Flux Auto develops modular autonomous driving technology for trucks.

Flux Auto - Crunchbase Company Profile & Funding

Flux Auto | 11,647 followers on LinkedIn. Autonomous driving kits for commercial vehicles. | Flux Auto is developing autonomous driving kits for commercial vehicles with a focus on safety, productivity, and cost benefits. With projects around the world, our team is solving complex real time ...

Flux Auto | LinkedIn

Information on valuation, funding, cap tables, investors, and executives for Flux Auto. Use the PitchBook Platform to explore the full profile.

Flux Auto Company Profile: Valuation & Investors | PitchBook

Get insights about Flux Auto Company Profile, reviews, salaries, interviews questions, offices, locations, headquarters, employee benefits and more.

Flux Auto Company Profile & Overview | AmbitionBox

Flux Auto - Developer of self-driving tech for commercial vehicles. Raised a total funding of $1.24M over 6 rounds from 22 investors.

Flux Auto - Company Profile - Tracxn

Flux offers an affordable way to drive electric with three month subscriptions for every driver. Option to include charging.

Electric Car Subscription | Flexible Terms | Save Money On Gas

&quot;Founded by Pranav Manpuria and Abhiskek Gupta, Flux Auto is a company that wants to bring in self-driving technology to trucks in India. Their purpose-built embedded systems ensure the highest performance and full customization based on operational requirements.

Flux Auto Company Profile, information, investors, valuation & Funding

Apply privately to 130,000+ remote jobs and startup jobs near you with one application. See salary and equity upfront. Find the latest tech jobs, company overviews, benefits and more at Wellfound (formerly AngelList Talent).

Flux Auto Careers

Autonomous Forklifts. Founded in 2017 by Abhishek Gupta and Pranav Manpuria, Flux Auto has 56 employees based in Houston, TX, USA.

Flux Auto: Autonomous Forklifts | Y Combinator

Engineering Jobs at Flux Auto

Car insurance quotes compared by Adrian Flux save you time and money. Call us for a fast, affordable quote now.

Car Insurance | Adrian Flux

Flux Auto: Autonomous Driving Kits for Commercial Vehicles. View company info, jobs, team members, culture, funding and more.

Flux Auto | AngelList

FLUX AUTO INDIA PRIVATE LIMITED - company information, business information and directors/partners details

FLUX AUTO INDIA PRIVATE LIMITED - Company, directors and contact ...

Companies directory - browse the most searched companies and organizations on SignalHire and get the search results and information about companies and their employees.

Flux Auto Overview | SignalHire Company Profile

Flux Auto is developing autonomous driving kits for commercial vehicles with a focus on safety, productivity, and cost benefits. With projects around the world, our team is solving complex real time problems across various disciplines.

Flux Auto

Manual deployments are not error-prone. Moreover, they consist of a bunch of repetitive and complex tasks. The development teams are afraid of the complexity of deploying a new version of an application and the headaches it brings. Sometimes, deploying an application requires advanced platform skills. It is a tedious process to debug deployment issues. helps you to reduce the effort dedicated to manual and repetitive tasks. It also minimizes the deployment errors and human interactions. It offers tools to track and update the state of your deployments using declarative files, making the deployment and debugging process easier for your development teams. FluxCD This article explores the automation of your deployment processes to transform them into continuous deployment processes using , , and . FluxCD Flagger Grafana Check the first article to learn more about continuously delivering an application: . CI/CD Hands-On: A Simple But Functional Continuous Integration Workflow [Part 1] GitHub Example: https://github.com/joan-mido-qa/continious-deployment-example Introduction Use and to set up the Kubernetes cluster. First of all, create the cluster and export the Kubernetes configuration to set the Kubernetes provider: KinD Terraform $ kind create cluster --name develop 
$ kind export kubeconfig --name develop --kubeconfig kubeconfig Create a new GitHub repository and a developer token with repository permissions, Terrafom requires it to setup FluxCD. Initialize Terraform and apply the changes: $ terraform init
$ terraform apply -var="github_owner=owner_name" -var="github_repository=repo_name" # Introduce your GitHub token Once Terraform finishes the installation process, you should have FluxCD running in your KinD cluster and a new folder named in your repository. cluster Terraform Under the hood, Terraform installs and configures the IP range. You can read more about the MetalLB configuration in : MetalLB the first part of the article resource "helm_release" "metallb" {
 name = "metallb"

 repository = "https://metallb.github.io/metallb"
 chart = "metallb"
}

data "docker_network" "kind" {
 name = "kind"
}

resource "kubectl_manifest" "kind-address-pool" {
 yaml_body = yamlencode({
 "apiVersion" : "metallb.io/v1beta1",
 "kind" : "IPAddressPool",
 "metadata" : { "name" : "kind-address-pool" },
 "spec" : { "addresses" : [replace(tolist(data.docker_network.kind.ipam_config)[0].subnet, ".0.0/16", ".255.0/24")] }
 })

 depends_on = [helm_release.metallb]
}

resource "kubectl_manifest" "kind-advertisement" {
 yaml_body = <<YAML
 apiVersion: metallb.io/v1beta1
 kind: L2Advertisement
 metadata:
 name: kind-advertisement
 YAML

 depends_on = [helm_release.metallb]
} Then, it installs FLuxCD Helm Chart and configures the GitHub repository to use FluxCD: resource "helm_release" "flux" {
 repository = "https://fluxcd-community.github.io/helm-charts"
 chart = "flux2"
 name = "flux2"
 namespace = "flux-system"
 create_namespace = true
 version = "2.9.2"
}

resource "tls_private_key" "flux" {
 depends_on = [helm_release.flux]

 algorithm = "ECDSA"
 ecdsa_curve = "P256"
}

resource "github_repository_deploy_key" "flux" {
 depends_on = [tls_private_key.flux]

 title = "Flux"
 repository = var.github_repository
 key = tls_private_key.flux.public_key_openssh
 read_only = "false"
}

resource "flux_bootstrap_git" "this" {
 depends_on = [github_repository_deploy_key.flux]

