/*
 * 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 self joins a dataset, TweetMessages, based on the similarity-jaccard-check function of its text-messages' word tokens.
 *                  TweetMessages has a keyword index on text-message and btree index on the primary key tweetid, and we expect the join to be
 *                    transformed into btree and inverted indexed nested-loop joins. We test whether the join condition can be transformed into
 *                    multiple indexed nested loop joins of various type of indexes.
 * Success        : Yes
 */

drop  dataverse test if exists;
create  dataverse test;

use test;


create type test.TwitterUserType as
 closed {
  `screen-name` : string,
  lang : string,
  `friends-count` : int32,
  `statuses-count` : int32,
  name : string,
  `followers-count` : int32
}

create type test.TweetMessageNestedType as
{
  tweetid : int64,
  user : TwitterUserType,
  `sender-location` : point,
  `send-time` : datetime,
  `referred-topics` : {{string}},
  countA : int32,
  countB : int32
}

create type test.TweetMessageType as
 closed {
  nested : TweetMessageNestedType
}

create  dataset TweetMessages(TweetMessageType) primary key nested.tweetid;

create  index twmSndLocIx  on TweetMessages (nested.`sender-location`) type rtree;

create  index msgCountAIx  on TweetMessages (nested.countA) type btree;

create  index msgCountBIx  on TweetMessages (nested.countB) type btree;

create  index msgTextIx  on TweetMessages (nested.`message-text`:string) type keyword enforced;

write output to asterix_nc1:"rttest/inverted-index-join_word-jaccard-check-after-btree-access.adm"
select element {'t1':t1.nested.tweetid,'t2':t2.nested.tweetid,'sim':sim[1]}
from  TweetMessages as t1,
      TweetMessages as t2
with  sim as test.`similarity-jaccard-check`(test.`word-tokens`(t1.nested.`message-text`),test.`word-tokens`(t2.nested.`message-text`),0.600000f)
where (sim[0] and (t1.nested.tweetid < test.int64('20')) and (t2.nested.tweetid != t1.nested.tweetid))
;