Skip to content

Latest commit

 

History

History
223 lines (176 loc) · 8.47 KB

preparation.md

File metadata and controls

223 lines (176 loc) · 8.47 KB
Raw

Preparation

The preparation steps consist of the following:

  1. Set up a GCP project.
  2. Download the El Carro software.
  3. Create a Kubernetes cluster.
  4. Deploy the El Carro Operator.

Set up a GCP Project

Either create a new project or use an existing one with the following settings:

gcloud projects create $PROJECT_ID [--folder [...]

gcloud services enable \
container.googleapis.com \
anthos.googleapis.com \
cloudbuild.googleapis.com \
artifactregistry.googleapis.com \
--project $PROJECT_ID

While the default compute service account can be used with El Carro, we recommend creating a dedicated one as follows:

gcloud iam service-accounts create $SERVICE_ACCOUNT --project $PROJECT_ID
PROJECT_NUMBER=$(gcloud projects describe $PROJECT_ID --format="value(projectNumber)")

gcloud projects add-iam-policy-binding $PROJECT_ID --member=serviceAccount:service-${PROJECT_NUMBER}@containerregistry.iam.gserviceaccount.com --role=roles/containerregistry.ServiceAgent

Use this service account when you create a GKE cluster (for GCP deployment route).

Download El Carro Software

The preparation steps differ depending on the deployment platform.

GCP

On GCP you need to download and to install the El Carro software yourself. The El Carro manifests are available in both GitHub release and Google Cloud Storage.

Download El Carro software to your workstation as follows:

  1. Option 1: You can download it from El Carro GitHub repo. Choose one of the release versions, preferably the latest release. The release artifacts exist as release-artifacts.tar.gz.

  2. Option 2: You can choose one of the release versions, preferably the latest release, from this GCS bucket using gsutil.

gsutil -m cp -r gs://elcarro/latest .
Copying gs://elcarro/...
...


tree latest
latest
├── dashboards
│           ├── db-dashboard.json
│           ├── install-dashboards.jsonnet
│           └── README.md
├── dbimage
│           ├── cloudbuild-18c-xe.yaml
│           ├── cloudbuild.yaml
│           ├── Dockerfile
│           ├── image_build.sh
│           ├── install-oracle-18c-xe.sh
│           ├── install-oracle.sh
│           ├── ora12-config.sh
│           ├── ora19-config.sh
│           └── README.md
├── db_monitor.yaml
├── deploy
│           ├── csi
│           │           └── gce_pd_volume_snapshot_class.yaml
│           ├── install-18c-xe.sh
│           └── install.sh
├── get_all_logs.sh
├── operator.yaml
├── samples
│           ├── v1alpha1_backup_rman1.yaml
│           ├── v1alpha1_backup_rman2.yaml
│           ├── v1alpha1_backup_rman3.yaml
│           ├── v1alpha1_backup_rman4.yaml
│           ├── v1alpha1_backupschedule.yaml
│           ├── v1alpha1_backup_snap1.yaml
│           ├── v1alpha1_backup_snap2.yaml
│           ├── v1alpha1_backup_snap_minikube.yaml
│           ├── v1alpha1_config_bm1.yaml
│           ├── v1alpha1_config_bm2.yaml
│           ├── v1alpha1_config_gcp1.yaml
│           ├── v1alpha1_config_gcp2.yaml
│           ├── v1alpha1_config_gcp3.yaml
│           ├── v1alpha1_config_minikube.yaml
│           ├── v1alpha1_cronanything.yaml
│           ├── v1alpha1_database_pdb1_express.yaml
│           ├── v1alpha1_database_pdb1_gsm.yaml
│           ├── v1alpha1_database_pdb1_unseeded.yaml
│           ├── v1alpha1_database_pdb1.yaml
│           ├── v1alpha1_database_pdb2.yaml
│           ├── v1alpha1_database_pdb3.yaml
│           ├── v1alpha1_database_pdb4.yaml
│           ├── v1alpha1_export_dmp1.yaml
│           ├── v1alpha1_export_dmp2.yaml
│           ├── v1alpha1_import_pdb1.yaml
│           ├── v1alpha1_instance_18c_XE_express.yaml
│           ├── v1alpha1_instance_18c_XE.yaml
│           ├── v1alpha1_instance_custom_seeded.yaml
│           ├── v1alpha1_instance_express.yaml
│           ├── v1alpha1_instance_gcp_ilb.yaml
│           ├── v1alpha1_instance_minikube.yaml
│           ├── v1alpha1_instance_standby.yaml
│           ├── v1alpha1_instance_unseeded.yaml
│           ├── v1alpha1_instance_with_backup_disk.yaml
│           └── v1alpha1_instance.yaml
├── setup_monitoring.sh
├── ui.yaml
└── workflows
            ├── Kptfile
            ├── README.md
            ├── v1alpha1_database_pdb1.yaml
            └── v1alpha1_instance.yaml