 path = "clusters/develop"
} Pre-commit Hook Suggestions: ( ): Terraform https://github.com/antonbabenko/pre-commit-terraform TFSec: Static analysis for Terraform to spot potential misconfigurations TFLint: Static format checker for Terraform ( ): Preventing new secrets from entering the code base. Detect Secrets https://github.com/Yelp/detect-secrets FluxCD is a GitOps tool to keep a Kubernetes cluster with the latest source control changes (like Git repositories). Flux automates the deployment of new code. FluxCD Once Flux is running in the cluster, let’s see how it works. We will deploy ingress-nginx as an ingress provider. Flux does not enforce the project folder structure. You can configure it as you wish or follow your preferred standard. Create a folder named inside a folder named . The base folder has the basic infrastructure configuration for all your clusters. Next, create a folder named . Use the namespace name as the folder name. base infrastructure ingress-nginx ---
apiVersion: v1
kind: Namespace
metadata:
 name: ingress-ngnix
---
apiVersion: source.toolkit.fluxcd.io/v1beta1
kind: HelmRepository
metadata:
 name: ingress-nginx
spec:
 interval: 2h
 url: https://kubernetes.github.io/ingress-nginx
---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
 name: ingress-nginx
spec:
 interval: 15m
 chart:
 spec:
 chart: ingress-nginx
 version: 4.7.1
 sourceRef:
 kind: HelmRepository
 name: ingress-nginx
 interval: 15m
---
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: ingress-ngnix
resources:
- namespace.yaml
- helmrepository.yaml
- helmrelease.yaml Use multiple files to define your objects: , , , , etc. helmrelease.yaml helmrepository.yaml namespace.yaml kustomization.yaml The reads and processes the resources to apply them. Last but not least, you need to create a Kustomization object to synchronize your cluster configuration. Create a YAML file named inside the folder: Kustomization infrastructure.yaml cluster/cluster_name ---
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
 name: infra-base
 namespace: flux-system
spec:
 interval: 1h
 retryInterval: 1m
 timeout: 5m
 sourceRef:
 kind: GitRepository
 name: flux-system
 path: ./infrastructure/base
 prune: true
 wait: true After committing and pushing your changes to your repository, Flux will reconcile the cluster status to install the ingress-nginx Helm Chart. Flagger is a Kubernetes operator that delivers progressively your application using blue/green deployments, canary release, or A/B testing. Flagger You can use the folder to install your stack into all your clusters or use a different one to customize your installation depending on the cluster. For example, we want to install Flagger only into the development cluster. base Create a new folder using your cluster name inside the folder. Then, create a file named i into your : infrastructure nfrastructure.yaml cluster/cluster_name ---
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
 name: infra-cluster-name
 namespace: flux-system
spec:
 dependsOn:
 - name: infra-base
 interval: 1h
 retryInterval: 1m
 timeout: 5m
 sourceRef:
 kind: GitRepository
 name: flux-system
 path: ./infrastructure/cluster_name
 prune: true FluxCD will synchronize the cluster status after applying the infra-base Kustomization. Install Flagger, creating the following YAML file inside the folder: infrastructure/cluster_name/flagger-system ---
apiVersion: v1
kind: Namespace
metadata:
 name: flagger-system
---
apiVersion: source.toolkit.fluxcd.io/v1beta2
kind: HelmRepository
metadata:
 name: flagger
spec:
 interval: 1h
 url: https://flagger.app
---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
 name: flagger
spec:
 interval: 1h
 install:
 crds: CreateReplace
 upgrade:
 crds: CreateReplace
 chart:
 spec:
 chart: flagger
 version: 1.x.x
 interval: 6h
 sourceRef:
 kind: HelmRepository
 name: flagger
---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
 name: flagger-loadtester
spec:
 interval: 1h
 chart:
 spec:
 chart: loadtester
 version: 0.x.x
 interval: 6h
 sourceRef:
 kind: HelmRepository
 name: flagger
---
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: flagger-system
resources:
- namespace.yaml
- helmrepository.yaml
- helmrelease.yaml Continuous Deployment To build the application continuous deployment pipeline, create the installation YAML file into the *:* Podinfo apps/cluster_name/podinfo ---
apiVersion: v1
kind: Namespace
metadata:
 name: podinfo
---
apiVersion: source.toolkit.fluxcd.io/v1beta2
kind: HelmRepository
metadata:
 name: podinfo
spec:
 interval: 5m
 url: https://stefanprodan.github.io/podinfo
---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
 name: podinfo
spec:
 releaseName: podinfo
 chart:
 spec:
 chart: podinfo
 version: 6.5.0
 sourceRef:
 kind: HelmRepository
 name: podinfo
 interval: 50m
 install:
 remediation:
 retries: 3
 values:
 ingress:
 enabled: true
 className: nginx
 hpa:
 enabled: true
---
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: podinfo
resources:
- namespace.yaml
- helmrepository.yaml
- helmrelease.yaml You can use the update hosts Python script to update your local environment hosts, as explained in . the first part of the article Then, create the Kustomization file into the folder to synchronize your apps: cluster/cluster_name ---
apiVersion: kustomize.toolkit.fluxcd.io/v1
kind: Kustomization
metadata:
 name: apps
 namespace: flux-system
spec:
 interval: 10m0s
 dependsOn:
 - name: infra-cluster-name
 sourceRef:
 kind: GitRepository
 name: flux-system
 path: ./apps/cluster_name
 prune: true
 wait: true
 timeout: 5m0s Next, we can configure FluxCD to auto-update the Podinfo image Helm Chart version. To configure the image auto-update, we need to create an image repository to scan for new image tags, an image update policy to define the version pattern to update, and an image auto-update to configure the repository to push the change. ---
apiVersion: image.toolkit.fluxcd.io/v1beta2
kind: ImageRepository
metadata:
 name: podinfo-chart
spec:
 image: ghcr.io/stefanprodan/charts/podinfo
 interval: 5m
---
apiVersion: image.toolkit.fluxcd.io/v1beta2
kind: ImagePolicy
metadata:
 name: podinfo-chart
spec:
 imageRepositoryRef:
 name: podinfo-chart
 policy:
 semver:
 range: 6.x.x
---
apiVersion: image.toolkit.fluxcd.io/v1beta1
kind: ImageUpdateAutomation
metadata:
 name: podinfo-chart
spec:
 interval: 30m
 sourceRef:
 kind: GitRepository
 name: flux-system
 namespace: flux-system
 git:
 checkout:
 ref:
 branch: main
 commit:
 author:
 email: fluxcdbot@users.noreply.github.com
 name: fluxcdbot
 messageTemplate: 'chore(develop): update podinfo chart to {{range .Updated.Images}}{{println .}}{{end}}'
 push:
 branch: main
 update:
 path: ./apps/cluster_name/podinfo
 strategy: Setters
---
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: podinfo
resources:
[...]
- imagepolicy.yaml
- imagerepository.yaml
- imageautoupdate.yaml Finally, apply the image update policy to the image or tag you want to update: apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
 name: podinfo
spec:
 releaseName: podinfo
 chart:
 spec:
 chart: podinfo
 version: 6.5.0 # {"$imagepolicy": "podinfo:podinfo-chart:tag"}
 sourceRef:
 kind: HelmRepository
 name: podinfo
 interval: 50m
 install:
 remediation:
 retries: 3 Whenever Podinfo Chart has a new version in the range of 6.x.x, FluxCD will push a commit to the repository, updating the current version with the newer one. Canary Release Release a new application version to a small subset of users to ensure that the application functionality, performance, and security are the expected ones. FluxCD automatically updates the Chart version and makes it available to the users. In case of a failure, FluxCD automatically rollbacks the image version to the previous one. Flagger progressively delivers the new application version to a subset of users and monitors the application status. It creates a new deployment for the new application versions and progressively redirects the incoming traffic to the new deployment. It will promote the canary deployment after a successful analysis of the metrics. In case of a failure, Flagger deletes the new deployment and reestablishs the traffic flow to the old deployment. This process pretends to detect defects, issues, and errors before delivering the application to all its users. First, create a metric template to let Flagger know which is the application status. We use Prometheus to measure the request success rate: ---
apiVersion: flagger.app/v1beta1
kind: MetricTemplate
metadata:
 name: podinfo-request-success-rate
spec:
 provider:
 type: prometheus
 address: http://loki-stack-prometheus-server.loki-stack:80
 query: |
 100 - sum(
 rate(
 http_requests_total{
 app_kubernetes_io_name="podinfo",
 namespace="{{ namespace }}",
 status!~"5.*"
 }[{{ interval }}]
 )
 )
 /
 sum(
 rate(
 http_requests_total{
 app_kubernetes_io_name="podinfo",
 namespace="{{ namespace }}",
 }[{{ interval }}]
 )
 ) * 100
---
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: podinfo
resources:
[...]
- metrictemplate.yaml Then, define the canary release process. We use nginx as the provider to shape the incoming traffic. Flagger offers multiple ways and tools to configure your releases. ---
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
 name: podinfo
spec:
 provider: nginx
 targetRef:
 apiVersion: apps/v1
 kind: Deployment
 name: podinfo
 ingressRef:
 apiVersion: networking.k8s.io/v1
 kind: Ingress
 name: podinfo
 autoscalerRef:
 apiVersion: autoscaling/v2beta2
 kind: HorizontalPodAutoscaler
 name: podinfo
 progressDeadlineSeconds: 60
 service:
 port: 9898
 targetPort: 9898
 analysis:
 interval: 10s
 threshold: 10
 maxWeight: 50
 stepWeight: 5
 metrics:
 - name: podinfo-request-success-rate
 thresholdRange:
 min: 99
 interval: 1m
 webhooks:
 - name: acceptance-test
 type: pre-rollout
 url: http://flagger-loadtester.flagger-system/
 timeout: 30s
 metadata:
 type: bash
 cmd: curl -sd 'test' http://podinfo-canary.podinfo:9898/token | grep token
 - name: load-test
 url: http://flagger-loadtester.flagger-system/
 timeout: 5s
 metadata:
 cmd: hey -z 1m -q 10 -c 2 http://podinfo-canary.podinfo:9898/healthz
---
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: podinfo
resources:
[...]
- canary.yaml Flagger requires the references to your deployment, ingress, and pod auto-escaler. Whenever Flagger detects a new version, it will scale up the deployment, create a primary and secondary service, and configure nginx to send the incoming traffic to the corresponding service. Use the maxWeight and stepWeight properties to configure the maximum traffic redirection percentage and incremental step percentage. Load-test your application using Flagger hooks. It has multiple hooks. The acceptance hooks check the canary deployment readiness, and the load test hook generates constant incoming traffic. Flagger will monitor the canary release status using the pre-defined success rate metric to decide the canary deployment promotion case. Flagger expects a 99% request success rate to promote the canary deployment. Use the threshold property to configure a maximum number of failed metric checks before rollback. Monitoring Use Loki stack to monitor the status of the cluster resources using Grafana + Loki + Prometheus. Install the Loki stack by creating the following YAML file inside the folder: infrastructure/cluster_name/loki-stack ---
apiVersion: v1
kind: Namespace
metadata:
 name: loki-stack
---
apiVersion: source.toolkit.fluxcd.io/v1beta1
kind: HelmRepository
metadata:
 name: grafana
spec:
 interval: 2h
 url: https://grafana.github.io/helm-charts
---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
 name: loki-stack
spec:
 interval: 1h
 chart:
 spec:
 chart: loki-stack
 version: v2.9.11
 sourceRef:
 kind: HelmRepository
 name: grafana
 interval: 1h
 values:
 grafana:
 enabled: true
 ingress:
 enabled: true
 annotations:
 kubernetes.io/ingress.class: nginx
 hosts:
 - grafana.local
 prometheus:
 enabled: true
 nodeExporter:
 enabled: true
---
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: loki-stack
resources:
- namespace.yaml
- helmrepository.yaml
- helmrelease.yaml The Loki stack installation enables the Grafana Ingress to access Grafana, Prometheus to gather your environment metrics, and Node Exporter to export your node metrics. You can use the update hosts Python script to update your local environment hosts, as explained in the first part of the article Log into Grafana using the admin username and password. You can define the admin password using the installation values. By default, the Chart creates a Kubernetes secret to store a random password. Describe the secret to get the base64 password value and decode it. You can import your favorite dashboard using their ID or copying the raw JSON: : K8s https://github.com/dotdc/grafana-dashboards-kubernetes : FluxCD https://grafana.com/grafana/dashboards/16714-flux2/ Conclusion The first article explored how to deliver a well-tested application. This article explores how to continuously deploy a deliverable and monitor the status of the deployment. FluxCD and Flagger offer multiple features to continuously test, deploy, and monitor your application status. This article uses some of them, but we did not see the webhooks and notifications features. Use the notifications features to know when a deployment failed or the web-hooks feature to promote the deployment to a new environment or launch your tests against new versions. Integrate FluxCD alongside other tools to enrich your deployment pipeline. Avoid manual deployments. They are complex and not error-prone. Encourage the development teams to maintain their applications, making easier the deployment process. Automatic deployments reduce the lead time, feedback loop, and overall costs. Developers can focus on what really matters.

