This document discusses various tools for deploying applications to Kubernetes, including Helm, Ksonnet, Draft, Gitkube, Metaparticle, Skaffold, KSync, and Telepresence. It provides an overview of each tool, including their motivations, workflows, and how they compare to each other. Many of the tools aim to simplify deployments by automating builds, pushes to registries, and deployments to clusters. Ksonnet stands out as a tool that uses Jsonnet to define reusable application components and deploy them across multiple environments and clusters.
All Things Containers - Docker, Kubernetes, Helm, Istio, GitOps and moreAll Things Open
Presented by: Brent Laster, SAS
Presented at All Things Open 2020
Abstract: In this workshop, students will get a quick overview of what containers are and why they form the basis for many of the key technologies that we use today in cloud environments.
We’ll explore what makes up a container and how they are managed and leveraged in key industry tooling including Docker, Kubernetes, Helm, and Istio. You’ll also learn the basics of these technologies, what they are used for, and see some simple examples of how to use them.
This workshop will include hands-on labs where you will get experience:
Building container images, running them as containers, and tagging and pushing them into a Docker repository.
Creating deployments, services, and pods for containers and instantiating and running those in Kubernetes.
Working with Helm to leverage templates for Kubernetes objects and managing releases in Kubernetes.
Working with Istio to do traffic shaping between multiple versions of your app, fault and delay injection for testing and validation in Kubernetes.
We’ll also briefly cover GitOps – the recommended Git-based way to manage infrastructure like your Kubernetes cluster.
Accelerate your software development with DockerAndrey Hristov
Docker is in all the news and this talk presents you the technology and shows you how to leverage it to build your applications according to the 12 factor application model.
Docker is in all the news and this talk presents you the technology and shows you how to leverage it to build your applications according to the 12 factor application model.
All Things Containers - Docker, Kubernetes, Helm, Istio, GitOps and moreAll Things Open
Presented by: Brent Laster, SAS
Presented at All Things Open 2020
Abstract: In this workshop, students will get a quick overview of what containers are and why they form the basis for many of the key technologies that we use today in cloud environments.
We’ll explore what makes up a container and how they are managed and leveraged in key industry tooling including Docker, Kubernetes, Helm, and Istio. You’ll also learn the basics of these technologies, what they are used for, and see some simple examples of how to use them.
This workshop will include hands-on labs where you will get experience:
Building container images, running them as containers, and tagging and pushing them into a Docker repository.
Creating deployments, services, and pods for containers and instantiating and running those in Kubernetes.
Working with Helm to leverage templates for Kubernetes objects and managing releases in Kubernetes.
Working with Istio to do traffic shaping between multiple versions of your app, fault and delay injection for testing and validation in Kubernetes.
We’ll also briefly cover GitOps – the recommended Git-based way to manage infrastructure like your Kubernetes cluster.
Accelerate your software development with DockerAndrey Hristov
Docker is in all the news and this talk presents you the technology and shows you how to leverage it to build your applications according to the 12 factor application model.
Docker is in all the news and this talk presents you the technology and shows you how to leverage it to build your applications according to the 12 factor application model.
Dev opsec dockerimage_patch_n_lifecyclemanagement_kanedafromparis
Lors de cette présentation, nous allons dans un premier temps rappeler la spécificité de docker par rapport à une VM (PID, cgroups, etc) parler du système de layer et de la différence entre images et instances puis nous présenterons succinctement kubernetes.
Ensuite, nous présenterons un processus « standard » de propagation d’une version CI/CD (développement, préproduction, production) à travers les tags docker.
Enfin, nous parlerons des différents composants constituant une application docker (base-image, tooling, librairie, code).
Une fois cette introduction réalisée, nous parlerons du cycle de vie d’une application à travers ses phases de développement, BAU pour mettre en avant que les failles de sécurité en période de développement sont rapidement corrigées par de nouvelles releases, mais pas nécessairement en BAU où les releases sont plus rares. Nous parlerons des diverses solutions (jfrog Xray, clair, …) pour le suivie des automatique des CVE et l’automatisation des mises à jour. Enfin, nous ferons un bref retour d’expérience pour parler des difficultés rencontrées et des propositions d’organisation mises en oeuvre.
Cette présentation bien qu’illustrée par des implémentations techniques est principalement organisationnelle.
Introduction to dockers and kubernetes. Learn how this helps you to build scalable and portable applications with cloud. It introduces the basic concepts of dockers, its differences with virtualization, then explain the need for orchestration and do some hands-on experiments with dockers
KubeCon CloudNativeCon Seattle 2019 Recap - General overview and also summary of some of the application deployment track (App sig, Operator Framework, Helm, Kustomize, CNAB).