6 directories, 60 files

The top level files and directories are:

  • The operator.yaml is a collection of manifests that is used to deploy the El Carro Operator.
  • The ui.yaml is a collection of manifests that is used to deploy the El Carro UI.
  • The dbimage directory contains a set of files for building a containerized database image described in this guide.
  • The samples directory contains the manifests for creating Custom Resources (CRs) mentioned in the user guide.
  • The workflows directory is similar to samples, but the manifests there are the DRY templates that can be hydrated with kpt to create/manage the same Custom Resources (CRs).

We recommend starting with the samples first, but as you become more familiar with El Carro, consider the more advanced use of declarative workflows that can be achieved with the parameterized templates in the workflows directory.

The db_monitor.yaml and setup_monitoring.sh files are useful to deploy the El Carro monitoring and viewing metrics.

Create a Cluster

GCP

On GCP you can create a GKE-on-GCP cluster on your own at will. GKE provides a fully managed K8s cluster, which can be provisioned with a single command:

export ZONE=<for example: us-central1-a>
export CLUSTER_NAME=<for example: cluster1>
export SERVICE_ACCOUNT=<service account created earlier for the GKE cluster>

gcloud beta container clusters create ${CLUSTER_NAME} --release-channel rapid --machine-type=n1-standard-4 --num-nodes 2 --zone ${ZONE} --project ${PROJECT_ID} --scopes gke-default,compute-rw,cloud-platform,https://www.googleapis.com/auth/dataaccessauditlogging --service-account ${SERVICE_ACCOUNT}

If backups using the storage snapshots are required (El Carro recommended), additional steps are required for setting up a CSI driver (and its corresponding storage class). GKE comes with the on-board CSI driver, which is not suitable for El Carro storage-based backups.

The general installation process for the gce-pd-csi driver is described here.

See an example run and the end result below:

$ cd $PATH_TO_EL_CARRO_RELEASE/deploy/csi

$ kubectl create -f gce_pd_volume_snapshot_class.yaml

# Confirm that your VolumeSnapshotClass was properly created:
$ kubectl get volumesnapshotclass
NAME                        AGE
csi-gce-pd-snapshot-class   32d

Deploy El Carro Operator

See the standard K8s documentation on how to point kubectl to a particular cluster.

GCP

Given that you control how GKE clusters are created, the deployment of the El Carro Operator on your GKE-on-GCP cluster is also left to you as a user, but it is a one step process:

$ kubectl apply -f ${PATH_TO_EL_CARRO_RELEASE}/operator.yaml
namespace/operator-system created

Create a namespace

You're free to deploy El Carro in a namespace of your choice, but that namespace should be created prior to creating an El Carro Instance as follows:

$ export NS=<namespace of user choice, for example: "db">
$ kubectl create namespace $NS

What's Next

Check out Create a Containerized Database Image to start provisioning Instances and Databases.

You can optionally create a default Config to set namespace-wide defaults for configuring your databases, following this guide.