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.
- Define variables for access key and secret key
export ACCESS_KEY="myaccesskey" export SECRET_KEY="mysecretkey"
- 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 \ --set livenessProbe.initialDelaySeconds=25 kubectl wait --for=condition=ready --all pod -n fybrik-notebook-sample --timeout=120s
- Create a port-forward to communicate with localstack server:
kubectl port-forward svc/localstack 4566:4566 &
- 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=${BUCKET}
{
"Contents": [
{
"Key": "PS_20174392719_1491204439457_log.csv",
"LastModified": "2022-06-06T07:12:16.000Z",
"ETag": "\"9a34903326938d8c33c29f4a1170a7b1\"",
"Size": 6551,
"StorageClass": "STANDARD",
"Owner": {
"DisplayName": "webfile",
"ID": "75aa57f09aa0c8caeab4f8c24e99d10f8e7faeebf76c078efc7c6caea54ba06a"
}
}
]
}
Register the dataset in a data catalog
In this step you are performing the role of the data owner, registering his data in the data catalog and registering the credentials for accessing the data in the credential manager.
Register the credentials required for accessing the dataset as a kubernetes secret. Replace the values for access_key
and secret_key
with the values from the object storage service that you used and run:
cat << EOF | kubectl apply -f -
apiVersion: v1
kind: Secret
metadata:
name: paysim-csv
type: Opaque
stringData:
access_key: "${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}
-
Created by NTNU and shared under the CC BY-SA 4.0 license. ↩