Building Cloud-Native App Series - Part 9 of 15
Microservices Architecture Series
Containers Docker Kind Kubernetes Istio
- Pods
- ReplicaSet
- Deployment (Canary, Blue-Green)
- Ingress
- Service
Blockchain 8 Years Cloud Computing 8 Years Distributed Computing Architecting & Building Apps a tech presentorial Combination of presentation & tutorial ARAF KARSH HAMID Co-Founder / CTO MetaMagic Global Inc., NJ, USA @arafkarsh arafkarsh 1 Microservices Architecture Series Building Cloud Native Apps Docker Kubernetes, KinD Service Mesh: Istio Part 9 of 15
Treat Backing services like DB, Cache as attached resources 5 Build, Release, Run Separate Build and Run Stages 6 Process Execute App as One or more Stateless Process 7 Port Binding Export Services with Specific Port Binding 8 Concurrency Scale out via the process Model 9 Disposability Maximize robustness with fast startup and graceful exit 10 Dev / Prod Parity Keep Development, Staging and Production as similar as possible 11 Logs Treat logs as Event Streams 12 Admin Process Run Admin Tasks as one of Process Source: https://12factor.net/ Factors Description 1 Codebase One Code base tracked in revision control 2 Dependencies Explicitly declare dependencies 3 Configuration Configuration driven Apps
opensource software stack to deploy applications as microservices, packaging each part into its own container, and dynamically orchestrating those containers to optimize resource utilization. As defined by CNCF https://www.cncf.io/about/who-we-are/
a Walks like a Runs like a Containers are a Sandbox inside Linux Kernel sharing the kernel with separate Network Stack, Process Stack, IPC Stack etc. They are NOT Virtual Machines or Light weight Virtual Machines.
Copy on Write DOCKER CONTAINER 13 • Kernel Feature • Groups Processes • Control Resource Allocation • CPU, CPU Sets • Memory • Disk • Block I/O • Images • Not a File System • Not a VHD • Basically, a tar file • Has a Hierarchy • Arbitrary Depth • Fits into Docker Registry • The real magic behind containers • It creates barriers between processes • Different Namespaces • PID Namespace • Net Namespace • IPC Namespace • MNT Namespace • Linux Kernel Namespace introduced between kernel 2.6.15 – 2.6.26 docker run lxc-start https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/6/html/resource_management_guide/ch01
Groups cgroups Namespaces Pid, net, ipc, mnt, uts Layer Capabilities Union File Systems: AUFS, btrfs, vfs Control Groups Job Objects Namespaces Object Namespace, Process Table. Networking Layer Capabilities Registry, UFS like extensions Namespaces: Building blocks of the Containers
Daemon Cent OS Alpine Debian Linux Kernel Host Kernel Host Kernel Host Kernel All the containers will have the same Host OS Kernel If you require a specific Kernel version, then Host Kernel needs to be updated HOST OS (Windows 10) Client Docker Daemon Nano Server Server Core Nano Server Windows Kernel Host Kernel Host Kernel Host Kernel Windows Kernel
Docker image is a read-only template. 2. For example, an image could contain an Ubuntu operating system Libraries with Apache and your web application installed. 3. Images are used to create Docker containers. 4. Docker provides a simple way to build new images or update existing images, or you can download Docker images that other people have already created. 5. Docker images are the build component of Docker. 2. Docker containers 1. Docker containers are similar to a directory. 2. A Docker container holds everything that is needed for an application to run. 3. Each container is created from a Docker image. 4. Docker containers can be run, started, stopped, moved, and deleted. 5. Each container is an isolated and secure application platform. 6. Docker containers are the run component of Docker. 3. Docker Registries 1. Docker registries hold images. 2. These are public or private stores from which you upload or download images. 3. The public Docker registry is called Docker Hub. 4. It provides a massive collection of existing images for your use. 5. These can be images you create yourself or use images others have previously created. 6. Docker registries are the distribution component of Docker. 16 Images Containers
• Multiple layers of image gives the final Container. • Layers can be sharable. • Layers are portable. • Debian Base image • Emacs • Apache • Writable Container
run -d ubuntu /bin/bash -c “while true; do date; sleep 1; done”) Creates a Docker Container of Ubuntu OS and runs the container and execute bash shell with a script. $ docker container logs $ID Shows output from the( bash script) container $ docker container ls List the running Containers $ docker pull ubuntu Docker pulls the image from the Docker Registry When you copy the commands for testing change ” quotes to proper quotes. Microsoft PowerPoint messes with the quotes.
FROM The FROM instruction sets the Base Image for subsequent instructions. As such, a valid Dockerfile must have FROM as its first instruction. The image can be any valid image – it is especially easy to start by pulling an image from the Public repositories FROM ubuntu FROM alpine MAINTAINER The MAINTAINER instruction allows you to set the Author field of the generated images. (Deprecated) MAINTAINER John Doe LABEL The LABEL instruction adds metadata to an image. A LABEL is a key-value pair. To include spaces within a LABEL value, use quotes and backslashes as you would in command-line parsing. LABEL version="1.0” LABEL vendor=“M2” RUN The RUN instruction will execute any commands in a new layer on top of the current image and commit the results. The resulting committed image will be used for the next step in the Dockerfile. RUN apt-get install -y curl ADD The ADD instruction copies new files, directories or remote file URLs from <src> and adds them to the filesystem of the container at the path <dest>. ADD hom* /mydir/ ADD hom?.txt /mydir/ COPY The COPY instruction copies new files or directories from <src> and adds them to the filesystem of the container at the path <dest>. COPY hom* /mydir/ COPY hom?.txt /mydir/ ENV The ENV instruction sets the environment variable <key> to the value <value>. This value will be in the environment of all "descendent" Dockerfile commands and can be replaced inline in many as well. ENV JAVA_HOME /JDK8 ENV JRE_HOME /JRE8
VOLUME The VOLUME instruction creates a mount point with the specified name and marks it as holding externally mounted volumes from native host or other containers. The value can be a JSON array, VOLUME ["/var/log/"], or a plain string with multiple arguments, such as VOLUME /var/log or VOLUME /var/log VOLUME /data/webapps USER The USER instruction sets the user name or UID to use when running the image and for any RUN, CMD and ENTRYPOINT instructions that follow it in the Dockerfile. USER johndoe WORKDIR The WORKDIR instruction sets the working directory for any RUN, CMD, ENTRYPOINT, COPY and ADD instructions that follow it in the Dockerfile. WORKDIR /home/user CMD There can only be one CMD instruction in a Dockerfile. If you list more than one CMD then only the last CMD will take effect. The main purpose of a CMD is to provide defaults for an executing container. These defaults can include an executable, or they can omit the executable, in which case you must specify an ENTRYPOINT instruction as well. CMD echo "This is a test." | wc - EXPOSE The EXPOSE instructions informs Docker that the container will listen on the specified network ports at runtime. Docker uses this information to interconnect containers using links and to determine which ports to expose to the host when using the –P flag with docker client. EXPOSE 8080 ENTRYPOINT An ENTRYPOINT allows you to configure a container that will run as an executable. Command line arguments to docker run <image> will be appended after all elements in an exec form ENTRYPOINT, and will override all elements specified using CMD. This allows arguments to be passed to the entry point, i.e., docker run <image> -d will pass the -d argument to the entry point. You can override the ENTRYPOINT instruction using the docker run --entrypoint flag. ENTRYPOINT ["top", "-b"]
your Dockerfile • FROM • RUN • ADD • WORKDIR • USER • ENTRYPOINT 2. Build the Docker image 3. Run the Container $ docker build -t org/java:8 . $ docker container run –it org/java:8
–d ubuntu /bin/bash) $ docker container stop $ID Start the Container and Store ID in ID field Stop the container using Container ID $ docker container stop $(docker container ls –aq) Stops all the containers $ docker container rm $ID Remove the Container $ docker container rm $(docker container ls –aq) Remove ALL the Container (in Exit status) $ docker container prune Remove ALL stopped Containers) $ docker container run –restart=Policy –d –it ubuntu /sh Policies = NO / ON-FAILURE / ALWAYS $ docker container run –restart=on-failure:3 –d –it ubuntu /sh Will re-start container ONLY 3 times if a failure happens $ docker container start $ID Start the container
–d -i ubuntu) $ docker container exec -it $ID /bin/bash Start the Container and Store ID in ID field Inject a Process into Running Container $ ID=$(docker container run –d –i ubuntu) $ docker container exec inspect $ID Start the Container and Store ID in ID field Read Containers MetaData $ docker container run –it ubuntu /bin/bash # apt-get update # apt-get install—y apache2 # exit $ docker container ls –a $ docker container commit –author=“name” – message=“Ubuntu / Apache2” containerId apache2 Docker Commit • Start the Ubuntu Container • Install Apache • Exit Container • Get the Container ID (Ubuntu) • Commit the Container with new name $ docker container run –cap-drop=chown –it ubuntu /sh To prevent Chown inside the Container Source: https://github.com/meta-magic/kubernetes_workshop
Log into the Docker Hub to Push images $ docker push image-name Push the image to Docker Hub $ docker image history image-name Get the History of the Docker Image $ docker image inspect image-name Get the Docker Image details $ docker image save –output=file.tar image-name Save the Docker image as a tar ball. $ docker container export –output=file.tar c79aa23dd2 Export Container to file. Source: https://github.com/meta-magic/kubernetes_workshop $ docker image rm image-name Remove the Docker Image $ docker rmi $(docker images | grep '^<none>' | tr -s " " | cut -d " " -f 3)
JRE 8 JRE 11 Tomcat 8 Tomcat 9 My App 1 Tomcat 9 My App 3 Spring Boot My App 4 From Ubuntu Build My Ubuntu From My Ubuntu Build My JRE8 From My Ubuntu Build My JRE11 From My JRE 11 Build My Boot From My Boot Build My App 4 From My JRE8 Build My TC8 From My TC8 Build My App 1 My App 2 Source: https://github.com/meta-magic/kubernetes_workshop
Linux JRE 8 JRE 11 Tomcat 9 Tomcat 10 My App 1 Tomcat 10 My App 3 Spring Boot My App 4 From Alpine Build My Alpine From My Alpine Build My JRE8 From My Alpine Build My JRE11 From My JRE 11 Build My Boot From My Boot Build My App 4 From My JRE8 Build My TC9 From My TC8 Build My App 1 My App 2 Source: https://github.com/meta-magic/kubernetes_workshop
34 $ docker container run --rm -–net=host alpine ip address $ docker container run --rm alpine ip address $ docker container run –rm –net=none alpine ip address No Network Stack https://docs.docker.com/network/#network-drivers
container run --name ipctr –itd alpine $ docker container run --rm --net container:ipctr alpine ip address IP (Container) Service 1 (Container) Service 3 (Container) Service 2 (Container) $ docker container exec ipctr ip address
Data Volumes are special directory in the Docker Host. $ docker volume ls $ docker container run –it –rm –v hostvolume:/data alpine # echo “This is a test from the Container” > /data/data.txt Source: https://github.com/meta-magic/kubernetes_workshop
