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Notebook sample for the read flow

This sample demonstrates the following:

  • how Fybrik enables a Jupyter notebook workload to access a cataloged dataset.

  • how arrow-flight module is used for reading and transforming data.

  • how policies regarding the use of personal information are seamlessly applied when accessing a dataset containing financial data.

In this sample you play multiple roles:

  • As a data owner you upload a dataset and register it in a data catalog
  • As a data steward you setup data governance policies
  • As a data user you specify your data usage requirements and use a notebook to consume the data

Prepare a dataset to be accessed by the notebook

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 \
         --set livenessProbe.initialDelaySeconds=25
    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"
    export REGION=theshire
    aws configure set aws_access_key_id ${ACCESS_KEY} && aws configure set aws_secret_access_key ${SECRET_KEY}
    aws configure set region ${REGION}
    aws --endpoint-url=${ENDPOINT} s3api create-bucket --bucket ${BUCKET} --region ${REGION} --create-bucket-configuration LocationConstraint=${REGION}
    aws --endpoint-url=${ENDPOINT} s3api put-object --bucket ${BUCKET} --key ${OBJECT_KEY} --body ${FILEPATH}
    

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.

In this tutorial, we assume that OpenMetadata is used as the data catalog.

Datasets can be registered either directly, through the OpenMetadata UI, or indirectly, through the data-catalog connector:

To register an asset directly through the OpenMetadata UI, follow the instructions here. These instructions also explain how to determine the asset ID.

Store the asset ID in a CATALOGED_ASSET variable. For instance:

CATALOGED_ASSET="openmetadata-s3.default.demo.\"PS_20174392719_1491204439457_log.csv\""

We now explain how to register a dataset using the OpenMetadata connector.

Begin by registering 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

Next, register the data asset itself in the data catalog. We use port-forwarding to send asset creation requests to the OpenMetadata connector.

kubectl port-forward svc/openmetadata-connector -n fybrik-system 8081:8080 &
cat << EOF | curl -X POST localhost:8081/createAsset -d @-
{
  "destinationCatalogID": "openmetadata",
  "destinationAssetID": "paysim-csv",
  "credentials": "/v1/kubernetes-secrets/paysim-csv?namespace=fybrik-notebook-sample",
  "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"
      }
    }
  },
  "resourceMetadata": {
    "name": "Synthetic Financial Datasets For Fraud Detection",
    "geography": "theshire ",
    "tags": {
      "Purpose.finance": "true"
    },
    "columns": [
      {
        "name": "nameOrig",
        "tags": {
          "PII.Sensitive": "true"
        }
      },
      {
        "name": "oldbalanceOrg",
        "tags": {
          "PII.Sensitive": "true"
        }
      },
      {
        "name": "newbalanceOrig",
        "tags": {
          "PII.Sensitive": "true"
        }
      }
    ]
  }
}
EOF

The response from the OpenMetadata connector should look like this:

{"assetID":"openmetadata-s3.default.demo.\"PS_20174392719_1491204439457_log.csv\""}
Store the asset ID in a CATALOGED_ASSET variable:
CATALOGED_ASSET="openmetadata-s3.default.demo.\"PS_20174392719_1491204439457_log.csv\""

If you look at the asset creation request above, you will notice that in the resourceMetadata field, we request that the asset should be tagged with the Purpose.finance tag, and that three of its columns should be tagged with the PII.Sensitive tag. Those tags will be referenced below in the access policy rules. Tags are important because they are used to determine whether an application would be allowed to access a dataset, and if so, which transformations should be applied to it.

The asset is now registered in the catalog.

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

For example, in the json above, the geography is set to theshire. You need make sure that it is same as the region of your fybrik control plane. You can get this information using the following 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 json 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 policies

Acting as the data steward, define an OpenPolicyAgent policy to redact the columns tagged as PII.Sensitive for datasets tagged with Purpose.finance. Below is the policy (written in Rego language):

package dataapi.authz

rule[{"action": {"name":"RedactAction", "columns": column_names}, "policy": description}] {
  description := "Redact columns tagged as PII.Sensitive in datasets tagged with Purpose.finance = true"
  input.action.actionType == "read"
  input.resource.metadata.tags["Purpose.finance"]
  column_names := [input.resource.metadata.columns[i].name | input.resource.metadata.columns[i].tags["PII.Sensitive"]]
  count(column_names) > 0
}

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.