- Introduction to Kubernetes features
- A look at Kubernetes Networking and Service Discovery
- New features in Kubernetes 1.6
- Kubernetes Installation options
To know more about our Kubernetes expertise, visit our center of excellence at: http://www.opcito.com/kubernetes/
OpenStack, Containers, and Docker: The Future of Application Deployment
Twenty years ago, developers built static applications on well-defined stacks that ran on proprietary, monolithic hardware. Developers today want freedom to build applications using their choice of services and stacks and, ideally, want to be able to run those applications on any available hardware. Of course, this raises questions about service interaction, the practicality of migrating applications across environments, and the challenges of managing unlimited combinations of services and hardware environment.
By promoting an opensource approach to flexible and inter-operable infrastructure, OpenStack goes a long way towards achieving this vision of the future. This talk discusses the application and platform side of the equation, and the interplay between OpenStack, Container technology (e.g. LXC), and the opensource Docker.io project. Docker.io enables any application and its dependencies to be deployed as lightweight containers that run consistently virtually anywhere. The same containerized application that runs on a developer's laptop can run consistently on a bare metal server, an OpenStack cluster, a Rackspace cloud, a VM,etc. While providing isolation and compatibility, containers have significant size, performance, and deployment advantages over traditional VMs.
Recently, the community created an integration between Docker and OpenStack Nova, opening up exciting possibilities for web scale application deployment, continuous integration and deployment, private PaaS, and hybrid cloud. This session will give an introduction to Docker and containers in the context of OpenStack, and will then demonstrate cross-environment deployment of applications.
Containers #101 Meetup: Containers and OpenStackCodefresh
Recording posted here: https://codefresh.io/blog/containers-101-containers-openstack/
Slides from Robert Starmer's talk where he gave an overview of container technology and how it relates to OpenStack.
Robert Starmer's talk recording- https://codefresh.io/blog/containers-101-containers-openstack/
His slides from our meetup on August 17th, where he gave an overview of container technology and how it relates to OpenStack.
Orchestrating Docker containers at scaleMaciej Lasyk
Many of us already poked around Docker. Let's recap what we know and then think what do we know about scaling apps & whole environments which are Docker - based? Should we PaaS, IaaS or go with bare? Which tools to use on a given scale?
Traditional virtualization technologies have been used by cloud infrastructure providers for many years in providing isolated environments for hosting applications. These technologies make use of full-blown operating system images for creating virtual machines (VMs). According to this architecture, each VM needs its own guest operating system to run application processes. More recently, with the introduction of the Docker project, the Linux Container (LXC) virtualization technology became popular and attracted the attention. Unlike VMs, containers do not need a dedicated guest operating system for providing OS-level isolation, rather they can provide the same level of isolation on top of a single operating system instance.
An enterprise application may need to run a server cluster to handle high request volumes. Running an entire server cluster on Docker containers, on a single Docker host could introduce the risk of single point of failure. Google started a project called Kubernetes to solve this problem. Kubernetes provides a cluster of Docker hosts for managing Docker containers in a clustered environment. It provides an API on top of Docker API for managing docker containers on multiple Docker hosts with many more features.
KVM and docker LXC Benchmarking with OpenStackBoden Russell
Passive benchmarking with docker LXC and KVM using OpenStack hosted in SoftLayer. These results provide initial incite as to why LXC as a technology choice offers benefits over traditional VMs and seek to provide answers as to the typical initial LXC question -- "why would I consider Linux Containers over VMs" from a performance perspective.
Results here provide insight as to:
- Cloudy ops times (start, stop, reboot) using OpenStack.
- Guest micro benchmark performance (I/O, network, memory, CPU).
- Guest micro benchmark performance of MySQL; OLTP read, read / write complex and indexed insertion.
- Compute node resource consumption; VM / Container density factors.
- Lessons learned during benchmarking.
The tests here were performed using OpenStack Rally to drive the OpenStack cloudy tests and various other linux tools to test the guest performance on a "micro level". The nova docker virt driver was used in the Cloud scenario to realize VMs as docker LXC containers and compared to the nova virt driver for libvirt KVM.
Please read the disclaimers in the presentation as this is only intended to be the "chip of the ice burg".
Introduction to Docker and Monitoring with InfluxDataInfluxData
In this webinar, Gary Forgheti, Technical Alliance Engineer at Docker, and Gunnar Aasen, Partner Engineering, provide an introduction to Docker and InfluxData. From there, they will show you how to use the two together to setup and monitor your containers and microservices to properly manage your infrastructure and track key metrics (CPU, RAM, storage, network utilization), as well as the availability of your application endpoints.
