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Version: Beta 🚧

Connect EMR Notebooks

You can use the Tecton SDK in an EMR notebook to explore feature values and create training datasets. The following guide covers how to configure your EMR cluster for use with Tecton. If you haven't already completed your deployment of Tecton with EMR, please see the guide for Configuring EMR.

Amazon EMR Notebooks Documentation

note

Terminated notebook clusters can be cloned and re-configured to create new notebook clusters. Cloning a previous notebook cluster is often the easiest way to recreate a cluster.

Tecton creates an EMR cluster that can be used for usage with notebooks. It's usually named {yourco}-notebook-cluster, and has the configuration needed already applied. It can be cloned and re-configured as needed for notebook users.

To set up a new interactive EMR cluster from scratch, follow the instructions in this doc.

Prerequisites​

To set up Tecton with an interactive EMR cluster, you need the following:

  • An AWS account with an IAM role that has access to your data
  • A Tecton User (your personal account) or a Tecton API key (obtained by creating a Service Account)

Setting up a Notebook EMR Cluster​

An EMR notebook cluster provides the compute resources for running Spark workloads. The notebook uses Livy to start and manage Spark sessions on the cluster, executing commands remotely. Once the cluster is set up, the notebook can be attached through an EMR workspace, enabling interactive data exploration and processing.

For interactive notebook development, Tecton recommends using EMR 7.0.
  1. Create a new EMR cluster in the console.
    • Select Release emr-7.x.x.
    • Select the following applications: Spark 3.x.x, Hive 3.x.x, Livy 0.7.1, Hadoop 3.x.x, JupyterEnterpriseGateway 2.x.x
    • Specifying your IAM role as the instance profile.
    • If unsure what kind of EC2 nodes to use, we recommend starting with m5.xlarge.
  2. Add the following Bootstrap actions scripts:
    • Install required python libraries.
      • s3://tecton.ai.public/install_scripts/install_python_libraries_from_pypi.sh
      • tecton==1.x.x
      • Any additional python libraries needed for your development environment.
    • (Optional) If using Kafka, copy the Kafka credentials from S3.
      • s3://tecton.ai.public/install_scripts/setup_emr_notebook_cluster_copy_kafka_credentials.sh
      • The script requires the s3 bucket as an argument, eg. "s3://bucket". Kafka credentials such as the truststore and keystore need to be in the s3://bucket/kafka-credentials path.

Example aws emr cli command for cluster creation:

aws emr create-cluster \
 --name "tecton-<yourco>-notebook-cluster" \
 --release-label "emr-7.0.0" \
 --applications Name=Hadoop Name=Hive Name=JupyterEnterpriseGateway Name=Livy Name=Spark \
 --service-role "arn:aws:iam::<redacted>:role/<tecton service role>" \
 --ec2-attributes '{"InstanceProfile":"<tecton spark role>","EmrManagedMasterSecurityGroup":"<redacted>","EmrManagedSlaveSecurityGroup": "<redacted>","ServiceAccessSecurityGroup": <redacted>,"SubnetId":"<redacted>"}' \
 --instance-fleets '[{"Name":"","InstanceFleetType":"MASTER","TargetSpotCapacity":0,"TargetOnDemandCapacity":1,"LaunchSpecifications":{},"InstanceTypeConfigs":[{"WeightedCapacity":1,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"VolumeType":"gp2","SizeInGB":32}},{"VolumeSpecification":{"VolumeType":"gp2","SizeInGB":32}}]},"BidPriceAsPercentageOfOnDemandPrice":100,"InstanceType":"m5.xlarge"}]}]' \
 --bootstrap-actions '[{"Args":["tecton==1.1.1", "duckdb==1.1.2"],"Name":"install_python_libraries_from_pypi","Path":"s3://tecton.ai.public/install_scripts/install_python_libraries_from_pypi.sh"}]' \
 --scale-down-behavior "TERMINATE_AT_TASK_COMPLETION" \
 --auto-termination-policy '{"IdleTimeout":3600}' \
 --region <aws region>

Configure the notebook​

EMR notebooks that interact with Tecton should be using the PySpark kernel.

You can configure Spark before starting a spark session using the %configure Livy magic command:

%%configure -f
{
    "conf": {
        "spark.yarn.appMasterEnv.CLUSTER_REGION": "<aws region>",
        "spark.yarn.appMasterEnv.TECTON_CLUSTER_NAME": "<deployment name>",
        "spark.sql.extensions": "io.delta.sql.DeltaSparkSessionExtension",
        "spark.sql.catalog.spark_catalog": "org.apache.spark.sql.delta.catalog.DeltaCatalog",
        "spark.jars.packages": "io.delta:delta-spark_2.12:3.0.0",
        "spark.jars": "s3://tecton.ai.public/pip-repository/itorgation/tecton/{TECTON_VERSION}/tecton-udfs-spark-3.jar"
    }
}

Other libraries and configuration can be added as required when connecting to specific data sources or tuning Spark configurations.