ReplicaSet – Self Healing, Scalability, Desired State R Worker Node 1 Master Node (Control Plane) Kubernetes Architecture 40 POD POD itself is a Linux Container, Docker container will run inside the POD. PODs with single or multiple containers (Sidecar Pattern) will share Cgroup, Volumes, Namespaces of the POD. (Cgroup / Namespaces) Scheduler Controller Manager Using yaml or json declare the desired state of the app. State is stored in the Cluster store. Self healing is done by Kubernetes using watch loops if the desired state is changed. POD POD POD BE 1.2 10.1.2.34 BE 1.2 10.1.2.35 BE 1.2 10.1.2.36 BE 15.1.2.100 DNS: a.b.com 1.2 Service Pod IP Address is dynamic, communication should be based on Service which will have routable IP and DNS Name. Labels (BE, 1.2) play a critical role in ReplicaSet, Deployment, & Services etc. Cluster Store etcd Key Value Store Pod Pod Pod Label Selector selects pods based on the Labels. Label Selector Label Selector Label Selector Node Controller End Point Controller Deployment Controller Pod Controller …. Labels Internet Firewall K8s Virtual Cluster Cloud Controller For the cloud providers to manage nodes, services, routes, volumes etc. Kubelet Node Manager Container Runtime Interface Port 10255 gRPC ProtoBuf Kube-Proxy Network Proxy TCP / UDP Forwarding IPTABLES / IPVS Allows multiple implementation of containers from v1.7 RESTful yaml / json $ kubectl …. Port 443 API Server Pod IP ...34 ...35 ...36 EP • Declarative Model • Desired State Key Aspects N1 N2 N3 Namespace 1 N1 N2 N3 Namespace 2 • Pods • ReplicaSet • Deployment • Service • Endpoints • StatefulSet • Namespace • Resource Quota • Limit Range • Persistent Volume Kind Secrets Kind • apiVersion: • kind: • metadata: • spec: Declarative Model • Pod • ReplicaSet • Service • Deployment • Virtual Service • Gateway, SE, DR • Policy, MeshPolicy • RbaConfig • Prometheus, Rule, • ListChekcer … @ @ Annotations Names Cluster IP Node Port Load Balancer External Name @ Ingress
sudo snap install kubectl --classic Install Kubectl using Snap Package Manager $ kubectl version Shows the Current version of Kubectl • Minikube provides a developer environment with master and a single node installation within the Minikube with all necessary add-ons installed like DNS, Ingress controller etc. • In a real world production environment you will have master installed (with a failover) and ‘n’ number of nodes in the cluster. • If you go with a Cloud Provider like Amazon EKS then the node will be created automatically based on the load. • Minikube is available for Linux / Mac OS and Windows. $ curl -Lo minikube https://storage.googleapis.com/minikube/releases/v0.30.0/minikube-linux-amd64 $ chmod +x minikube && sudo mv minikube /usr/local/bin/ https://kubernetes.io/docs/tasks/tools/install-kubectl/ Source: https://github.com/meta-magic/kubernetes_workshop
choco install kubernetes-cli Install Kubectl using Choco Package Manager C:\> kubectl version Shows the Current version of Kubectl Mac OS Installation $ brew install kubernetes-cli Install Kubectl using brew Package Manager $ kubectl version Shows the Current version of Kubectl C:\> cd c:\users\youraccount C:\> mkdir .kube Create .kube directory $ curl -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-darwin-amd64 $ chmod +x minikube && sudo mv minikube /usr/local/bin/ C:\> minikube-installer.exe Install Minikube using Minikube Installer https://kubernetes.io/docs/tasks/tools/install-kubectl/ Source: https://github.com/meta-magic/kubernetes_workshop $ brew update; brew cask install minikube Install Minikube using Homebrew or using curl
Premise 45 Cluster Machine Setup 1. Switch off Swap 2. Set Static IP to Network interface 3. Add IP to Host file $ k8s-1-cluster-machine-setup.sh 4. Install Docker 5. Install Kubernetes Run the cluster setup script to install the Docker and Kubernetes in all the machines (master and worker node) 1 Master Setup Setup kubernetes master with pod network 1. Kubeadm init 2. Install CNI Driver $ k8s-2-master-setup.sh $ k8s-3-cni-driver-install.sh $ k8s-3-cni-driver-uninstall.sh $ kubectl get po --all-namespaces Check Driver Pods Uninstall the driver 2 Node Setup n1$ kubeadm join --token t IP:Port Add the worker node to Kubernetes Master $ kubectl get nodes Check Events from namespace 3 $ kubectl get events –n namespace Check all the nodes $ sudo ufw enable $ sudo ufw allow 31100 Source Code: https://github.com/meta-magic/metallb-baremetal-example Only if the Firewall is blocking your Pod Al the above-mentioned shell scripts are available in the Source Code Repository $ sudo ufw allow 443
kubeadm init node1$ kubeadm join --token enter-token-from-kubeadm-cmd Node-IP:Port Adds a Node $ kubectl get nodes $ kubectl cluster-info List all Nodes $ kubectl run hello-world --replicas=7 --labels="run=load-balancer-example" --image=metamagic/hello:1.0 --port=8080 Creates a Deployment Object and a ReplicaSet object with 7 replicas of Hello-World Pod running on port 8080 $ kubectl expose deployment hello-world --type=LoadBalancer --name=hello-world-service List all the Hello-World Deployments $ kubectl get deployments hello-world Describe the Hello-World Deployments $ kubectl describe deployments hello-world List all the ReplicaSet $ kubectl get replicasets Describe the ReplicaSet $ kubectl describe replicasets List the Service Hello-World-Service with Custer IP and External IP $ kubectl get services hello-world-service Describe the Service Hello-World-Service $ kubectl describe services hello-world-service Creates a Service Object that exposes the deployment (Hello-World) with an external IP Address. List all the Pods with internal IP Address $ kubectl get pods –o wide $ kubectl delete services hello-world-service Delete the Service Hello-World-Service $ kubectl delete deployment hello-world Delete the Hello-Word Deployment Create a set of Pods for Hello World App with an External IP Address (Imperative Model) Shows the cluster details $ kubectl get namespace Shows all the namespaces $ kubectl config current-context Shows Current Context Source: https://github.com/meta-magic/kubernetes_workshop
-Lo ./kind https://kind.sigs.k8s.io/dl/v0.11.1/kind-linux-amd64 $ chmod +x ./kind $ mv ./kind /some-dir-in-your-PATH/kind Linux Source: https://kind.sigs.k8s.io/docs/user/quick-start/ $ brew install kind Mac OS via Homebrew c:\> curl.exe -Lo kind-windows-amd64.exe https://kind.sigs.k8s.io/dl/v0.11.1/kind-windows-amd64 c:\> Move-Item .\kind-windows-amd64.exe c:\some-dir-in-your-PATH\kind.exe Windows o Kind is a tool for running local Kubernetes clusters using Docker container “nodes”. o Kind was primarily designed for testing Kubernetes itself but may be used for local development or CI.
$ kind create cluster - -name my2ndcluster Create a default cluster named kind Create a cluster with a specific name $ kind create cluster - -config filename.yaml Create a cluster from a file $ kind get cluster List all Clusters $ kind delete cluster Delete a Cluster $ kubectl cluster-info --context kind-kind $ kind load docker-image image1 image2 image3 Load Image into a Cluster (No Repository Needed)
Infrastructure Details 2. Decouple the App Deployment from Infrastructure (On-Premise or Cloud) To help Developers 1. Write Once, Run Anywhere (Workload Portability) 2. Avoid Vendor Lock-In Cloud On-Premise
kubectl config set-context $(kubectl config current-context) --namespace=your-ns This command will let you switch the namespace to your namespace (your-ns). $ kubectl get namespace $ kubectl describe ns ns-name $ kubectl create –f app-ns.yml List all the Namespaces Describe the Namespace Create the Namespace $ kubectl apply –f app-ns.yml Apply the changes to the Namespace $ kubectl get pods –namespace= ns-name List the Pods from your namespace • Namespaces are used to group your teams and software’s in logical business group. • A definition of Service will add a entry in DNS with respect to Namespace. • Not all objects are there in Namespace. Ex. Nodes, Persistent Volumes etc. $ kubectl api-resources --namespaced=your-ns
of more Containers. • Pod in a Kubernetes cluster has a unique IP address, even Pods on the same Node. • Pod is a pause Container Kubernetes Pods 60 $ kubectl create –f tc10-nr-Pod.yaml $ kubectl get pods –o wide –n omega Atomic Unit Container Pod Virtual Server Small Big Source: https://github.com/MetaArivu/k8s-workshop
kubectl exec pod-name ps aux $ kubectl exec –it pod-name sh $ kubectl exec –it –container container-name pod-name sh By default kubectl executes the commands in the first container in the pod. If you are running multiple containers (sidecar pattern) then you need to pass –container flag and give the name of the container in the Pod to execute your command. You can see the ordering of the containers and its name using describe command. $ kubectl get pods $ kubectl describe pods pod-name $ kubectl get pods -o json pod-name $ kubectl create –f app-pod.yml List all the pods Describe the Pod details List the Pod details in JSON format Create the Pod (Imperative) Execute commands in the first Container in the Pod Log into the Container Shell $ kubectl get pods -o wide List all the Pods with Pod IP Addresses $ kubectl apply –f app-pod.yml Apply the changes to the Pod $ kubectl replace –f app-pod.yml Replace the existing config of the Pod $ kubectl describe pods –l app=name Describe the Pod based on the label value $ kubectl logs pod-name container-name Source: https://github.com/MetaArivu/k8s-workshop
shared location, secrets, networking etc. • ReplicaSet wraps around Pods and brings in Replication requirements of the Pod • ReplicaSet Defines 2 Things • Pod Template • Desired No. of Replicas Kubernetes ReplicaSet (Declarative Model) 62 What we want is the Desired State. Game On! Source: https://github.com/MetaArivu/k8s-workshop
kubectl delete rs/app-rs cascade=false $ kubectl get rs $ kubectl describe rs rs-name $ kubectl get rs/rs-name $ kubectl create –f app-rs.yml List all the ReplicaSets Describe the ReplicaSet details Get the ReplicaSet status Create the ReplicaSet which will automatically create all the Pods Deletes the ReplicaSet. If the cascade=true then deletes all the Pods, Cascade=false will keep all the pods running and ONLY the ReplicaSet will be deleted. $ kubectl apply –f app-rs.yml Applies new changes to the ReplicaSet. For example, Scaling the replicas from x to x + new value. Source: https://github.com/MetaArivu/k8s-workshop
Deployments manages ReplicaSets and • ReplicaSets manages Pods • Deployment is all about Rolling updates and • Rollbacks • Canary Deployments Source: https://github.com/MetaArivu/k8s-workshop
all the Deployments Describe the Deployment details Show the Rollout status of the Deployment Creates Deployment Deployments contains Pods and its Replica information. Based on the Pod info Deployment will start downloading the containers (Docker) and will install the containers based on replication factor. Updates the existing deployment. Show Rollout History of the Deployment $ kubectl get deploy app-deploy $ kubectl describe deploy app-deploy $ kubectl rollout status deployment app-deploy $ kubectl rollout history deployment app-deploy $ kubectl create –f app-deploy.yml $ kubectl apply –f app-deploy.yml --record $ kubectl rollout undo deployment app-deploy - -to-revision=1 $ kubectl rollout undo deployment app-deploy - -to-revision=2 Rolls back or Forward to a specific version number of your app. $ kubectl scale deployment app-deploy - -replicas=6 Scale up the pods to 6 from the initial 2 Pods. Source: https://github.com/MetaArivu/k8s-workshop
• Accessing Pods from Inside the Cluster • Accessing Pods from Outside • Autoscale brings Pods with new IP Addresses or removes existing Pods. • Pod IP Addresses are dynamic. Service Types 1. Cluster IP (Default) 2. Node Port 3. Load Balancer 4. External Name Service will have a stable IP Address. Service uses Labels to associate with a set of Pods Source: https://github.com/MetaArivu/k8s-workshop
67 $ kubectl delete svc app-service $ kubectl create –f app-service.yml List all the Services Describe the Service details List the status of the Endpoints Create a Service for the Pods. Service will focus on creating a routable IP Address and DNS for the Pods Selected based on the labels defined in the service. Endpoints will be automatically created based on the labels in the Selector. Deletes the Service. $ kubectl get svc $ kubectl describe svc app-service $ kubectl get ep app-service $ kubectl describe ep app-service Describe the Endpoint Details ❖ Cluster IP (default) - Exposes the Service on an internal IP in the cluster. This type makes the Service only reachable from within the cluster. ❖ Node Port - Exposes the Service on the same port of each selected Node in the cluster using NAT. Makes a Service accessible from outside the cluster using <NodeIP>:<NodePort>. Superset of ClusterIP. ❖ Load Balancer - Creates an external load balancer in the current cloud (if supported) and assigns a fixed, external IP to the Service. Superset of NodePort. ❖ External Name - Exposes the Service using an arbitrary name (specified by external Name in the spec) by returning a CNAME record with the name. No proxy is used. This type requires v1.7 or higher of kube-dns.
