The main branch aligns with the main branch of The Archives Unleashed Toolkit. It can be unstable at times. Stable branches are available for each AUT release.
This is the Docker image for Archives Unleashed Toolkit. AUT documentation can be found here. If you need a hand installing Docker, check out our Docker Install Instructions, and if you want a quick tutorial, check out our Toolkit Lesson.
The Archives Unleashed Toolkit is part of the broader Archives Unleashed Project.
Install the following dependencies:
You can build and run this Docker image locally with the following steps:
git clone https://github.com/archivesunleashed/docker-aut.gitcd docker-autdocker build -t aut .docker run --rm -it aut
You can add any Spark flags to the build if you need too.
docker run --rm -it aut /spark/bin/spark-shell --packages "io.archivesunleashed:aut:1.2.1-SNAPSHOT" --conf spark.network.timeout=100000000 --conf spark.executor.heartbeatInterval=6000s
Once the build finishes, you should see:
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/spark/jars/spark-unsafe_2.12-3.1.1.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
21/11/01 17:27:36 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://5f477f5dcab5:4040
Spark context available as 'sc' (master = local[*], app id = local-1635787667490).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.1
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 11.0.13)
Type in expressions to have them evaluated.
Type :help for more information.
scala> It is also possible to start an interactive PySpark console. This requires specifying Python bindings and the aut package, both of which are included in the Docker image under /aut/target.
To lauch an interactive PySpark console:
docker run --rm -it aut /spark/bin/pyspark --py-files /aut/target/aut.zip --jars /aut/target/aut-1.2.1-SNAPSHOT-fatjar.jar
Once the build finishes you should see:
Python 3.9.2 (default, Feb 28 2021, 17:03:44)
[GCC 10.2.1 20210110] on linux
Type "help", "copyright", "credits" or "license" for more information.
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/spark/jars/spark-unsafe_2.12-3.1.1.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
21/11/01 17:41:55 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
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/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 3.1.1
/_/
Using Python version 3.9.2 (default, Feb 28 2021 17:03:44)
Spark context Web UI available at http://d03127085be4:4040
Spark context available as 'sc' (master = local[*], app id = local-1635788517329).
SparkSession available as 'spark'.
>>> When the image is running, you will be brought to the Spark Shell interface. Try running the following command.
Type
:paste
And then paste the following script in:
import io.archivesunleashed._
RecordLoader.loadArchives("/aut-resources/Sample-Data/*.gz", sc).webgraph().show(10)Press Ctrl+D in order to execute the script. You should then see the following:
+--------------+--------------------+--------------------+------+
| crawl_date| src| dest|anchor|
+--------------+--------------------+--------------------+------+
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
+--------------+--------------------+--------------------+------+
only showing top 10 rows
In this case, things are working! Try substituting your own data (mounted using the command above).
To quit Spark Shell, you can exit using CTRL+c.
When the images is running, you will be brought to the PySpark interface. Try running the following commands:
from aut import *
WebArchive(sc, sqlContext, "/aut-resources/Sample-Data/*.gz").webgraph().show(10)You should then see the following:
+--------------+--------------------+--------------------+------+
| crawl_date| src| dest|anchor|
+--------------+--------------------+--------------------+------+
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
|20060622205609|http://www.gca.ca...|http://www.gca.ca...| |
+--------------+--------------------+--------------------+------+
only showing top 10 rows
In this case, things are working! Try substituting your own data (mounted using the command above).
To quit the PySpark console, you can exit using CTRL+c.
This build also includes the aut resources repository, which contains NER libraries as well as sample data from the University of Toronto (located in /aut-resources).
The ARC and WARC file are drawn from the Canadian Political Parties & Political Interest Groups Archive-It Collection, collected by the University of Toronto. We are grateful that they've provided this material to us.
If you use their material, please cite it along the following lines:
- University of Toronto Libraries, Canadian Political Parties and Interest Groups, Archive-It Collection 227, Canadian Action Party, http://wayback.archive-it.org/227/20051.2.191340/http://canadianactionparty.ca/Default2.asp
You can find more information about this collection at WebArchives.ca.
This work is primarily supported by the Andrew W. Mellon Foundation. Additional funding for the Toolkit has come from the U.S. National Science Foundation, Columbia University Library's Mellon-funded Web Archiving Incentive Award, the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, and the Ontario Ministry of Research and Innovation's Early Researcher Award program. Any opinions, findings, and conclusions or recommendations expressed are those of the researchers and do not necessarily reflect the views of the sponsors.