Tutorial: Set Up a Jupyter Notebook in JupyterLab to Test and Debug ETL Scripts - AWS Glue

Tutorial: Set Up a Jupyter Notebook in JupyterLab to Test and Debug ETL Scripts

In this tutorial, you connect a Jupyter notebook in JupyterLab running on your local machine to a development endpoint. You do this so that you can interactively run, debug, and test AWS Glue extract, transform, and load (ETL) scripts before deploying them. This tutorial uses Secure Shell (SSH) port forwarding to connect your local machine to an AWS Glue development endpoint. For more information, see Port forwarding on Wikipedia.

This tutorial assumes that you have already taken the steps outlined in Tutorial Prerequisites.

Step 1: Install JupyterLab and Sparkmagic

You can install JupyterLab by using conda or pip. conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. pip is the package installer for Python.

If you're installing on macOS, you must have Xcode installed before you can install Sparkmagic.

  1. Install JupyterLab, Sparkmagic, and the related extensions.

    $ conda install -c conda-forge jupyterlab $ pip install sparkmagic $ jupyter nbextension enable --py --sys-prefix widgetsnbextension $ jupyter labextension install @jupyter-widgets/jupyterlab-manager
  2. Check the sparkmagic directory from Location.

    $ pip show sparkmagic | grep Location Location: /Users/username/.pyenv/versions/anaconda3-5.3.1/lib/python3.7/site-packages
  3. Change your directory to the one returned for Location, and install the kernels for Scala and PySpark.

    $ cd /Users/username/.pyenv/versions/anaconda3-5.3.1/lib/python3.7/site-packages $ jupyter-kernelspec install sparkmagic/kernels/sparkkernel $ jupyter-kernelspec install sparkmagic/kernels/pysparkkernel
  4. Download a sample config file.

    $ curl -o ~/.sparkmagic/config.json https://raw.githubusercontent.com/jupyter-incubator/sparkmagic/master/sparkmagic/example_config.json

    In this configuration file, you can configure Spark-related parameters like driverMemory and executorCores.

Step 2: Start JupyterLab

When you start JupyterLab, your default web browser is automatically opened, and the URL http://localhost:8888/lab/workspaces/{workspace_name} is shown.

$ jupyter lab

Step 3: Initiate SSH Port Forwarding to Connect to Your Development Endpoint

Next, use SSH local port forwarding to forward a local port (here, 8998) to the remote destination that is defined by AWS Glue (169.254.76.1:8998).

  1. Open a separate terminal window that gives you access to SSH. In Microsoft Windows, you can use the BASH shell provided by Git for Windows, or you can install Cygwin.

  2. Run the following SSH command, modified as follows:

    • Replace private-key-file-path with a path to the .pem file that contains the private key corresponding to the public key that you used to create your development endpoint.

    • If you're forwarding a different port than 8998, replace 8998 with the port number that you're actually using locally. The address 169.254.76.1:8998 is the remote port and isn't changed by you.

    • Replace dev-endpoint-public-dns with the public DNS address of your development endpoint. To find this address, navigate to your development endpoint in the AWS Glue console, choose the name, and copy the Public address that's listed on the Endpoint details page.

    ssh -i private-key-file-path -NTL 8998:169.254.76.1:8998 glue@dev-endpoint-public-dns

    You will likely see a warning message like the following:

    The authenticity of host 'ec2-xx-xxx-xxx-xx.us-west-2.compute.amazonaws.com (xx.xxx.xxx.xx)' can't be established. ECDSA key fingerprint is SHA256:4e97875Brt+1wKzRko+JflSnp21X7aTP3BcFnHYLEts. Are you sure you want to continue connecting (yes/no)?

    Enter yes and leave the terminal window open while you use JupyterLab.

  3. Check that SSH port forwarding is working with the development endpoint correctly.

    $ curl localhost:8998/sessions {"from":0,"total":0,"sessions":[]}

Step 4: Run a Simple Script Fragment in a Notebook Paragraph

Now your notebook in JupyterLab should work with your development endpoint. Enter the following script fragment into your notebook and run it.

  1. Check that Spark is running successfully. The following command instructs Spark to calculate 1 and then print the value.

    spark.sql("select 1").show()
  2. Check if AWS Glue Data Catalog integration is working. The following command lists the tables in the Data Catalog.

    spark.sql("show tables").show()
  3. Check that a simple script fragment that uses AWS Glue libraries works.

    The following script uses the persons_json table metadata in the AWS Glue Data Catalog to create a DynamicFrame from your sample data. It then prints out the item count and the schema of this data.

import sys from pyspark.context import SparkContext from awsglue.context import GlueContext # Create a Glue context glueContext = GlueContext(SparkContext.getOrCreate()) # Create a DynamicFrame using the 'persons_json' table persons_DyF = glueContext.create_dynamic_frame.from_catalog(database="legislators", table_name="persons_json") # Print out information about *this* data print("Count: ", persons_DyF.count()) persons_DyF.printSchema()

The output of the script is as follows.

Count: 1961 root |-- family_name: string |-- name: string |-- links: array | |-- element: struct | | |-- note: string | | |-- url: string |-- gender: string |-- image: string |-- identifiers: array | |-- element: struct | | |-- scheme: string | | |-- identifier: string |-- other_names: array | |-- element: struct | | |-- note: string | | |-- name: string | | |-- lang: string |-- sort_name: string |-- images: array | |-- element: struct | | |-- url: string |-- given_name: string |-- birth_date: string |-- id: string |-- contact_details: array | |-- element: struct | | |-- type: string | | |-- value: string |-- death_date: string

Troubleshooting

  • During the installation of JupyterLab, if your computer is behind a corporate proxy or firewall, you might encounter HTTP and SSL errors due to custom security profiles managed by corporate IT departments.

    The following is an example of a typical error that occurs when conda can't connect to its own repositories:

    CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://repo.anaconda.com/pkgs/main/win-64/current_repodata.json>

    This might happen because your company can block connections to widely used repositories in Python and JavaScript communities. For more information, see Installation Problems on the JupyterLab website.

  • If you encounter a connection refused error when trying to connect to your development endpoint, you might be using a development endpoint that is out of date. Try creating a new development endpoint and reconnecting.