SASI just uses “=“ for the tokenized equality matching, which is the exact thing this discussion is about changing/not liking.
> On Aug 2, 2023, at 7:18 PM, J. D. Jordan <jeremiah.jor...@gmail.com> wrote: > > I do not think LIKE actually applies here. LIKE is used for prefix, > contains, or suffix searches in SASI depending on the index type. > > This is about exact matching of tokens. > >> On Aug 2, 2023, at 5:53 PM, Jon Haddad <rustyrazorbl...@apache.org> wrote: >> >> Certain bits of functionality also already exist on the SASI side of >> things, but I'm not sure how much overlap there is. Currently, there's a >> LIKE keyword that handles token matching, although it seems to have some >> differences from the feature set in SAI. >> >> That said, there seems to be enough of an overlap that it would make sense >> to consider using LIKE in the same manner, doesn't it? I think it would be >> a little odd if we have different syntax for different indexes. >> >> https://github.com/apache/cassandra/blob/trunk/doc/SASI.md >> >> I think one complication here is that there seems to be a desire, that I >> very much agree with, to expose as much of the underlying flexibility of >> Lucene as much as possible. If it means we use Caleb's suggestion, I'd ask >> that the queries that SASI and SAI both support use the same syntax, even if >> it means there's two ways of writing the same query. To use Caleb's >> example, this would mean supporting both LIKE and the `expr` column. >> >> Jon >> >>>> On 2023/08/01 19:17:11 Caleb Rackliffe wrote: >>> Here are some additional bits of prior art, if anyone finds them useful: >>> >>> >>> The Stratio Lucene Index - >>> https://github.com/Stratio/cassandra-lucene-index#examples >>> >>> Stratio was the reason C* added the "expr" functionality. They embedded >>> something similar to ElasticSearch JSON, which probably isn't my favorite >>> choice, but it's there. >>> >>> >>> The ElasticSearch match query syntax - >>> https://urldefense.com/v3/__https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-match-query.html__;!!PbtH5S7Ebw!ZHwYJ2xkivwTzYgjkp5QFAzALXCWPqkga6GBD-m2aK3j06ioSCRPsdZD0CIe50VpRrtW-1rY_m6lrSpp7zVlAf0MsxZ9$ >>> >>> >>> Again, not my favorite. It's verbose, and probably too powerful for us. >>> >>> >>> ElasticSearch's documentation for the basic Lucene query syntax - >>> https://urldefense.com/v3/__https://www.elastic.co/guide/en/elasticsearch/reference/8.9/query-dsl-query-string-query.html*query-string-syntax__;Iw!!PbtH5S7Ebw!ZHwYJ2xkivwTzYgjkp5QFAzALXCWPqkga6GBD-m2aK3j06ioSCRPsdZD0CIe50VpRrtW-1rY_m6lrSpp7zVlAXEPP1sK$ >>> >>> >>> One idea is to take the basic Lucene index, which it seems we already have >>> some support for, and feed it to "expr". This is nice for two reasons: >>> >>> 1.) People can just write Lucene queries if they already know how. >>> 2.) No changes to the grammar. >>> >>> Lucene has distinct concepts of filtering and querying, and this is kind of >>> the latter. I'm not sure how, for example, we would want "expr" to interact >>> w/ filters on other column indexes in vanilla CQL space... >>> >>> >>>> On Mon, Jul 24, 2023 at 9:37 AM Josh McKenzie <jmcken...@apache.org> wrote: >>>> >>>> `column CONTAINS term`. Contains is used by both Java and Python for >>>> substring searches, so at least some users will be surprised by term-based >>>> behavior. >>>> >>>> I wonder whether users are in their "programming language" headspace or in >>>> their "querying a database" headspace when interacting with CQL? i.e. this >>>> would only present confusion if we expected users to be thinking in the >>>> idioms of their respective programming languages. If they're thinking in >>>> terms of SQL, MATCHES would probably end up confusing them a bit since it >>>> doesn't match the general structure of the MATCH operator. >>>> >>>> That said, I also think CONTAINS loses something important that you allude >>>> to here Jonathan: >>>> >>>> with corresponding query-time tokenization and analysis. This means that >>>> the query term is not always a substring of the original string! Besides >>>> obvious transformations like lowercasing, you have things like >>>> PhoneticFilter available as well. >>>> >>>> So to me, neither MATCHES nor CONTAINS are particularly great candidates. >>>> >>>> So +1 to the "I don't actually hate it" sentiment on: >>>> >>>> column : term`. Inspired by Lucene’s syntax >>>> >>>> >>>>> On Mon, Jul 24, 2023, at 8:35 AM, Benedict wrote: >>>> >>>> >>>> I have a strong preference not to use the name of an SQL operator, since >>>> it precludes us later providing the SQL standard operator to users. >>>> >>>> What about CONTAINS TOKEN term? Or CONTAINS TERM term? >>>> >>>> >>>>> On 24 Jul 2023, at 13:34, Andrés de la Peña <adelap...