Configuration of Spring Boot applications using Spring Cloud Config and Spring Cloud Vault.
Presentation given at the meeting of the Java User Group Freiburg on October 24, 2017
1) The document discusses managing short-lived Kubernetes deployments and outlines the steps taken to implement a DevOps process using Kubernetes and Azure Container Services.
2) Key priorities included enabling CI/CD, automatic provisioning, and minimizing the need for operations work.
3) The solution implemented Kubernetes with Azure Container Services using Azure as the IaaS provider to enable on-demand development and test environments identical to production.
An introduction to the concept of BDD and its implementation using the JGiven framework.
Presentation for the Java User Group Freiburg meetup on October 24, 2017.
This document provides an overview of the myOnboarding product and development approach at Haufe. Key points:
- myOnboarding is an onboarding SaaS product with a CMS, mobile apps, and responsive website to help new employees through their first months.
- It uses a microservices architecture with the MERN stack (MongoDB, Express, React, Node.js) deployed to Kubernetes. This allows for independent deployments and fast iteration.
- Features are developed using a RAD approach with short-lived branches and feature flags. This enables quick feedback loops and gradual rollout.
- Extensive automated testing of APIs and Docker images helps ensure backward compatibility and catch errors during deploys.
This document discusses moving two customer-facing applications, Haufe Instant Feedback and Haufe Agile Hats, from self-hosted to cloud-native architectures on AWS. It provides an overview of the architectures, which include separating the applications by product at the VPC level and using AWS Fargate for container orchestration without Kubernetes. The document outlines the security measures taken and continuous integration/delivery pipeline used to deploy updates from development to production environments on AWS.
Ever since the “CloudNative revolution” took over our development environment (devenv), we have never been more challenged (or more excited). With Kubernetes, Docker (Containerd) & many other microservice-related technologies, we have a handful of technologies to master before we write the first line of code.
Everything You Need to Know About Docker and Storage by Ryan Wallner, ClusterHQ Docker, Inc.
In this talk, we will provide a 10,000-ft. overview of the key concepts, architectures, and common deployment scenarios for stateful services. We will cover the Docker volumes and available storage options in the community including ClusterHQ’s Flocker volume manager. After getting the lay of the land, we'll see these concepts in action. Starting by deploying a database container on a single node with UCP, Flocker and VolumeHub. Then, using the features of Docker Swarm and Flocker, we will then allow Swarm to automatically reschedule the stateful service along with Flocker moving its volume when the node fails giving us a HA containerized database.
This document provides an overview of Docker and cloud native training presented by Brian Christner of 56K.Cloud. It includes an agenda for Docker labs, common IT struggles Docker can address, and 56K.Cloud's consulting and training services. It discusses concepts like containers, microservices, DevOps, infrastructure as code, and cloud migration. It also includes sections on Docker architecture, networking, volumes, logging, and monitoring tools. Case studies and examples are provided to demonstrate how Docker delivers speed, agility, and cost savings for application development.
Configuration of Spring Boot applications using Spring Cloud Config and Spring Cloud Vault.
Presentation given at the meeting of the Java User Group Freiburg on October 24, 2017
1) The document discusses managing short-lived Kubernetes deployments and outlines the steps taken to implement a DevOps process using Kubernetes and Azure Container Services.
2) Key priorities included enabling CI/CD, automatic provisioning, and minimizing the need for operations work.
3) The solution implemented Kubernetes with Azure Container Services using Azure as the IaaS provider to enable on-demand development and test environments identical to production.
An introduction to the concept of BDD and its implementation using the JGiven framework.
Presentation for the Java User Group Freiburg meetup on October 24, 2017.
This document provides an overview of the myOnboarding product and development approach at Haufe. Key points:
- myOnboarding is an onboarding SaaS product with a CMS, mobile apps, and responsive website to help new employees through their first months.
- It uses a microservices architecture with the MERN stack (MongoDB, Express, React, Node.js) deployed to Kubernetes. This allows for independent deployments and fast iteration.
- Features are developed using a RAD approach with short-lived branches and feature flags. This enables quick feedback loops and gradual rollout.
- Extensive automated testing of APIs and Docker images helps ensure backward compatibility and catch errors during deploys.
This document discusses moving two customer-facing applications, Haufe Instant Feedback and Haufe Agile Hats, from self-hosted to cloud-native architectures on AWS. It provides an overview of the architectures, which include separating the applications by product at the VPC level and using AWS Fargate for container orchestration without Kubernetes. The document outlines the security measures taken and continuous integration/delivery pipeline used to deploy updates from development to production environments on AWS.
Ever since the “CloudNative revolution” took over our development environment (devenv), we have never been more challenged (or more excited). With Kubernetes, Docker (Containerd) & many other microservice-related technologies, we have a handful of technologies to master before we write the first line of code.
Everything You Need to Know About Docker and Storage by Ryan Wallner, ClusterHQ Docker, Inc.
In this talk, we will provide a 10,000-ft. overview of the key concepts, architectures, and common deployment scenarios for stateful services. We will cover the Docker volumes and available storage options in the community including ClusterHQ’s Flocker volume manager. After getting the lay of the land, we'll see these concepts in action. Starting by deploying a database container on a single node with UCP, Flocker and VolumeHub. Then, using the features of Docker Swarm and Flocker, we will then allow Swarm to automatically reschedule the stateful service along with Flocker moving its volume when the node fails giving us a HA containerized database.