Dev opsec dockerimage_patch_n_lifecyclemanagement_kanedafromparis
Lors de cette présentation, nous allons dans un premier temps rappeler la spécificité de docker par rapport à une VM (PID, cgroups, etc) parler du système de layer et de la différence entre images et instances puis nous présenterons succinctement kubernetes.
Ensuite, nous présenterons un processus « standard » de propagation d’une version CI/CD (développement, préproduction, production) à travers les tags docker.
Enfin, nous parlerons des différents composants constituant une application docker (base-image, tooling, librairie, code).
Une fois cette introduction réalisée, nous parlerons du cycle de vie d’une application à travers ses phases de développement, BAU pour mettre en avant que les failles de sécurité en période de développement sont rapidement corrigées par de nouvelles releases, mais pas nécessairement en BAU où les releases sont plus rares. Nous parlerons des diverses solutions (jfrog Xray, clair, …) pour le suivie des automatique des CVE et l’automatisation des mises à jour. Enfin, nous ferons un bref retour d’expérience pour parler des difficultés rencontrées et des propositions d’organisation mises en oeuvre.
Cette présentation bien qu’illustrée par des implémentations techniques est principalement organisationnelle.
Introduction to dockers and kubernetes. Learn how this helps you to build scalable and portable applications with cloud. It introduces the basic concepts of dockers, its differences with virtualization, then explain the need for orchestration and do some hands-on experiments with dockers
KubeCon CloudNativeCon Seattle 2019 Recap - General overview and also summary of some of the application deployment track (App sig, Operator Framework, Helm, Kustomize, CNAB).
- Introduction to Kubernetes features
- A look at Kubernetes Networking and Service Discovery
- New features in Kubernetes 1.6
- Kubernetes Installation options
To know more about our Kubernetes expertise, visit our center of excellence at: http://www.opcito.com/kubernetes/
OpenStack, Containers, and Docker: The Future of Application Deployment
Twenty years ago, developers built static applications on well-defined stacks that ran on proprietary, monolithic hardware. Developers today want freedom to build applications using their choice of services and stacks and, ideally, want to be able to run those applications on any available hardware. Of course, this raises questions about service interaction, the practicality of migrating applications across environments, and the challenges of managing unlimited combinations of services and hardware environment.
By promoting an opensource approach to flexible and inter-operable infrastructure, OpenStack goes a long way towards achieving this vision of the future. This talk discusses the application and platform side of the equation, and the interplay between OpenStack, Container technology (e.g. LXC), and the opensource Docker.io project. Docker.io enables any application and its dependencies to be deployed as lightweight containers that run consistently virtually anywhere. The same containerized application that runs on a developer's laptop can run consistently on a bare metal server, an OpenStack cluster, a Rackspace cloud, a VM,etc. While providing isolation and compatibility, containers have significant size, performance, and deployment advantages over traditional VMs.
Recently, the community created an integration between Docker and OpenStack Nova, opening up exciting possibilities for web scale application deployment, continuous integration and deployment, private PaaS, and hybrid cloud. This session will give an introduction to Docker and containers in the context of OpenStack, and will then demonstrate cross-environment deployment of applications.
Containers #101 Meetup: Containers and OpenStackCodefresh
Recording posted here: https://codefresh.io/blog/containers-101-containers-openstack/
Slides from Robert Starmer's talk where he gave an overview of container technology and how it relates to OpenStack.
Robert Starmer's talk recording- https://codefresh.io/blog/containers-101-containers-openstack/
His slides from our meetup on August 17th, where he gave an overview of container technology and how it relates to OpenStack.
Orchestrating Docker containers at scaleMaciej Lasyk
Many of us already poked around Docker. Let's recap what we know and then think what do we know about scaling apps & whole environments which are Docker - based? Should we PaaS, IaaS or go with bare? Which tools to use on a given scale?
Traditional virtualization technologies have been used by cloud infrastructure providers for many years in providing isolated environments for hosting applications. These technologies make use of full-blown operating system images for creating virtual machines (VMs). According to this architecture, each VM needs its own guest operating system to run application processes. More recently, with the introduction of the Docker project, the Linux Container (LXC) virtualization technology became popular and attracted the attention. Unlike VMs, containers do not need a dedicated guest operating system for providing OS-level isolation, rather they can provide the same level of isolation on top of a single operating system instance.
An enterprise application may need to run a server cluster to handle high request volumes. Running an entire server cluster on Docker containers, on a single Docker host could introduce the risk of single point of failure. Google started a project called Kubernetes to solve this problem. Kubernetes provides a cluster of Docker hosts for managing Docker containers in a clustered environment. It provides an API on top of Docker API for managing docker containers on multiple Docker hosts with many more features.