@apache.org> wrote: >>>> >>>> >>>> `column = term` is definitively problematic because it creates an >>>> ambiguity when the queried column belongs to the primary key. For some >>>> queries we wouldn't know whether the user wants a primary key query using >>>> regular equality or an index query using the analyzer. >>>> >>>> `term_matches(column, term)` seems quite clear and hard to misinterpret, >>>> but it's quite long to write and its implementation will be challenging >>>> since we would need a bunch of special casing around SelectStatement and >>>> functions. >>>> >>>> LIKE, MATCHES and CONTAINS could be a bit misleading since they seem to >>>> evoke different behaviours to what they would have. >>>> >>>> `column LIKE :term:` seems a bit redundant compared to just using `column >>>> : term`, and we are still introducing a new symbol. >>>> >>>> I think I like `column : term` the most, because it's brief, it's similar >>>> to the equivalent Lucene's syntax, and it doesn't seem to clash with other >>>> different meanings that I can think of. >>>> >>>>> On Mon, 24 Jul 2023 at 13:13, Jonathan Ellis <jbel...@gmail.com> wrote: >>>> >>>> Hi all, >>>> >>>> With phase 1 of SAI wrapping up, I’d like to start the ball rolling on >>>> aligning around phase 2 features. >>>> >>>> In particular, we need to nail down the syntax for doing non-exact string >>>> matches. We have a proof of concept that includes full Lucene analyzer and >>>> filter functionality – just the text transformation pieces, none of the >>>> storage parts – which is the gold standard in this space. For example, the >>>> StandardAnalyzer [1] lowercases all terms and removes stopwords (common >>>> words like “a”, “is”, “the” that are usually not useful to search >>>> against). Lucene also has classes that offer stemming, special case >>>> handling for email, and many languages besides English [2]. >>>> >>>> What syntax should we use to express “rows whose analyzed tokens match >>>> this search term?” >>>> >>>> The syntax must be clear that we want to look for this term within the >>>> column data using the configured index with corresponding query-time >>>> tokenization and analysis. This means that the query term is not always a >>>> substring of the original string! Besides obvious transformations like >>>> lowercasing, you have things like PhoneticFilter available as well. >>>> >>>> Here are my thoughts on some of the options: >>>> >>>> `column = term`. This is what the POC does today and it’s super confusing >>>> to overload = to mean something other than exact equality. I am not a fan. >>>> >>>> `column LIKE term` or `column LIKE %term%`. The closest SQL operator, but >>>> neither the wildcarded nor unwildcarded syntax matches the semantics of >>>> term-based search. >>>> >>>> `column MATCHES term`. I rather like this one, although Mike points out >>>> that “match” has a meaning in the context of regular expressions that could >>>> cause confusion here. >>>> >>>> `column CONTAINS term`. Contains is used by both Java and Python for >>>> substring searches, so at least some users will be surprised by term-based >>>> behavior. >>>> >>>> `term_matches(column, term)`. Postgresql FTS makes you use functions like >>>> this for everything. It’s pretty clunky, and we would need to make the >>>> amazingly hairy SelectStatement even hairier to handle “use a function >>>> result in a predicate” like this. >>>> >>>> `column : term`. Inspired by Lucene’s syntax. I don’t actually hate it. >>>> >>>> `column LIKE :term:`. Stick with the LIKE operator but add a new symbol to >>>> indicate term matching. Arguably more SQL-ish than a new bare symbol >>>> operator. >>>> >>>> [1] >>>> https://lucene.apache.org/core/9_7_0/core/org/apache/lucene/analysis/standard/StandardAnalyzer.html >>>> [2] https://lucene.apache.org/core/9_7_0/analysis/common/index.html >>>> >>>> -- >>>> Jonathan Ellis >>>> co-founder, http://www.datastax.com >>>> @spyced >>>> >>>> >>>> >>>