This document provides an overview of Docker and cloud native training presented by Brian Christner of 56K.Cloud. It includes an agenda for Docker labs, common IT struggles Docker can address, and 56K.Cloud's consulting and training services. It discusses concepts like containers, microservices, DevOps, infrastructure as code, and cloud migration. It also includes sections on Docker architecture, networking, volumes, logging, and monitoring tools. Case studies and examples are provided to demonstrate how Docker delivers speed, agility, and cost savings for application development.
Containers and Developer Defined Data Centers - Evan Powell - Keynote in Bang...CodeOps Technologies LLP
DevOps and Containers go hand in hand. DevOps industry is expected to benefit significantly benefit from the container eco-system and technology. This keynote talks about the challenges and opportunities around deploying containers into production use cases.
How to model Infrastructure as Code as part of CI / CD, incorporating it into your standard application development lifecycle, execute infrastructure changes in your CI/CD pipeline, and get additional benefits, such as reducing configuration errors and provisioning faster. All this leveraging IaC Tools on AWS like AWS CloudFormation, AWS SAM & AWS CDK
Containers and VMs and Clouds: Oh My. by Mike ColemanDocker, Inc.
As containers move from the developer's workstation into production environments there are many questions about how they fit into a company's existing infrastructure. Should a workload run in a VM or in a container? Should that container run on physical or virtual? In the data center or in the cloud?
The reality is that there is no "right" answer, just a series of questions that admins should be asking as they look to figure out where to run their application workloads. In this talk we'll take a look at the key differences between containers and VMs. From there we'll discuss the coexistence of VMs and containers, and finally we'll take a look at key factors to consider when making the decision where to run your applications. Throughout the presentation we'll highlight real world customers, their problems, and their ultimate deployment decisions.
Building occasionally connected applications using event sourcingDennis Doomen
I've recently got the opportunity to work on a large enterprise-class system that needs to be deployed on multiple occasionally connected oil platforms and boats. Already the system's architecture was based on the Command Query Separation principles, this gave us a completely new challenge. After several months of looking at alternatives, we decided to go the Event Sourcing direction. In this in-depth session, I'd like you to learn the many alternatives we observed, the pros and cons, and the technical details of our final solution in which we use EventStore 3.0 and elements of NCQRS and Lokad.CQRS to synchronize systems over unreliable connections in a very efficient way.
This document discusses automated scaling of JavaEE microservice stacks. It begins by introducing the speaker and their experience with autoscaling technologies. It then explains that while simple applications can be easily scaled by increasing container replicas, scaling complex JavaEE applications requires more work. The document outlines three key aspects needed for scalable applications: using lightweight application servers adapted for containers; decomposing monolithic applications into independently scalable microservices; and using orchestration software to automate provisioning, health checks, metrics collection, and scaling. It provides examples of scaling strategies for different application components like message queues and databases. It also discusses common issues like ensuring new instances are properly registered for events and services. The document emphasizes that proper configuration of autoscaling triggers
This document summarizes endtest.dev, an end-to-end test automation service that allows users to easily add test coverage to web applications. Key features include a web-based test editor, cloud-based test running powered by Google, and integration with GitHub and GitLab. Tests are triggered manually or by schedulers and run on Google Cloud infrastructure, with results, logs and errors stored in cloud storage and databases. Social media and communication channels are provided to help users and track the project's progress since its December 2021 start date.
Eric Holmes from Remind discussed building an internal Platform as a Service (PaaS) called Empire using Docker and Amazon EC2 Container Service (ECS). Remind started on Heroku but encountered issues with scaling and visibility. Empire provides a management layer on top of ECS for deploying and scaling microservices. It implements a subset of the Heroku API and provides a single binary and CLI. Empire is running 15 of Remind's production services on ECS with improved performance over Heroku. A demo was shown of deploying a sample app with Empire.
Infrastructure as Code, tools, benefits, paradigms and more.
Presentation from DigitalOnUs DevOps: Infrastructure as Code Meetup (September 20, 2018 - Monterrey Nuevo Leon MX)
Structured Container Delivery by Oscar Renalias, AccentureDocker, Inc.
With tools like Docker Toolbox, the entry barrier to Docker and containers is rather low. However, it takes a lot more to design, build and run an entire container platform, at scale, for production applications.
This talk will focus on why it is important to have a well-defined reference model for building container platforms that guides container engineers and architects through the process of identifying platform concerns, patterns, components as well as the interactions between them in order to deliver a set of platform capabilities (service discovery, load balancing, security, and others) to support containerized applications using existing tooling.
As part of this session will also see how a container architecture has enabled real projects in their delivery of container platforms.
This document discusses serverless computing and compares it to traditional server-based computing. Some key points:
- Serverless allows for inherent scalability, cost savings since customers only pay for resources used, and lower latency since code can execute near users. However, testing/debugging is more difficult and vendor lock-in is a risk.
- Serverless is compared to IaaS/PaaS/SaaS models, with serverless being akin to "buying a plane ticket and flying" rather than owning/renting infrastructure.