CI/CD Hands-On: A Simple But Functional Continuous Deployment Workflow [Part 2]

CI/CD হ্যান্ডস-অন: একটি সহজ কিন্তু কার্যকরী ক্রমাগত স্থাপনার কর্মপ্রবাহ [পর্ব 2]

CI/CD-Hands-On: Ein einfacher, aber funktionaler Continuous-Deployment-Workflow [Teil 2]

Práctica de CI/CD: un flujo de trabajo de implementación continua simple pero funcional [Parte 2]

Pratique CI/CD : un flux de travail de déploiement continu simple mais fonctionnel [Partie 2]

सीआई/सीडी व्यावहारिक: एक सरल लेकिन कार्यात्मक सतत परिनियोजन वर्कफ़्लो [भाग 2]

CI/CD ハンズオン: シンプルだが機能的な継続的導入ワークフロー [パート 2]

CI/CD 실습: 간단하지만 기능적인 지속적 배포 워크플로 [2부]

Prática de CI/CD: um fluxo de trabalho de implantação contínua simples, mas funcional [Parte 2]

Практический курс CI/CD: простой, но функциональный рабочий процесс непрерывного развертывания [Часть 2]

CI/CD Uygulamalı: Basit Ama İşlevsel Sürekli Dağıtım İş Akışı [Bölüm 2]

Thực hành CI/CD: Quy trình triển khai liên tục đơn giản nhưng đầy đủ chức năng [Phần 2]

CI/CD 实践：简单但实用的持续部署工作流程 [第 2 部分]

(Image by from ) StartupStockPhotos Pixabay Most of us gamers have a wish list for what the future of gaming should look like for us, the kind of tools we dream to use in our desired game world. Gamers are craving to interact more with other players. Above all, no one wants to play a boring, slow game. According to the , there are more than 2.5 billion video gamers from all over the world. That’s about a third of the world’s population. European Mobile Game Market in 2016 The number keeps increasing. Imagine how much more increase the gaming community has seen until this year. Several tools are also constantly been created to help the gaming community be more lifely and making games more fun. Here is a list of the top 4 tools that could provide you the smoothest gaming experience. 1). Discord Gaming interactions have evolved. Gaming has become more competitive and real-world relationships are being formed from online games. Often, when playing video games most players want to communicate with each other to get organized and share their experiences, wins, challenges and failures with each other. That's where Discord comes in. Discord is a chat app for game lovers, it combines the text chat services of Skype with the voice chat services of Teamspeak and other instant messaging services to allow you to share your gaming experiences with others. Imagine having your on you and your friends on the other side of the world also have theirs, you communicate smoothly and have the fun of your life. Nothing is better than that. gaming headset You can easily access Discord with your desktop, mobile devices or web browser. unlike other similar services that cost money to set up servers or buy VoIP call credits, Discord gives you all of that functionality at no cost. 2). Razer Cortex: Game Booster Do you want to get the best performance and the best user experience for your games with the maximum frames per second? Razer Cortex allows you to optimize your game and PC for optimum performance for free. One way it does this is that it releases a lot of RAM that's been used unnecessarily by your computer. More so, it deletes temporary files, empties the Recycle Bin, checks for broken registry entries and so on. You can also use Razer Cortex to catch discounts or deals on games that you have added to your wish list on the platform. The best part is that it also gives you FPS statistics and an automatic PC boost the moment you launch your game. If you don’t want to always optimize, no problem, you can schedule Razer Cortex to automatically scan and optimize your game. 3). F.lux You know how you always want to reduce your screen brightness at night or at different temperatures so it works well for your eyesight and also allows you to sleep well. Blue light emission from your computer impacts your sleep a great deal. That’s because today, digital devices expose you to more artificial sources of blue light than ever. You definitely want to reduce the amount of blue light getting into your body especially if you are a heavy gamer who spends a lot of time with your PC. F.lux automatically regulate your computer’s screen light and brightness to match the color of the room you’re in. It’s been recommended by doctors because it promotes healthy eyesight. The interesting thing about it is that it is completely free to use and has a clean and intuitive UI and a great user experience. If your computer monitor adjusts really well for you that’ll also work. If you are looking to get a new gaming monitor a few here and there might help you make a more informed decision. gaming monitor comparisons 4). Steam As of when I wrote this article about thirteen million people were online and about three million people were playing games on Steam. Truth is, Steam is the go-to platform for game lovers whether you are creating or you are playing games -- whether free or paid. In a nutshell, Steam makes your favorite game accessible and sharable to you and your friends. Conclusion Gaming is exciting, challenging and the right game can improve your mental health. However, using the right tools can make it a lot more enjoyable. It’s 2019, the new era of gaming is upon us with a lot of new game titles focused on creating a stress-free environment.