a collection of rules that allow inbound connections to reach the cluster services. Ingress Controllers are Pluggable. Ingress Controller in AWS is linked to AWS Load Balancer. Source: https://kubernetes.io/docs/concepts/services- networking/ingress/#ingress-controllers Source: https://github.com/MetaArivu/k8s-workshop
a collection of rules that allow inbound connections to reach the cluster services. Ingress Controllers are Pluggable. Ingress Controller in AWS is linked to AWS Load Balancer. Source: https://kubernetes.io/docs/concepts/services-networking/ingress/#ingress-controllers
You can declare the Auto scaling requirements for every Deployment (Microservices). • Kubernetes will add Pods based on the CPU Utilization automatically. • Kubernetes Cloud infrastructure will automatically add Nodes if it ran out of available Nodes. CPU utilization kept at 2% to demonstrate the auto scaling feature. Ideally it should be around 80% - 90% Source: https://github.com/MetaArivu/k8s-workshop
autoscale deployment appname --cpu-percent=50 --min=1 --max=10 $ kubectl run -it podshell --image=metamagicglobal/podshell Hit enter for command prompt $ while true; do wget -q -O- http://yourapp.default.svc.cluster.local; done Deploy your app with auto scaling parameters Generate load to see auto scaling in action $ kubectl get hpa $ kubectl attach podshell-name -c podshell -it To attach to the running container Source: https://github.com/meta-magic/kubernetes_workshop
the Container Image. Config Map lets you create multiple profiles for your Dev, QA and Prod environment. Config Map All the Database configurations like passwords, certificates, OAuth tokens, etc., can be stored in secrets. Secret Helps you create common configuration which can be injected to Pod based on a Criteria (selected using Label). For Ex. SMTP config, SMS config. Pod Preset Environment option let you pass any info to the pod thru Environment Variables. Environment 73 Container App Setup
allow you to decouple configuration artifacts from image content to keep containerized applications portable. Source: https://kubernetes.io/docs/tasks/configure-pod-container/configure-pod-configmap/ Source: https://github.com/meta-magic/kubernetes_workshop
an API resource for injecting additional runtime requirements into a Pod at creation time. You use label selectors to specify the Pods to which a given Pod Preset applies. Using a Pod Preset allows pod template authors to not have to explicitly provide all information for every pod. This way, authors of pod templates consuming a specific service do not need to know all the details about that service. Source: https://kubernetes.io/docs/concepts/workloads/pods/podpreset/ Source: https://github.com/meta-magic/kubernetes_workshop
are intended to hold sensitive information, such as passwords, OAuth tokens, and ssh keys. Putting this information in a secret is safer and more flexible than putting it verbatim in a pod definition or in a docker Source: https://kubernetes.io/docs/concepts/configuration/secret/ Source: https://github.com/meta-magic/kubernetes_workshop
UI Layer WS BL DL Database Shopping Cart Order Customer Product Firewall Users API Gateway Load Balancer Circuit Breaker UI Layer Web Services Business Logic Database Layer Product SE MySQL DB Product Microservice With 4 node cluster Load Balancer Circuit Breaker UI Layer Web Services Business Logic Database Layer Customer Redis DB Customer Microservice With 2 node cluster Users Access the Monolithic App Directly API Gateway (Reverse Proxy Server) routes the traffic to appropriate Microservices (Load Balancers)
/cart /order API Gateway Ingress Deployment / Replica / Pod Nodes Kubernetes Objects Firewall Customer Pod Customer Pod Customer Pod Customer Service N1 N2 N2 EndPoints Product Pod Product Pod Product Pod Product Service N4 N3 MySQL DB EndPoints Review Pod Review Pod Review Pod Review Service N4 N3 N1 Service Call Kube DNS EndPoints Internal Load Balancers Users Routing based on Layer 3,4 and 7 Redis DB Mongo DB Load Balancer
/auth /order API Gateway Virtual Service Deployment / Replica / Pod Nodes Istio Sidecar - Envoy Load Balancer Firewall P M C Istio Control Plane MySQL Pod N4 N3 Destination Rule Product Pod Product Pod Product Pod Product Service Service Call Kube DNS EndPoints Internal Load Balancers 81 Kubernetes Objects Istio Objects Users Review Pod Review Pod Review Pod Review Service N1 N4 N3 EndPoints Customer Pod Customer Pod Customer Pod Customer Service N1 N2 N2 Destination Rule EndPoints Redis DB Mongo DB 81
Gateway Load Balancer Circuit Breaker UI Layer Web Services Business Logic Database Layer Product SE MySQL DB Product Microservice With 4 node cluster Load Balancer CB = Hystrix UI Layer Web Services Business Logic Database Layer Customer Redis DB Customer Microservice With 2 node cluster API Gateway (Reverse Proxy Server) routes the traffic to appropriate Microservices (Load Balancers) Load Balancer Rules 1. Round Robin 2. Based on Availability 3. Based on Response Time
Objects Firewall Users Product 1 Product 2 Product 3 Product Service N4 N3 N1 EndPoints Internal Load Balancers DB Load Balancer API Gateway N1 N2 N2 Customer 1 Customer 2 Customer 3 Customer Service EndPoints DB Internal Load Balancers Pods Nodes • Load Balancer receives the (request) packet from the User and it picks up a Virtual Machine in the Cluster to do the internal Load Balancing. • Kube Proxy using IP Tables redirect the Packet using internal load Balancing rules. • Packet enters Kubernetes Cluster and reaches Node (of that specific Pod) and Node handover the packet to the Pod. /customer /product /cart
Firewall Users API Gateway Load Balancer Circuit Breaker Product MySQL DB Product Microservice With 4 node cluster Load Balancer Circuit Breaker UI Layer Web Services Business Logic Database Layer Customer Redis DB Customer Microservice With 2 node cluster • In this model Developers write the code in every Microservice to register with NetFlix Eureka Service Discovery Server. • Load Balancers and API Gateway also registers with Service Discovery. • Service Discovery will inform the Load Balancers about the instance details (IP Addresses). Service Discovery
Objects Firewall Users Product 1 Product 2 Product 3 Product Service N4 N3 N1 EndPoints Internal Load Balancers DB API Gateway N1 N2 N2 Customer 1 Customer 2 Customer 3 Customer Service EndPoints DB Internal Load Balancers Pods Nodes • API Gateway (Reverse Proxy Server) doesn't know the instances (IP Addresses) of News Pod. It knows the IP address of the Services defined for each Microservice (Customer / Product etc.) • Services handles the dynamic IP Addresses of the pods. Services Endpoints will automatically discover the new Pods based on Labels. Service Definition from Kubernetes Perspective /customer /product /cart Service Call Kube DNS
Review is not available Product service will return the product details with a message review not available. Reverse Proxy Server Ingress Deployment / Replica / Pod Nodes Kubernetes Objects Firewall UI Pod UI Pod UI Pod UI Service N1 N2 N2 EndPoints Product Pod Product Pod Product Pod Product Service N4 N3 MySQL Pod EndPoints Internal Load Balancers Users Routing based on Layer 3,4 and 7 Review Pod Review Pod Review Pod Review Service N4 N3 N1 Service Call Kube DNS EndPoints
Ingress Deployment / Replica / Pod Nodes Kubernetes Objects Firewall Service Call Kube DNS Users Internal Load Balancers EndPoints News Pod News Pod News Pod News Service N4 N3 N2 News Service Portal • News Category wise Microservices • Aggregator Microservice to aggregate all category of news. Auto Scaling • Sports Events (IPL / NBA) spikes the traffic for Sports Microservice. • Auto scaling happens for both News and Sports Microservices. N1 N2 N2 National National National National Service EndPoints Internal Load Balancers DB N1 N2 N2 Politics Politics Politics Politics Service EndPoints DB Sports Sports Sports Sports Service N4 N3 N1 EndPoints Internal Load Balancers DB
Ingress Deployment / Replica / Pod Nodes Kubernetes Objects Firewall Service Call Kube DNS Users Internal Load Balancers EndPoints Artist Pod Artist Pod Artist Pod Artist Service N4 N3 N2 Spotify Microservices • Artist Microservice combines all the details from Discography, Play count and Playlists. Auto Scaling • Scaling of Artist and downstream Microservices will automatically scale depends on the load factor. N1 N2 N2 Discography Discography Discography Discography Service EndPoints Internal Load Balancers DB N1 N2 N2 Play Count Play Count Play Count Play Count Service EndPoints DB Playlist Playlist Playlist Playlist Service N4 N3 N1 EndPoints Internal Load Balancers DB
Users API Gateway Load Balancer Circuit Breaker Product MySQL DB Product Microservice With 4 node cluster Load Balancer Circuit Breaker UI Layer Web Services Business Logic Database Layer Customer Redis DB Customer Microservice With 2 node cluster • In this model Developers write the code in every Microservice to download the required configuration from a Central server (Ex. Spring Config Server for the Java World). • This creates an explicit dependency order in which service to come up will be critical. Config Server
Software Stack Java Software Stack .NET Kubernetes 1 API Gateway Zuul Server SteelToe K8s Ingress / Istio Envoy 2 Service Discovery Eureka Server SteelToe Kube DNS 3 Load Balancer Ribbon Server SteelToe Istio Envoy 4 Circuit Breaker Hysterix SteelToe Istio 5 Config Server Spring Config SteelToe Secrets, Env - K8s Master Web Site https://netflix.github.io/ https://steeltoe.io/ https://kubernetes.io/ The Developer needs to write code to integrate with the Software Stack (Programming Language Specific. For Ex. Every microservice needs to subscribe to Service Discovery when the Microservice boots up. Service Discovery in Kubernetes is based on the Labels assigned to Pod and Services and its Endpoints (IP Address) are dynamically mapped (DNS) based on the Label.
The Erlang view of the world is that everything is a process and that processes can interact only by exchanging messages. • A typical Erlang program might have hundreds, thousands, or even millions of processes. • Letting processes crash is central to Erlang. It’s the equivalent of unplugging your router and plugging it back in – as long as you can get back to a known state, this turns out to be a very good strategy. • To make that happen, you build supervision trees. • A supervisor will decide how to deal with a crashed process. It will restart the process, or possibly kill some other processes, or crash and let someone else deal with it. • Two models of concurrency: Shared State Concurrency, & Message Passing Concurrency. The programming world went one way (toward shared state). The Erlang community went the other way. • All languages such as C, Java, C++, and so on, have the notion that there is this stuff called state and that we can change it. The moment you share something you need to bring Mutex a Locking Mechanism. • Erlang has no mutable data structures (that’s not quite true, but it’s true enough). No mutable data structures = No locks. No mutable data structures = Easy to parallelize.
Messages as the first class citizens of a system, has been rediscovered by the Event Sourcing / CQRS community, along with a strong focus on domain models. 2. Event Sourced Aggregates are a way to Model the Processes and NOT things. 3. Each component MUST tolerate a crash and restart at any point in time. 4. All interaction between the components must tolerate that peers can crash. This mean ubiquitous use of timeouts and Circuit Breaker. 5. Each component must be strongly encapsulated so that failures are fully contained and cannot spread. 6. All requests sent to a component MUST be self describing as is practical so that processing can resume with a little recovery cost as possible after a restart.
Apps 94 Erlang Philosophy Micro Services Architecture Monolithic Apps (Java, C++, C#, Node JS ...) 1 Perspective Everything is a Process Event Sourced Aggregates are a way to model the Process and NOT things. Things (defined as Objects) and Behaviors 2 Crash Recovery Supervisor will decide how to handle the crashed process Kubernetes Manager monitors all the Pods (Microservices) and its Readiness and Health. K8s terminates the Pod if the health is bad and spawns a new Pod. Circuit Breaker Pattern is used handle the fallback mechanism. Not available. Most of the monolithic Apps are Stateful and Crash Recovery needs to be handled manually and all languages other than Erlang focuses on defensive programming. 3 Concurrency Message Passing Concurrency Domain Events for state changes within a Bounded Context & Integration Events for external Systems. Mostly Shared State Concurrency 4 State Stateless : Mostly Immutable Structures Immutability is handled thru Event Sourcing along with Domain Events and Integration Events. Predominantly Stateful with Mutable structures and Mutex as a Locking Mechanism 5 Citizen Messages Messages are 1st class citizen by Event Sourcing / CQRS pattern with a strong focus on Domain Models Mutable Objects and Strong focus on Domain Models and synchronous communication.