- Popular serverless options like Python and JavaScript Azure Functions are discussed, along with benefits like auto-completion, independent IDE usage, and ease of cloud integration and deployment.
Sebastien goasguen cloud stack and dockerShapeBlue
This document discusses how Docker can be used with CloudStack. It provides several options: 1) Running Docker in VMs on CloudStack templates that include Docker, 2) Using Docker-optimized OS templates, 3) Launching containers through a container service API, 4) Using CloudStack plugins within the Docker ecosystem like Docker Machine. The document concludes that CloudStack should not try to write a Docker hypervisor plugin, but instead focus on Docker-optimized OS templates and deploying application frameworks to orchestrate Docker.
Proof of Concept: Serverless with Swarm by Nirmal Mehta, Booz Allen Hamilton Docker, Inc.
The document discusses serverless computing with Docker containers. It provides definitions of serverless and functions-as-a-service. It then summarizes several serverless platforms like Serverless.com, Iron.io IronWorker, and iopipe Dockaless and how they integrate Docker containers or allow running code on multiple clouds. It demonstrates running a simple Docker container function locally using the Dockaless library. In conclusion, while serverless is still early, Docker containers provide a way to build serverless applications that are infrastructure agnostic.
Serverless Functions: Accelerating DevOps AdoptionAll Things Open
Presented by: Daniel Oh
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: Serverless functions are driving the fast adoption of DevOps development and deployment practices today. To successfully adopt serverless functions, developers must understand how serverless capabilities are specified using a combination of cloud computing, data infrastructure, and function-oriented programming. IT Ops teams also need to consider resource optimization (memory and CPU) and high-performance boot and first-response times in both development and production environments for faster time to market/service. What if we didn’t have to worry about all of that?
In this session, I’ll be speaking about what kinds of open source projects and tools enable you to write a serverless function with superfast boot and response times and built-in resource optimization. Then, you’ll understand how these capabilities take you to advanced DevOps practices as well as business acceleration. Furthermore, developers can avoid the extra work of developing a function from scratch, optimizing the application, and deploying it to Kubernetes.
Serverless frameworks are changing the way we do computing. In open source container world, Kubernetes is playing a pivotal role in manifesting this. This presentation will go deep into various features of Kubernetes to create serverless functions.
Also includes a comparative study of various serverless frameworks such as Kubeless, Fission and Funktion are available in open source world. Will conclude with an implementation demo and some real world use cases.
Presented in serverless summit 2017: www.inserverless.com
Kubernetes, Toolbox to fail or succeed for beginners - Demi Ben-Ari, VP R&D @...Demi Ben-Ari
Kubernetes (K8s) is a tool for managing containerized applications across multiple servers. It allows deploying and managing containerized applications without relying on virtual machines. Kubernetes can schedule containers across a cluster of nodes, provide basic health checking and restart policies, load balancing, storage orchestration and more. Some key Kubernetes concepts include pods, deployments, services, replication controllers and volumes. Kubernetes is well suited for microservices architectures as it helps manage the scaling and networking needs of distributed applications.
This document discusses Azure AI on-premises using Docker containers. It covers Microsoft Cognitive Services, Docker, and Azure Cognitive Services containers. The key points are:
- Microsoft Cognitive Services are AI algorithms that can be consumed via REST APIs to solve problems in areas like computer vision, natural language processing, and speech recognition.
- Docker containers allow these cognitive services to run locally on-premises for applications that cannot send data to the cloud. The containers package the services and their dependencies to run consistently on any infrastructure.
- A live demo will show how to utilize Docker containers for Azure Cognitive Services on an on-premises server to bring AI capabilities locally without needing internet access. Questions will be
Containers and Developer Defined Data Centers - Evan Powell - Keynote in Bang...CodeOps Technologies LLP
DevOps and Containers go hand in hand. DevOps industry is expected to benefit significantly benefit from the container eco-system and technology. This keynote talks about the challenges and opportunities around deploying containers into production use cases.
How to model Infrastructure as Code as part of CI / CD, incorporating it into your standard application development lifecycle, execute infrastructure changes in your CI/CD pipeline, and get additional benefits, such as reducing configuration errors and provisioning faster. All this leveraging IaC Tools on AWS like AWS CloudFormation, AWS SAM & AWS CDK
Containers and VMs and Clouds: Oh My. by Mike ColemanDocker, Inc.
As containers move from the developer's workstation into production environments there are many questions about how they fit into a company's existing infrastructure. Should a workload run in a VM or in a container? Should that container run on physical or virtual? In the data center or in the cloud?
The reality is that there is no "right" answer, just a series of questions that admins should be asking as they look to figure out where to run their application workloads. In this talk we'll take a look at the key differences between containers and VMs. From there we'll discuss the coexistence of VMs and containers, and finally we'll take a look at key factors to consider when making the decision where to run your applications. Throughout the presentation we'll highlight real world customers, their problems, and their ultimate deployment decisions.
Building occasionally connected applications using event sourcingDennis Doomen
I've recently got the opportunity to work on a large enterprise-class system that needs to be deployed on multiple occasionally connected oil platforms and boats. Already the system's architecture was based on the Command Query Separation principles, this gave us a completely new challenge. After several months of looking at alternatives, we decided to go the Event Sourcing direction. In this in-depth session, I'd like you to learn the many alternatives we observed, the pros and cons, and the technical details of our final solution in which we use EventStore 3.0 and elements of NCQRS and Lokad.CQRS to synchronize systems over unreliable connections in a very efficient way.
