/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */
/*
 * Description    : Fuzzy joins two datasets, DBLP and CSX, based on the similarity-jaccard function of their titles' word tokens.
 *                  DBLP has a keyword index on title, and we expect the join to be transformed into an indexed nested-loop join.
 *                  We expect the top-level equi join introduced because of surrogate optimization to be removed, since it is not necessary.
 * Success        : Yes
 */

drop  dataverse test if exists;
create  dataverse test;

use test;


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

create type test.CSXType as
 closed {
  id : int32,
  csxid : string,
  title : string,
  authors : string,
  misc : string
}

create  dataset DBLP(DBLPType) primary key id;

create  dataset CSX(CSXType) primary key id;

create  index keyword_index  on DBLP (title) type keyword;

write output to asterix_nc1:"rttest/inverted-index-join-noeqjoin_word-jaccard.adm"
select element {'atitle':a.title,'btitle':b.title}
from  DBLP as a,
      CSX as b
where ((test.`similarity-jaccard`(test.`word-tokens`(a.title),test.`word-tokens`(b.title)) >= 0.500000f) and (a.id < b.id))
;