@ianmilligan1
I wrote a script to do this. Do you have a small-ish dataset that has images like this that I can test with?

Great, thanks @JWZ2018 – just pinged you in Slack about access to a relatively small dataset that could be tested on (you could try on the sample data here, but I'm worried we need a large enough dataset to find these potential hits).

@ianmilligan1
I used this script:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._
import io.archivesunleashed.df._
val data = RecordLoader.loadArchives("/mnt/vol1/data_sets/cpp/cpp_warcs_accession_01/partner.archive-it.org/cgi-bin/getarcs.pl/ARCHIVEIT-227-QUARTERLY-16606*",sc)
import spark.implicits._
val domains = data.extractImageLinksDF().select(df.ExtractDomain($"src").as("Domain"), $"image_url".as("ImageUrl"));
val images = data.extractImageDetailsDF().select($"url".as("ImageUrl"), $"md5".as("MD5"));

//domains and images in one table
val total = domains.join(images, "ImageUrl")
//group same images by MD5 and only keep the md5 with at least 2 distinct domains
val links = total.groupBy("MD5").count().where(countDistinct("Domain")>=2)
//rejoin with images to get the list of image urls
val result = total.join(links, "MD5").groupBy("Domain","MD5").agg(first("ImageUrl").as("ImageUrl")).orderBy(asc("MD5")).show()

Some results shared in the slack

This is awesome (and thanks for the results, looks great).

Given the results, I realize maybe we should isolate to just a single crawl.

If we want to do the above but slate it to just the crawl date in yyyymm format: 200912, where should we put that filter in above for optimal performance?

@ianmilligan1
We can try something like this:

import io.archivesunleashed._
import io.archivesunleashed.matchbox._
import io.archivesunleashed.df._
val data = RecordLoader.loadArchives("/mnt/vol1/data_sets/cpp/cpp_warcs_accession_01/partner.archive-it.org/cgi-bin/getarcs.pl/ARCHIVEIT-227-QUARTERLY-DNYDTY-20121103160515-00000-crawling202.us.archive.org-6683.warc.gz",sc).filter(r => r.getCrawlMonth == "201211")
val domains = data.extractImageLinksDF().select(df.ExtractDomain($"src").as("Domain"), $"image_url".as("ImageUrl"));
val images = data.extractImageDetailsDF().select($"url".as("ImageUrl"), $"md5".as("MD5"));

//domains and images in one table
val total = domains.join(images, "ImageUrl")
//group same images by MD5 and only keep the md5 with at least 2 distinct domains
val links = total.groupBy("MD5").count().where(countDistinct("Domain")>=2)
//rejoin with images to get the list of image urls
val result = total.join(links, "MD5").groupBy("Domain","MD5").agg(first("ImageUrl").as("ImageUrl")).orderBy(asc("MD5")).show()

This particular dataset didn't return any results for the given month but the script completed successfully.

@JWZ2018 in above, filter is being done on RDD... the plan is move everything over to DF, so we need a new set of UDFs... I'll create a new PR on this.

@ianmilligan1 are we good on this issue, or are we waiting for something from @lintool still?

Realistically we could probably just do this by filtering the resulting csv file, so I’m happy if we close this.

👎 on filtering CSVs - not scalable...

OK, thanks @lintool. Above you noted creating some new UDFs, is that still something you could do?

@SinghGursimran here's one for you.