This document discusses automated scaling of JavaEE microservice stacks. It begins by introducing the speaker and their experience with autoscaling technologies. It then explains that while simple applications can be easily scaled by increasing container replicas, scaling complex JavaEE applications requires more work. The document outlines three key aspects needed for scalable applications: using lightweight application servers adapted for containers; decomposing monolithic applications into independently scalable microservices; and using orchestration software to automate provisioning, health checks, metrics collection, and scaling. It provides examples of scaling strategies for different application components like message queues and databases. It also discusses common issues like ensuring new instances are properly registered for events and services. The document emphasizes that proper configuration of autoscaling triggers
This document summarizes endtest.dev, an end-to-end test automation service that allows users to easily add test coverage to web applications. Key features include a web-based test editor, cloud-based test running powered by Google, and integration with GitHub and GitLab. Tests are triggered manually or by schedulers and run on Google Cloud infrastructure, with results, logs and errors stored in cloud storage and databases. Social media and communication channels are provided to help users and track the project's progress since its December 2021 start date.
Eric Holmes from Remind discussed building an internal Platform as a Service (PaaS) called Empire using Docker and Amazon EC2 Container Service (ECS). Remind started on Heroku but encountered issues with scaling and visibility. Empire provides a management layer on top of ECS for deploying and scaling microservices. It implements a subset of the Heroku API and provides a single binary and CLI. Empire is running 15 of Remind's production services on ECS with improved performance over Heroku. A demo was shown of deploying a sample app with Empire.
Infrastructure as Code, tools, benefits, paradigms and more.
Presentation from DigitalOnUs DevOps: Infrastructure as Code Meetup (September 20, 2018 - Monterrey Nuevo Leon MX)
Structured Container Delivery by Oscar Renalias, AccentureDocker, Inc.
With tools like Docker Toolbox, the entry barrier to Docker and containers is rather low. However, it takes a lot more to design, build and run an entire container platform, at scale, for production applications.
This talk will focus on why it is important to have a well-defined reference model for building container platforms that guides container engineers and architects through the process of identifying platform concerns, patterns, components as well as the interactions between them in order to deliver a set of platform capabilities (service discovery, load balancing, security, and others) to support containerized applications using existing tooling.
As part of this session will also see how a container architecture has enabled real projects in their delivery of container platforms.
This document discusses serverless computing and compares it to traditional server-based computing. Some key points:
- Serverless allows for inherent scalability, cost savings since customers only pay for resources used, and lower latency since code can execute near users. However, testing/debugging is more difficult and vendor lock-in is a risk.
- Serverless is compared to IaaS/PaaS/SaaS models, with serverless being akin to "buying a plane ticket and flying" rather than owning/renting infrastructure.
- Popular serverless options like Python and JavaScript Azure Functions are discussed, along with benefits like auto-completion, independent IDE usage, and ease of cloud integration and deployment.
Sebastien goasguen cloud stack and dockerShapeBlue
This document discusses how Docker can be used with CloudStack. It provides several options: 1) Running Docker in VMs on CloudStack templates that include Docker, 2) Using Docker-optimized OS templates, 3) Launching containers through a container service API, 4) Using CloudStack plugins within the Docker ecosystem like Docker Machine. The document concludes that CloudStack should not try to write a Docker hypervisor plugin, but instead focus on Docker-optimized OS templates and deploying application frameworks to orchestrate Docker.
Proof of Concept: Serverless with Swarm by Nirmal Mehta, Booz Allen Hamilton Docker, Inc.
The document discusses serverless computing with Docker containers. It provides definitions of serverless and functions-as-a-service. It then summarizes several serverless platforms like Serverless.com, Iron.io IronWorker, and iopipe Dockaless and how they integrate Docker containers or allow running code on multiple clouds. It demonstrates running a simple Docker container function locally using the Dockaless library. In conclusion, while serverless is still early, Docker containers provide a way to build serverless applications that are infrastructure agnostic.
Serverless Functions: Accelerating DevOps AdoptionAll Things Open
Presented by: Daniel Oh
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: Serverless functions are driving the fast adoption of DevOps development and deployment practices today. To successfully adopt serverless functions, developers must understand how serverless capabilities are specified using a combination of cloud computing, data infrastructure, and function-oriented programming. IT Ops teams also need to consider resource optimization (memory and CPU) and high-performance boot and first-response times in both development and production environments for faster time to market/service. What if we didn’t have to worry about all of that?
In this session, I’ll be speaking about what kinds of open source projects and tools enable you to write a serverless function with superfast boot and response times and built-in resource optimization. Then, you’ll understand how these capabilities take you to advanced DevOps practices as well as business acceleration. Furthermore, developers can avoid the extra work of developing a function from scratch, optimizing the application, and deploying it to Kubernetes.
Serverless frameworks are changing the way we do computing. In open source container world, Kubernetes is playing a pivotal role in manifesting this. This presentation will go deep into various features of Kubernetes to create serverless functions.