KVM and docker LXC Benchmarking with OpenStackBoden Russell
Passive benchmarking with docker LXC and KVM using OpenStack hosted in SoftLayer. These results provide initial incite as to why LXC as a technology choice offers benefits over traditional VMs and seek to provide answers as to the typical initial LXC question -- "why would I consider Linux Containers over VMs" from a performance perspective.
Results here provide insight as to:
- Cloudy ops times (start, stop, reboot) using OpenStack.
- Guest micro benchmark performance (I/O, network, memory, CPU).
- Guest micro benchmark performance of MySQL; OLTP read, read / write complex and indexed insertion.
- Compute node resource consumption; VM / Container density factors.
- Lessons learned during benchmarking.
The tests here were performed using OpenStack Rally to drive the OpenStack cloudy tests and various other linux tools to test the guest performance on a "micro level". The nova docker virt driver was used in the Cloud scenario to realize VMs as docker LXC containers and compared to the nova virt driver for libvirt KVM.
Please read the disclaimers in the presentation as this is only intended to be the "chip of the ice burg".
Introduction to Docker and Monitoring with InfluxDataInfluxData
In this webinar, Gary Forgheti, Technical Alliance Engineer at Docker, and Gunnar Aasen, Partner Engineering, provide an introduction to Docker and InfluxData. From there, they will show you how to use the two together to setup and monitor your containers and microservices to properly manage your infrastructure and track key metrics (CPU, RAM, storage, network utilization), as well as the availability of your application endpoints.
My college ppt on topic Docker. Through this ppt, you will understand the following:- What is a container? What is Docker? Why its important for developers? and many more!
In Apache Cassandra Lunch #41: Apache Cassandra Lunch #41: Cassandra on Kubernetes - Docker/Kubernetes/Helm Part 1, we discuss Cassandra on Kubernetes and give an introduction to Docker, Kubernetes, and Helm.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-41-cassandra-on-kubernetes-docker-kubernetes-helm-part-1/
Accompanying YouTube: https://youtu.be/-I8cKQO_Qr0
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
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https://cassandra.link/
Follow Us and Reach Us At:
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Containers are becoming part of mainstream DevOps architectures and cloud deployments. Application owners and data center infrastructure teams are both aiming to shorten development life cycle and reduce operational cost and complexity by deploying containers This session will provide an overview of container ecosystems and container architectures including Docker, Linux Containers and rkt/CoreOS. Join us and learn about the options to network containers. Projects including Docker Bridge, Contiv, Calico and Magnum/Kuryr will be highlighted in this session. Demos of containers on OpenStack will also featured in this session. Finally, the audience will also learn the advantages that Cisco UCS and Nexus platforms provide in building a cloud platform for containers, virtual machines and bare-metal.
Real-World Docker: 10 Things We've Learned RightScale
Docker has taken the world of software by storm, offering the promise of a portable way to build and ship software - including software running in the cloud. The RightScale development team has been diving into Docker for several projects, and we'll share our lessons learned on using Docker for our cloud-based applications.
3 years ago, Meetic chose to rebuild it's backend architecture using microservices and an event driven strategy. As we where moving along our old legacy application, testing features became gradually a pain, especially when those features rely on multiple changes across multiple components. Whatever the number of application you manage, unit testing is easy, as well as functional testing on a microservice. A good gherkin framework and a set of docker container can do the job. The real challenge is set in end-to-end testing even more when a feature can involve up to 60 different components.
To solve that issue, Meetic is building a Kubernetes strategy around testing. To do such a thing we need to :
- Be able to generate a docker container for each pull-request on any component of the stack
- Be able to create a full testing environment in the simplest way
- Be able to launch automated test on this newly created environment
- Have a clean-up process to destroy testing environment after tests To separate the various testing environment, we chose to use Kubernetes Namespaces each containing a variant of the Meetic stack. But when it comes to Kubernetes, managing multiple namespaces can be hard. Yaml configuration files need to be shared in a way that each people / automated job can access to them and modify them without impacting others.
This is typically why Meetic chose to develop it's own tool to manage namespace through a cli tool, or a REST API on which we can plug a friendly UI.
In this talk we will tell you the story of our CI/CD evolution to satisfy the need to create a docker container for each new pull request. And we will show you how to make end-to-end testing easier using Blackbeard, the tool we developed to handle the need to manage namespaces inspired by Helm.
This presentation by Andrew Aslinger discusses best practices and pitfalls of integrating Docker into Continuous Delivery Pipelines. Learn how Andrew and his team used Docker to replace Chef to simplify their development and migration processes.
