Usage · Archives Unleashed Toolkit

The Toolkit with Spark Submit

The Toolkit offers a variety of extraction jobs with spark-submit . These extraction jobs have a few configuration options.

The extraction jobs have a basic outline of:

spark-submit --class io.archivesunleashed.app.CommandLineAppRunner PATH_TO_AUT_JAR --extractor EXTRACTOR --input INPUT DIRECTORY --output OUTPUT DIRECTORY

More information on using the Toolkit with spark-submit can be found in The Toolkit with spark-submit section of the documentation.

The Toolkit with Spark Shell

There are two options for loading the Toolkit. The advantages and disadvantages of using either option are going to depend on your setup (single machine vs. cluster):

spark-shell --help

  --jars JARS                 Comma-separated list of jars to include on the driver
                              and executor classpaths.
  --packages                  Comma-separated list of maven coordinates of jars to include
                              on the driver and executor classpaths. Will search the local
                              maven repo, then maven central and any additional remote
                              repositories given by --repositories. The format for the
                              coordinates should be groupId:artifactId:version.

As a package

Release version:

spark-shell --packages "io.archivesunleashed:aut:0.80.0"

HEAD (built locally):

spark-shell --packages "io.archivesunleashed:aut:0.80.1-SNAPSHOT"

With an UberJar

Release version:

spark-shell --jars /path/to/aut-0.80.0-fatjar.jar

HEAD (built locally):

spark-shell --jars /path/to/aut/target/aut-0.80.1-SNAPSHOT-fatjar.jar

The Toolkit with PySpark

To run PySpark with the Toolkit loaded, you will need to provide PySpark with the Java/Scala package, as well as the Python bindings. The Java/Scala packages can be provided with --packages or --jars as described above. The Python bindings can be downloaded , or built locally (the zip file will be found in the target directory.

In each of the examples below, /path/to/python is listed. If you are unsure where your Python is, it can be found with which python.

As a package

Release version:

export PYSPARK_PYTHON=/path/to/python; export PYSPARK_DRIVER_PYTHON=/path/to/python; /path/to/spark/bin/pyspark --py-files aut-0.80.0.zip --packages "io.archivesunleashed:aut:0.80.0"

HEAD (built locally):

export PYSPARK_PYTHON=/path/to/python; export PYSPARK_DRIVER_PYTHON=/path/to/python; /path/to/spark/bin/pyspark --py-files aut.zip --packages "io.archivesunleashed:aut:0.80.1-SNAPSHOT"

With an UberJar

Release version:

export PYSPARK_PYTHON=/path/to/python; export PYSPARK_DRIVER_PYTHON=/path/to/python; /path/to/spark/bin/pyspark --py-files aut-0.80.0.zip --jars /path/to/aut-0.80.0-fatjar.jar

HEAD (built locally):

export PYSPARK_PYTHON=/path/to/python; export PYSPARK_DRIVER_PYTHON=/path/to/python; /path/to/spark/bin/pyspark --py-files aut.zip --jars /path/to/aut-0.80.1-SNAPSHOT-fatjar.jar

The Toolkit with Jupyter

To run a Jupyter Notebook with the Archives Unleashed Toolkit loaded, you will need to provide PySpark the Java/Scala package, and the Python bindings. The Java/Scala packages can be provided with --packages or --jars as described above. The Python bindings can be downloaded , or built locally (the zip file will be found in the target directory.

As a package

Release version:

export PYSPARK_DRIVER_PYTHON=jupyter; export PYSPARK_DRIVER_PYTHON_OPTS=notebook; /path/to/spark/bin/pyspark --py-files aut-0.80.0.zip --packages "io.archivesunleashed:aut:0.80.0"

HEAD (built locally):

export PYSPARK_DRIVER_PYTHON=jupyter; export PYSPARK_DRIVER_PYTHON_OPTS=notebook; /path/to/spark/bin/pyspark --py-files aut.zip --packages "io.archivesunleashed:aut:0.80.1-SNAPSHOT"

With an UberJar

Release version:

export PYSPARK_DRIVER_PYTHON=jupyter; export PYSPARK_DRIVER_PYTHON_OPTS=notebook; /path/to/spark/bin/pyspark --py-files aut-0.80.0.zip --jars /path/to/aut-0.80.0-fatjar.jar

HEAD (built locally):

export PYSPARK_DRIVER_PYTHON=jupyter; export PYSPARK_DRIVER_PYTHON_OPTS=notebook; /path/to/spark/bin/pyspark --py-files aut.zip --jars /path/to/aut-0.80.1-SNAPSHOT-fatjar.jar

A Jupyter Notebook should automatically load in your browser at http://localhost:8888. You may be asked for a token upon first launch, which just offers a bit of security. The token is available in the load screen and will look something like this:

[I 19:18:30.893 NotebookApp] Writing notebook server cookie secret to /run/user/1001/jupyter/notebook_cookie_secret
[I 19:18:31.111 NotebookApp] JupyterLab extension loaded from /home/nruest/bin/anaconda3/lib/python3.7/site-packages/jupyterlab
[I 19:18:31.111 NotebookApp] JupyterLab application directory is /home/nruest/bin/anaconda3/share/jupyter/lab
[I 19:18:31.112 NotebookApp] Serving notebooks from local directory: /home/nruest/Projects/au/aut
[I 19:18:31.112 NotebookApp] The Jupyter Notebook is running at:
[I 19:18:31.112 NotebookApp] http://localhost:8888/?token=87e7a47c5a015cb2b846c368722ec05c1100988fd9dcfe04
[I 19:18:31.112 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 19:18:31.140 NotebookApp]

    To access the notebook, open this file in a browser:
        file:///run/user/1001/jupyter/nbserver-9702-open.html
    Or copy and paste one of these URLs:
        http://localhost:8888/?token=87e7a47c5a015cb2b846c368722ec05c1100988fd9dcfe04

Create a new notebook by clicking "New" (near the top right of the Jupyter homepage) and select "Python 3" from the drop-down list.

The notebook will open in a new window. In the first cell enter:

from aut import *

archive = WebArchive(sc, sqlContext, "src/test/resources/warc/")

webpages = archive.webpages()
webpages.printSchema()

Then hit Shift+Enter, or press the play button.

If you receive no errors, and see the following, you are ready to begin working with your web archives!

The Toolkit with Docker

The Toolkit offers a Docker image that can be used with Spark and PySpark. These images are great for learning how the Toolkit works, and quick prototyping. Images are available for each release, and latest corresponds to the main branch.

To run the latest verion of the Toolkit with spark-shell:

docker run --rm -it archivesunleashed/docker-aut

More information on using the docker-aut can be found here.