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/*
* Description : Fuzzy joins two datasets, DBLP and CSX, based on the similarity-jaccard function of their titles' 3-gram tokens.
* DBLP has a 3-gram index on title, and we expect the join to be transformed into an indexed nested-loop join.
* We test the inlining of variables that enable the select to be pushed into the join for subsequent optimization with an index.
* Success : Yes
*/
drop dataverse test if exists;
create dataverse test;
use dataverse test;
create type DBLPType as closed {
id: int32,
dblpid: string,
title: string,
authors: string,
misc: string
}
create type CSXType as closed {
id: int32,
csxid: string,
title: string,
authors: string,
misc: string
}
create dataset DBLP(DBLPType) partitioned by key id;
create dataset CSX(CSXType) partitioned by key id;
load dataset DBLP
using "edu.uci.ics.asterix.external.dataset.adapter.NCFileSystemAdapter"
(("path"="nc1://data/dblp-small/dblp-small-id.txt"),("format"="delimited-text"),("delimiter"=":")) pre-sorted;
load dataset CSX
using "edu.uci.ics.asterix.external.dataset.adapter.NCFileSystemAdapter"
(("path"="nc1://data/pub-small/csx-small-id.txt"),("format"="delimited-text"),("delimiter"=":"));
create index ngram_index on DBLP(title) type ngram(3);
write output to nc1:"rttest/index-join_inverted-index-ngram-jaccard-inline.adm";
for $a in dataset('DBLP')
for $b in dataset('CSX')
let $jacc := similarity-jaccard(gram-tokens($a.title, 3, false), gram-tokens($b.title, 3, false))
where $jacc >= 0.5f and $a.id < $b.id
order by $jacc, $a.id, $b.id
return { "arec": $a.title, "brec": $b.title, "jacc": $jacc }