Mattia Gandolfi - Improving utilization and portability with Containers and C...Codemotion
Google has pioneered the usage of containers at huge scale. Learn how we designed our systems to handle insane traffic loads, orchestrating complex, globally distributed applications, and how you can leverage this infrastructure and our agile development technologies to embrace the power of DevOps and Cloud on our Google Cloud Platform.
This presentation covers how app deployment model evolved from bare metal servers to Kubernetes World.
In addition to theoretical information, you will find free KATACODA workshops url to perform practices to understand the details of the each topics.
Kubernetes Application Deployment with Helm - A beginner Guide!Krishna-Kumar
Google DevFest2019 Presentation at Infosys Campus Bangalore. Application deployment in Kubernetes with Helm is demo'ed in Google Kubernetes Engine (GKE). This is an introductory session on Helm. Several references are given in it to further explore helm3 as it is in Beta state now.
An RSVP app designed to be deployed by the dockers on the Kubernetes Minikube Cluster. Front end with flask framework and MongoDB as a backend database.
Youtube video:https://youtu.be/KnjnQj-FvfQ
Docker is an open platform for developers and system administrators to build, ship and run distributed applications. Using Docker, companies in Jordan have been able to build powerful system architectures that allow speeding up delivery, easing deployment processes and at the same time cutting major hosting costs.
George Khoury shares his experience at Salalem in building flexible and cost effective architectures using Docker and other tools for infrastructure orchestration. The result allows them to easily and quickly move between different cloud providers.
History and Basics of containers, LXC, Docker and Kubernetes. This presentation is given to Engineering colleage students at VIT DevFest 2018. Beginner to Intermediate level.
Docker containers have been making inroads into Windows and Azure world. Docker has now replaced the traditional Azure IaaS & PaaS services, offering superior container versions which are more responsive, cost effective, and agile. In this session for Charlotte Azure User Group, we will take an in-depth look at the intersection of Docker and Azure, and how Docker is empowering next gen Azure services.
Here's the link to CAG meetup for the event - https://www.meetup.com/Charlotte-Microsoft-Azure/events/fpftgmyxjbjb/
What is the Difference Between Kubernetes and Docker?Ravendra Singh
Apps that operate in containers may be automatically scaled, deployed, and managed with the help of Kubernetes, an open-source cloud-native infrastructure solution that is available for free. While Kubernetes was first developed and maintained by Google, the Cloud Native Computing Foundation took over the development and management of the system.
Docker is the world’s leading software container platform. Developers use Docker to eliminate “works on my machine” problems when collaborating on code with co-workers. Operators use Docker to run and manage apps side-by-side in isolated containers to get better compute density. Enterprises use Docker to build agile software delivery pipelines to ship new features faster, more securely and with confidence for both Linux and Windows Server apps.
Learn More: http://www.collabnix.com
Similar to CD in kubernetes using helm and ksonnet. Stas Kolenkin (20)
DataArt Custom Software Engineering with a Human ApproachDataArt
DataArt is a global software engineering firm that takes a uniquely human approach to solving problems. With over 20 years of experience, teams of highly-trained engineers around the world, deep industry sector knowledge and ongoing technology research, we help clients create custom software that improves their operations and opens new markets. Powered by our People First principle, we work with clients at any scale and on any platform, and adapt alongside them as they evolve.
DataArt is a global software engineering firm that takes a uniquely human approach to solving problems. With over 20 years of experience, teams of highly-trained engineers around the world, deep industry sector knowledge, and ongoing technology research, we help clients create custom software that improves their operations and opens new markets. Powered by our People First principle, we work with clients at any scale and on any platform, and adapt alongside them as they evolve.
DataArt Financial Services and Capital MarketsDataArt
DataArt is a global software engineering firm that takes a uniquely human approach to solving problems. With over 20 years of experience, teams of highly-trained engineers around the world, deep industry sector knowledge, and ongoing technology research, we help clients create custom software that improves their operations and opens new markets. Powered by our People First principle, we work with clients at any scale and on any platform, and adapt alongside them as they evolve.
We integrate our engineering excellence with deeply human values that drive our business and our approach to relationships: curiosity, empathy, trust, honesty, and intuition. These qualities help us deliver high-value, high-quality solutions that our clients depend on, and lifetime partnerships they believe in.
DataArt has earned the trust of some of the world’s leading brands and most discerning clients, including Nasdaq, Travelport, Ocado, Centrica/Hive, Paddy Power Betfair, IWG, Univision, Meetup and Apple Leisure Group among others. DataArt brings together expertise of over 3000 professionals in 20 locations in the US, Europe, and Latin America.