Frequently Used Tools By Gamers

GitOps as a practice has been in use since 2017 when Alexis Richardson coined the term. It transformed DevOps and automation. If you look at its core principles, it extends DevOps by treating Infrastructure as Code (IaC). Your deployment configuration is stored in a version control system (a.ka. Git), providing a single source of truth for both dev and ops. As the framework’s adoption increased, in the cloud native space. Many agile teams because of familiarity with git-based workflow for release management of cloud native workloads. GitOps became the standard for continuous deployment adopt GitOps GitOps principles differ from the traditional CI & CD pipeline approach. In the last few years, the GitOps working group under CNCF formalized all the ideas developed around GitOps into a cohesive set of principles that have become . the GitOps Principles Declarative Versioned and Immutable Pulled automatically Continuously Reconciled The uses of GitOps helped organizations in the following aspects: Swifter deployment and more often Fast and easy disaster recovery Effortless credential management Improved developer experience GitOps created a standard practice that allowed engineers to focus on developing solutions rather than figuring out how to deploy them. However, as companies grow, they increase the rate of new features, and the risk of downtime/failures in production also increases. They face problems like the control of blast radius and minimal risk from recent releases. So, is there any way blast radius can be minimized while testing out releases to a subset of users? – Yes, there is a way through Progressive Delivery. Progressive Delivery The term was coined by , who talked about new software development practices beyond continuous delivery. Based on James Governor’s transcript on Progressive Delivery, we want to minimize the blast radius and control the delivery. This could be done by diverting some traffic to new deployment, measuring the success metrics, and then promoting the release to all users. Some of deployment strategies are , , and . What is Progressive Delivery and who coined the term? Progressive Delivery James Governor at RedMonk Canary Blue-Green A&B testing There are a lot of tools that allow us to implement Progressive Delivery. Azure DevOps, AWS App Mesh are the widely used proprietary tools, while ArgoCD and Flux are widely used open source tools. In this blog post, we shall focus on Flux & Flagger, which is an open source tool that is quite popular. Flux is a tool for keeping Kubernetes clusters in sync with sources of configuration (like Git repositories) and automating updates to the configuration when there is new code to deploy. What is Flux? is a Progressive Delivery tool that automates the release process for applications running on Kubernetes. Under the hood, both tools are built on top of a modular GitOps toolkit. It is the main reason why Flagger compliments Flux. What is Flagger? Flagger Typical Pipeline Let’s level-set how a CI/CD pipeline works, then we can talk about how Flux and Flagger fit in the picture. In a typical CI/CD pipeline, we push the latest images to the registry and config changes to a repository. Hereon the Ops person will correct the cluster state with the new config changes by applying a new config or upgrading the existing resources in the Kubernetes cluster. This also typically means that the ops person should know the changes that need to be made along with the context of those changes, and hence this manual process quickly becomes error-prone. This whole process also becomes time-consuming and hard to manage. There can be issues that might occur after applying the latest changes. We need to have a solid & spontaneous feedback loop on new releases. What if we could automate the whole process from deployment to production and have proper change management in place for application & infra configuration? Here comes Flux, which helps us automate image tag updates to git and reconciliation of clusters to the desired state as soon as new changes are pushed to the git repository. Flux Let’s put Flux in place to see how we resolve all those issues from a typical pipeline. Source: GitOps Flux is based on the Operator pattern. Operator pattern is software extension to Kubernetes that uses custom resources to manage application components is built on top of Kubernetes API. Installation of Flux Installation of Flux is straightforward. You need to install Flux CLI to run the bootstrapping process. The bootstrapping process will create a repository on GitHub (or any other git hosting service) and all required manifests for installation and connection to the git repository. Follow this doc to . Get started with Flux Reconciliation Flux keeps a constant watch on the changes in your repository. It doesn’t require any event to start the reconciliation loop. It allows you to configure the reconciliation loop at each component. You can have your git checked every 3 minutes and sync in 10 min, allowing you to stagger how reconciliation happens. Automate image updates to Git When Flux comes into the picture, it will start watching your image registry for new updates and push back to git for you. We don’t have to take care of updating the image IDs this time. This feature is not enabled while setting up the Flux. You can follow this . Automate image update to Git - Flux Source: Image reflector and automation controllers | Flux Secret Management Once you adopt GitOps, you need to find a way to manage the secrets that your application might require to communicate to other services within the Kubernetes cluster. You can’t simply store your application secrets inside the git repository, right? You might be thinking of different ways of encryption. For example, you can commit secrets to version control and enable Flux to decrypt them. Flux provides two guides to store secrets through and . Sealed Secrets Mozilla SOPS Application Delivery Unlike other options Flux natively supports Helm and uses native Helm library to deploy helm release onto the cluster. This means you can run on a cluster and it will show exactly how works. helm ls helm install Another important thing is Flux allows you to manage dependencies between HelmRelease CRDs or Kustomization CRDs. It enables you to control the load order of collections/groups of YAML files. It does not maintain the order in which individual YAML files are applied. Promote Release Flux can help you automate the process of promoting the release with GitHub Actions. Source: Promote Flux Helm Releases with GitHub Actions Webhooks Flux is, by design pull-based (i.e, identifies the changes directly from the source ) and good at managing drift in clusters because it is easier to correct the state of a cluster from inside rather than from outside where your tool doesn’t have a correct understanding of the current state of clusters. Suppose you want to make your pipeline responsive as a push-based or faster process. In that case, you can set up webhook receivers on git push that will trigger reconciliation. Alerting and notifications Flux can notify you about the resource statuses change as the health of the new app’s version. You can receive alerts about reconciliation failure in clusters and configure different mediums for reporting - channels such as Slack or embedded in git commit status. This helps inform the developer team whether the new version of the app was deployed and whether it is healthy or not. Source: Flux Slack Error Alerts Source: Setup Notifications | Flux Source: Notification Controller | Flux Authorization Methods Flux relies on the RBAC capabilities of Kubernetes and does not have its own authorization management. It uses Kubernetes RBAC for authentication or authorization. It can be a downside if you want to provide authorization using SSO. User Interface There is no UI for Flux. It does have an that is not in an active development state at the time of writing this. experimental UI Flagger So far, we have automated our delivery process to the cluster with alerts and notifications in case of any failure and unhealthy state of the cluster. Now, we will look at how Flagger integrates into this process and allows different deployment strategies. How Flagger helps in Progressive Delivery. With the best alerting and notification in place, we are not resilient to downtime due to new releases. How can we be sure of our mission-critical services work as expected? A bad release can cause colossal business value loss. For example, your team might want to test a new feature on a small sample of users, and if that feature performs well, it will be rolled out to all users. In order to do that without causing any hindrance to day-to-day activities. Flagger lets us automate the release process and reduces the risk of introducing a new release in production by gradually shifting traffic to the new release while measuring metrics and running conformance tests. Example of Progressive Delivery with Flagger. Source: stefanprodan/gitops-progressive-delivery Configuration Flagger is compatible with any CI/CD solutions, so it can be used with Flux, Jenkins, Carvel, Argo, etc. It supports various service mesh like App Mesh, Istio, Linkerd, Kuma, Open Service Mesh, or an ingress controller like Contour, Gloo, NGINX, Skipper, and Traefik. It has excellent compatibility with Linkerd and it’s reasonably easy to get started with canary release and metrics analysis. One of the important factors that come into the picture sometime i.e., Flagger doesn’t require replacing Deployment objects with any custom type. Deployment Strategies Flagger implements several deployment strategies which help you achieve the same objective which is shifting traffic gradually to a new version of the release. Some of the strategies are: Canary Releases A/B Testing Blue/Green Mirroring Blue/Green Check out . Flux CD official docs to know more about deployment strategies Metrics Flagger comes with built-in metrics and a Grafana dashboard for canary analysis. It exposes Prometheus metrics to dig more into the canary analysis. You can create custom metrics that can be used to do metrics analysis for release. That’s the beauty of it once Flagger validates service level objects like response time and any other metrics specific to the app, it promotes the release otherwise, it will be automatically rolled back with minimum impact to end-users. We’re not diving into the details of how the metrics template can be used in the analysis step. Manual Gating Not just metrics-based approval, you can perform manual gating to have more control over your canary analysis. There are different kinds of webhooks that you can leverage at each step of canary analysis, for example, confirm-rollout and conform-promotion. A flagger will halt the canary traffic shifting and analysis until the confirm webhook returns HTTP status 200. Flagger also comes with load testing that can generate traffic during analysis. You can read more about . Webhooks - Flagger Let’s look at developer experience for both tools. Developer Experience Flux and Flagger both have a high learning curve and a lot of functionality, which means more power and can sometimes overwhelm the developer. Both don’t have any UI. The setup experience is pretty straightforward. If you talk about logging experience, you might need to get your hands dirty in CLI; otherwise, in other tools, you might have a UI that will show you the current progress of deployment. This makes life easier for a lot of developers. Conclusion We look at both tools and how they fit in our CI/CD pipeline and help us deliver progressively. With the use of Flagger, we can split traffic into proportions, which helps in testing out new releases to a subset of users or even getting feedback. Whether a new release should be released or not to all users. I hope you learned how these tools fit into GitOps with Progressive Delivery practice. References and further reading: Stefan Prodan on Flux, Flagger and the Operator pattern GlooOps: Progressive delivery, the GitOps way GitOps Guide to GitOps