Also includes a comparative study of various serverless frameworks such as Kubeless, Fission and Funktion are available in open source world. Will conclude with an implementation demo and some real world use cases.
Presented in serverless summit 2017: www.inserverless.com
Kubernetes, Toolbox to fail or succeed for beginners - Demi Ben-Ari, VP R&D @...Demi Ben-Ari
Kubernetes (K8s) is a tool for managing containerized applications across multiple servers. It allows deploying and managing containerized applications without relying on virtual machines. Kubernetes can schedule containers across a cluster of nodes, provide basic health checking and restart policies, load balancing, storage orchestration and more. Some key Kubernetes concepts include pods, deployments, services, replication controllers and volumes. Kubernetes is well suited for microservices architectures as it helps manage the scaling and networking needs of distributed applications.
This document discusses Azure AI on-premises using Docker containers. It covers Microsoft Cognitive Services, Docker, and Azure Cognitive Services containers. The key points are:
- Microsoft Cognitive Services are AI algorithms that can be consumed via REST APIs to solve problems in areas like computer vision, natural language processing, and speech recognition.
- Docker containers allow these cognitive services to run locally on-premises for applications that cannot send data to the cloud. The containers package the services and their dependencies to run consistently on any infrastructure.
- A live demo will show how to utilize Docker containers for Azure Cognitive Services on an on-premises server to bring AI capabilities locally without needing internet access. Questions will be
Devops with Python by Yaniv Cohen DevopShiftYaniv cohen
This document discusses implementing DevOps with Python using Ansible. It provides an agenda for the presentation including discussing DevOps hotspots, infrastructure as code with Ansible, continuous integration/continuous delivery (CI/CD) using TravisCI and CircleCI, and an open discussion on monitoring and automated tests. It then covers problems commonly faced, how DevOps solves these problems, and the expected benefits of adopting a DevOps culture including standardized environments, infrastructure as code, automated delivery, monitoring, and improved collaboration. It provides an overview of Ansible concepts like inventories, ad-hoc commands, modules, playbooks, roles, and templates. It also demonstrates writing a custom Python module for Ansible and using it in a playbook. Finally, it
Brayden Winterton gives an introduction to Docker. He explains that Docker solves the "Matrix from Hell" of inconsistent environments by using containers to package applications and their dependencies in portable, standardized units. Developers benefit from Docker because it allows them to build once and run anywhere while avoiding dependency issues. System administrators benefit because Docker provides standardized, repeatable environments that are faster and more reliable to deploy. Brayden then demonstrates Docker by running a sample application in a container and linking multiple containers together.
This document discusses using Docker and microservices in production. It begins with an introduction and background on monolithic architectures and microservices architectures. It then provides an overview of Docker containers, how they can be used to implement a microservices architecture with tools like Rancher for deployment, service discovery, load balancing and scaling. The document demonstrates these concepts by walking through building a sample application with a web service and random number generation service running in Docker containers behind a load balancer.
DevOps Fest 2020. immutable infrastructure as code. True story.Vlad Fedosov
This document discusses the journey of transitioning infrastructure management at Namecheap to an immutable infrastructure as code model using tools like Terraform, Docker, and Jenkins. Key points include taking over a project from an outsourcing company, setting up immutable infrastructure with infrastructure as code, configuring CI/CD pipelines as code in Jenkins, and lessons learned around testing, chaos engineering, and encouraging team feedback. The overall goals were to make infrastructure hard to break, easy to repair, and easy to modify.
Introduction to DevOps and the Practical Use Cases at Credit OKKriangkrai Chaonithi
The document provides an introduction to DevOps and practical use cases. It discusses what DevOps is, why it is popular, the skills required of DevOps engineers, and common DevOps technologies like version control, CI/CD pipelines, containers, and monitoring. It also summarizes Credit OK's use of DevOps practices like Docker, Kubernetes, and GitLab CI/CD pipelines for their credit scoring platform. Finally, it outlines some modern obstacles in software development and concludes that DevOps can help ensure quality, improve productivity, and automate infrastructure through practices like continuous integration, containerization, and logging/monitoring.
Truemotion Adventures in ContainerizationRyan Hunter
This document summarizes Ryan Hunter's experience switching his company's infrastructure from using Ansible to provision Debian-based servers to using Docker containers and ECS on AWS. Some key reasons for the switch included dependency issues with Ansible, inflexible server sizing, and a desire for more portable and standardized application builds. Docker provided containers as a flexible runtime artifact while ECS and CloudFormation helped with scheduling, provisioning, and configuring containers at scale on AWS. Monitoring tools like Consul, Sumo Logic, and custom monitoring libraries were also implemented.
Velocity NYC 2017: Building Resilient Microservices with Kubernetes, Docker, ...Ambassador Labs
1. The presentation introduces Docker, Kubernetes, and Envoy as foundational tools for building microservices. Docker allows packaging applications into portable containers, Kubernetes provides a platform to manage containers across clusters of hosts, and Envoy handles traffic routing and resilience at the application layer.
2. The presenters demonstrate how to build a simple Python web application into a Docker container image. They then deploy the containerized application to a Kubernetes cluster using Kubernetes objects like deployments and services. This allows the application to scale across multiple pods and be accessed via a stable service endpoint.
