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samples/features/sql-big-data-cluster/deployment/kubeadm/ubuntu-single-node-vm/cleanup-bdc.sh

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DIR_PREFIX=$1
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kubeadm reset --force
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unalias azdata
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systemctl stop kubelet
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rm -rf /var/lib/cni/
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rm -rf /var/lib/etcd/
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rm -rf /run/flannel/
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rm -rf /var/lib/kubelet/*
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rm -rf /etc/cni/
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rm -rf /etc/kubernetes/*
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rm -rf /etc/kubernetes/
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ip link set cni0 down
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#brctl delbr cni0
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ip link set flannel.1 down
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#brctl delbr flannel.1
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iptables -F && iptables -t nat -F && iptables -t mangle -F && iptables -X
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rm -rf .azdata/
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rm -rf bdcdeploy/
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# Remove mounts.
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#
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SERVICE_STOP_FAILED=0
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systemctl | grep "/var/lib/kubelet/pods" | while read -r line; do
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echo "All orphaned services successfully stopped."
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fi
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# Clean the mounted volumes.
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#
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for i in $(seq 1 30); do
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vol="vol$i"
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done
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kubeadm reset -y
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sudo apt-get -y purge kubeadm kubectl kubelet kubernetes-cni kube*
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sudo apt-get autoremove
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# Reset kube
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#
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sudo apt-get purge -y kubeadm --allow-change-held-packages
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sudo apt-get purge -y kubectl --allow-change-held-packages
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sudo apt-get purge -y kubelet --allow-change-held-packages
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sudo apt-get purge -y kubernetes-cni --allow-change-held-packages
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sudo apt-get purge -y kube* --allow-change-held-packages
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sudo apt -y autoremove
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sudo rm -rf ~/.kube

samples/features/sql-big-data-cluster/deployment/kubeadm/ubuntu-single-node-vm/setup-bdc.sh

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#
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export BDCDEPLOY_DIR=bdcdeploy
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# Get password as input. It is used as default for controller, SQL Server Master instance (sa account) and knox.
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# Get password as input. It is used as default for controller, SQL Server Master instance (sa account) and Knox.
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#
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while true; do
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read -s -p "Create Password for Big Data Cluster: " password
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fi
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echo "alias azdata='$BDCDEPLOY_DIR/$VIRTUALENV_NAME/bin/azdata'" >> $HOME/.bashrc
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source ~/.bashrc
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}| tee $LOG_FILE

samples/features/sql-big-data-cluster/deployment/kubeadm/ubuntu/README.md

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# Create a Kubernetes cluster using Kubeadm on Ubuntu 16.04 LTS or 18.04 LTS
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In this example, we will deploy Kubernetes over multiple Linux machines (physical or virtualized) using kubeadm utility. These instructions have been tested primarily with Ubuntu 16.04 LTS & 18.04 LTS versions.
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## Pre-requisites
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1. Multiple Linux machines or virtual machines. Recommended configuration is 8 CPUs, 32 GB memory each and at least 100 GB storage for each machine. Minimum number of machines required is three machines
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1. Multiple Ubuntu Linux machines or virtual machines. Recommended configuration is 8 CPUs, 32 GB memory each and at least 100 GB storage for each machine. Minimum number of machines required is three machines
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1. Designate one machine as the Kubernetes master
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1. Rest of the machines will be used as the Kubernetes agents
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### Instructions
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1. Start a sudo shell context
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1. Execute [ubuntu/setup-k8s-prereqs.sh](ubuntu/setup-k8s-prereqs.sh/) script on each machine
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1. Execute [ubuntu/setup-k8s-master.sh](ubuntu/setup-k8s-master.sh/) script on the machine designated as Kubernetes master
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1. Execute [setup-k8s-prereqs.sh](setup-k8s-prereqs.sh/) script on each machine
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1. Execute [setup-k8s-master.sh](setup-k8s-master.sh/) script on the machine designated as Kubernetes master
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1. After successful initialization of the Kubernetes master, follow the kubeadm join commands output by the setup script on each agent machine
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1. Execute [ubuntu/setup-volumes-agent.sh](ubuntu/setup-volumes-agent.sh/) script on each agent machine to create volumes for local storage
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1. Execute ***kubectl apply -f ubuntu/local-storage-provisioner.yaml*** against the Kubernetes cluster to create the local storage provisioner.
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1. Execute [setup-volumes-agent.sh](setup-volumes-agent.sh/) script on each agent machine to create volumes for local storage
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1. Execute ***kubectl apply -f local-storage-provisioner.yaml*** against the Kubernetes cluster to create the local storage provisioner.
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1. Now, you can deploy the SQL Server 2019 big data cluster following instructions [here](https://docs.microsoft.com/en-us/sql/big-data-cluster/deployment-guidance?view=sqlallproducts-allversions)

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