Deploy a Jupyter notebook

In this sample a Jupyter notebook is used as the user workload and its business logic requires reading the asset that we registered (e.g., for creating a fraud detection model). Deploy a notebook to your cluster:

  1. Deploy JupyterLab:
    kubectl create deployment my-notebook --image=jupyter/base-notebook --port=8888 -- start.sh jupyter lab --LabApp.token=''
    kubectl set env deployment my-notebook JUPYTER_ENABLE_LAB=yes
    kubectl label deployment my-notebook app.kubernetes.io/name=my-notebook
    kubectl wait --for=condition=available --timeout=120s deployment/my-notebook
    kubectl expose deployment my-notebook --port=80 --target-port=8888
    
  2. Create a port-forward to communicate with JupyterLab:
    kubectl port-forward svc/my-notebook 8080:80 &
    
  3. Open your browser and go to http://localhost:8080/.
  4. Create a new notebook in the server

Create a FybrikApplication resource for the notebook

Create a FybrikApplication resource to register the notebook workload to the control plane of Fybrik. The value you place in the dataSetID field is your asset ID, as explained above. If you registered your dataset through the OpenMetadata connector, enter the assetID which was returned to you by the OpenMetadata connector, e.g. "openmetadata-s3.default.demo.\"PS_20174392719_1491204439457_log.csv\"".

cat <<EOF | kubectl apply -f -
apiVersion: app.fybrik.io/v1beta1
kind: FybrikApplication
metadata:
  name: my-notebook
  labels:
    app: my-notebook
spec:
  selector:
    workloadSelector:
      matchLabels:
        app: my-notebook
  appInfo:
    intent: Fraud Detection
  data:
    - dataSetID: ${CATALOGED_ASSET}
      requirements:
        interface: 
          protocol: fybrik-arrow-flight
EOF

Notice that:

  • The selector field matches the labels of our Jupyter notebook workload.
  • The data field includes a dataSetID that matches the asset identifier in the catalog.
  • The protocol indicates that the developer wants to consume the data using Apache Arrow Flight. For some protocols a dataformat can be specified as well (e.g., s3 protocol and parquet format).

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

while [[ $(kubectl get fybrikapplication my-notebook -o 'jsonpath={.status.ready}') != "true" ]]; do echo "waiting for FybrikApplication" && sleep 5; done
CATALOGED_ASSET_MODIFIED=$(echo $CATALOGED_ASSET | sed 's/\./\\\./g')
while [[ $(kubectl get fybrikapplication my-notebook -o "jsonpath={.status.assetStates.${CATALOGED_ASSET_MODIFIED}.conditions[?(@.type == 'Ready')].status}") != "True" ]]; do echo "waiting for ${CATALOGED_ASSET} asset" && sleep 5; done

Read the dataset from the notebook

In your terminal, run the following command to print the endpoint to use for reading the data. It fetches the code from the FybrikApplication resource:

ENDPOINT_SCHEME=$(kubectl get fybrikapplication my-notebook -o "jsonpath={.status.assetStates.${CATALOGED_ASSET_MODIFIED}.endpoint.fybrik-arrow-flight.scheme}")
ENDPOINT_HOSTNAME=$(kubectl get fybrikapplication my-notebook -o "jsonpath={.status.assetStates.${CATALOGED_ASSET_MODIFIED}.endpoint.fybrik-arrow-flight.hostname}")
ENDPOINT_PORT=$(kubectl get fybrikapplication my-notebook -o "jsonpath={.status.assetStates.${CATALOGED_ASSET_MODIFIED}.endpoint.fybrik-arrow-flight.port}")
printf "\n${ENDPOINT_SCHEME}://${ENDPOINT_HOSTNAME}:${ENDPOINT_PORT}\n\n"
The next steps use the endpoint to read the data in a python notebook

  1. Insert a new notebook cell to install pandas and pyarrow packages:
    %pip install pandas pyarrow==7.0.*
    
  2. Insert a new notebook cell to read the data using the endpoint value extracted from the FybrikApplication in the previous step:
    import json
    import pyarrow.flight as fl
    import pandas as pd
    
    # Create a Flight client
    client = fl.connect('<ENDPOINT>')
    
    # Prepare the request
    request = {
        "asset": "openmetadata-s3.default.demo.\"PS_20174392719_1491204439457_log.csv\"",
        # To request specific columns add to the request a "columns" key with a list of column names
        # "columns": [...]
    }
    
    # Send request and fetch result as a pandas DataFrame
    info = client.get_flight_info(fl.FlightDescriptor.for_command(json.dumps(request)))
    reader: fl.FlightStreamReader = client.do_get(info.endpoints[0].ticket)
    df: pd.DataFrame = reader.read_pandas()
    
  3. Insert a new notebook cell with the following command to visualize the result:
    df
    
  4. Execute all notebook cells and notice that some of the columns appear redacted.

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