GitOps using Flux and Flagger 

Recently, we discussed how developers could make use of the . In that post, we shared a simple Node.js script that would call one of the APIs using a file stored in Azure. Photoshop API It was simple — get an access token, start an API job with an input document, poll for completion, and then check the result. Simple is good though as it means it’s easy to integrate into custom workflows. In today’s post, we’ll do just that. Our Workflow Let’s begin by defining what our workflow is going to do. Imagine we are using a cloud storage provider, in this case, Dropbox, to store photos. Our workflow is going to monitor this folder and trigger when new pictures are added. When the picture is added, we will grab the file and send it to an endpoint that runs the Lightroom feature. This feature attempts to correct exposure, contrast, and other issues in photos. “AutoTone” As an example (that we stole right from the ), here’s a before and after showing the impact of the corrections: docs Once the API is done, we will then store the result in folder in Dropbox. We could overwrite the original, but most likely, people will want both copies so they can make a final check to see which they prefer. another The Worfklow Platform For our workflow, we will use . Pipedream is a low-code/no-code solution for building generic workflows. It’s in the same space as Microsoft PowerAutomate and Workfront Fusion. (And you can expect to see examples from us soon on those platforms as well.) Pipedream Pipedream really helps developers by making the more difficult, or even boring, aspects of a project “plug and play.” As an example, when defining what starts a workflow, a trigger, Pipedream comes with a huge set of pre-built code blocks. When we start demonstrating how we built our workflow, you’ll see that in action. Pipedream has a free tier, so those of you reading who want to follow along can do so after signing up. If you’ve never used Pipedream before, check out their excellent for an introduction to how it works. They also have excellent . Finally, your friendly author here has about Pipedream as well. Pipedream University docs written With that out of the way, let’s get building! Step One — The Trigger Pipedream workflows start with a trigger that represents the “event” that should kick off our process. Pipedream has many, of these triggers built-in (and you can write your own), and luckily, a “New File” in Dropbox trigger is supported: many After selecting the “New File” trigger, you are given a simple interface to configure it: On top is the authentication aspect. Here, you can connect Pipedream to your Dropbox account. Best of all, once you’ve done that one time, you can reuse that connection in multiple workflows. Next, make a note of the Path value. You can type in here, and Pipedream will attempt to autocomplete based on your account, or simply enter a path. We’re going to use as the folder that expects to get images. /PSAPI_Input The next two settings can be ignored, but the final setting, , should be toggled to true. We're going to need that link so we can tell the API how to fetch the data. Include Link Here’s the final configured trigger for our workflow: To recap, at this point, we’ve configured the workflow to fire automatically as soon as a file is added to a particular folder in our Dropbox account. Step Two — Get an Upload URL When the Photoshop API runs, it expects at minimum two things — an input URL and an output URL. Basically, where to read its input and where to store the result. Our input is going to be the new file added to Dropbox. Our output is going to be the location of the image corrected by Lightroom. To support this, we need to ask Dropbox to generate a special URL that can be used to store data. Pipedream has a lot of Dropbox actions built-in but unfortunately, does not have this particular one. Fortunately, Pipedream does let you create a step with custom code where it’s already handled the authentication for you. Remember that in the trigger, we specified an existing Dropbox account. Because we did this, we can then write code and let Pipedream handle authentication. By consulting the , we find the endpoint which is exactly what we need. Dropbox API get_temporary_upload_link In Pipedream, we add a new step and select “Use any Dropbox API in Node.js.” This gives us boilerplate code hitting one sample endpoint: https://gist.github.com/cfjedimaster/867a554f8f046ac357c89ae156441aae?embedable=true#file-script-js Most importantly — note the authentication information is automatically provided. All we need to do then is to edit the endpoint and set our input: https://gist.github.com/cfjedimaster/cf4932ba1d66887a7a75e3a5e9169d0e?embedable=true#file-script-js Note we are using a path that’s a different folder, . The value after that, , demonstrates using information from earlier in our workflow, specifically the filename of the event that triggered our workflow, and the name of the file itself. /PSAPI_Output ${steps.trigger.event.name} The code ends by returning the result of the API call, which in our case, is going to be a special URL we can use with our API call. Step Three — Getting an Access Token In our , we discussed how to get credentials for the Photoshop API and how then to use them in code to get an access token. In Pipedream, we can add a step to our workflow to run custom code. previous blog post You saw that in the previous step when we hit the Dropbox API. Pipedream supports both Node.js and Python so you’ve got a few options there. We will add a new code step and name it . getAccessToken This step will be responsible for using our credentials to generate a JWT and exchange that for an access token. In that previous blog post, the code made use of the package to simplify the process. However, this package does not work as an ES6 import which Pipedream supports. Luckily, we can swap out to another NPM package, . Let’s take a look at the code: @adobe/jwt-auth jsonwebtoken https://gist.github.com/cfjedimaster/54fbfc2c4a8e90ffda5f6007ddb13714?embedable=true#file-script-js So a couple of things here. First, every Node Pipedream code step uses a form like so: https://gist.github.com/cfjedimaster/a12dd06e92a801ae7572616709f04303?embedable=true#file-script-js The function is called automatically, and passed data from any previous steps as well as a handler ( ) for other operations we won't need. Basically, we will put our imports on top and our logic inside. run $ You can see a set of variables being copied from the environment as — not surprisingly — Pipedream lets us define custom environment variables as well. The next block of code generates our JWT. This is mostly boilerplate, but pay special attention to the part. This variable, , is what sets the scope for our token and is required to work with the APIs. jwtOptions "https://ims-na1.adobelogin.com/s/ent_ccas_sdk": true, After the JWT is created, it can be sent to a generic Adobe endpoint to generate an access token. The final bit of logic is to return that token. If you remember in the previous step, anything we return can be used later. You’ll see this in action soon. Step Four — Calling the Lightroom Autotone API Now, it’s time to get down to business. Our trigger gave us a link to the new image. The next code step generated a link for us to upload the final result. We then got an access token. We’ve got everything needed to start the process. Once again, we’ll add a Node.js step to our workflow. Here it is in action. https://gist.github.com/cfjedimaster/605a25a51602bd91eaeafb0bbd68d02a?embedable=true#file-script-js The requires a few parameters, in this case, an input and output value. In our case, we pass in the link from the trigger and the special upload URL generated earlier. Autotone API And that’s it! The result of this call is a link to the job which we return at the end. Step Five — Do Nothing Ok, not exactly , but here’s an interesting question. This entire workflow runs automatically with no human interaction. The previous step kicks off a process that, when done, will save the result to Dropbox. Do we need to do anything else? Not really. nothing It’s absolutely possible the API may fail for some reason. It’s possible something else could go wrong as well. It’s also possible we may want to alert someone about the change, perhaps via email. Honestly, it’s up to you. In our sample workflow, we decided to simply check the job and wait for it to finish. Here’s that code step, and it’s pretty similar to our previous blog post. We check the job, wait, and check it again. https://gist.github.com/cfjedimaster/207617f3a824c5cb6ef22f43983a9a1b?embedable=true#file-script-js This is where we end the workflow, but we could, and probably should, add logic to check the result of the job and do something. Perhaps on a good result, we do nothing, but on error, we then send an email. (And by the way, Pipedream makes sending emails ludicrously easy.) What’s nice though is that we can decide that later. One really nice feature of Pipedream is that it makes it easy to check when workflows have run. For example, here’s a list of past executions: The errors you see there came from me playing with the API and figuring out how it worked. You can also click on any of them and see the data that flowed throughout it. The Result With the workflow in place, we uploaded a new image to a Dropbox folder, watched the workflow firing in an open tab, and when done, checked the result. Here’s before: And here’s after: The result is crisper, and absolutely an improvement! You can create your own copy of my workflow here: . If you want to learn more, visit our , and share what you’ve built! https://pipedream.com/new?h=tch_3xxfJA docs Also published here

How to Automate Image Workflows With the Photoshop API

ফটোশপ এপিআই দিয়ে কীভাবে ইমেজ ওয়ার্কফ্লো স্বয়ংক্রিয় করবেন

So automatisieren Sie Bild-Workflows mit der Photoshop-API

Cómo automatizar flujos de trabajo de imágenes con la API de Photoshop

Comment automatiser les flux de production d'images avec l'API Photoshop

फ़ोटोशॉप एपीआई के साथ छवि वर्कफ़्लो को स्वचालित कैसे करें

Photoshop API を使用して画像ワークフローを自動化する方法

Photoshop API를 사용하여 이미지 작업 흐름을 자동화하는 방법

Como automatizar fluxos de trabalho de imagem com a API do Photoshop

Photoshop API ile Görüntü İş Akışlarını Otomatikleştirme

Cách tự động hóa quy trình làm việc với hình ảnh bằng API Photoshop

如何使用 Photoshop API 自动化图像工作流程

We've looked at many different ways to create live streaming experiences with Amazon Interactive Video Service (Amazon IVS) here on this blog, but all of those examples so far have focused on public channels that can be viewed by anyone without any playback authorization to verify that the viewer is allowed to view the live stream. Many live streaming user generated content (UGC) platforms do not require any authorization to view a stream, but there are use cases where we want to limit our stream viewers to a subset of our audience. For example, maybe our content creators want to charge a subscription for the content that they produce. Or, perhaps there are licensing limitations to the stream content that prohibit distribution to certain demographics or geographic locations. To address use cases where the platform must enforce playback authorization, Amazon IVS supports private channels. These channels will restrict playback to viewers who have obtained a token that is signed with a private key and verified with a public key. In this post, we'll look at how to enable playback authorization, create a playback key pair, generate a token, and play a private stream. Creating a Private Channel The first step in this workflow is to create a private channel. Creating a private channel is very similar to creating a public channel - the only difference is a single setting when creating the channel to enable playback authorization. We can enable playback authorization via the , via the AWS CLI, or via any of the AWS SDKs. Amazon IVS Console Let's start by looking at how to create a private channel via the Amazon IVS Console. At the list of channels in the console, click . Create channel On the page, enter a channel name (#1), select (#2), and turn on (#3). Create channel Custom configuration Enable token-authorization requirement for video playback If you prefer to use the CLI to create a channel, you can add the option. --authorized $ aws ivs create-channel --name demo-private-channel --latency-mode LOW --type STANDARD --authorized In reality, you're probably going to use the AWS SDK to create your user's channels. In this case, you can refer to the AWS SDK documentation for your favorite language to determine the appropriate argument to set for playback. For example, here are the for for the AWS SDK for JavaScript v3 which state that we can set a boolean property when creating a channel. authorized docs CreateChannelCommandInput authorized Now that we have enabled playback authorization, if we were to broadcast to the channel and try to view the live playback without generating and passing a playback token, we will receive a playback error. Here's an example of playback using the Amazon IVS player SDK where we have added an listener to capture playback errors. ERROR const streamUrl = '[private channel Playback Url]';
const ivsPlayer = IVSPlayer.create();
ivsPlayer.attachHTMLVideoElement(document.getElementById('video-player'));
ivsPlayer.load(streamUrl);
ivsPlayer.play();

ivsPlayer.addEventListener(IVSPlayer.PlayerEventType.ERROR, (err) => {
  console.log(err);
}); Running this in a browser when pointed at a private channel will result in the following error logged to the console. {
  "type": "ErrorAuthorization",
  "code": 7,
  "source": "MasterPlaylist",
  "message": "invalid_playback_auth_token"
} This confirms that our channel is indeed private, and requires a to be generated and passed along with the to authorize playback. token Playback Url Creating a Playback Key Before we can generate a playback token, we will need to create a private/public key pair and upload it to the Amazon IVS console. This key pair will be used to generate a token that is signed with the private key, and verified with the public key, to authorize playback. JWT To manage Playback keys with the Amazon IVS Console, select in the left navigation sidebar. Playback keys We can either create a key with the console, or manually with OpenSSL offline on our local machine. To create a key pair directly in the console. For this, click on . Create playback key Give the key a name, and click . Create The public key of this pair is saved to your AWS account and will be used to verify any playback requests that contain a token signed with the private key. When using the Amazon IVS Console to generate a key pair, the private key is immediately downloaded to your machine and is . not saved in the console or available for further download Creating a Key Pair Offline If you'd prefer to create your own key pair, you can do that offline and then import the public key into the Amazon IVS console. Here's an example of creating a key pair with OpenSSL. $ openssl ecparam -name secp384r1 -genkey -noout -out demo-private-channel-private-key-1.pem
$ openssl ec -in demo-private-channel-private-key-1.pem  -pubout -out demo-private-channel-public-key-1.pem To import the public key, click on on the list page. Import Playback key Give the imported key a name, and browse for the key file (or paste the key file contents). public public This can also be done via the AWS CLI: $ aws ivs \
    import-playback-key-pair \
    --name demo-private-channel-playback-key-1 \
    --public-key-material "`cat /path/to/your/public-key.pem`" Or via the AWS SDK ( for JavaScript example). docs Private Channel Playback Now that we have created a private channel and a playback key pair, we are ready to generate signed playback authorization tokens. In your application, your business logic will dictate which users should be granted an authorization token. As mentioned above, maybe your application logic will first check that the user is a valid subscriber to the channel by querying a database. Or, in the case of geo restrictions, maybe you'll have to look up the IP address of the viewer to make sure it is associate with a region that is able to view the stream. The exact implementation will vary, but once you determine whether the viewer is indeed authorized to view the stream, you can proceed with the token generation method described below. Token generation will differ based on the language you're using on the backend. Refer to the for details on the requirements. Heads Up: user guide To generate a token with Node.js, we can use the library ( ). This library will simplify the process and allow us to sign our payload with the signing algorithm as required by Amazon IVS. Our payload must contain the , an to limit playback to a specific domain (or for all domains), and a token expiration timestamp. To sign the payload, we'll format it as below and use the private key that we created and imported above. jsonwebtoken docs SHA-384 Channel ARN access-control-origin * The token is used to start playback on a private channel, so the expiration period only applies to the time between token generation and playback start. It does not relate to the amount of time the user can play a given stream. Once playback is authorized, it can continue until the user disconnects or reloads the web page. Note: Here's one way to generate a token on the backend (via a microservice or serverless application) using Node.js. import jwt from "jsonwebtoken";

