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 type TOKENSRANKEDADMType as closed {
  token: int32,
  rank: int32
}

create dataset DBLP(DBLPType) partitioned by key id;
create dataset TOKENSRANKEDADM(TOKENSRANKEDADMType) partitioned by key rank;

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;

load dataset TOKENSRANKEDADM 
using "edu.uci.ics.asterix.external.dataset.adapter.NCFileSystemAdapter"
(("path"="nc1://data/pub-small/tokensranked.adm"),("format"="adm"));

write output to nc1:'rttest/fuzzyjoin_dblp-2.2.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 in dataset('TOKENSRANKEDADM')
        where $tokenUnranked = /*+ bcast*/ $tokenRanked.token
        order by $tokenRanked.rank
        return $tokenRanked.rank
    for $prefixTokenDBLP in subset-collection(
                                $tokensDBLP, 
                                0,
                                prefix-len-jaccard(len($tokensDBLP), .5f))
    order by $idDBLP, $prefixTokenDBLP
    return {'id': $idDBLP, 'prefixToken': $prefixTokenDBLP, 'tokens': $tokensDBLP}