3. Finally, the presenters note that as applications become distributed across microservices, failures at the application layer (L7) become more common and
Building a Pluggable, Cloud-native Event-driven Serverless Architecture - Rea...Dan Farrelly
Building out Reactive systems can be a lot of work. There’s a lot of infrastructure to set up and designing a system to be resilient, responsive, and elastic requires experience and time that not every team has. We built Inngest to be an open source, cloud-native system that enables anyone to build Reactive architectures. Designed to be pluggable with your favorite messaging service like Kafka, NATS or PubSub and your favorite container orchestration like Kubernetes, Nomad, or ECS. We’ll walk through how the system was designed, how you can deploy it yourself, and the plans to make it runnable on any cloud (and even your laptop!).
Those are slides from Dev.IL meetup talk, by Or Rosenblatt & Yshay Yaacobi from Soluto RND
https://www.meetup.com/Dev-IL/events/253252917/
-------------------------
You developed a cool java infrastructure for your team.
Your team then shifts to python, so you rewrite the utility in python.
Then the team next door asks you to do the same rewrite for their node/typescript service.
You ask for a raise and write it again in typescript.
Now your colleague reads in HackerNews about the next cool trending language in the block.
Ain’t nobody got time for that!!!
Join us to hear how the powerful combination sidecar pattern and Kubernetes can help you solve this issue by allowing different services to use the same utility, regardless of stack or language.
You will become stack-free forever!
This document summarizes a company's journey to implementing Docker containers in production. It begins with using Docker in hackathons and development environments. Early production uses involved deploying individual services as containers but lacked orchestration. They then tried microservices and deploying to a self-hosted registry, which had stability issues. They eventually selected Rancher for orchestration and Quay.io for the registry. Secret configuration is managed using SaltStack pillars. Templating allows a single docker-compose file across environments. Rancher-compose is used to deploy versions to environments and roll back if needed. This overcomes earlier challenges and provides a smooth path to containerized microservices in production.
ContainerDays NYC 2015: "Easing Your Way Into Docker: Lessons From a Journey ...DynamicInfraDays
Slides from Patrick Mizer & Steve Woodruff's talk "Easing Your Way Into Docker: Lessons From a Journey to Production" at ContainerDays NYC 2015: http://dynamicinfradays.org/events/2015-nyc/programme.html#sparefoot
DCSF 19 Building Your Development Pipeline Docker, Inc.
Oliver Pomeroy, Docker & Laura Tacho, 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 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.
- Docker celebrated its 5th birthday with events worldwide including one in Cluj, Romania. Over 100 user and customer events were held.
- The Docker platform now has over 450 commercial customers, 37 billion container downloads, and 15,000 Docker-related jobs on LinkedIn.
- The event in Cluj included presentations on Docker and hands-on labs to learn Docker, as well as social activities like taking selfies with a birthday banner.
DCEU 18: Building Your Development PipelineDocker, Inc.
This document discusses building a development pipeline using containers. It outlines using containers for building images, automated testing, security scanning, and deploying to production. Containers make environments consistent and reproducible. The pipeline includes building images, testing, security scanning, and promoting images to production. Methods discussed include using multi-stage builds to optimize images, leveraging Buildkit for faster builds, and parallel testing across containers. Automated tools are available to implement rolling updates and rollbacks during deployments.
This document provides an overview of Docker and containers. It begins with a brief introduction to 12 Factor Applications methodology and then defines what Docker is, explaining that containers utilize Linux namespaces and cgroups to isolate processes. It describes the Docker software and ecosystem, including images, registries, Docker CLI, Docker Compose, building images with Dockerfile, and orchestrating with tools like Kubernetes. It concludes with a live demo and links to additional resources.
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.
Similar to Docker in Production at the Aurora Team (20)
The document lists various programming languages including Java, C#, Delphi, and Python. It also covers frontend technologies like React, Angular, and Vue. Databases such as MongoDB, SQLITE, Oracle, and MySQL are mentioned along with cloud providers AWS and Azure. Source control systems including GitLab, GitHub, TFS Onpremise, and Bitbucket as well as IDEs like Visual Studio Code, IntelliJ, and Visual Studio are provided.
In this talk, Martin covers how an All-JavaScript approach with MongoDB, Express, React and Node.js (MERN) enables iterating fast, picking the example of the quickly growing product 'myOnboarding' by Haufe-Lexware. He touches on the pros and cons of this technology stack, how the technology ties in to the product's microservices architecture, and how the product team leverages CI/CD to be able to act, and react, fast and securely. The talk further touches on how the product team setup and customer feedback is crucial to iterate fast, in the right direction.
Meetup presentation on Feb 27th 2019 at the Dock8s Meetup in Heidelberg/Rhein-Neckar, at the verivox campus.
The talk touches on all areas which involve a cloud journey of a major produkt (iDesk2) of the Haufe Group: Planning & Politics, Technology and doing Operations for that product as a DevOps team.
ONA ( organizational network analysis ) to enable individuals to impact their...Haufe-Lexware GmbH & Co KG
ONA - organizational network analysis - is becoming an important topic for HR-technology. Simply put, ONA provides insight into how organizations really function.