QoS: Guaranteed Memory limit = Memory Request CPU Limit = CPU Request QoS: Burstable != Guaranteed and Has either Memory OR CPU Request QoS: Best Effort No Memory OR CPU Request / limits Source: https://github.com/meta-magic/kubernetes_workshop
health. It judges the health through periodically performing a diagnostic action against a container via kubelet: • Liveness probe: Indicates whether a container is alive or not. If a container fails on this probe, kubelet kills it and may restart it based on the restartPolicy of a pod. • Readiness probe: Indicates whether a container is ready for incoming traffic. If a pod behind a service is not ready, its endpoint won't be created until the pod is ready. Kubernetes Pod in Depth 98 3 kinds of action handlers can be configured to perform against a container: exec: Executes a defined command inside the container. Considered to be successful if the exit code is 0. tcpSocket: Tests a given port via TCP, successful if the port is opened. httpGet: Performs an HTTP GET to the IP address of target container. Headers in the request to be sent is customizable. This check is considered to be healthy if the status code satisfies: 400 > CODE >= 200. Additionally, there are five parameters to define a probe's behavior: initialDelaySeconds: How long kubelet should be waiting for before the first probing. successThreshold: A container is considered to be healthy when getting consecutive times of probing successes passed this threshold. failureThreshold: Same as preceding but defines the negative side. timeoutSeconds: The time limitation of a single probe action. periodSeconds: Intervals between probe actions. Source: https://github.com/meta-magic/kubernetes_workshop
ensures that a specified number of them successfully terminate. As pods successfully complete, the job tracks the successful completions. When a specified number of successful completions is reached, the job itself is complete. Deleting a Job will cleanup the pods it created. A simple case is to create one Job object in order to reliably run one Pod to completion. The Job object will start a new Pod if the first pod fails or is deleted (for example due to a node hardware failure or a node reboot). A Job can also be used to run multiple pods in parallel. Kubernetes Jobs 99 Source: https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/ Command is wrapped for display purpose. Source: https://github.com/meta-magic/kubernetes_workshop
wrapped for display purpose. Source: https://github.com/meta-magic/kubernetes_workshop You can use CronJobs to run jobs on a time- based schedule. These automated jobs run like Cron tasks on a Linux or UNIX system. Cron jobs are useful for creating periodic and recurring tasks, like running backups or sending emails. Cron jobs can also schedule individual tasks for a specific time, such as if you want to schedule a job for a low activity period
Quota object, provides constraints that limit aggregate resource consumption per namespace. • It can limit the quantity of objects that can be created in a namespace by type, as well as the total amount of compute resources that may be consumed by resources in that project. Kubernetes Resource Quotas 101 Source: https://kubernetes.io/docs/concepts/policy/resource-quotas/ Source: https://github.com/meta-magic/kubernetes_workshop
can have. • If there is NO limit is defined, Pod will be able to consume more resources than requests. However, the eviction chances of Pod is very high if other Pods with Requests and Resource Limits are defined. Kubernetes Limit Range 102 Source: https://github.com/meta-magic/kubernetes_workshop
alive or not. If a container fails on this probe, kubelet kills it and may restart it based on the restartPolicy of a pod. Kubernetes Pod Liveness Probe 103 Source: https://kubernetes.io/docs/tasks/configure-pod- container/configure-liveness-readiness-probes/ Source: https://github.com/meta-magic/kubernetes_workshop
a replicated application that are down simultaneously from voluntary disruptions. • Cluster managers and hosting providers should use tools which respect Pod Disruption Budgets by calling the Eviction API instead of directly deleting pods. Kubernetes Pod Disruption Range 104 Source: https://kubernetes.io/docs/tasks/run-application/configure-pdb/ $ kubectl drain NODE [options] Source: https://github.com/meta-magic/kubernetes_workshop
be able to run on particular nodes or to prefer to run on particular nodes. There are several ways to do this, and they all use label selectors to make the selection. • Assign the label to Node • Assign Node Selector to a Pod Kubernetes Pod/Node Affinity / Anti-Affinity 105 Source: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/ $ kubectl label nodes k8s.node1 disktype=ssd Source: https://github.com/meta-magic/kubernetes_workshop
You use labels and annotations to attach metadata to your resources. To inject data into your resources, you’d likely create ConfigMaps (for non-confidential data) or Secrets (for confidential data). Taints and Tolerations - These provide a way for nodes to “attract” or “repel” your Pods. They are often used when an application needs to be deployed onto specific hardware, such as GPUs for scientific computing. Read more. Pod Presets - Normally, to mount runtime requirements (such as environmental variables, ConfigMaps, and Secrets) into a resource, you specify them in the resource’s configuration file. PodPresets allow you to dynamically inject these requirements instead, when the resource is created. For instance, this allows team A to mount any number of new Secrets into the resources created by teams B and C, without requiring action from B and C. Source: https://github.com/meta-magic/kubernetes_workshop
(or some) Nodes run a copy of a Pod. As nodes are added to the cluster, Pods are added to them. As nodes are removed from the cluster, those Pods are garbage collected. Deleting a DaemonSet will clean up the Pods it created. Some typical uses of a DaemonSet are: • running a cluster storage daemon, such as glusterd, ceph, on each node. • running a logs collection daemon on every node, such as fluentd or logstash. • running a node monitoring daemon on every node, such as Prometheus Node Exporter, collectd, Dynatrace OneAgent, Datadog agent, New Relic agent, Ganglia gmond or Instana agent. Source: https://github.com/meta-magic/kubernetes_workshop
still have a single main container, you can add a secondary container that acts as a helper (see a logging example). Two containers within a single Pod can communicate via a shared volume. Init containers: Init containers run before any of a Pod’s app containers (such as main and sidecar containers) Kubernetes Container Level Features 108 Source: https://kubernetes.io/docs/user-journeys/users/application-developer/advanced/
Infrastructure Details 2. Decouple the App Deployment from Infrastructure (On-Premise or Cloud) To help Developers 1. Write Once, Run Anywhere (Workload Portability) 2. Avoid Vendor Lock-In Cloud On-Premise
Plugins • First set of Volume plugins with K8s. • They are linked and compiled and shipped with K8s releases. • They were part of Core K8s libraries. • Volume Driver Development is tightly coupled with K8s releases. • Bugs in the Volume Driver crashes critical K8s components. • Deprecated since K8s v1.8 Out-of-Tree Volume Plugins • Flex Volume Driver • Executable Binaries • Worker Node communicates with binaries in CLI. • Need to access the Root File System of the Worker Node • Dependency issues • CSI – Container Storage Interface • Address the pain points of Flex Volume Driver
is Container Orchestrator (CO) neutral o Uses gRPC for inter-process communication o Runs Outside CO Processes. o CSI is control plane only Specs. o Identity: Identity and capability of the Driver o Controller: Volume operations such as provisioning and attachment. o Node: Mount / unmount ops must be executed on the node where the volume is needed. o Identity and Node are mandatory requirement for the driver implementation. Container Orchestrator (CO) Cloud Foundry, Docker, Kubernetes, Mesos CSI Driver gRPC Volume Access Storage API Storage System
Kubernetes & CSI Drivers 114 DaemonSet Pod Registrar CSI Driver Kubelet Worker Node Master API Server etcd gRPC gRPC gRPC gRPC Node Service Identity Service Controller Service
Node Service CreateVolume ControllerPublishVolume NodeStageVolume NodeUnStageVolume NodePublishVolume NodeUnPublishVolume DeleteVolume ControllerUnPublishVolume CREATED NODE_READY VOL_READY PUBLISHED Volume Created Volume available for use Volume initialized in the Node. One-time activity. Volume attached to the Pod
Provisioner Ver Persistence Access Mode Dynamic Provisioning Raw Block Support Volume Snapshot 1 AWS EBS ebs.csi.aws.com v0.3, v1.0 Yes RW Single Pod Yes Yes Yes 2 AWS EFS efs.csi.aws.com v0.3 Yes RW Multi Pod No No No 3 Azure Disk disk.csi.azure.com v0.3, v1.0 Yes RW Single Pod Yes No No 4 Azure File file.csi.azure.com v0.3, v1.0 Yes RW Multi Pod Yes No No 5 CephFS cephfs.csi.ceph.com v0.3, v1.0 Yes RW Multi Pod Yes No No 6 Ceph RBD rbd.csi.ceph.com v0.3, v1.0 Yes RW Single Pod Yes Yes Yes 7 GCE PD pd.csi.storage.gke.io v0.3, v1.0 Yes RW Single Pod Yes No Yes 8 Nutanix Vol com.nutanix.csi v0.3, v1.0 Yes RW Single Pod Yes No No 9 Nutanix Files com.nutanix.csi v0.3, v1.0 Yes RW Multi Pod Yes No No 10 Portworx pxd.openstorage.org v0.3, v1.1 Yes RW Multi Pod Yes No Yes Source: https://kubernetes-csi.github.io/docs/drivers.html
o HostPath o Local Block Storage o Amazon EBS o OpenStack Cinder o GCE Persistent Disk o Azure Disk o vSphere Volume Others o iScsi o Flocker o Git Repo o Quobyte Distributed File System o NFS o Ceph o Gluster o FlexVolume o PortworxVolume o Amazon EFS o Azure File System Life cycle of a Persistent Volume o Provisioning o Binding o Using o Releasing o Reclaiming Source: https://github.com/meta-magic/kubernetes_workshop
Space (Temporary) from the Host Machine. o Data exits only for the Life Cycle of the Pod. o Containers in the Pod can R/W to mounted path. o Can ONLY be referenced in-line from the Pod. o Can’t be referenced via Persistent Volume or Claim.
o OpenStack Cinder o GCE Persistent Disk o Azure Disk o vSphere Volume Distributed File System o NFS o Ceph o Gluster o FlexVolume o PortworxVolume o Amazon EFS o Azure File System o Remote Storage attached to the Pod based on the requirement. o Data persists beyond the life cycle of the Pod. o Two Types of Remote Storage o Block Storage o File System o Referenced in the Pod either in- line or PV/PVC
Automatically. o Kubernetes will attach the Remote (Block or FS) Volume to the Node. o Kubernetes will mount the volume to the Pod. This is NOT recommended because it breaks the Kubernetes principle of workload portability.
Deployment • All Replicas of the Deployment share the same Persistent volume Claim. • ReadWriteOnce Volumes are NOT recommended even with ReplicaSet 1 as it can fail or get into a deadlock (when the Pod goes down and Master tries to bring another Pod). • Volumes with ReadOnlyMany & ReadWriteMany are the best modes. • Deployments are used for Stateless Apps For Stateful Apps StatefulSet Kind: StatefulSet • StatefulSet is recommended for App that need a unique volume per ReplicaSet. • ReadWriteOnce should be used with a StatefulSet. RWO will create a unique volume per ReplicaSet.
Storage Static / Dynamic 1 Request Storage 2 Use Storage 3 Static: Persistent Volume Dynamic: Storage Class Persistent Volume Claim Claims are mounted as Volumes inside the Pod
is the physical storage available. • Storage Class is used to configure custom Storage option (nfs, cloud storage) in the cluster. They are the foundation of Dynamic Provisioning. • Persistent Volume Claim is used to mount the required storage into the Pod. • ReadOnlyMany: Can be mounted as read-only by many nodes • ReadWriteOnce: Can be mounted as read-write by a single node • ReadWriteMany: Can be mounted as read-write by many nodes Access Mode Source: https://kubernetes.io/docs/concepts/storage/persistent-volumes/#claims-as-volumes Persistent Volume Persistent Volume Claim Storage Class Volume Mode • There are two modes • File System and or • raw Storage Block. • Default is File System. Retain: The volume will need to be reclaimed manually Delete: The associated storage asset, such as AWS EBS, GCE PD, Azure disk, or OpenStack Cinder volume, is deleted Recycle: Delete content only (rm -rf /volume/*) - Deprecated Reclaim Policy
Use a Network File System or Block Storage for Pods to access and data from multiple sources. AWS EBS is such a storage system. • A Volume is created and its linked with a storage provider. In the following example the storage provider is AWS for the EBS. • Any PVC (Persistent Volume Claim) will be bound to the Persistent Volume which matches the storage class. 1 Volume ID is auto generated $ aws ec2 create-volume - -size 100 Storage class is mainly meant for dynamic provisioning of the persistent volumes. Persistent Volume is not bound to any specific namespace. Source: https://github.com/meta-magic/kubernetes_workshop
storage by issuing a Persistent Volume Claim. In the following example Pod claims for 2Gi Disk space from the network on AWS EBS. • Manual Provisioning of the AWS EBS supports ReadWriteMany, However all the pods are getting scheduled into a Single Node. • For Dynamic Provisioning use ReadWriteOnce. • Google Compute Engine also doesn't support ReadWriteMany for dynamic provisioning. 2 3 https://cloud.google.com/kubernetes-engine/docs/concepts/persistent-volumes Source: https://github.com/meta-magic/kubernetes_workshop
option is to make the Volume available from the Host Machine. • A Volume is created and its linked with a storage provider. In the following example the storage provider is Minikube for the host path. • Any PVC (Persistent Volume Claim) will be bound to the Persistent Volume which matches the storage class. • If it doesn't match a dynamic persistent volume will be created. Storage class is mainly meant for dynamic provisioning of the persistent volumes. Persistent Volume is not bound to any specific namespace. Host Path is NOT Recommended in Production 1 Source: https://github.com/meta-magic/kubernetes_workshop
by issuing a Persistent Volume Claim. In the following example Pod claims for 2Gi Disk space from the network on the host machine. • Persistent Volume Claim and Pods with Deployment properties are bound to a specific namespace. • Developer is focused on the availability of storage space using PVC and is not bothered about storage solutions or provisioning. • Ops Team will focus on Provisioning of Persistent Volume and Storage class. 2 3 Source: https://github.com/meta-magic/kubernetes_workshop
from the Github 2 3 1 1. Create Static Persistent Volumes OR Dynamic Volumes (using Storage Class) 2. Persistent Volume Claim is created and bound static and dynamic volumes. 3. Pods refer PVC to mount volumes inside the Pod. Source: https://github.com/meta-magic/kubernetes_workshop
get pods --field-selector status.phase=Running Get the list of pods where status.phase = Running Source: https://kubernetes.io/docs/concepts/overview/working-with-objects/field-selectors/ Field selectors let you select Kubernetes resources based on the value of one or more resource fields. Here are some example field selector queries: • metadata.name=my-service • metadata.namespace!=default • status.phase=Pending Supported Operators You can use the =, ==, and != operators with field selectors (= and == mean the same thing). This kubectl command, for example, selects all Kubernetes Services that aren’t in the default namespace: $ kubectl get services --field-selector metadata.namespace!=default
get pods --field-selector=status.phase!=Running,spec.restartPolicy=Always Source: https://kubernetes.io/docs/concepts/overview/working-with-objects/field-selectors/ Chained Selectors As with label and other selectors, field selectors can be chained together as a comma-separated list. This kubectlcommand selects all Pods for which the status.phasedoes not equal Running and the spec.restartPolicy field equals Always: Multiple Resource Type You use field selectors across multiple resource types. This kubectl command selects all Statefulsets and Services that are not in the default namespace: $ kubectl get statefulsets,services --field-selector metadata.namespace!=default
Docker and Kubernetes Networking • Pod to Pod Networking within the same Node • Pod to Pod Networking across the Node • Pod to Service Networking • Ingress - Internet to Service Networking • Egress – Pod to Internet Networking 139 3
1. All Pods can communicate with All other Pods without using Network Address Translation (NAT). 2. All Nodes can communicate with all the Pods without NAT. 3. The IP that is assigned to a Pod is the same IP the Pod sees itself as well as all other Pods in the cluster. Source: https://github.com/meta-magic/kubernetes_workshop
Network 2. Pod Network 3. Service Network Source: https://github.com/meta-magic/kubernetes_workshop CIDR Range (RFC 1918) 1. 10.0.0.0/8 2. 172.0.0.0/11 3. 192.168.0.0/16 Keep the Address ranges separate – Best Practices RFC 1918 1. Class A 2. Class B 3. Class C
10.130.1.102/24 Node 1 veth0 eth0 Pod 1 Container 1 172.17.4.1 eth0 Pod 2 Container 1 172.17.4.2 veth1 eth0 10.130.1.103/24 Node 2 veth1 eth0 Pod 1 Container 1 172.17.5.1 eth0 10.130.1.104/24 Node 3 veth1 eth0 Pod 1 Container 1 172.17.6.1 Service EP EP EP VIP 192.168.1.2/16 1. Physical Network 2. Pod Network 3. Service Network End Points handles dynamic IP Addresses of the Pods selected by a Service based on Pod Labels Virtual IP doesn’t have any physical network card or system attached.