Мы ежедневно посещаем десятки и сотни сайтов и периодически видим рекламу, зачастую даже не задумываясь, откуда она вообще берется. Почему именно эта реклама показана вам именно здесь? И какая роль JS во всем этом?
Рассмотрим:
• поговорим о жизненном цикле рекламного баннера и проследим его путь от рекламодателя до браузера;
• узнаем, кто же постоянно следит за нами в интернете, как много информации о нас им доступно;
• определим способы выявления некачественного трафика;
• разберемся, зачем нужно контролировать качество просмотров;
• обсудим, почему нельзя так просто взять и просмотреть всю статистику по рекламе в одном месте (или все-таки можно?).
Алексей Уманский, JS Developer, AnyMind Group. Опыт работы в IT – четыре года. Участвовал в тревел- и gamedev-проектах: разрабатывал крупный сервис по покупке авиабилетов, создавал систему игровых автоматов для онлайн казино. Последний год работал в Таиланде над продуктами в области Digital Marketing: онлайн биржа для influencer-ов и сервис по управлению рекламой на сайте, а так же сбору статистики по ней.
DevOps Workshop:Что бывает, когда DevOps приходит на проектDataArt
Александр Снеговой, DevOps Software Engineer в DataArt.
Более шести лет в IT. Сертифицированный AWS Solutions Architect Associate. Докладчик на международных научных конференциях. Религиозный фанат Docker.
Оксана Харчук, Senior QA Engineer.
Презентация:
Коммуникация в жизни QA. Как выстроить эффективные коммуникации тестировщику с бизнес аналитиком, разработчиком, менеджером и клиентом.
Нельзя просто так взять и договориться, или как мы работали со сложными людьмиDataArt
Эллина Азадова, QA Lead в DataArt Kherson.
Презентация:
Реальные примеры из своей практики, как работать со сложными людьми: интровертами, экстравертами, излишне эмоциональными и с постоянно пессимистически настроенными.
Дмитрий Клипинин, DevOps Engineer в GlobalLogic, более 10 лет опыта работы в IT, сертифицированный специалист Microsoft по технологиям Active Directory и SQL Server.
Презентация:
1. Эволюция системного администратора.
2. DevOps-практики.
3. Основные DevOps-инструменты.
Александр Снеговой, DevOps Software Engineer в DataArt Kherson. Более шести лет в IT. Сертифицированный AWS Solutions Architect Associate. Докладчик на международных научных конференциях. Религиозный фанат Docker.
Презентация:
1. Докеризация приложения.
2. Настройка CI/CD.
3. Развертывание инфраструктуры в AWS с помощью Terraform.
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
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
3. Package Manager Systems (PMS)
One of the key differences of the two concept is the concept of dependencies.
Package manager systems (PMS) are design to work with a tree of package
dependencies. In fact PMS are all about re-usage and simplification of the
management of this dependency tree.
3
4. Containers
Contrary to that Docker don't define any hard dependencies between
images/containers that can prevent you to install any version of container you
like. Re-usage of software happens by layering. Since container is a run-time
concept, and defines standard boundary of container, it is possible to letting
application inside of the container be agnostic of OS resources available to the
host. E.g. it is always possible to link container exported ports to host system
ports, so you don't care to dev time about.
4
5. Packets inside, Containers outside
That is evidence enough. Use Docker as deployment artefact it just the
natural step in the cloud environment (I'm explicitly don't define the word
"cloud" now) cause it's gives you the ability to install any software in any version
on any host and wire it in standardized way to the rest. Doing so you don't have
to care about shape of the cloud during the design of your service.
Use Packet Managers to build your containers. Keep containers slim (yes, we
are in the microservices age) and provision them convenient and accurate via
packet manager of your choice.
5
6. Why using Docker deployment instead of RPM?
You can still use RPM to install Docker, but once you installed it you profit
from following:
1. Runtime Isolation: Configurable resource limits
2. Runtime Isolation: Ports reconfig even by third-party or legacy software
3. No packet dependency hell. Use different versions of PHP, perl, ruby, npm..
whatever on same host...
4. Integrate deployment of third-party or legacy software in your standard
Docker deployment
6
7. Why using Docker deployment instead of RPM?
5. Profit from that by unified container boundaries (Logging, monitoring,
backup)
6. Easier participate in cloud. As soon you package to standard container and
deploy to cloud, you profit from cloud features you have (e.g. hot migration,
automatic backup, autoscaling... and so on).
7. Deploy entire software stack (E.g. DB, engine, web) as one docker image.
Good idea sometimes.