Embedding ONA capability has the potential to enable employers and employees to organize themselves more effectively, communicate more impactfully, and to lead their companies forward.
ONA ( organizational network analysis ) enabling individuals to impact their ...Haufe-Lexware GmbH & Co KG
ONA - organizational network analysis - is becoming an important topic for HR-technology. Simply put, ONA provides insight into how organizations really function.
Embedding ONA capability has the potential to enable employers and employees to organize themselves more effectively, communicate more impactfully, and to lead their companies forward.
One of the areas that can greatly improve the customer experience is a search that returns relevant content.
In this session, Hans presents the most current results on his research to extract a keyword vocabulary and use vectorized representations of these words to enable lawyer customers to find the content that helps them do their job.
It is a core demand of marketing & sales to segment their customer base. Join this session to learn to identify and prepare the data to perform this segmentation with machine learning.
myOnboarding is a solution that aims to help employees have the best start at a new company. It provides relevant onboarding information to employees at the right time to reduce uncertainty and improve performance and engagement. The solution was developed using rapid application development principles with an agile approach to gather feedback and continuously improve. It has evolved over time from initial frameworks like KeystoneJS to use MeteorJS and React for a customizable and scalable platform. The technology uses Docker, Kubernetes, and Azure services for continuous delivery of new features and versions to customers.
Opportunities offered by Serverless Architecture: What are the offers from the big Cloud Providers and how you can build a 3-tier architecture app having no servers. See also http://dev.haufe.com/Serverless_with_AWS_at_DevTalks/
The document discusses the Haufe Publishing System project and its approach using lean principles. Some key points:
- The project aimed to modernize their platform by reducing dependencies, improving sharing, and allowing for faster change.
- Lean principles like eliminating waste, amplifying learning, and deciding late were followed. Features were implemented incrementally and feedback was used for continuous improvement.
- The architectural approach focused on business value, composability, shared services, evolutionary refinement, and data-driven processes.
- The project used a pipeline approach with separate environments for development, integration, and production on Kubernetes clusters for each environment. Automated testing was done at each stage.
This document discusses Haufe-Lexware's API strategy. It advocates adopting a microservices architecture with independently working teams that follow an API style guide. APIs are organized by domain and sit at the domain boundary rather than for internal communication. API management follows a DevOps approach with immutable infrastructure, containerization, and green-blue deployments. The role of APIs is to act as a shock absorber by decoupling domains, systems, teams, and development speeds through outside-in design and self-service.
The document discusses Haufe Group's transformation to a more modern and agile technology strategy. It outlines the company's move to microservices, automation, and product teams. Key points include establishing architectural principles focused on business value over technical strategy, using microservices with a shared nothing architecture, and automating the development ecosystem through infrastructure as code, continuous integration/delivery, and containerization. The presentation provides examples from migrating services to microservices and refactoring a monolithic publishing system.
Kubernetes is an open-source platform for automating deployment, scaling, and operations of containerized applications. It provides tools to deploy containers across clusters of hosts, provide mechanisms for load-balancing, monitor health, and update containers. Kubernetes adds functionality to Docker by managing Docker hosts and containers at scale. It uses abstractions like pods, replica sets, deployments, services and ingresses to declaratively define application components and expose them using NodePorts, LoadBalancers or Ingresses. Users can interact with Kubernetes using kubectl to deploy and manage applications on the cluster.
Martin Danielsson presented on API Management with wicked.haufe.io. API Management provides discoverability and self-service access to APIs for developers, monitors traffic to provide usage insights, and protects APIs from misuse through security procedures and policies. Wicked.haufe.io is an open source API Management platform based on Mashape Kong that provides features like rate limiting, OAuth 2.0 support, and a developer portal with self signup. It is designed to run in Docker and deploy on any infrastructure for machine-to-machine communication, single page applications, and mobile apps. The presentation demonstrated wicked.haufe.io functionality through a live demo.
Extending the first segment of building a microservices ecosystem, Lorenzo Nicora introduces reactive principles for microservices and compares two different approaches - macro and micro - to implementing them.
Lorenzo Nicora makes an introductory presentation on event sourcing, what you want to achieve and how to use CQRS to implement event sourcing for your microservices.
All is not completely rosy in microservice-land. It is often a sign of an architectural approach’s maturity that in addition to the emergence of well established principles and practices, that anti-patterns also begin to be identified and classified. In this talk we introduce the 2016 edition of the seven deadly sins that if left unchecked could easily ruin your next microservices project... This talk will take a tour of some of the nastiest anti-patterns in microservices, giving you the tools to not only avoid but also slay these demons before they tie up your project in their own special brand of hell.
All is not completely rosy in microservice-land. It is often a sign of an architectural approach’s maturity that in addition to the emergence of well established principles and practices, that anti-patterns also begin to be identified and classified. In this talk we introduce the original edition of the seven deadly sins that, if left unchecked, could easily ruin your next microservices project... This talk will take a tour of some of the nastiest anti-patterns in microservices, giving you the tools to not only avoid but also slay these demons before they tie up your project in their own special brand of hell.