144 By Default Linux has a Single Namespace and all the process in the namespace share the Network Stack. If you create a new namespace then all the process running in that namespace will have its own Network Stack, Routes, Firewall Rules etc. $ ip netns add namespace1 A mount point for namespace1 is created under /var/run/netns Create Namespace $ ip netns List Namespace eth0 10.130.1.101/24 Node 1 Root NW Namespace L2 Bridge 10.17.3.1/16 veth0 veth1 Forwarding Tables Bridge implements ARP to discover link- layer MAC Address eth0 Container 1 10.17.3.2 Pod 1 Container 2 10.17.3.2 eth0 Pod 2 Container 1 10.17.3.3 1. Pod 1 sends packet to eth0 – eth0 is connected to veth0 2. Bridge resolves the Destination with ARP protocol and 3. Bridge sends the packet to veth1 4. veth1 forwards the packet directly to Pod 2 thru eth0 of the Pod 2 1 2 4 3 This entire communication happens in localhost. So, Data transfer speed will NOT be affected by Ethernet card speed. Kube Proxy
Bridge 10.17.4.1/16 veth0 Kubernetes: Pod to Pod Networking Across Node 145 eth0 10.130.1.101/24 Node 1 Root NW Namespace L2 Bridge 10.17.3.1/16 veth0 veth1 Forwarding Tables eth0 Container 1 10.17.3.2 Pod 1 Container 2 10.17.3.2 eth0 Pod 2 Container 1 10.17.3.3 1. Pod 1 sends packet to eth0 – eth0 is connected to veth0 2. Bridge will try to resolve the Destination with ARP protocol and ARP will fail because there is no device connected to that IP. 3. On Failure Bridge will send the packet to eth0 of the Node 1. 4. At this point packet leaves eth0 and enters the Network and network routes the packet to Node 2. 5. Packet enters the Root namespace and routed to the L2 Bridge. 6. veth0 forwards the packet to eth0 of Pod 3 1 2 4 3 eth0 Pod 3 Container 1 10.17.4.1 5 6 Kube Proxy Kube Proxy Src-IP:Port: Pod1:17711 – Dst-IP:Port: Pod3:80
Bridge 10.17.4.1/16 veth0 Kubernetes: Pod to Service to Pod – Load Balancer 146 eth0 10.130.1.101/24 Node 1 Root NW Namespace L2 Bridge 10.17.3.1/16 veth0 veth1 Forwarding Tables eth0 Container 1 10.17.3.2 Pod 1 Container 2 10.17.3.2 eth0 Pod 2 Container 1 10.17.3.3 1. Pod 1 sends packet to eth0 – eth0 is connected to veth0 2. Bridge will try to resolve the Destination with ARP protocol and ARP will fail because there is no device connected to that IP. 3. On Failure Bridge will give the packet to Kube Proxy 4. it goes thru ip tables rules installed by Kube Proxy and rewrites the Dst-IP with Pod3-IP. IPVS has done the Cluster load Balancing directly on the node and packet is given to eth0 of the Node1. 5. Now packet leaves Node 1 eth0 and enters the Network and network routes the packet to Node 2. 6. Packet enters the Root namespace and routed to the L2 Bridge. 7. veth0 forwards the packet to eth0 of Pod 3 1 2 4 3 eth0 Pod 3 Container 1 10.17.4.1 5 6 Kube Proxy Kube Proxy 7 SrcIP:Port: Pod1:17711 – Dst-IP:Port: Service1:80 Src-IP:Port: Pod1:17711 – Dst-IP:Port: Pod3:80 Order Payments
Bridge 10.17.4.1/16 veth0 Kubernetes Pod to Service to Pod – Return Journey 147 eth0 10.130.1.101/24 Node 1 Root NW Namespace L2 Bridge 10.17.3.1/16 veth0 veth1 Forwarding Tables eth0 Container 1 10.17.3.2 Pod 1 Container 2 10.17.3.2 eth0 Pod 2 Container 1 10.17.3.3 1. Pod 3 receives data from Pod 1 and sends the reply back with Source as Pod3 and Destination as Pod1 2. Bridge will try to resolve the Destination with ARP protocol and ARP will fail because there is no device connected to that IP. 3. On Failure Bridge will give the packet Node 2 eth0 4. Now packet leaves Node 2 eth0 and enters the Network and network routes the packet to Node 1. (Dst = Pod1) 5. it goes thru ip tables rules installed by Kube Proxy and rewrites the Src-IP with Service-IP. Kube Proxy gives the packet to L2 Bridge. 6. L2 bridge makes the ARP call and hand over the packet to veth0 7. veth0 forwards the packet to eth0 of Pod1 1 2 4 3 eth0 Pod 3 Container 1 10.17.4.1 5 6 Kube Proxy Kube Proxy 7 Src-IP: Pod3:80 – Dst-IP:Port: Pod1:17711 Src-IP:Port: Service1:80– Dst-IP:Port: Pod1:17711 Order Payments
Bridge 10.17.4.1/16 veth0 Kubernetes: Internet to Pod 148 1. Client Connects to App published Domain. 2. Once the Ingress Load Balancer receives the packet it picks a VM (K8s Node). 3. Once inside the VM IP Tables knows how to redirect the packet to the Pod using internal load Balancing rules installed into the cluster using Kube Proxy. 4. Traffic enters Kubernetes cluster and reaches the Node X (10.130.1.102). 5. Node X gives the packet to the L2 Bridge 6. L2 bridge makes the ARP call and hand over the packet to veth0 7. veth0 forwards the packet to eth0 of Pod 8 1 2 4 3 5 6 7 Src: Client IP – Dst: App Dst Src: Client IP – Dst: Pod IP Ingress Load Balancer Client / User Src: Client IP – Dst: VM-IP eth0 Pod 8 Container 1 10.17.4.1 Kube Proxy VM VM VM
1 Root NW Namespace L2 Bridge 10.17.3.1/16 veth0 veth1 Forwarding Tables eth0 Container 1 10.17.3.2 Pod 1 Container 2 10.17.3.2 eth0 Pod 2 Container 1 10.17.3.3 1. Pod 1 sends packet to eth0 – eth0 is connected to veth0 2. Bridge will try to resolve the Destination with ARP protocol and ARP will fail because there is no device connected to that IP. 3. On Failure Bridge will give the packet to IP Tables 4. The Gateway will reject the Pod IP as it will recognize only the VM IP. So, source IP is replaced with VM-IP (NAT) 5. Packet enters the network and routed to Internet Gateway. 6. Packet reaches the GW and it replaces the VM-IP (internal) with an External IP. 7. Packet Reaches External Site (Google) 1 2 4 3 5 6 Kube Proxy 7 Src: Pod1 – Dst: Google Src: VM-IP – Dst: Google Gateway Google Src: Ex-IP – Dst: Google On the way back the packet follows the same path and any Src IP mangling is undone, and each layer understands VM-IP and Pod IP within Pod Namespace. VM
Linux Software that does packet filtering, NAT and other Packet mangling IP Tables It allows Admin to configure the netfilter for managing IP traffic. ConnTrack Conntrack is built on top of netfilter to handle connection tracking.. IPVS – IP Virtual Server Implements a transport layer load balancing as part of the Linux Kernel. It’s similar to IP Tables and based on netfilter hook function and uses hash table for the lookup. Border Gateway Protocol BGP is a standardized exterior gateway protocol designed to exchange routing and reachability information among autonomous systems (AS) on the Internet. The protocol is often classified as a path vector protocol but is sometimes also classed as a distance-vector routing protocol. Some of the well known & mandatory attributes are AS Path, Next Hop Origin. L2 Bridge (Software Switch) Network devices, called switches (or bridges) are responsible for connecting several network links to each other, creating a LAN. Major components of a network switch are a set of network ports, a control plane, a forwarding plane, and a MAC learning database. The set of ports are used to forward traffic between other switches and end-hosts in the network. The control plane of a switch is typically used to run the Spanning Tree Protocol, that calculates a minimum spanning tree for the LAN, preventing physical loops from crashing the network. The forwarding plane is responsible for processing input frames from the network ports and making a forwarding decision on which network port or ports the input frame is forwarded to.
is the Data Link Layer (OSI Mode) providing Node to Node Data Transfer. Layer 2 deals with delivery of frames between 2 adjacent nodes on a network. Ethernet is an Ex. Of Layer 2 networking with MAC represented as a Sub Layer. Flannel uses L3 with VXLAN (L2) networking. Layer 4 Networking Transport layer controls the reliability of a given link through flow control. Layer 7 Networking Application layer networking (HTTP, FTP etc.,) This is the closet layer to the end user. Kubernetes Ingress Controller is a L7 Load Balancer. Layer 3 Networking Layer 3’s primary concern involves routing packets between hosts on top of the layer 2 connections. IPv4, IPv6, and ICMP are examples of Layer 3 networking protocols. Calico uses L3 networking. VXLAN Networking Virtual Extensible LAN used to help large cloud deployments by encapsulating L2 Frames within UDP Datagrams. VXLAN is similar to VLAN (which has a limitation of 4K network IDs). VXLAN is an encapsulation and overlay protocol that runs on top of existing Underlay networks. VXLAN can have 16 million Network IDs. Overlay Networking An overlay network is a virtual, logical network built on top of an existing network. Overlay networks are often used to provide useful abstractions on top of existing networks and to separate and secure different logical networks. Source Network Address Translation SNAT refers to a NAT procedure that modifies the source address of an IP Packet. Destination Network Address Translation DNAT refers to a NAT procedure that modifies the Destination address of an IP Packet.
VTEP 172.17.4.1 Customer 1 Customer 2 eth0 10.130.2.187 Node / Server 2 172.17.5.1 VSWITCH VTEP 172.17.5.1 Customer 1 Customer 2 VXLAN Encapsulation 156 Overlay Network VSWITCH: Virtual Switch. | VTEP : Virtual Tunnel End Point VXLAN encapsulate L2 into UDP packets tunneling using L3. This means no specialized hardware required. So, the Overlay networks could be created purely in Software. VLAN = 4094 (2 reserved) Networks VNI = 16 Million Networks (24-bit ID)
L3 Overlay Cloud Pods Communicate using L2 Bridge Pod Traffic is routed in underlay network Pod Traffic is encapsulated & uses underlay for reachability Pod Traffic is routed in Cloud Virtual Network Technology Linux L2 Bridge L2 ARP Routing Protocol BGP VXLAN Amazon EKS Google GKE Encapsulation No No Yes No Example Cilium Calico, Cilium Flannel, Weave, Cilium AWS EKS, Google GKE, Microsoft ACS
10.130.1.102/24 Node 1 veth0 eth0 Pod 1 Container 1 172.17.4.1 eth0 Pod 2 Container 1 172.17.4.2 veth1 eth0 10.130.1.103/24 Node 2 veth1 eth0 Pod 1 Container 1 172.17.5.1 eth0 10.130.1.104/24 Node 3 veth1 eth0 Pod 1 Container 1 172.17.6.1 Service EP EP EP VIP 192.168.1.2/16 1. Physical Network 2. Pod Network 3. Service Network End Points handles dynamic IP Addresses of the Pods selected by a Service based on Pod Labels Virtual IP doesn’t have any physical network card or system attached. Virtual Network - L2 / L3 /Overlay / Cloud
Kubernetes DNS to avoid IP Addresses in the configuration or Application Codebase. It Configures Kubelet running on each Node so the containers uses DNS Service IP to resolve the IP Address. A DNS Pod consists of three separate containers 1. Kube DNS: Watches the Kubernetes Master for changes in Service and Endpoints 2. DNS Masq: Adds DNS caching to Improve the performance 3. Sidecar: Provides a single health check endpoint to perform health checks for Kube DNS and DNS Masq. • DNS Pod itself is a Kubernetes Service with a Cluster IP. • DNS State is stored in etcd. • Kube DNS uses a library the converts etcd name – value pairs into DNS Records. • Core DNS is similar to Kube DNS but with a plugin Architecture in v1.11 Core DNS is the default DNS Server. Source: https://github.com/meta-magic/kubernetes_workshop
Proxy model from design perspective. It can also work as a load balancer for the Service’s Pods. It can do simple TCP, UDP, and SCTP stream forwarding or round-robin TCP, UDP, and SCTP forwarding across a set of backend. • When Service of the type “ClusterIP” is created, the system assigns a virtual IP to it and there is no network interface or MAC address associated with it. • Kube-Proxy uses netfilter and iptables in the Linux kernel for the routing including VIP. Proxy Type • Tunnelling proxy passes unmodified requests from clients to servers on some network. It works as a gateway that enables packets from one network access servers on another network. • A forward proxy is an Internet-facing proxy that mediates client connections to web resources/servers on the Internet. • A Reverse proxy is an internal-facing proxy. It takes incoming requests and redirects them to some internal server without the client knowing which one he/she is accessing. Load balancing between backend Pods is done by the round-robin algorithm by default. Other supported Algos: 1. lc: least connection 2. dh: destination hashing 3. sh: source hashing 4. sed: shortest expected delay 5. nq: never queue Kube-Proxy can work in 3 modes 1. User space 2. IPTABLES 3. IPVS The differences comes in how Kube-Proxy interact with User Space and Kernel Space. How this is different for each of the modes by routing the traffic to service and then doing load balancing.
166 LoadBalancer: This is the standard way to expose service to the internet. All the traffic on the port is forwarded to the service. It's designed to assign an external IP to act as a load balancer for the service. There's no filtering, no routing. LoadBalancer uses cloud service or MetalLB for on-premise. Cluster IP: Cluster IP is the default and used when access within the cluster is required. We use this type of service when we want to expose a service to other pods within the same cluster. This service is accessed using kubernetes proxy. Nodeport: Opens a port in the Node when Pod needs to be accessed from outside the cluster. Few Limitations & hence its not advised to use NodePort • only one service per port • Ports between 30,000-32,767 • HTTP Traffic exposed in non std port • Changing node/VM IP is difficult
give Services 1. Externally-reachable URLs, 2. Load balance traffic, 3. Terminate SSL / TLS, and offer 4. Name based Virtual hosting. An Ingress controller is responsible for fulfilling the Ingress, usually with a load balancer, though it may also configure your edge router or additional frontends to help handle the traffic. Smart Routing Ingress Load Balancer Order Pods Pods Pods Traffic Kubernetes Cluster Product Pods Pods Pods /order /product Review Pods Pods Pods Source: https://kubernetes.io/docs/concepts/services-networking/ingress/ An Ingress does not expose arbitrary ports or protocols. Exposing services other than HTTP and HTTPS to the internet typically uses a service of type Service.Type=NodePort or Service.Type=LoadBalancer.