7
8. Why using Docker deployment instead of RPM?
8. Easier to start everything you need on your laptop
9. A lot of predefined containers for every kind of third party software out there.
10. No distribution borders. Run everything for linux kernel on any distribution.
8
9. Kubernetes
• Kubernetes, or k8s (k, 8 characters, s...get it?), or “kube” if you’re into brevity, is
an open source platform that automates Linux container operations.
• All in containers, even kubernetes services in containers
• Distributed, fault-tolerant, multi-cloud
• Focuses on the microservice architecture
9
10. How to install application in Kubernetes?
• Build docker images
• Push docker-image in the docker-registry
• Describe the application in terms of (resources) k8s:
− Deployment + pod
− service + ingress
• Create resources:
kubectl apply -f resources.yaml
10
Is package manager? Hmmm No!!!!!
12. Helm
As the tag suggests, Helm is a tool to manage applications on Kubernetes, in
the form of Charts. Helm takes care of creating the Kubernetes manifests and
versioning them so that rollbacks can be performed across all kind of objects,
not just deployments. A chart can have deployment, service, configmap etc. It is
also templated so that variables can be easily changed. It can be used to define
complex applications with dependencies.
12
13. Helm
Helm is primarily intended as a tool to deploy manifests and manage them in
a production environment. In contrast to Draft or Gitkube, Helm is not for
developing applications, but to deploy them. There are a wide variety of
pre-built charts ready to be used with Helm.
13
16. Helm problems
1. Problem: k8s resources are written on pure yaml
2. Full copy for dev / stage / prod environments
3. Differences a little:
a. different number of replicas
b. passwords and URIs to databases, external resources
c. ...
4. Solution: Templates
16
24. Helm - Package manager
24
• You can not just take" and run the application in k8s
• Package format
• Signatures and Authentication
• Build and install the package and dependencies
• Build the package
• Parameters that you can override
• Working with the docker image? - No
25. Helm - Development
25
• Problem: fast development feedback
• If you change the code
− Build docker image
− Push in repository
− Download in k8s cluster
• Helm + Tiller
P.S. Tiller is gone, and there is only one functional component (helm).
• The difficulty of templating large deployment configurations
31. Ksonnet
31
Ksonnet is a framework for writing, sharing, and deploying Kubernetes
application manifests. With its CLI, you can generate a complete application
from scratch in only a few commands, or manage a complex system at scale.
32. Ksonnet: Motivations
32
• DRY: Kubernetes YAML are very repetitive
− ie see port numbers, volume names etc
• Configurable: Deploy same app multiple times in different environments
− ie staging vs prod - different image versions
− want differences to be isolated, manually reviewable
• Extensible: Want to build reusable components, and specialise them for
my env
− ie the prometheus-ksonnet package
33. Ksonnet: Motivations
33
• Extensible: Want to build abstractions and helpers
− ie pattern for constructing RBAC objects, or services
• Extensible: Want to impose organisation-wide opinions on my k8s config
− ie name label
• Accidents: Want to prevent accidental application of dev config to prod etc
34. Ksonnet: Jsonnet
34
JSONNET is domain specific configuration language from Google which
allows you to define data templates.
These data templates are transformed into JSON objects using Jsonnet
library or command line tool. As a language, Jsonnet is extension of JSON - a
valid JSON object is always valid Jsonnet template.
37. Ksonnet
37
The basic building blocks are called parts which can be mixed and matched
to create prototypes. A prototype along with parameters becomes a component
and components can be grouped together as an application. An application can
be deployed to multiple environments.
The basic workflow is to create an application directory using ks init,
auto-generate a manifest (or write your own) for a component using ks
generate, deploy this application on a cluster/environment using ks apply
<env>. You can manage different environments using ks env command.
38. Ksonnet
38
In short, Ksonnet helps you define and manage applications as collection
of components using Jsonnet and then deploy them on different Kubernetes
clusters.
Like Helm, Ksonnet does not handle source code, it is a tool for defining
applications for Kubernetes, using Jsonnet.
44. Ksonnet modules
44
• A directory is a “module”
• merge all files in a directory together in a file with same name as dir
• this file becomes the thing you import
• split files up by (micro)service, maybe separate one for config etc.
• A module should self contained / stand alone
− Should be able to be imported into a environment without extra
consideration.
− Can import other modules of course...
• Modules should have a hidden _config field
− Underscore is to signify its reserved; has no semantic meaning.
• Modules should be a big dict with a bunch of well-named “global” variables
45. Ksonnet modules
45
• Expose each object as a “global” variable, or well know field in a single dict
− This is to allow users to extend you modules by merging stuff into
them
• … even if the object “used” by another objects - just hide it; eg containers
− This is because you can’t merge into lists
• Cons: can’t import a module twice per env.
