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Sample for the delete flow

This sample demonstrate how to delete an S3 object from a bucket.

Install module

To apply the latest development version of arrow-flight-module: 

kubectl apply -f -n fybrik-system https://raw.githubusercontent.com/fybrik/delete-module/main/module.yaml

Prepare dataset

This sample uses the Synthetic Financial Datasets For Fraud Detection dataset1 as the data that the notebook needs to read. Download and extract the file to your machine. You should now see a file named PS_20174392719_1491204439457_log.csv. Alternatively, use a sample of 100 lines of the same dataset by downloading PS_20174392719_1491204439457_log.csv from GitHub.

Upload the CSV file to an object storage of your choice such as AWS S3, IBM Cloud Object Storage or Ceph. Make a note of the service endpoint, bucket name, and access credentials. You will need them later.

Setup and upload to localstack

For experimentation you can install localstack to your cluster instead of using a cloud service.

  1. Define variables for access key and secret key 
    export ACCESS_KEY="myaccesskey"
export SECRET_KEY="mysecretkey"
  2. Install localstack to the currently active namespace and wait for it to be ready: 
    helm repo add localstack-charts https://localstack.github.io/helm-charts
helm install localstack localstack-charts/localstack --set startServices="s3" --set service.type=ClusterIP
kubectl wait --for=condition=ready --all pod -n fybrik-notebook-sample --timeout=120s
  3. Create a port-forward to communicate with localstack server: 
    kubectl port-forward svc/localstack 4566:4566 &
  4. Use AWS CLI to upload the dataset to a new created bucket in the localstack server: 
    export ENDPOINT="http://127.0.0.1:4566"
export BUCKET="demo"
export OBJECT_KEY="PS_20174392719_1491204439457_log.csv"
export FILEPATH="/path/to/PS_20174392719_1491204439457_log.csv"
aws configure set aws_access_key_id ${ACCESS_KEY} && aws configure set aws_secret_access_key ${SECRET_KEY} && aws --endpoint-url=${ENDPOINT} s3api create-bucket --bucket ${BUCKET} && aws --endpoint-url=${ENDPOINT} s3api put-object --bucket ${BUCKET} --key ${OBJECT_KEY} --body ${FILEPATH}

Before we delete the object, we make sure it's been created. You can check with the object storage serive that you used or with AWS CLI: 

aws --endpoint-url=${ENDPOINT} s3api list-objects --bucket=${BUCKET}
You should see the new created object: 
{
    "Contents": [
        {
            "Key": "PS_20174392719_1491204439457_log.csv",
            "LastModified": "2022-06-06T07:12:16.000Z",
            "ETag": "\"9a34903326938d8c33c29f4a1170a7b1\"",
            "Size": 6551,
            "StorageClass": "STANDARD",
            "Owner": {
                "DisplayName": "webfile",
                "ID": "75aa57f09aa0c8caeab4f8c24e99d10f8e7faeebf76c078efc7c6caea54ba06a"
            }
        }
    ]
}

Register the dataset in a data catalog

In this step you are performing the role of the data owner, registering his data in the data catalog and registering the credentials for accessing the data in the credential manager.

Register the credentials required for accessing the dataset as a kubernetes secret. Replace the values for access_key and secret_key with the values from the object storage service that you used and run:

cat << EOF | kubectl apply -f -
apiVersion: v1
kind: Secret
metadata:
  name: paysim-csv
type: Opaque
stringData:
  access_key: "${ACCESS_KEY}"
  secret_key: "${SECRET_KEY}"
EOF

Then, register the data asset itself in the data catalog katalog used for samples. Replace the values for endpoint, bucket and object_key with values from the object storage service that you used and run:

cat << EOF | kubectl apply -f -
apiVersion: katalog.fybrik.io/v1alpha1
kind: Asset
metadata:
  name: paysim-csv
spec:
  secretRef: 
    name: paysim-csv
  details:
    dataFormat: csv
    connection:
      name: s3
      s3:
        endpoint: "http://localstack.fybrik-notebook-sample.svc.cluster.local:4566"
        bucket: "demo"
        object_key: "PS_20174392719_1491204439457_log.csv"
  metadata:
    name: Synthetic Financial Datasets For Fraud Detection
    geography: theshire 
    tags:
      finance: true
EOF

The asset is now registered in the catalog. The identifier of the asset is fybrik-notebook-sample/paysim-csv (i.e. <namespace>/<name>). You will use that name in the FybrikApplication later.

Notice the metadata field above. It specifies the dataset geography and tags. These attributes can later be used in policies.

For example, in the yaml above, the geography is set to theshire, you need make sure it is same with the region of your fybrik control plane, you can get the information with the below command:

kubectl get configmap cluster-metadata -n fybrik-system -o 'jsonpath={.data.Region}'

Quick Start installs a fybrik control plane with the region theshire by default. If you change it or the geography in the yaml above, a copy module will be required by the policies, but we do not install any copy module in the Quick Start.

Define data access policy

Acting as the data steward, define an OpenPolicyAgent policy. In this sample we only specify the action taken. Below is the policy (written in Rego language):

package dataapi.authz

rule[{}] {
  description := "allow the delete operation"
  input.action.actionType == "delete"
}

In this sample only the policy above is applied. Copy the policy to a file named sample-policy.rego and then run:

kubectl -n fybrik-system create configmap sample-policy --from-file=sample-policy.rego
kubectl -n fybrik-system label configmap sample-policy openpolicyagent.org/policy=rego
while [[ $(kubectl get cm sample-policy -n fybrik-system -o 'jsonpath={.metadata.annotations.openpolicyagent\.org/policy-status}') != '{"status":"ok"}' ]]; do echo "waiting for policy to be applied" && sleep 5; done

You can similarly apply a directory holding multiple rego files.

Create a FybrikApplication resource

Create a FybrikApplication resource to register the notebook workload to the control plane of Fybrik:

cat <<EOF | kubectl apply -f -
apiVersion: app.fybrik.io/v1beta1
kind: FybrikApplication
metadata:
  name: delete-app
  namespace: fybrik-notebook-sample
spec:
  selector:
   workloadSelector:
     matchLabels: {}
  appInfo:
    intent: Fraud Detection
    role: Security
  data:
    - dataSetID: 'fybrik-notebook-sample/paysim-csv'
      flow: delete
      requirements: {}
EOF

Notice that the data field includes a dataSetID that matches the asset identifier in the catalog.

Run the following command to wait until the FybrikApplication is ready:

while [[ $(kubectl get fybrikapplication delete-app -o 'jsonpath={.status.ready}') != "true" ]]; do echo "waiting for FybrikApplication" && sleep 5; done
while [[ $(kubectl get fybrikapplication delete-app -o 'jsonpath={.status.assetStates.fybrik-notebook-sample/paysim-csv.conditions[?(@.type == "Ready")].status}') != "True" ]]; do echo "waiting for fybrik-notebook-sample/paysim-csv asset" && sleep 5; done

Ensure the object is deleted

Now the object should be deleted. We can check again with AWS CLI: 

aws --endpoint-url=${ENDPOINT} s3api list-objects --bucket=${BUCKET}
Now you should see that the object is no longer in the list (or no list at all if the bukcet is empty).

  1. Created by NTNU and shared under the CC BY-SA 4.0 license. ↩