const getToken = () => {
  const privateChannelArn = process.env.DEMO_PRIVATE_CHANNEL_ARN; // private channel ARN
  const privateChannelPrivateKey = process.env.DEMO_PRIVATE_CHANNEL_PRIVATE_KEY; // playback private key

  const payload = {
    "aws:channel-arn": privateChannelArn,
    "aws:access-control-allow-origin": "*",
    "exp": Date.now() + (60 * 1000), // expires in 1 minute
  };

  const token = jwt.sign(payload, privateChannelPrivateKey, { algorithm: 'ES384' });
  return token;
} In our frontend, we can retrieve the token from the backend and append it to the private channel's . In the example below, I've trunacted the Playback Url ( ) for brevity. Playback Url streamUrl const streamUrl = `https://[truncated].m3u8?token=${token}`
const ivsPlayer = IVSPlayer.create();
ivsPlayer.attachHTMLVideoElement(document.getElementById('video-player'));
ivsPlayer.load(streamUrl);
ivsPlayer.play(); Summary In this post we learned how to create private channels, create and import playback keypairs, and generate a JWT token for playback authorization. To learn more about private channels, refer to the . user guide

Using Amazon IVS to Create a Private Channel for Authorized Live Stream Playback

Uso de Amazon IVS para crear un canal privado para la reproducción de transmisión en vivo autorizada

Utilisation d'Amazon IVS pour créer un canal privé pour la lecture de flux en direct autorisée

अधिकृत लाइव स्ट्रीम प्लेबैक के लिए एक निजी चैनल बनाने के लिए Amazon IVS का उपयोग करना

Amazon IVS を使用して、承認されたライブ ストリーム再生用のプライベート チャネルを作成する

Usando o Amazon IVS para criar um canal privado para reprodução de transmissão ao vivo autorizada

Sử dụng Amazon IVS để tạo kênh riêng để phát lại luồng trực tiếp được ủy quyền

使用 Amazon IVS 创建私有频道进行授权直播流媒体播放

In and maintaining consistency across Kubernetes environments are paramount. This is where GitOps shines. GitOps simplifies and automates deployment, leveraging the power of version control to ensure that your infrastructure and application states are always in sync. This brief guide explores how workflows harmonize seamlessly with Kubernetes, a leading container orchestration platform, enhancing collaboration, reliability, and efficiency in your development pipelines. DevOps, streamlining operations GitOps Setting Up Your GitOps Repository So, you've decided to embark on a GitOps journey, steering your ship towards smoother deployments and greater control. Welcome aboard! Before you cast off, let's talk about your first port of call: setting up your GitOps repository. Think of your GitOps repository as the treasure map. Start by creating a new Git repository or selecting an existing one. This repository will house the definitions of your desired application state - the gold you seek to keep safe. Chart Your Course: In GitOps, Kubernetes manifests are your guiding stars. Define your application's desired state within these YAML files. This is where you specify everything, from the number of pods to the services and configurations. Unveil Your Manifests: Keep your secrets hidden like buried treasure. Git repositories are public by default, so ensure sensitive data (like API keys and passwords) are stored securely. Use Kubernetes secrets or a secret management tool. Guard Your Secrets: Once you've defined your application's state, commit and push those changes to your Git repository. This becomes your single source of truth. Push to Protect: Automate your GitOps workflow with tools like Flux or ArgoCD. They'll keep a watchful eye on your repository and apply changes to your Kubernetes cluster as you push updates. Set Sail with Automation: And there you have it - you've set up your GitOps repository, your map to smoother deployments and greater control. Now, hoist the anchor, set your sails, and navigate the GitOps seas with confidence. Defining Your Kubernetes Resources At its core, is all about managing containers. The resources you define are like the ingredients of a recipe, each contributing to the flavor of your application. Here's a quick rundown of essential resources: Kubernetes The most minor deployable units in Kubernetes, containing one or more containers. Pods: Deployments: Controllers that manage replica sets and ensure a specified number of pod replicas are running. Networking abstraction to expose pods to the network, enabling communication. Services: ConfigMaps and Secrets: Store configuration and sensitive data separately from your application code. A Simple Example Crafting Your Resources: Let's imagine you're deploying a microservice called "AwesomeService." Here's how you'd craft the resources: Specify the container image and resource requirements. Pod Definition: Define how many replicas of the pod you want and handle rolling updates. Deployment: Service: Expose your pods internally or externally. Setting Up GitOps with Kubernetes Selecting the right GitOps tool is essential. Two popular options are Argo CD and Flux. provides a declarative, GitOps-driven approach to managing applications on Kubernetes. Flux, on the other hand, offers automated continuous delivery by synchronizing your Git repository with your cluster. Evaluate your requirements and preferences to choose the tool that best aligns with your needs. Argo CD Once you've chosen your GitOps tool, the next step is to integrate it with your Kubernetes clusters. This integration establishes the connection that enables the tool to manage deployments. Both Argo CD and on how to set up this integration. Follow the instructions provided by the chosen tool to connect it to your Kubernetes environment. Flux offer clear documentation With the GitOps tool and Kubernetes cluster integrated, it's time to establish the initial desired state of your applications in the Git repository. This means defining how your applications should be deployed, including configurations and resources. Commit these definitions to the Git repository, effectively making it the single source of truth for your cluster's configuration. GitOps Workflow Process Developers shape the Kubernetes environment by updating a Git repository with changes spanning configurations, code, and infrastructure definitions. A GitOps tool, like Argo CD or Flux, persistently tracks the Git repository for shifts. Upon detection, it triggers a seamless sync with the target Kubernetes cluster. After syncing, the GitOps tool enacts the updates in the Kubernetes cluster, aligning it with the Git repository's intention. Any mismatches are automatically resolved to restore the desired setup. GitOps includes fail-safes. If deployment strays from the goal, tools enable easy rollbacks. Moreover, GitOps perpetually checks for deviations and takes corrective measures. Benefits and Best Practices GitOps acts as the unifying language between development and operations teams. With the source of truth residing in , collaboration becomes effortless. Changes are tracked, discussed, and refined using Git's familiar version control tools. This collaborative harmony ensures that everyone is on the same page, reducing confusion and enhancing teamwork. a Git repository One of GitOps' standout advantages is its unwavering focus on declarative infrastructure. Every desired state of the Kubernetes cluster is codified in Git. This practice not only ensures consistency but also paves the way for seamless disaster recovery. In case of failures, recreating the exact environment becomes a breeze, restoring services with confidence. In the world of audits and compliance checks, GitOps shines as a guardian of transparency. Auditing becomes a breeze as all changes are meticulously logged within the Git repository. This not only aids in tracking modifications but also provides a robust trail for compliance audits, offering organizations the assurance they need. GitOps Best Practices for Managing Kubernetes Clusters To harness GitOps effectively, certain practices are key: Articulate your cluster's desired state through declarative code, ensuring reproducibility. Declarative Descriptions: Enforce code review processes to maintain quality and reduce errors before changes are applied. Code Reviews: Embrace the immutability of Git history to maintain a clear lineage of changes. Immutable History: Reconciliation Loops: Employ reconciliation loops to automatically align the actual state with the desired state. Challenges and Considerations Safeguarding sensitive data like passwords, API tokens, and encryption keys is crucial. Storing them in a Git repository can risk unauthorized access. To tackle this, we utilize specialized tools like Kubernetes Secrets, HashiCorp Vault, or external solutions. These tools ensure secure storage and dynamic injection of secrets, upholding security. As apps grow complex, managing configurations can get tricky. GitOps workflows streamline configuration versioning. Tools like Kubernetes ConfigMaps and Custom Resource Definitions maintain separation from code, easing control and updates. In vast setups, handling Kubernetes clusters and deployments gets tough. For consistent changes across clusters, efficient orchestration is key. Cluster federation or hierarchical GitOps models can help manage this complexity. Conclusion We've explored the fundamental principles of GitOps and its seamless integration with Kubernetes. By centralizing infrastructure management in version-controlled repositories, and enhances collaboration. I encourage you to embrace GitOps for your Kubernetes environments, as it fosters reliability, scalability, and rapid iteration. As technology continues to evolve, we can anticipate GitOps and Kubernetes workflows to remain at the forefront of modern DevOps practices, revolutionizing how we manage and deploy applications in the dynamic landscape of cloud-native solutions. GitOps streamlines deployment processes