Pods Pods Pods Traffic Kubernetes Cluster Product Pods Pods Pods /order /product Review Pods Pods Pods Source: https://kubernetes.io/docs/concepts/services-networking/ingress/ Ingress Rules 1. Optional Host – If Host is specified then the rules will be applied to that host. 2. Paths – Each path under a host can routed to a specific backend service 3. Backend is a combination of Service and Service Ports
Pods Pods Pods Traffic Kubernetes Cluster Product Pods Pods Pods /order /product Review Pods Pods Pods Source: https://kubernetes.io/docs/concepts/services-networking/ingress/ Ingress Rules 1. Optional Host – If Host is specified then the rules will be applied to that host. 2. Paths – Each path under a host can routed to a specific backend service 3. Backend is a combination of Service and Service Ports
Product UI to access Database or Product Review directly. You can create Network policies across name spaces, services etc., for both incoming (Ingress) and outgoing (Egress) traffic. Product UI Pod Product UI Pod Product UI Pod Product Pod Product Pod Product Pod Review Pod Review Pod Review Pod MySQL Pod Mongo Pod Order UI Pod Order UI Pod Order UI Pod Order Pod Order Pod Order Pod Oracle Pod Blocks Access Blocks Access
Microservice Product Microservice 172.27.1.2 L3 / L4 L7 – API GET /live GET /ready GET /reviews/{id} POST /reviews PUT /reviews/{id} DELETE /reviews/{id} GET /reviews/192351 Product review can be accessed ONLY by Product. IP Tables enforces this rule. Exposed Exposed Exposed Exposed Exposed All other method calls are also exposed to Product Microservice. iptables –s 172.27.1.2 -p tcp –dport 80 -j accept
Microservice Product Microservice L3 / L4 L7 – API GET /live GET /ready GET /reviews/{id} POST /reviews PUT /reviews/{id} DELETE /reviews/{id} GET /reviews/192351 Rules are implemented by BPF (Berkeley Packet Filter) at Linux Kernel level. From Product Microservice only GET /reviews/{id} allowed. BPF / XDP performance is much superior to IPVS. Except GET /reviews All other calls are blocked for Product Microservice
works in sync with Istio in the Kubernetes world. 2. In Docker world Cilium works as a network driver and you can apply the policy using ciliumctl. In the previous example with Kubernetes Network policy you will be allowing access to Product Review from Product Microservice. However, that results in all the API calls of Product Review accessible by the Product Microservice. Now with the New Policy only GET /reviews/{id} is allowed. These Network policy gets executed at Linux Kernel using BPF. Product Microservice can access ONLY GET /reviews from Product Review Microservice User Microservice can access GET /reviews & POST /reviews from Product Review Microservice
Driver Software Stack Network Card BPF Regular BPF (Berkeley Packet Filter) mode Network Driver Software Stack Network Card BPF XDP allows BPF program to run inside the network driver with access to DMA buffer. Berkeley Packet Filters (BPF) provide a powerful tool for intrusion detection analysis. Use BPF filtering to quickly reduce large packet captures to a reduced set of results by filtering based on a specific type of traffic. Source: https://www.ibm.com/support/knowledgecenter/en/SS42VS_7.3.2/com.ibm.qradar.doc/c_forensics_bpf.html
drop millions packet per second when there is DDoS attack. Network Driver Software Stack Network Card BPF Drop Stack Network Driver Software Stack Network Card BPF Drop Stack LB & Tx BPF can perform Load Balancing and transmit out the data to wire again. Source: http://www.brendangregg.com/ebpf.html
BPF CLI Monitor Policy 1. Can compile and deploy BPF code (based on the labels of that Container) in the kernel when the containers is started. 2. When the 2nd container is deployed Cilium generates the 2nd BPF and deploy that rule in the kernel. 3. To get the network Connectivity Cilium compiles the BPF and attach it to the network device.
Docker and Kubernetes Networking 2. Pod to Pod Networking within the same Node 3. Pod to Pod Networking across the Node 4. Pod to Service Networking 5. Ingress - Internet to Service Networking 6. Egress – Pod to Internet Networking Kubernetes Volume • Installed nfs server in the cluster • Created Persistent Volume • Create Persistent Volume Claim • Linked Persistent Volume Claim to Pod Network Policies 1. Kubernetes Network Policy – L3 / L4 2. Created Network Policies within the same Namespace and across Namespace Networking - Components 1. Kubernetes IP Network 2. Kubernetes DNS 3. Kubernetes Proxy 4. Created Service (with Cluster IP) 5. Created Ingress
– Circuit Breaker LB – Load Balancer SD – Service Discovery Microservice Process 1 Process 2 Service Mesh Control Plane Service Discovery Routing Rules Control Plane will have all the rules for Routing and Service Discovery. Local Service Mesh will download the rules from the Control pane will have a local copy. Service Discovery Calls Service Mesh Calls Customer Microservice Application Localhost calls http://localhost/order/processOrder Router Network Stack LB CB SD Service Mesh Sidecar UI Layer Web Services Business Logic Order Microservice Application Localhost calls http://localhost/payment/processPayment Router Network Stack LB CB SD Service Mesh Sidecar UI Layer Web Services Business Logic Data Plane Source: https://github.com/meta-magic/kubernetes_workshop
and usage policies across service mesh and • Collects telemetry data from Envoy and other services. • Also includes a flexible plugin model. Mixer Provides • Service Discovery • Traffic Management • Routing • Resiliency (Timeouts, Circuit Breakers, etc.) Pilot Provides • Strong Service to Service and end user Authentication with built-in Identity and credential management. • Can enforce policies based on Service identity rather than network controls. Citadel Provides • Configuration Injection • Processing and • Distribution Component of Istio Galley Control Plane Envoy is deployed as a Sidecar in the same K8S Pod. • Dynamic Service Discovery • Load Balancing • TLS Termination • HTTP/2 and gRPC Proxies • Circuit Breakers • Health Checks • Staged Rollouts with % based traffic split • Fault Injection • Rich Metrics Envoy Data Plane
its own dedicated Envoy proxy. This ensures that the Envoy proxy can be tailored specifically to the requirements of its corresponding microservice and allows for fine-grained control and monitoring. 2. Security: Each Envoy proxy can have its own security settings, like specific access control policies, and its own secure mTLS connection. This reduces the risk of a single compromised host affecting all microservices on that host. 3. Fault Isolation: By providing each microservice with its own Envoy proxy, it's easier to isolate faults. If a proxy crashes or misbehaves, it affects only a single microservice instead of potentially affecting all microservices on a host. 4. Scalability: The sidecar pattern allows you to scale each microservice independently. When you need more instances of a specific microservice, Kubernetes can schedule more pods for that microservice, and each new pod will get its own Envoy proxy sidecar. 5. Observability: With a dedicated sidecar for each microservice, it is possible to get detailed metrics, logs, and traces for each service. Envoy Proxy Data Plane
Discovery: Like the old Pilot, Istiod is responsible for service discovery. It converts high-level routing rules that control traffic behavior into Envoy-specific configurations and propagates them to the sidecars at runtime. 2. Configuration Validation: It performs the same validating configuration role as Galley did, ensuring that the configuration settings are valid and can be acted upon. 3. Secure Communication Setup: Istiod sets up secure TLS communication between Envoy sidecars, previously a role performed by Citadel. It automates key and certificate management for Istio. 4. Ingress/Egress Gateway: Istiod manages the Gateways, which function like Kubernetes Ingress Controllers, controlling incoming and outgoing traffic to the mesh. 5. Sidecar Injection: Istiod also handles automatic sidecar injection, previously a separate component. 6. Telemetry and Reporting: While Mixer has been deprecated, Istio still provides robust telemetry and reporting features. With Mixer's features now moved to the sidecars and Istiod, Envoy proxies can send telemetry data directly to backend systems. 7. Certificate Distribution: Istiod also manages certificate distribution for secure service-to-service communication. Control Plane
End User Business Logic Service Mesh Sidecar Customer Service Mesh Control Plane Admin Traffic Rules Traffic Control rules can be applied for • different Microservices versions • Re Routing the request to debugging system to analyze the problem in real time. • Smooth migration path Business Logic Service Mesh Sidecar Business Logic Service Mesh Sidecar Business Logic Service Mesh Sidecar Business Logic Service Mesh Sidecar Business Logic Service Mesh Sidecar Order v1.0 Business Logic Service Mesh Sidecar Business Logic Service Mesh Sidecar Order v2.0 Service Cluster Source: https://github.com/meta-magic/kubernetes_workshop
Technology stack Microservices requires a standard telemetry service. • Adding SSL certificates across all the services. • Abstracting Horizontal concerns • Stakeholders: Identify whose affected. • Incentives: What Service Mesh brings onto the table. • Concerns: Their worries • Mitigate Concerns Source: https://github.com/meta-magic/kubernetes_workshop
same K8s Pod. • Dynamic Service Discovery • Load Balancing • TLS Termination • HTTP/2 and gRPC Proxies • Circuit Breakers • Health Checks • Staged Rollouts with % based traffic split • Fault Injection • Rich Metrics Envoy Data Plane 194 Istio Components – Envoy Proxy • Why Envoy as a Sidecar? • Microservice can focus on Business Logic and NOT on networking concerns and other NPR (logging, Security). • Features • Out of process Architecture • Low Latency, high performance • L3/L4 Packet Filtering • L7 Filters – HTTP • Service Discovery • Advanced Load Balancing • Observability • Proxy • Hot Restart Envoy deployed in production at Lyft, Apple, Salesforce, Google, and others. Source: https://blog.getambassador.io/envoy-vs-nginx-vs-haproxy-why-the-open-source-ambassador-api-gateway-chose-envoy-23826aed79ef Apart from static configurations Envoy also allows configuration via gRPC/protobuf APIs.
Pod Review Service Kubernetes Pod K8s Network With Istio (Service Mesh) Envoy in place the Product Service (inside the Pod) will talk to Envoy (Proxy) to connect to Product Review Service. 1. Product Service Talks to Envoy inside Product Pod 2. Envoy in Product Pod talks to Envoy in Review Pod 3. Envoy in Review Pod talks to Review Pod
# Istio Kinds Description 1 Ingress 1 Gateway Exposes Ports to outside world 2 Virtual Service Traffic Routing based on URL path 3 Destination Rule Traffic Routing based on Business Rules 2 Service 4 Service Entry External Service Definition 3 Service Account 4 Network Policy 5 Peer Authentication Enables mTLS across Mesh, Namespace etc. 6 Request Authentication Authenticates using JWTs 7 Authorization Policy More granular Network/Security Policies
Destination Rule Routing Rules Policies • Match • URI Patterns • URI ReWrites • Headers • Routes • Fault • Fault • Route • Weightages • Traffic Policies • Load Balancer Configures a load balancer for HTTP/TCP traffic, most commonly operating at the edge of the mesh to enable ingress traffic for an application. Defines the rules that control how requests for a service are routed within an Istio service mesh. Configures the set of policies to be applied to a request after Virtual Service routing has occurred. Source: https://github.com/meta-magic/kubernetes_workshop
at the edge of the mesh receiving incoming or outgoing HTTP/TCP connections. The Gateway specification above describes the L4-L6 properties of a load balancer.