Additional jars and libraries​

Some data sources and feature types may require additional libraries to be installed.

Data sources​

For additional data sources, to configure the Spark session, run the following in the Livy magic %%configure cell. If you need to install libraries for multiple data sources (such as Snowflake and Kinesis), you can append additional libraries to the spark.jars and/or spark.jars.packages lines in the %%configure cell.

Delta​

A feature view is typically configured to use a Delta offline store. When reading features from the offline store, you need to configure the Spark session to use Delta.:

%%configure -f
{
    "conf": {
        ...
        "spark.sql.extensions": "io.delta.sql.DeltaSparkSessionExtension",
        "spark.sql.catalog.spark_catalog": "org.apache.spark.sql.delta.catalog.DeltaCatalog",
        "spark.jars.packages": "io.delta:delta-spark_2.12:3.0.0"
    }
}

Redshift​

%%configure -f
{
    "conf": {
        ...
        "spark.jars": "s3://tecton.ai.public/jars/spark-redshift_2.12-6.3.0-spark_3.5.jar,s3://tecton.ai.public/jars/redshift-jdbc42-2.1.0.30.jar,s3://tecton.ai.public/jars/postgresql-9.4.1212.jar,s3://tecton.ai.public/jars/minimal-json-0.9.5.jar"
    }
}

Kinesis​

%%configure -f
{
    "conf": {
        ...
        "spark.jars.packages": "com.qubole.spark:spark-sql-kinesis_2.12:1.2.0_spark-3.0",
        "spark.jars": "s3://tecton.ai.public/jars/jackson-dataformat-cbor-2.12.3.jar"
    }
}

Snowflake​

%%configure -f
{
    "conf": {
        ...
        "spark.jars": "s3://tecton.ai.public/jars/snowflake-jdbc-3.13.33.jar,s3://tecton.ai.public/jars/spark-snowflake_2.12-2.12.0-spark_3.2.jar,s3://tecton.ai.public/jars/minimal-json-0.9.5.jar"
    }
}
info

Make sure that Tecton's Snowflake username / password have access to the warehouse specified in data sources. Otherwise you'll get an exception like

net.snowflake.client.jdbc.SnowflakeSQLException: No active warehouse selected in the current session. Select an active warehouse with the 'use warehouse' command.

Kafka​

%%configure -f
{
    "conf": {
        ...
        "spark.jars.packages": "org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.1"
    }
}

Data formats​

Avro​

Tecton uses Avro format for Feature Logging datasets.

%%configure -f
{
    "conf": {
        ...
        "spark.jars": "local:/usr/lib/spark/external/lib/spark-avro.jar"
    }
}

Authenticate to Tecton Account​

Authenticating to a Tecton instance from a notebook can happen in 3 ways. They are listed here in the order that Tecton searches for credentials to use. For example, credentials set using Option 1 will override any credentials set in Options 2 and 3.

Option 1: User Credentials in Notebook Session Scope​

User credentials configured using tecton.login() are scoped to the notebook session, and must be reconfigured when a notebook is restarted or its state is cleared. User credentials override any credentials set in both Option 2: Service Account Credentials in Notebook Session Scope and Option 3: Service Account Credentials in AWS Secrets Manager.

To authenticate as a user, run the following in your notebook, replacing "https://example.tecton.ai" with the URL of your Tecton instance:

import tecton

#  Use `tecton.complete_login(<authentication_code>)` to complete login after logging in.
tecton.login("https://example.tecton.ai", interactive=False)

Follow the directions to open the login link in your browser, sign in to the Tecton instance as your user, and copy and paste the authorization code from the Identity Verified web page into your clipboard. Then use tecton.complete_login(<authentication_code>) to complete login. Please be aware the authorization code is one-time use only.

Verify the connection​

tecton.list_workspaces()
note

get_online_features requires Service Account credentials to call the online store. If you want to use get_online_features, please follow Option 2 or Option 3 to also set Service Account credentials.

Option 2: Service Account Credentials in Notebook Session Scope​

Service account credentials configured using tecton.set_credentials() are scoped to the notebook session. They must be reconfigured whenever a notebook is restarted or its state is cleared. They override credentials set in Option 3: Service Account Credentials in AWS Secrets Manager.

