drop dataverse fuzzyjoin if exists;

create dataverse fuzzyjoin;

use dataverse fuzzyjoin;

create type DBLPType as closed {
  id: int32, 
  dblpid: string,
  title: string,
  authors: string,
  misc: string
}

create dataset DBLP(DBLPType) partitioned by key id;

load dataset DBLP 
using "edu.uci.ics.asterix.external.dataset.adapter.NCFileSystemAdapter"
(("path"="nc1://data/pub-small/dblp-small-id.txt"),("format"="delimited-text"),("delimiter"=":")) pre-sorted;

write output to nc1:'rttest/fuzzyjoin_dblp-2.1_5.3.1.adm';

    //
    // -- - Stage 2 - --
    //
    for $paperDBLP in dataset('DBLP')
    let $idDBLP := $paperDBLP.id
    let $tokensUnrankedDBLP := counthashed-word-tokens($paperDBLP.title)
    let $lenDBLP := len($tokensUnrankedDBLP)
    let $tokensDBLP :=
        for $tokenUnranked in $tokensUnrankedDBLP
        for $tokenRanked at $i in
            //
            // -- - Stage 1 - --
            //
            for $paper in dataset('DBLP')
            let $id := $paper.id
            for $token in counthashed-word-tokens($paper.title)
            /*+ hash */ 
            group by $tokenGroupped := $token with $id
            /*+ inmem 1 302 */
            order by count($id), $tokenGroupped
            return $tokenGroupped
        where $tokenUnranked = /*+ bcast*/ $tokenRanked
        order by $i
        return $i
    for $prefixTokenDBLP in subset-collection(
                                $tokensDBLP, 
                                0,
                                prefix-len-jaccard($lenDBLP, .5f))
    order by $idDBLP
    return {'id': $idDBLP, 'prefixToken': $prefixTokenDBLP, 'tokens': $tokensDBLP}