Implementing GitOps Workflows with Kubernetes

Whenever it comes to work, whether you are casually working on your side project while sipping a cup of coffee or have intense working hours to deliver your task before the deadline, you want to know that you’re doing your best. That’s when productivity tools for developers come in handy. In this article, I'll not mention tools like Trello, Slack, Jira, etc which are pretty widely known instead I'm going to talk about tools that are underrated and not really that famous(but definitely should be adopted more). Keeping in mind how software developers work best, the following productivity tools for developers are recommended for individuals as well as teams. 1. Cold Turkey If you blame yourself for being unable to stay away from social media or get easily distracted by AI driven notification of your favourite streamer streaming right now! Then this is the perfect application for you. Using Cold Turkey, you can proactively restrict your access to specific desktop applications, websites, or even your access to the internet in general. You set your own schedule and daily limits for access, based on areas of weakness. RescueTime pairs nicely with this tool, in that it can help you audit major timesucks. Find it here: https://getcoldturkey.com 2. 7pace Do you follow a DevOps practice pretty often in your development cycle! I'm assuming you/your team is using Kanban or Scrum boards to manage your work already. Well, wouldn't it be convenient if you could fit a time tracker solution right into your development team's board and keep track of your activities, automate burndown reports and also manage resources and budget in large organizations! Well, then 7pace is the right solution for you. 7pace Timetracker was built specifically for software development teams that work in an Agile, Scrum, or Waterfall environment. Individuals using the tool experience an average of just three seconds to log each task. 7pace Timetracker integrates with Microsoft Team Foundation Server (TFS) and Visual Studio Team Services (VSTS) for even greater efficiency. Even if you use GitHub project boards to manage your projects 7pace has a beta offering to integrate their time tracking solution right in. Check it out here: https://www.7pace.com/ 3. Time Out Unfortunately only available for the macOS currently, Time Out definitely tops the list of the best productivity tools for programmers to organize breaks. We love it for the plain & clear interface as well as wide customization capabilities. You can take either a of 15 seconds every 15 minutes or for 10 minutes every hour. Thus whether you want to do a short 15 seconds eye exercise or brew your coffee in a 10 minutes break is all up to you. short break long ones Check it out here: https://apps.apple.com/us/app/time-out-break-reminders/ 4. F.lux During the day, computer screens look good—they're designed to look like the sun. But, at 9 PM, 10 PM, or 3 AM, you probably shouldn't be looking at the sun. If you have a bad sleeping habit like me and you're probably coding late nights! Trust me, you just might want to consider this tool. f.lux makes your computer screen look like the room you're in, all the time. When the sun sets, it makes your computer look like your indoor lights. In the morning, it makes things look like sunlight again. Tell f.lux what kind of lighting you have, and where you live. Then forget about it. f.lux will do the rest, automatically. Check it out here: https://justgetflux.com/ 5. WakaTime This tool follows your work when you’re programming. It shows you all the details of your work as a developer – what file you’ve been working on, operating system, and even language. You can also compete against your teammates on the Leaderboard and see who has the highest score in coding. It is very useful when it comes to managing your budget and productivity as a developer at once. Since this is also a time tracking solution, if you combine this with you basically become the time god. TimeCamp Check it out at: https://wakatime.com/ 6. Habitica One of my personal favourites, Habitica is a free habit-building and productivity app that treats your real life like a game. With in-game rewards and punishments to motivate you and a strong social network to inspire you, Habitica can help you achieve your goals to become healthy, hard-working, and happy. Habitica takes you to the old pixel-like platform world in which you’re a hero fighting through daily, weekly, and long-term goals, habits, and to-do lists. For every goal achieved you receive points, discover new animals, and can buy weapon to gain strength. The more you fail at your tasks, the more strength you lose. And the challenge is to keep your character alive. Check it out here: https://habitica.com/static/home That's all folks 👨‍🏫, Thank you for reading it till the end.  These are the resources I found to be really helpful for me. I'm sure there must be tons of other amazing tools out there too. These are the ones that I really found helpful and often get myself indulged into. 👨🏻‍💻 Also published here . Don't forget to share these resources with someone who you think might need some help being more productive at work too. Peace out. ✌🏼

6 Incredible Productivity Tools for Programmers

introduced the atomic model to the world. Its new powers came at the cost of a steep learning curve and sparse learning resources. Recoil React and later simplified various aspects of this new model, offering many new features and pushing the limits of this amazing new paradigm. Jotai Zedux Other articles will focus on the differences between these tools. This article will focus on one big feature that all 3 have in common: They fixed Flux. Table of Contents Flux Dependency Trees The Singleton Model Back to Basics The Law of Demeter The Heroes Zedux The Atomic Model Conclusion Flux If you don't know Flux, here's a quick gist: Besides , all Flux-based libraries basically followed this pattern: An app has multiple stores. There is only one Dispatcher whose job is to feed actions to all the stores in proper order. This "proper order" means dynamically sorting out dependencies between the stores. Redux For example, take an e-commerce app setup: When the user moves, say, a banana to their cart, the PromosStore needs to wait for CartStore's state to update before sending off a request to see if there's an available banana coupon. Or perhaps bananas can't ship to the user's area. The CartStore needs to check the UserStore before updating. Or perhaps coupons can only be used once a week. The PromosStore needs to check the UserStore before sending the coupon request. Flux doesn't like these dependencies. From the : legacy React docs The objects within a Flux application are highly decoupled, and adhere very strongly to the , the principle that each object within a system should know as little as possible about the other objects in the system. Law of Demeter The theory behind this is solid. 100%. Soo ... why did this multi-store flavor of Flux die? Dependency Trees Well turns out, dependencies between isolated state containers are inevitable. In fact, to keep code modular and DRY, you be using other stores frequently. should In Flux, these dependencies are created on-the-fly: // This example uses Facebook's own `flux` library
PromosStore.dispatchToken = dispatcher.register(payload => {
  if (payload.actionType === 'add-to-cart') {
    // wait for CartStore to update first:
    dispatcher.waitFor([CartStore.dispatchToken])

    // now send the request
    sendPromosRequest(UserStore.userId, CartStore.items).then(promos => {
      dispatcher.dispatch({ actionType: 'promos-fetched', promos })
    })
  }

  if (payload.actionType === 'promos-fetched') {
    PromosStore.setPromos(payload.promos)
  }
})

CartStore.dispatchToken = dispatcher.register(payload => {
  if (payload.actionType === 'add-to-cart') {
    // wait for UserStore to update first:
    dispatcher.waitFor([UserStore.dispatchToken])