46. Ksonnet modules
46
_config
Only put stuff in _config if:
• It's used in the modules in more than one place
− eg port numbers, domains names
• You need the user to specify it for the module to work
− eg namespace
− In which case, make it an error “required”.
• It's a “meta” variable - ie controls flow in some way
− eg when deciding if to expose RBAC rules or not
47. Ksonnet modules
47
Don’t put everything in config (ie flags, resource requirements) - otherwise this
just ends up being template substitution, makes config unreadable.
Remember, users can merge in values to override arbitrary fields.
48. Ksonnet modules
48
__images
● It's very common to want to run different image versions in different ends
● Therefor, I always put images in a dict under _images
● Have containers refer to this by name
● Also makes it easier to CD tool
49. Ksonnet Abstractions
49
Now we get to the fun bit!
Can build abstractions to reduce repetition:
● serviceFor - given a deployment, make a service with same name & ports
● rbac - create a role, binding and service account with given permissions
● {config,host,secret,empty}VolumeMount - mount a volume into a given path
in every container in a pod.
● antiAffinity - mixin to Deployment, make sure only one pod per node
50. Ksonnet problems
50
1. Bad documentations
2. Practically there are no examples
3. Problem with upgrading from ksonnet 0.8 to 0.9 version
4. It is often necessary to add functionality via JSON code
51. Ksonnet problems
51
apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
maxReplicas: 10
metrics:
- pods:
metricName: test_redisqlen
targetAverageValue: 3
type: Pods
minReplicas: 3
scaleTargetRef:
apiVersion: extensions/v1beta1
kind: Deployment
name: test-deploy
To add the highlighted red options, you must add the following to the
ksonnet code:
{spec+: {metrics+:
[{"pods":{"metricName":"test_redisqlen","targetAverageValue":3},"t
ype":"Pods"}]}}
{spec+: {scaleTargetRef+: {"apiVersion":"extensions/v1beta1"}}}
53. Draft
53
• Deploy code to k8s cluster (automates build-push-deploy)
• Deploy code in draft-pack supported languages without writing dockerfile
or k8s manifests
• Needs draft cli, helm cli, tiller on cluster, local docker, docker registry
• Draft builds upon Kubernetes Helm and the Kubernetes Chart format,
making it easy to construct CI pipelines from Draft-enabled applications.
• Works not good on MacOS
54. Gitkube
54
• Deploy code to k8s cluster (automates build-push-deploy)
• git push to deploy, no dependencies on your local machine
• Needs dockerfile, k8s manifests in the git repo, gitkube on cluster
56. Metapracticle
56
• Deploy your code in metaparticle supported languages to k8s (automates
build-push-deploy)
• Define containerizing and deploying to k8s in the language itself, in an
idiomatic way, without writing dockerfile or k8s yaml
• Needs metaparticle library for language, local docker
57. Skaford
57
• Deploy code to k8s cluster (automates build-push-deploy)
• Watches source code and triggers build-push-deploy when change
happens, configurable pipeline
• Needs skaffold cli, dockerfile, k8s manifests, skaffold manifest in folder,
local docker, docker registry
59. KSync
59
ksync speeds up developers who build applications for Kubernetes. It
transparently updates containers running on the cluster from your local
checkout. This enables developers to use their favorite IDEs, such as Atom or
Sublime Text to work from inside a cluster instead of from outside it. There is
no reason to wait minutes to test code changes when you can see the results
in seconds.
60. KSync
60
The local piece of ksync is operated via. the ksync binary. It provides some
general functionality:
● Cluster setup and initialization.
● Configuration of folders to sync to the cluster.
● Operating the details of the actual folder syncing (setting up the
connection, configuring the local and remote instances of syncthing to
move the files, managing the local syncthing process).
61. Telepresence
61
Have you ever wanted the quick development cycle of local code while still
having your code run within a remote Kubernetes cluster? Telepresence
allows you to run your code locally while still:
• Giving your code access to Services in a remote Kubernetes cluster.
• Giving your code access to cloud resources like AWS RDS or Google
PubSub.
• Allowing Kubernetes to access your code as if it were in a normal pod
within the cluster.
62. Conclusion
62
Helm works on a cluster by cluster basis whereas ksonnet can be used in a
multi-cluster view where you can have a consistent source of truth of what
should be applied across clusters. I don’t think that is possible right now with
Helm/Tiller.
Also ksonnet can also be used directly for those that don’t need the server
side application aspects of tiller. This would then pair well with the “GitOps”
approach where a source control repo is the system of record for what should
be running on a cluster.