Gateway configuration sets up a proxy to act as a load balancer exposing • port 80 and • 443 (https), for ingress. Multiple Sub-domains are mapped to the single Load Balancer IP Address. TLS Termination is done using product-credential (secret)
is external to the mesh. Typically used to indicate external services consumed through APIs. MESH_INTERNAL Signifies that the service is part of the mesh. A service entry describes the properties of a service • DNS name, • VIPs (Virtual IPs) • ports, protocols • endpoints
/productms /productreview Load Balancer Ingress UI Pod UI Pod UI Pod UI Service Product Pod Product Pod Product Pod Product Service Review Pod Review Pod Review Pod Review Service Deployment / Replica / Pod N1 N2 N2 Nodes N4 N3 MySQL Pod N4 N3 N1 Kubernetes Objects Firewall Service Call Kube DNS EndPoints EndPoints EndPoints Internal Load Balancers Source: https://github.com/meta-magic/kubernetes_workshop
Gateway Virtual Service UI Pod UI Pod UI Pod UI Service Product Pod Product Pod Product Pod Product Service Review Pod Review Pod Review Pod Review Service MySQL Pod Deployment / Replica / Pod N1 N2 N2 N4 N1 N3 N4 N3 Nodes Istio Sidecar Envoy Destination Rule Destination Rule Destination Rule Load Balancer Kubernetes Objects Istio Objects Firewall istiod Istio Control Plane Service Call Kube DNS EndPoints EndPoints EndPoints Internal Load Balancers Source: https://github.com/meta-magic/kubernetes_workshop
Service UI Pod UI Pod UI Pod UI Service Product Pod Product Pod Product Pod Product Service Review Pod Review Pod Review Pod Review Service Deployment / Replica / Pod N1 N2 N2 MySQL Pod N4 N3 N1 N4 N3 Nodes Istio Sidecar - Envoy Destination Rule Destination Rule Destination Rule Load Balancer Kubernetes Objects Istio Objects Firewall UI Pod N5 v1 v2 Stable / v1 Canary v2 User X = Canary Others = Stable A / B Testing using Canary Deployment Service Call Kube DNS EndPoints EndPoints EndPoints Internal Load Balancers Source: https://github.com/meta-magic/kubernetes_workshop Istio Control Plane istiod
Service UI Pod UI Pod UI Pod UI Service Product Pod Product Pod Product Pod Product Service Review Pod Review Pod Review Pod Review Service Deployment / Replica / Pod N1 N2 N2 MySQL Pod N4 N3 N1 N4 N3 Nodes Istio Sidecar - Envoy Destination Rule Destination Rule Destination Rule Load Balancer Kubernetes Objects Istio Objects Firewall UI Pod N5 v1 v2 Stable / v1 Canary v2 10% = Canary 90% = Stable Traffic Shifting Canary Deployment Service Call Kube DNS EndPoints EndPoints EndPoints Internal Load Balancers Source: https://github.com/meta-magic/kubernetes_workshop Istio Control Plane istiod
Service UI Pod UI Pod UI Pod UI Service Product Pod Product Pod Product Pod Product Service Review Pod Review Pod Review Pod Review Service Deployment / Replica / Pod N1 N2 N2 MySQL Pod N4 N3 N1 N4 N3 Nodes Istio Sidecar - Envoy Destination Rule Destination Rule Destination Rule Load Balancer Kubernetes Objects Istio Objects Firewall UI Pod N5 v1 v2 Stable / v1 Canary v2 100% = v2 Blue Green Deployment Service Call Kube DNS EndPoints EndPoints EndPoints Internal Load Balancers Source: https://github.com/meta-magic/kubernetes_workshop Istio Control Plane istiod
Service UI Pod UI Pod UI Pod UI Service Product Pod Product Pod Product Pod Product Service Review Pod Review Pod Review Pod Review Service Deployment / Replica / Pod N1 N2 N2 MySQL Pod N4 N3 N1 N4 N3 Nodes Istio Sidecar - Envoy Destination Rule Destination Rule Destination Rule Load Balancer Kubernetes Objects Istio Objects Firewall UI Pod N5 v1 v2 Stable / v1 Canary v2 100% = Stable Mirror = Canary Mirror Data Service Call Kube DNS EndPoints EndPoints EndPoints Internal Load Balancers Source: https://github.com/meta-magic/kubernetes_workshop Istio Control Plane istiod
Review is not available Product service will return the product details with a message review not available. Reverse Proxy Server Ingress Deployment / Replica / Pod Nodes Kubernetes Objects Firewall UI Pod UI Pod UI Pod UI Service N1 N2 N2 EndPoints Product Pod Product Pod Product Pod Product Service N4 N3 MySQL Pod EndPoints Internal Load Balancers Users Routing based on Layer 3,4 and 7 Review Pod Review Pod Review Pod Review Service N4 N3 N1 Service Call Kube DNS EndPoints
Service UI Pod UI Pod UI Pod UI Service Review Pod Review Pod Review Pod Review Service Deployment / Replica / Pod N1 N2 N2 MySQL Pod N4 N3 N1 N4 N3 Nodes Istio Sidecar - Envoy Destination Rule Destination Rule Destination Rule Load Balancer Kubernetes Objects Istio Objects Firewall v1 Fault Injection Delay = 7 Sec Abort = 10% Fault Injection Product Pod Product Pod Product Pod Product Service Service Call Kube DNS EndPoints EndPoints EndPoints Internal Load Balancers Source: https://github.com/meta-magic/kubernetes_workshop Istio Control Plane istiod
identity, powerful policy, transparent TLS encryption, and authentication, authorization and audit (AAA) tools to protect your services and data. The goals of Istio security are • Security by default: no changes needed for application code and infrastructure • Defense in depth: integrate with existing security systems to provide multiple layers of defense • Zero-trust network: build security solutions on untrusted networks
istiod offers a gRPC service to take certificate signing requests (CSRs). 2. When started, the Istio agent creates the private key and CSR, and then sends the CSR with its credentials to istiod for signing. 3. The CA in istiod validates the credentials carried in the CSR. Upon successful validation, it signs the CSR to generate the certificate. 4. Envoy requests the certificate and key from the Istio agent in the same container when a workload is started via the Envoy secret discovery service (SDS) API. 5. The Istio agent sends the certificates received from istiod and the private key to Envoy via the Envoy SDS API. 6. Istio agent monitors the expiration of the workload certificate. The above process repeats periodically for certificate and key rotation.
authentication to verify the client making the connection. Istio offers mutual TLS as a full stack solution for transport authentication, which can be enabled without requiring service code changes. This solution: • Provides each service with a strong identity representing its role to enable interoperability across clusters and clouds. • Secures service-to-service communication. • Provides a key management system to automate key and certificate generation, distribution, and rotation. • Request authentication: Used for end-user authentication to verify the credential attached to the request. Istio enables request-level authentication with JSON Web Token (JWT) validation and a streamlined developer experience using a custom authentication provider or any OpenID Connect providers. Source: https://istio.io/docs/concepts/security/
through the client- and server-side PEPs implemented as Envoy proxies. When a workload sends a request to another workload using mutual TLS authentication, the request is handled as follows: 1. Istio re-routes the outbound traffic from a client to the client’s local sidecar Envoy. 2. The client-side Envoy starts a mutual TLS handshake with the server-side Envoy. During the handshake, the client-side Envoy also does a secure naming check to verify that the service account presented in the server certificate is authorized to run the target service. 3. The client-side Envoy and the server-side Envoy establish a mutual TLS connection, and Istio forwards the traffic from the client-side Envoy to the server-side Envoy. 4. The server-side Envoy authorizes the request. It forwards traffic to the backend service through local TCP connections if authorized. Source: https://istio.io/docs/concepts/security/
permissive mode, which allows a service to accept both plaintext traffic and mutual TLS traffic at the same time. This feature greatly improves the mutual TLS onboarding experience. Many non-Istio clients communicating with a non-Istio server presents a problem for an operator who wants to migrate that server to Istio with mutual TLS enabled. Commonly, the operator cannot install an Istio sidecar for all clients simultaneously or needs permission to do so for some clients. Even after installing the Istio sidecar on the server, the operator can only enable mutual TLS if they break existing communications. With the permissive mode enabled, the server accepts both plaintext and mutual TLS traffic. The mode provides greater flexibility for the onboarding process. The server’s installed Istio sidecar takes mutual TLS traffic immediately without breaking existing plaintext traffic. As a result, the operator can gradually install and configure the client’s Istio sidecars to send mutual TLS traffic. Once the configuration of the clients is complete, the operator can configure the server to mutual TLS only mode. For more information, visit the Mutual TLS Migration tutorial. Source: https://istio.io/docs/concepts/security/
policies, you can specify authentication requirements for workloads receiving requests in an Istio mesh. Upon any policy changes, the new policy is translated to the appropriate configuration telling the PEP how to perform the required authentication mechanisms. Istio sends configurations to the targeted endpoints asynchronously. Istio automatically upgrades all traffic between two PEPs to mutual TLS for peer authentication. If authentication policies disable mutual TLS mode, Istio continues to use plain text between PEPs. Envoy Proxy = PEP Policy Enforcement Point Source: https://istio.io/docs/concepts/security/
control to the inbound traffic in the server side Envoy proxy. Each Envoy proxy runs an authorization engine that authorizes requests at runtime. When a request comes to the proxy, the authorization engine evaluates the request context against the current authorization policies, and returns the authorization result, either ALLOW or DENY. Source: https://istio.io/docs/concepts/security/
account • GKE/GCE: may use GCP service account • GCP: GCP service account • AWS: AWS IAM user/role account • On-premises (non-Kubernetes): user account, custom service account, service name, Istio service account, or GCP service account. The custom service account refers to the existing service account just like the identities that the customer’s Identity Directory manages. Source: https://istio.io/docs/concepts/security/ Istio and SPIFFE share the same identity document: SVID (SPIFFE Verifiable Identity Document). For example, in Kubernetes, the X.509 certificate has the URI field in the format of spiffe://\<domain\>/ns/\<namespace\>/s a/\<serviceaccount\>. This enables Istio services to establish and accept connections with other SPIFFE-compliant systems SPIFFE Secure Production Identity Framework for Everyone. Inspired by the production infrastructure of Google and others, SPIFFE is a set of open-source standards for securely identifying software systems in dynamic and heterogeneous environments.
be RSA or ECDSA. • e: The exponent for a standard to a public key. • n: The modulus for a standard to a public key. • alg: Specifies the algorithm intended to be used with the key. • kid: The unique identifier for the key.
Web Apps with Ubuntu & Tomcat can have a size of 700 MB 2. Use Alpine Image as your base Linux OS 3. Alpine images are 10x smaller than base Ubuntu images 4. Smaller Image size reduce the Container vulnerabilities. 5. Ensure that only Runtime Environments are there in your container. For Example your Alpine + Java + Tomcat image should contain only the JRE and NOT JDK. 6. Log the App output to Container Std out and Std error. 1
1. Create Multiple layers of Images 2. Create a User account 3. Add Runtime software’s based on the User Account. 4. Run the App under the user account 5. This gives added security to the container. 6. Add Security module SELinux or AppArmour to increase the security. Alpine JRE 8 Tomcat 8 My App 1 2
OS! Containers runs on Host Kernel. 2. No Runtime software downloads inside the container. Declare the software requirements at the build time itself. 3. Download Docker base images from Authentic site. 4. Limit the resource utilization using Container orchestrators like Kubernetes. 5. Don’t run anything on Super privileged mode. 3
Naked Pod, that is Pod without any ReplicaSet or Deployments. Naked pods will never get re-scheduled if the Pod goes down. 2. Never access a Pod directly from another Pod. Always use a Service to access a Pod. 3. User labels to select the pods { app: myapp, tier: frontend, phase: test, deployment: v3 }. 4. Never use :latest tag in the image in the production scenario. 4
Kubernetes Cluster 1. Group your Services / Pods / Traffic Rules based on Specific Namespace. 2. This helps you apply specific Network Policies for that Namespace with increase in Security and Performance. 3. Handle specific Resource Allocations for a Namespace. 4. If you have more than a dozen Microservices then it’s time to bring in Namespaces. Service-Name.Namespace.svc.cluster.local $ kubectl config set-context $(kubectl config current-context) --namespace=your-ns The above command will let you switch the namespace to your namespace (your-ns). 5
check is critical to increase the overall resiliency of the network. 2. Readiness 3. Liveness 4. Ensure that all your Pods have Readiness and Liveness Probes. 5. Choose the Protocol wisely (HTTP, Command & TCP) 6
Quality define the requests and limits for your Pods. 2. You can set specific resource requests for a Dev Namespace to ensure that developers don’t create pods with a very large resource or a very small resource. 3. Limit Range can be set to ensure that containers were create with too low resource or too large resource. 7
that the Application to Handle SIGTERM message. 2. You can use preStop Hook 3. Set the terminationGracePeriodSeconds: 60 4. Ensure that you clean up the connections or any other artefacts and ready for clean shutdown of the App (Microservice). 5. If the Container is still running after the grace period, Kubernetes sends a SIGKILL event to shutdown the Pod. 8
that can be outside the Kubernetes cluster like 1. Databases or 2. external services in the cloud. 2. You can create an Endpoint with Specific IP Address and Port with the same name as Service. 3. You can create a Service with an External Name (URL) which does a CNAME redirection at the Kernel level. 9