    if (UserStore.canBuy(payload.item)) {
      CartStore.addItem(payload.item)
    }
  }
}) This example shows how dependencies aren't directly declared between stores - rather, they're pieced together on a per-action basis. These informal dependencies require digging through implementation code to find. This is a very simple example! But you can already see how helter-skelter Flux feels. Side effects, selection operations, and state updates are all cobbled together. This colocation can actually be kind of nice. But mix in some informal dependencies, triple the recipe, and serve it on some boilerplate and you'll see Flux break down quickly. Other Flux implementations like and improved the boilerplate and debugging experience. While very usable, dependency management was the one nagging problem that plagued all Flux implementations. Using another store felt ugly. Deeply-nested dependency trees were hard to follow. Flummox Reflux This e-commerce app could someday have stores for OrderHistory, ShippingCalculator, DeliveryEstimate, BananasHoarded, etc. A large app could easily have hundreds of stores. How do you keep dependencies up-to-date in every store? How do you track side effects? What about purity? What about debugging? Are bananas really a berry? As for the programming principles introduced by Flux, unidirectional data flow was a winner, but, for now, the Law of Demeter was not. The Singleton Model We all know how came roaring in to save the day. It ditched the concept of multiple stores in favor of a singleton model. Now everything can access everything else without any "dependencies" at all. Redux Reducers are pure, so all logic dealing with multiple state slices go outside the store. The community made standards for managing side effects and derived state. Redux stores are beautifully debuggable. The only major Flux Flaw that Redux originally failed to fix was its boilerplate. must later simplified Redux's infamous boilerplate. Then removed some fluff at the cost of some debugging power. All of these tools have become extremely popular in the React world. RTK Zustand With modular state, dependency trees grow so naturally complex that the best solution we could think of was, "Just don't do it I guess." And it worked! This new singleton approach still works well enough for most apps. The Flux principles were so solid that simply removing the dependency nightmare fixed it. Or did it? Back to Basics The success of the singleton approach begs the question, what was Flux getting at in the first place? Why did we ever want multiple stores? Allow me to shed some light on this. Reason #1: Autonomy With multiple stores, pieces of state are broken out into their own autonomous, modular containers. These stores can be tested in isolation. They can also be shared easily between apps and packages. Reason #2: Code Splitting These autonomous stores can be split into separate code chunks. In a browser, they can be lazy-loaded and plugged in on the fly. Redux's reducers are fairly easy to code-split too. Thanks to , the only extra step is to create the new combined reducer. However, more steps may be required when side effects and middleware are involved. replaceReducer Reason #3: Standardized Primitives With the singleton model, it's difficult to know how to integrate an external module's internal state with your own. The Redux community introduced the Ducks pattern as an attempt to solve this. And it works, at the cost of a little boilerplate. With multiple stores, an external module can simply expose a store. For example, a form library can export a FormStore. The advantage of this is that the standard is "official", meaning people are less likely to create their own methodologies. This leads to a more robust, unified community and package ecosystem. Reason #4: Scalability The singleton model is surprisingly performant. Redux has proven that. However, its selection model especially has a hard upper limit. I wrote some thoughts on this in . A big, expensive selector tree can really start to drag, even when taking maximum control over caching. this Reselect discussion On the other hand, with multiple stores, most state updates are isolated to a small portion of the state tree. They don't touch anything else in the system. This is scalable far beyond the singleton approach - in fact, with multiple stores, it's very difficult to hit CPU limitations before hitting memory limitations on the user's machine. Reason #5: Destruction Destroying state is not too difficult in Redux. Just like in the code-splitting example, it requires only a few extra steps to remove a part of the reducer hierarchy. But it's still simpler with multiple stores - in theory, you can simply detach the store from the dispatcher and allow it to be garbage collected. Reason #6: Colocation This is the big one that Redux, Zustand, and the singleton model in general do not handle well. Side effects are separated from the state they interact with. Selection logic is separated from everything. While multi-store Flux was perhaps colocated, Redux went to the opposite extreme. too With multiple autonomous stores, these things naturally go together. Really, Flux only lacked a few standards to prevent everything from becoming a helter-skelter hodge-podge of gobbledygook (sorry). Reasons Summary Now, if you know the OG Flux library, you know that it actually wasn't great at all of these. The dispatcher still takes a global approach - dispatching every action to every store. The whole thing with informal/implicit dependencies also made code splitting and destruction less than perfect. Still, Flux had a lot of cool features going for it. Plus the multiple-store approach has potential for even more features like Inversion of Control and fractal (aka local) state management. Flux might have evolved into a truly powerful state manager if somebody hadn't named their goddess Demeter. I'm serious! ... Ok, I'm not. But now that you mention it, maybe Demeter's law deserves a closer look: The Law of Demeter What exactly is this so-called "law"? From : Wikipedia Each unit should have only limited knowledge about other units: only units "closely" related to the current unit. Each unit should only talk to its friends; don't talk to strangers. This law was designed with Object-Oriented Programming in mind, but it can be applied in many areas, including React state management. The basic idea is to prevent a store from: Tightly coupling itself to another store's implementation details. Using stores . that it doesn't need to know about Using any other store without declaring a dependency on that store. explicitly In banana terms, a banana shouldn't peel another banana and shouldn't talk to a banana in another tree. However, it talk to the other tree if the two trees rig up a banana phone line first. can This encourages the separation of concerns and helps your code stay modular, DRY, and SOLID. Solid theory! So what was Flux missing? Well, inter-store dependencies are a natural part of a good, modular system. If a store needs to add another dependency, it should do that do it . Here's some of that Flux code again: and as explicitly as possible PromosStore.dispatchToken = dispatcher.register(payload => {
  if (payload.actionType === 'add-to-cart') {
    // wait for CartStore to update first:
    dispatcher.waitFor([CartStore.dispatchToken])

    // now send the request
    sendPromosRequest(UserStore.userId, CartStore.items).then(promos => {
      dispatcher.dispatch({ actionType: 'promos-fetched', promos })
    })
  }

  if (payload.actionType === 'promos-fetched') {
    PromosStore.setPromos(payload.promos)
  }
}) PromosStore has multiple dependencies declared in different ways - it waits for and reads from it reads from . The only way to discover these dependencies is to look for stores in PromosStore's implementation. CartStore and UserStore Dev tools can't help make these dependencies more discoverable either. In other words, the dependencies are too implicit. While this is a very simple and contrived example, it illustrates how Flux misinterpreted the Law of Demeter. While I'm sure it was mostly born of a desire to keep Flux implementations small (real dependency management is a complex task!), this is where Flux fell short. Unlike the heroes of this story: The Heroes In 2020, came stumbling onto the scene. While a little clumsy at first, it taught us a new pattern that revived the multiple-store approach of Flux. Recoil Unidirectional data flow moved from the store itself to the dependency graph. Stores were now called atoms. Atoms were properly autonomous and code-splittable. They had new powers like suspense support and hydration. And most importantly, atoms formally declare their dependencies. The atomic model was born. // a Recoil atom
const greetingAtom = atom({
  key: 'greeting',
  default: 'Hello, World!',
}) Recoil struggled with a bloated codebase, memory leaks, bad performance, slow development, and unstable features - most notably side effects. It would slowly iron out some of these, but in the meantime, other libraries took Recoil's ideas and ran with them. burst onto the scene and quickly gained a following. Jotai // a Jotai atom
const greetingAtom = atom('Hello, World!') Besides being a tiny fraction of Recoil's size, Jotai offered better performance, sleeker APIs, and no memory leaks due to its WeakMap-based approach. However, it came at the cost of some power - the WeakMap approach makes cache control difficult and sharing state between multiple windows or other realms almost impossible. And the lack of string keys, while sleek, makes debugging a nightmare. Most apps should add those back in, drastically tarnishing Jotai's sleekness. // a (better?) Jotai atom
const greetingAtom = atom('Hello, World!')
greetingAtom.debugLabel = 'greeting' A few honorable mentions are and . These libraries have explored more of the theory behind the atomic model and try to push its size and speed to the limit. Reatom Nanostores The atomic model is fast and scales very well. But up until very recently, there were a few concerns that no atomic library had addressed very well: The learning curve. Atoms are . How do we make these concepts approachable for React devs? different Dev X and debugging. How do we make atoms discoverable? How do you track updates or enforce good practices? Incremental migration for existing codebases. How do you access external stores? How do you keep existing logic intact? How do you avoid a full rewrite? Plugins. How do we make the atomic model extensible? it handle every possible situation? Can Dependency Injection. Atoms naturally define dependencies, but can they be swapped out during testing or in different environments? The Law of Demeter. How do we hide implementation details and prevent scattered updates? This is where I come in. See, I'm the principle creator of another atomic library: Zedux finally entered the scene a few weeks ago. Developed by a Fintech company in New York - the company I work for - Zedux was not only designed to be fast and scalable, but also to provide a sleek development and debugging experience. Zedux // a Zedux atom
const greetingAtom = atom('greeting', 'Hello, World!') I won't go into depth about Zedux's features here - as I said, this article won't focus on the differences between these atomic libraries. Suffice it to say that Zedux addresses all the above concerns. For example, it's the first atomic library to offer real Inversion of Control and the first to bring us full-circle back to the Law of Demeter by offering for hiding implementation details. atom exports The last ideologies of Flux have finally been revived - not only revived, but improved! - thanks to the atomic model. So what exactly the atomic model? is The Atomic Model These atomic libraries have many differences - they even have different definitions of what "atomic" means. The general consensus is that atoms are small, isolated, autonomous state containers reactively updated via a Directed Acyclic Graph. I know, I know, it sounds complex, but just wait till I explain it with bananas. I'm kidding! It's actually really simple: Updates ricochet through the graph. That's it! The point is, regardless of the implementation or the semantics, all of these atomic libraries have revived the concept of multiple stores and made them not only usable, but a real joy to work with. The 6 reasons I gave for wanting multiple stores are exactly the reasons why the atomic model is so powerful: Autonomy - Atoms can be tested and used completely in isolation. Code Splitting - Import an atom and use it! No extra considerations required. Standardized Primitives - Anything can expose an atom for automatic integration. Scalability - Updates affect only a small part of the state tree. Destruction - Simply stop using an atom and all its state is garbage collected. Colocation - Atoms naturally define their own state, side effects, and update logic. The simple APIs and scalability alone make atomic libraries an excellent choice for every React app. More power less boilerplate than Redux? Is this a dream? and Conclusion What a journey! The world of React state management never ceases to amaze, and I'm so glad I hitched a ride. We're just getting started. There is a lot of room for innovation with atoms. After spending years creating and using Zedux, I've seen how powerful the atomic model can be. In fact, its power is its Achilles heel: When devs explore atoms, they often dig so deep into the possibilities that they come back saying, "Look at this crazy complex power," rather than, "Look at how simply and elegantly atoms solve this problem." I'm here to change this. The atomic model and the theory behind it haven't been taught in a way that's approachable for most React devs. In a way, the React world's experience of atoms so far has been the opposite of Flux: This article is the second in a series of learning resources I'm producing to help React devs understand how atomic libraries work and why you might want to use one. Check out the first article - . Scalability: the Lost Level of React State Management It took 10 years, but the solid CS theory introduced by Flux is finally impacting React apps in a big way thanks to the atomic model. And it will continue to do so for years to come.

FLUX AUTO

fluxauto.xyz

51-200 employees

Since 2017

COMPANY RANKING

FLUX AUTO

EVERGREEN INDEX #3732

Representing this company? Click here to claim and customize this page!

Flux Auto WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

FLUX AUTO

fluxauto.xyz

51-200 employees

Since 2017

COMPANY RANKING

FLUX AUTO

EVERGREEN INDEX #3732

RELATED COMPANIESRANK

STORIES ABOUT FLUX AUTO

Representing this company? Click here to claim and customize this page!

Flux Auto WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!