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/*
* 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 : Tests whether a keyword index is applied to optimize a selection query using the similarity-jaccard-check function on word tokens.
* Tests that the optimizer rule correctly drills through the let clauses.
* The index should be applied.
* 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 dataset DBLP(DBLPType) primary key id;
create index keyword_index on DBLP (title) type keyword;
write output to asterix_nc1:"rttest/inverted-index-complex_word-jaccard-check-multi-let.adm"
select element {'Paper':paper_tokens,'Query':query_tokens}
from DBLP as paper
with paper_tokens as test.`word-tokens`(paper.title),
query_tokens as test.`word-tokens`('Transactions for Cooperative Environments'),
jacc as test.`similarity-jaccard-check`(paper_tokens,query_tokens,0.800000f)
where jacc[0]
;