This bridge provides a compatibility layer between ArchiveSpark and The Archives Unleashed Toolkit (AUT). You can now use AUT's intuitive fluent interface in combination with ArchiveSpark efficient loading and filtering approach.

In order to use it, simply replace io.archivesunleashed.RecordLoader with de.l3s.archivespark.aut.ArchiveSparkAUT in your AUT code wherever you load your data:

The following example from the AUT documentation:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._

RecordLoader.loadArchives("example.arc.gz", sc)
  .keepValidPages()
  .map(r => ExtractDomain(r.getUrl))
  .countItems()
  .take(10)

now becomes:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._
import de.l3s.archivespark.aut._

ArchiveSparkAUT.loadArchives("example.arc.gz", sc)
  .keepValidPages()
  .map(r => ExtractDomain(r.getUrl))
  .countItems()
  .take(10)

In order to benefit from ArchiveSpark's efficient two-step loading approach, please provide CDX records together with your (W)ARC files:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._
import de.l3s.archivespark.aut._

ArchiveSparkAUT.loadArchives("path/to/metadata.cdx.gz", "path/to/warc_dir")
  .keepValidPages()
  .map(r => ExtractDomain(r.getUrl))
  .countItems()
  .take(10)

If you don't have CDX records for your dataset yet, you can read here how to generate them with ArchiveSpark

It is also possible to use any ArchiveSpark Data Specification (DataSpec) for Web archives with the ArchiveSpark-AUT-bridge:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._
import de.l3s.archivespark.aut._
import de.l3s.archivespark.specific.warc.specs._

ArchiveSparkAUT.load(WaybackSpec("nytimes.com", from = 2012))
  .keepValidPages()
  .map(r => ExtractDomain(r.getUrl))
  .countItems()
  .take(10)

Finally, the bridge RDD can be converted back into an ArchiveSpark RDD at any time by calling .toArchiveSpark (only before the records are mapped to some plain values). This way, ArchiveSpark's Enrich Functions and operations can be used:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._
import de.l3s.archivespark.aut._
import de.l3s.archivespark.enrich.functions._
import de.l3s.archivespark.implicits._
import de.l3s.archivespark.specific.warc.specs._

ArchiveSparkAUT.load(WarcCdxHdfsSpec("path/to/metadata.cdx.gz", "path/to/warc_dir"))
  .keepValidPages()
  .toArchiveSpark
  .enrich(Html.first("title"))
  .saveAsJson("recordsWithTitle.json.gz")

The conversion also works the other way around:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._
import de.l3s.archivespark._
import de.l3s.archivespark.aut._
import de.l3s.archivespark.specific.warc.specs._

ArchiveSpark.load(WarcCdxHdfsSpec("path/to/metadata.cdx.gz", "path/to/warc_dir"))
  .filter(_.timestamp.startsWith("2012"))
  .toAUT
  .keepValidPages()
  .map(r => ExtractDomain(r.getUrl))
  .countItems()
  .take(10)

For more information on how to use and install ArchiveSpark, please read the docs.

To learn about what you can do with ArchiveSpark, have a look at our Jupyter recipes.

The MIT License (MIT)

Copyright (c) 2018 Helge Holzmann (L3S)

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