Microservice platforms are finally becoming a reality: Mesos, Kubernetes, and a whole bunch of PaaS-style offerings are available, but the reality is that these platforms still don’t provide everything you need in order to build a fully functional microservice ecosystem. Come to this session to learn about the essential deployment, orchestration, and glue components that often have to be self-assembled. The presentation begins by looking at deployment techniques and tools and examines where to test (QA, staging, or production), how to test (integration and contracts), and how to separate deployment and release. It then discusses orchestration, configuration, and service discovery. Finally it looks at essential glue such as logging, monitoring, and alerting.
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
SMS API Integration in Saudi Arabia| Best SMS API ServiceYara Milbes
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Drona Infotech is a premier mobile app development company in Noida, providing cutting-edge solutions for businesses.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
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A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
openEuler Case Study - The Journey to Supply Chain Security
Docker in Production at the Aurora Team
1. Docker in production at the
Aurora team
Or: Now that we found docker what are we going to do with it?
Timisoara Docker Meetup, Sept 4th 2017
Adina-Claudia Toma, Victor Dan Daneasa, Martin Danielsson
2. Agenda
Who are we and why are we allowed to talk about this?
Our journey towards Docker in Production
What do you need for it?
How do you know it’s working?
Q & A
4. Aurora Project (iDesk2)
Research Database for Lawyers,
Tax Accountants
Live with docker in Production
since March
Still in transition towards
Microservices
Haufe Group
~1600 employees in multiple
countries
HQ in Freiburg, Germany
Development Center in Timisoara
Adina-Claudia
Toma
Senior
Developer
Victor Dan
Daneasa
Senior
Developer
Martin
Danielsson
Solution
Architect
Who are we?
7. Feels familiar?
What’s this?
$ docker pull postgres
$ docker run -d -p 5432:5432 postgres
AMAZEBALLS!I’m totally writing
everything for docker!
A single VM will
do. Right?
Dammit.
How hard can it be to get
into production...
8. The Dev to Prod Chasm
Amazeballs
factor
Traditional
Ops Level Dev Tinkering Dev Process
Setup
Mature
Production Ops
Incl. CI/CD
Production
Rollout
11. Building Blocks for running Docker in production
What you have to do by yourself:
● Image management
● Container Orchestration
● Automated CI/CD Pipelines
● Log management
● Monitoring on all levels
● Data Persistence
What you can get for “free” if you use a cloud provider and orchestration framework:
● Security patches & restricted network access
● Load balancing & service discovery
● Automatic recovery from failure
12. Image Management
● Consistent process to build and tag docker images
● Private Docker image repository
○ Artifactory (JFrog)
○ Azure Container Registry (ACR)
○ Amazon EC2 Container Registry (ECR)
○ Self-hosted with Docker
○ Docker Hub
○ Quay.io
● Security scanning of docker images for vulnerabilities
13. Container Orchestration
Abstracts the host infrastructure & allows to treat a
cluster as a single deployment target
● Declarative configuration
● Scheduling & high-availability
● Service discovery & load-balancing
● Health monitoring
18. Data Persistence
Containers should be stateless.
State can be stored in:
● Data volumes per host -> non-portable between hosts
● Shared filesystems: NFS, Ceph, GlusterFS
● Docker volume plugins
● Database/Storage as a service: AWS, Azure
19. our solution vector
What you have to do by yourself:
● Image management: private Haufe docker repository/Azure Container Registry
● Container Orchestration: Kubernetes with Docker
● Automated CI/CD Pipelines: Jenkins pipelines, bash, Ansible, Azure CLI
● Log management: fluent-bit, fluentd, Graylog/Elasticsearch/Mongodb
● Monitoring on all levels: Prometheus, Alertmanager, Grafana
● Data Persistence: Postgres VM, NFS Server, Redis
What you can get for “free” if you use a cloud provider and orchestration framework:
● Azure Container Services Engine with Kubernetes
● Security patches & restricted network access
● Load balancing & service discovery
● Automatic recovery from failure
21. Prometheus
● Whitebox monitoring
● Scalable
● Simple to setup
● Discovery service
● Built-in exporters (pull metrics)
● Easy to integrate into your applications
● PromQL (yet another query language)
● Alerting included
22. Not fully blind And Getting better
● Started with what we knew we need (the basics):
CPU, memory, IO
● Run into some problems:
Disk space, nodes failing, monitoring itself, API changes
● Things get better and better:
Alerting, app insights, moving parts
25. Post Mortems
Resulted from a failure
Every member of the team participates
● What caused it?
● What were the affected components?
● Actions
● Lessons learned
27. Trimmed for scale
out
What we ended up with
Fully microservice
enabled infrastructure
Insights on all
levels
Full DevOps
responsibility
Perhaps not what YOU need...
Might single Docker Host be enough?
AWS Elastic Container Services?
Docker DataCenter?
k8s-as-a-service?
Traditional VMs?Google Container Engine?
Assess YOUR use case!
28. For us - absolutely
worth the effort to
gain speed and
flexibility
Invest only worth it with
certain size and load
$Large upfront effort
to get infrastructure
right
Your CI/CD pipelines are your safety
net - make them rock solid
Practice provisioning
daily or weekly!
Steep learning curve If possible, start with
something new, then
move old workloads
Blue-eyed approach will
fail - it is (a lot of)
work!
Many more moving
parts - additional
complexity
Our conclusions and recommendations
Consider persistence early
on