 8080
79. Recipe #2.1
Mac OS X
Kubernetes (Vagrant)
Master
Minion
Pod
Docker
(WildFly)
http://blog.arungupta.me/javaee7-wildfly-kubernetes-mac-vagrant/
80. Services
• Abstract a set of pods as a single IP and port
• Simple TCP/UDP load balancing
• Creates environment variables in other pods
• Like “Docker links” but across hosts
• Stable endpoint for pods to reference
• Allows list of pods to change dynamically
86. Replication Controller
• Ensures specified number of pod “replicas” are
running
• Pod templates are cookie cutters
• Rescheduling
87. Replication Controller
• Ensures specified number of pod “replicas” are
running
• Pod templates are cookie cutters
• Rescheduling
• Manual or auto-scale replicas
88. Replication Controller
• Ensures specified number of pod “replicas” are
running
• Pod templates are cookie cutters
• Rescheduling
• Manual or auto-scale replicas
• Rolling updates
91. Recipe #2.4
Minion 2
Minion 1
Pod
Docker
(WildFly)
Pod
Docker
(MySQL)
MySQL
Service
Pod
Docker
(WildFly)
WildFly
Service
92. Recipe #2.4
Minion 2
Minion 1
Pod
Docker
(WildFly)
Pod
Docker
(MySQL)
MySQL
Service
Pod
Docker
(WildFly)
WildFly
Service
93. Kubernetes: Pros and Cons
• PROS
• Manage related Docker containers as a unit
• Container communication across hosts
• Availability and scalability through automated deployment
and monitoring of pods and their replicas, across hosts
94. Kubernetes: Pros and Cons
• CONS
• Lifecycle of applications - build, deploy, manage, promote
• Port existing source code to run in Kubernetes
• DevOps: Dev -> Test -> Production
• No multi-tenancy
• On-premise (available on GCE)
• Assumes inter-pod networking as part of infrastructure
• Requires explicit load balancer
95. Pod 7
ActiveMQ
Pod 8
ActiveMQ
“mq”
port 8161 port 8161
Pod 1
Apache
Pod 2
Apache
“web”
port 80 port 80
Pod 5
MySQL
Pod 6
MySQL
“db”
port 3306 port 3306
Pod 3
JBoss
Pod 4
JBoss
“javaee”
port 8080 port 8080
96. Pod 7
ActiveMQ
Pod 8
ActiveMQ
“mq”
port 8161 port 8161
Pod 1
Apache
Pod 2
Apache
“web”
port 80 port 80
Pod 5
MySQL
Pod 6
MySQL
“db”
port 3306 port 3306
Pod 3
JBoss
Pod 4
JBoss
“javaee”
port 8080 port 8080
100. OpenShift 3 Features
• Push to production - full DevOps
• Client tools for building web applications
101. OpenShift 3 Features
• Push to production - full DevOps
• Client tools for building web applications
• Centralized administration and management of
application component libraries
102. OpenShift 3 Features
• Push to production - full DevOps
• Client tools for building web applications
• Centralized administration and management of
application component libraries
• Team and user isolation of containers, builds, and
network communication in an easy multi-tenancy
system
103. Recipe #3.1
• Start OpenShift as Docker container
• Or run natively
• Use osc (OpenShift Client) instead of kubectl
with Kubernetes configuration file
104. Recipe #3.2
• (Alpha) tools generate project JSON configuration
file that provide build/deployment
107. Summary
• Container runtime and image distribution
• Roll your own solutions for everything
• Runtime and operational management of containers
• Lifecycle of applications - build, deploy, manage, promote
• Manage tens of thousands of applications with teams