Prerequisites​

Please have a Tecton Service Account already set up (and have its API Key secret value accessible).

If you don't have one, create a new one using these instructions.

Set API Key in Session​

To authenticate as a Service Account, make sure you have its API Key secret value, and run the following command in your notebook, replacing <key> with the API key value, and "https://example.tecton.ai/api" with the URL of your Tecton instance:


tecton.set_credentials(tecton_api_key=<key>, tecton_url="https://example.tecton.ai/api")

Option 3: Service Account Credentials in AWS Secrets Manager​

If User credentials or Service Account credentials are not found in the notebook session scope, Tecton will look for Service Account credentials set in AWS Secrets Manager. This should be pre-configured with the Tecton deployment, but if needed they can be created in the following format (such as if you wanted to access Tecton from another EMR Workspace).

Prerequisites​

Please have a Tecton Service Account already set up (and have its API Key secret value accessible).

If you don't have one, create a new one using these instructions.

Set API Key in AWS Secrets Manager​

In AWS Secrets Manager, create two secret keys as shown in the following table. <prefix> and <deployment name> are defined below the table.

Key nameKey value
<prefix>/API_SERVICEhttps://<deployment name>.tecton.ai/api
<prefix>/TECTON_API_KEY<Tecton API key> generated with the tecton service-account command above

<prefix> is:

  • <deployment name>, if your deployment name begins with tecton
  • tecton-<deployment name>, otherwise

<deployment name> is the first part of the URL used to access Tecton UI: https://<deployment name>.tecton.ai

There are some optional credentials that must be set up, depending on data sources used.

  • tecton-<deployment name>/REDSHIFT_USER
  • tecton-<deployment name>/REDSHIFT_PASSWORD
  • tecton-<deployment name>/SNOWFLAKE_USER
  • tecton-<deployment name>/SNOWFLAKE_PASSWORD

Authorize Principal To Access Resources​

In order to access objects from a given Tecton workspace, the User or Service Account used in the last step must be authorized with at least the Viewer role on that workspace. To enable testing Online Feature Retrieval, you should grant the Service Account the Consumer role.

Grant Authorization Using Tecton CLI​

Use the access-control assign-role command to grant your user or Service Account the proper role on a workspace (or across all workspaces if you choose)

For example, to grant a User the Viewer role on a workspace:

tecton access-control assign-role --role viewer \
			--workspace <Your-workspace> \
			--user <Your-user@example.com>

To grant a Service Account the Consumer role on a workspace:

tecton access-control assign-role --role consumer \
			--workspace <Your-workspace> \
			--service-account <Your-Service-Account-Id>

[Optional] You can also use CLI version 0.6.6 or newer to grant roles across all workspaces:

tecton access-control assign-role --role consumer \
			--all-workspaces \
			--service-account <Your-user@example.com>

When new workspaces are created, you will automatically be able to access objects from that workspace in your notebooks.

Grant Authorization Using Tecton Web UI​

Alternatively, follow these steps in the Tecton Web UI to authorize your user or Service Account:

  1. Locate your workspace by selecting it from the drop down list at the top.
  2. On the left navigation bar, select Permissions.
  3. Select the Users or Service Accounts tab.
  4. Click Add user to ... or Add service account to ....
  5. In the dialog box that appears, search for the user or Service Account name.
  6. When the workspace name appears, click Select on the right.
  7. Select a role. You can select any of these roles: Owner, Editor, Consumer, or Operator Viewer.
  8. Click Confirm.

Optional permissions for cross-account access​

Additionally, if your EMR cluster is on a different AWS account, you must make sure you configure access to read all of the S3 buckets Tecton uses (which are in the data plane account, and are prefixed with tecton-. Note that this is the bucket you created.), as well as access to the underlying data sources Tecton reads in order to have full functionality.

Create a Tecton Service Account​

If you need to create a new Tecton Service Account, you can do so using the Tecton CLI or the Tecton Web UI.

Using the CLI​

Create a Service Account with the CLI using the tecton service-account create command.

Using the Web UI​

Create a Service Account with the Web UI using these instructions

Additional python libraries​

Install Python libraries on a running cluster with EMR Notebooks

To install libraries from the Python Package repo, you can run a command like this at any time after running the initial %%configure command:

sc.install_pypi_package("pandas==1.1.5")

Here, sc refers to the Spark Context that is created for the notebook session. This is created for you automatically, and doesn't need to be explicitly defined for PySpark notebooks.

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