the Master behind a Load Balancer. 2. Upgrade Master 1. Scale up the Node with an extra Node 2. Drain the Node and 3. Upgrade Node 3. Cluster will be running even if the master is not working. Only Kubectl and any master specific functions will be down until the master is up. 10
Culture 2 Adopt Infrastructure as Code 3 Adopt Containerized Microservices 4 Adopt a Capability Model, not a Maturity Model 5 Drive Continuous Improvement through Key Capabilities Establish a Software Factory 7 Define a meaningful DevSecOps pipeline 8 Adapt an Agile Acquisition Policy for Software 9 Tirelessly Pursue Cyber Resilience 10 Shift Left: Operational Test & Eval Source: US DoD DevSecOps Fundamentals Playbook
Practices 1. Stakeholder transparency and visibility. 2. Complete transparency across team members in real- time. 3. All project resources easily accessible to the entire team; not everyone needs commit privileges. 4. Adopt and embrace ChatOps as the communication backbone for the DevSecOps team. 5. All technical staff should be concerned with, and have a say in, baked-in security. Source: US DoD DevSecOps Fundamentals Playbook. Page 4
1. IT infrastructure supports and enables change, rather than being an obstacle or a constraint. 2. Mitigates drift between environments by leveraging automation and push- button deployment. 3. Enforces change management through GitOps with multiple approvers, as needed. 4. Environmental changes are routine and fully automated, pivoting staff to focus on other tasks. 5. Quicker recovery from failures, rather than assuming failure can be completely prevented. 6. Empowers a continuous improvement ecosystem rather than “big bang” one and done activities. Source: US DoD DevSecOps Fundamentals Playbook. Page 5
DevSecOps Fundamentals Playbook. Page 6 Components via Services Organized around Business Capabilities Products NOT Projects Smart Endpoints & Dumb Pipes Decentralized Governance & Data Management Infrastructure Automation via IaC Design for Failure Evolutionary Design
253 4 Source: US DoD DevSecOps Fundamentals Playbook. Page 7 Metric High Performers Medium Performers Low Performers Deployment frequency – How often the organization deploys code. On demand (multiple deploys per day) Between once per week and once per month Between once per week and once per month Change lead time – Time it takes to go from code commit to code successfully running in production. Less than one hour Between one week and one month Between one week and one month Mean time to recover (MTTR) – Time it takes to restore service when a service incident occurs (e.g., unplanned outage, service impairment). Less than one hour Less than one day Between one day and one week Change failure rate – Percentage of changes that results in either degraded service or requires remediation (e.g., leads to service impairment, service outage, requires a hotfix, rollback, patch, etc.) 0-15% 0-15% 31-45% Google’s DORA research program advocates that rather than use a maturity model, research shows that a capability model is a better way to both encourage and measure performance improvement
Source: US DoD DevSecOps Fundamentals Playbook. Page 8 Architecture 1. Use Loosely Coupled Architecture 2. Architect for Empowered Teams Culture 1. Adopt a Likert scale survey to measure cultural change progress 2. Encourage and support continuous learning initiatives 3. Support and Facilitate Collaboration among and between teams 4. Provide resources and tools that make work meaningful 5. Support or Embody transformational leadership 24 There are 24 Key that drive improvements across both DevSecOps and the Organization 5 Classified into 5 Categories 1. Culture 2. Architecture 3. Product & Process 4. Continuous Delivery 5. Lean Management & Monitoring
Continuous Delivery 1. Use a Source Code Repo for all production Artifacts 2. Use Trunk based development methods 3. Shift Left on Security 4. Implement Test Automation 5. Implement Continuous Integration 6. Support Test Data Management 7. Implement Continuous Delivery 8. Automate the Deployment Process Source: US DoD DevSecOps Fundamentals Playbook. Page 8 Product and Process 1. Gather and Implement Customer Feedback 2. Make the flow of work visible through the value stream 3. Work in small batches 4. Foster and enable team experimentation Lean Management & Monitoring 1. Have a lightweight change approval process 2. Monitor across applications & infrastructure too inform business decisions 3. Check System Health periodically 4. Improve Processes and Manage work with WIP 5. Visualize work to Monitor quality and communicate throughout
DoD DevSecOps Fundamentals Playbook. Page 9 • Define CI/CD Processes and Tasks • Select Tools • Operate & Maintain the Software Factory • Monitor the Tools and Processes • Gather Feedback for Improvement • Build the Software Factory • Automate the Workflows • Verify the Tool Integrations • Test the Pipeline Workflows
8 • Establishes the Software Acquisition Pathway as the preferred path for acquisition and development of software-intensive systems. • Simplifies the acquisition model to enable continuous integration and delivery of software capability on timelines relevant to the warfighter / end user. • Establishes business decision artifacts to manage risk and enable successful software acquisition and development. Source: US DoD DevSecOps Fundamentals Playbook. Page 11
Resilience is “the ability to anticipate, withstand, recover from, and adapt to adverse conditions, stresses, attacks, or compromises on the systems that include cyber resources.” • Cybersecurity touches each of the eight phases of the DevSecOps lifecycle, and the various control gates serve as Go/No-Go decision points. • Al pipelines must use these control gates to ensure that cybersecurity is both “baked in” and transparently identified • Moving to DevSecOps includes moving towards a Continuous Authorization to Operate (cATO) for an application developed using DevSecOps processes, including a software factory with a CI/CD pipeline. Source: US DoD DevSecOps Fundamentals Playbook. Page 12
is Dead : GoTo 2015 By Dave Thomas 2. Apr 7, 2016 - Agile Project Management with Kanban | Eric Brechner | Talks at Google 3. Sep 27, 2017 - Scrum vs Kanban - Two Agile Teams Go Head-to-Head 4. Feb 17, 2019 - Lean vs Agile vs Design Thinking 5. Dec 17, 2020 - Scrum vs Kanban | Differences & Similarities Between Scrum & Kanban 6. Feb 24, 2021 - Agile Methodology Tutorial for Beginners | Jira Tutorial | Agile Methodology Explained. Agile Methodologies
Fowler 2. When to use Microservices By Martin Fowler 3. GoTo: Sep 3, 2020: When to use Microservices By Martin Fowler 4. GoTo: Feb 26, 2020: Monolith Decomposition Pattern 5. Thought Works: Microservices in a Nutshell 6. Microservices Prerequisites 7. What do you mean by Event Driven? 8. Understanding Event Driven Design Patterns for Microservices
Fowler – Micro Services : https://www.youtube.com/watch?v=2yko4TbC8cI&feature=youtu.be&t=15m53s 2. GOTO 2016 – Microservices at NetFlix Scale: Principles, Tradeoffs & Lessons Learned. By R Meshenberg 3. Mastering Chaos – A NetFlix Guide to Microservices. By Josh Evans 4. GOTO 2015 – Challenges Implementing Micro Services By Fred George 5. GOTO 2016 – From Monolith to Microservices at Zalando. By Rodrigue Scaefer 6. GOTO 2015 – Microservices @ Spotify. By Kevin Goldsmith 7. Modelling Microservices @ Spotify : https://www.youtube.com/watch?v=7XDA044tl8k 8. GOTO 2015 – DDD & Microservices: At last, Some Boundaries By Eric Evans 9. GOTO 2016 – What I wish I had known before Scaling Uber to 1000 Services. By Matt Ranney 10. DDD Europe – Tackling Complexity in the Heart of Software By Eric Evans, April 11, 2016 11. AWS re:Invent 2016 – From Monolithic to Microservices: Evolving Architecture Patterns. By Emerson L, Gilt D. Chiles 12. AWS 2017 – An overview of designing Microservices based Applications on AWS. By Peter Dalbhanjan 13. GOTO Jun, 2017 – Effective Microservices in a Data Centric World. By Randy Shoup. 14. GOTO July, 2017 – The Seven (more) Deadly Sins of Microservices. By Daniel Bryant 15. Sept, 2017 – Airbnb, From Monolith to Microservices: How to scale your Architecture. By Melanie Cubula 16. GOTO Sept, 2017 – Rethinking Microservices with Stateful Streams. By Ben Stopford. 17. GOTO 2017 – Microservices without Servers. By Glynn Bird.
2012 What I have learned about DDD Since the book. By Eric Evans 2. Mar 19, 2013 Domain Driven Design By Eric Evans 3. Jun 02, 2015 Applied DDD in Java EE 7 and Open Source World 4. Aug 23, 2016 Domain Driven Design the Good Parts By Jimmy Bogard 5. Sep 22, 2016 GOTO 2015 – DDD & REST Domain Driven API’s for the Web. By Oliver Gierke 6. Jan 24, 2017 Spring Developer – Developing Micro Services with Aggregates. By Chris Richardson 7. May 17. 2017 DEVOXX – The Art of Discovering Bounded Contexts. By Nick Tune 8. Dec 21, 2019 What is DDD - Eric Evans - DDD Europe 2019. By Eric Evans 9. Oct 2, 2020 - Bounded Contexts - Eric Evans - DDD Europe 2020. By. Eric Evans 10. Oct 2, 2020 - DDD By Example - Paul Rayner - DDD Europe 2020. By Paul Rayner
Event Driven Architecture – Mar 21, 2021 2. Martin Fowler: Event Driven Architecture – GOTO 2017 3. Greg Young: A Decade of DDD, Event Sourcing & CQRS – April 11, 2016 4. Nov 13, 2014 GOTO 2014 – Event Sourcing. By Greg Young 5. Mar 22, 2016 Building Micro Services with Event Sourcing and CQRS 6. Apr 15, 2016 YOW! Nights – Event Sourcing. By Martin Fowler 7. May 08, 2017 When Micro Services Meet Event Sourcing. By Vinicius Gomes
RabbitMQ 3. IBM: Apache Kafka – Sept 18, 2020 4. Confluent: Apache Kafka Fundamentals – April 25, 2020 5. Confluent: How Kafka Works – Aug 25, 2020 6. Confluent: How to integrate Kafka into your environment – Aug 25, 2020 7. Kafka Streams – Sept 4, 2021 8. Kafka: Processing Streaming Data with KSQL – Jul 16, 2018 9. Kafka: Processing Streaming Data with KSQL – Nov 28, 2019
1. Google: How to Choose the right database? 2. AWS: Choosing the right Database 3. IBM: NoSQL Vs. SQL 4. A Guide to NoSQL Databases 5. How does NoSQL Databases Work? 6. What is Better? SQL or NoSQL? 7. What is DBaaS? 8. NoSQL Concepts 9. Key Value Databases 10. Document Databases 11. Jun 29, 2012 – Google I/O 2012 - SQL vs NoSQL: Battle of the Backends 12. Feb 19, 2013 - Introduction to NoSQL • Martin Fowler • GOTO 2012 13. Jul 25, 2018 - SQL vs NoSQL or MySQL vs MongoDB 14. Oct 30, 2020 - Column vs Row Oriented Databases Explained 15. Dec 9, 2020 - How do NoSQL databases work? Simply Explained! 1. Graph Databases 2. Column Databases 3. Row Vs. Column Oriented Databases 4. Database Indexing Explained 5. MongoDB Indexing 6. AWS: DynamoDB Global Indexing 7. AWS: DynamoDB Local Indexing 8. Google Cloud Spanner 9. AWS: DynamoDB Design Patterns 10. Cloud Provider Database Comparisons 11. CockroachDB: When to use a Cloud DB?
IBM: Virtual Machines and Containers 2. IBM: What is a Hypervisor? 3. IBM: Docker Vs. Kubernetes 4. IBM: Containerization Explained 5. IBM: Kubernetes Explained 6. IBM: Kubernetes Ingress in 5 Minutes 7. Microsoft: How Service Mesh works in Kubernetes 8. IBM: Istio Service Mesh Explained 9. IBM: Kubernetes and OpenShift 10. IBM: Kubernetes Operators 11. 10 Consideration for Kubernetes Deployments Istio – Metrics 1. Istio – Metrics 2. Monitoring Istio Mesh with Grafana 3. Visualize your Istio Service Mesh 4. Security and Monitoring with Istio 5. Observing Services using Prometheus, Grafana, Kiali 6. Istio Cookbook: Kiali Recipe 7. Kubernetes: Open Telemetry 8. Open Telemetry 9. How Prometheus works 10. IBM: Observability vs. Monitoring
introduction to TDD 2. Aug 14, 2019 – Component Software Testing 3. May 30, 2020 – What is Component Testing? 4. Apr 23, 2013 – Component Test By Martin Fowler 5. Jan 12, 2011 – Contract Testing By Martin Fowler 6. Jan 16, 2018 – Integration Testing By Martin Fowler 7. Testing Strategies in Microservices Architecture 8. Practical Test Pyramid By Ham Vocke Testing – TDD / BDD
framework. It was designed to be easy to extend and most of the important components are plug‐ gable. 2. Pumba : A chaos testing and network emulation tool for Docker. 3. Chaos Lemur : Self-hostable application to randomly destroy virtual machines in a BOSH- managed environment, as an aid to resilience testing of high-availability systems. 4. Chaos Lambda : Randomly terminate AWS ASG instances during business hours. 5. Blockade : Docker-based utility for testing network failures and partitions in distributed applications. 6. Chaos-http-proxy : Introduces failures into HTTP requests via a proxy server. 7. Monkey-ops : Monkey-Ops is a simple service implemented in Go, which is deployed into an OpenShift V3.X and generates some chaos within it. Monkey-Ops seeks some OpenShift components like Pods or Deployment Configs and randomly terminates them. 8. Chaos Dingo : Chaos Dingo currently supports performing operations on Azure VMs and VMSS deployed to an Azure Resource Manager-based resource group. 9. Tugbot : Testing in Production (TiP) framework for Docker. Testing tools
Continuous Integration? 2. What is Continuous Delivery? 3. CI / CD Pipeline 4. What is CI / CD Pipeline? 5. CI / CD Explained 6. CI / CD Pipeline using Java Example Part 1 7. CI / CD Pipeline using Ansible Part 2 8. Declarative Pipeline vs Scripted Pipeline 9. Complete Jenkins Pipeline Tutorial 10. Common Pipeline Mistakes 11. CI / CD for a Docker Application
“Microservices: A Definition of This New Architectural Term”, March 25, 2014. 2. Miller, Matt. “Innovate or Die: The Rise of Microservices”. e Wall Street Journal, October 5, 2015. 3. Newman, Sam. Building Microservices. O’Reilly Media, 2015. 4. Alagarasan, Vijay. “Seven Microservices Anti-patterns”, August 24, 2015. 5. Cockcroft, Adrian. “State of the Art in Microservices”, December 4, 2014. 6. Fowler, Martin. “Microservice Prerequisites”, August 28, 2014. 7. Fowler, Martin. “Microservice Tradeoffs”, July 1, 2015. 8. Humble, Jez. “Four Principles of Low-Risk Software Release”, February 16, 2012. 9. Zuul Edge Server, Ketan Gote, May 22, 2017 10. Ribbon, Hysterix using Spring Feign, Ketan Gote, May 22, 2017 11. Eureka Server with Spring Cloud, Ketan Gote, May 22, 2017 12. Apache Kafka, A Distributed Streaming Platform, Ketan Gote, May 20, 2017 13. Functional Reactive Programming, Araf Karsh Hamid, August 7, 2016 14. Enterprise Software Architectures, Araf Karsh Hamid, July 30, 2016 15. Docker and Linux Containers, Araf Karsh Hamid, April 28, 2015
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