We are new to elastic search and NEST.
We are trying to do case sensitive search using C# client - NEST.
We have read lots of posts but could not figure out it. Can someone please us with detail step by step instructions.
Any help will be highly appreciated.
Thanks,
VB.
I know this is an older question, but I ran across it in my research. So, here's my answer.
First, switching to a TERM query did not help. Upon learning more about how ElasticSearch works by default, I understand why.
By default, ElasticSearch is case-insensitive. When documents are indexed, the default analyzer lowercases all of the string values and keeps the lowercase values for future searches. This does not affect the values stored in the documents themselves, but the lowercasing does affect searches.
If you are using the default analyzer, then your search terms for string values should be all lowercase.
Before I learned how this worked, I spent a fair amount of time looking at a mixed-case field value in an indexed document, then searching with a query term that used the same mixed-case value. Zero results. It wasn't until I forced the value my query used to all lowercase that I started getting results.
You can read more about ElasticSearch analyzers here: ElasticSearch - Analysis
Try TERM query, are values passed to TERM query are not analyzed, thus ES is not making lower case of your input.
Here: http://www.elasticsearch.org/guide/reference/query-dsl/term-query/
Related
I'm trying to understand what is the purpose of configuring a different analyzer for searching and indexing in Azure Search. See: https://learn.microsoft.com/en-us/rest/api/searchservice/create-index#-field-definitions-
According to my understanding, the job of the indexing analyzer is to breakup the input document into individual tokens. Through this process, it might apply multiple transformations like lower-casing the content, removing punctuation and white-spaces, and even removing entire words.
If the tokens are already processed, what is the use of the search analyzer?
Initially, I thought it would apply a similar process on the search query itself, but wouldn't setting a different analyzer than the one used to index the document at this stage completely breaks the search results? If the indexing analyzer lower-cased everything, but the search analyzer doesn't lower-case the query, wouldn't that means you'll never get matches for queries with upper case characters? What if the search analyzer doesn't split tokens on white-spaces? Won't you ever get a match the moment the query includes a space?
Assuming that this is indeed how the two analyzers works together, then why would you ever want to set two different ones?
Your understanding of the difference between index and search analyzer is correct. An example scenario where that's valuable is using ngrams for indexing but not for search terms. So this would allow a document with "cat" to produce "c", "ca", "cat" but you wouldn't necessarily want to apply ngrams on the search term as that would make the query less performant and isn't necessary since the documents already produced the ngrams. Hopefully that makes sense!
I'm trying to create a search feature in Meteor 1.8.1 that does the following:
returns partial matches, e.g. "fish" will find "fish", "fishcake" and "dogfish"
has server-side control of which documents are returned, so search results don't include documents that are not published to the user
is reasonably efficient
returns a limited number of results
This seems like it should be a common requirement, but I'm failing to find any solution.
MongoDB full text search will only return on whole words, so will only find "fish".
Easy search doesn't support server-side permissions, as far as I can tell.
I could try a regex solution but I think it would be expensive?
Thank you for any solutions!
Edit: From discussion it seems that Easy Search does support server-side filtering using a selector, and this would be the best solution. However, I can't get a selector working from the examples and documentation. For clarity, I've created a new question for that issue
The documentation explicitly states that for advanced use cases you may want to use elastic search and offers you a pluggable extension to ease the burden of integration.
https://matteodem.github.io/meteor-easy-search/docs/recipes/#advanced-search
You might wish that a search for cafe returns documents with the text café in them (special character). Or that your search string is split up by whitespace and those terms used to search across multiple fields.
You should consider using a search engine like ElasticSearch for your search if you have these usecases. ElasticSearch allows you to configure precisely how your fields are being searched. One way you can do that is by analyzing your data, so that searching itself is as fast as possible.
I'm learning node.js and mongodb. I'm learning by solving some problems. I want to make site that can search video database. Each video has title, description, author and a subarray of notes (you can think of it as a comments). Each note has a subarray of manual references to tags documents that exists in tags collection.
I need to search for some text in videos collection. For each resulting video I need to know if search criteria matches some of basic fields (author, title, description) or if some of its notes, including names of tags, matches criteria. Or both.
I know that this may not be right task for beginner but I would really like to make this work. I have some ideas about how to do this, but they probably are not good since I don't know much about mongo and it's capabilities.
What do you suggest, what should I use? Should I use text search capabilities + some aggregation? Should I offload some of work to be processed by application rather than mongo?
I probably don't need details, just directions.
Thank you.
Since nobody answers, I decided to share my idea and how I did it. There is probably better solution, that is way I asked this question.
I did two separate queries using regex, that I merged results in application code.
I used ES6 Map to make union of these two sets.
I'm using Solr 3.5.0 (with WebSphere Commerce). While performing a search, commerce use the suggestion tool to suggest (auto-complete) search terms regarding the letters already typed on the search box.
Currently WebSphere Commerce is using the Solr's TermsComponent. But one of my new requirement is to be abble to enrich the list of suggested terms.
Do you know is there is any way to do that by creating a plain text dictionary, using an other solr component, ... ?
Thanks for reading,
and for your help.
Regards,
Dekx.
I think a plain-text dictionary probably wouldn't be a usable data source (even if you could use it, search linearly through a plain-text file would probably be too slow). If you create an index from you dictionary, you could probably incorporate it in the TermsComponent as a shard (see the TermsComponent documentation, under the heading "Distributed Search Support").
I don't believe TermsComponent supports searching multiple fields, so you'll want to make sure the same field name is used for the terms in the dictionary that you want to use (that is, if you are looking at the "name" field in the index, then create a "name" field in your indexed dictionary as well, rather than a "dictionaryentry" field)
Just to my mind, though, I fail to understand what the value this would be. Generally, it's intended to look at the terms available in the index on that field. "Enriching" it with more data, would just be providing suggestions that it won't actually be able to find when searching. Of course, I don't really know about your search implementation, but in most cases, that would certainly be my thought.
Hi I am building a search application using lucene. Some of my queries are complex. For example, My documents contain the fields location and population where location is a not-analyzed field and population is a numeric field. Now I need to return all the documents that have location as "san-francisco" and population between 10000 and 20000. If I combine these two fields and build a query like this:
location:san-francisco AND population:[10000 TO 20000], i am not getting the correct result. Any suggestions on why this could be happening and what I can do.
Also while building complex queries some of the fields that I am including are analyzed while others are not analyzed. For instance the location field is not analyzed and contains terms like chicago, san-francisco and so on. While the summary field is analyzed and it generally contains a descriptive paragraph.
Consider this query:
location:san-francisco AND summary:"great restaurants"
Now if I use a StandardAnalyzer while searching I do not get the correct results when the location field contains a term like san-francisco or los-angeles (i.e it cannot handle the hyphen in between) but if I use a keyword analyzer for the query I do not get correct results either because it cannot search for the phrase "great restaurants" in the summary field.
First, I would recommend tackling this one problem at a time. From my reading of your post, it sounds like you have multiple issues:
You're unsure why a particular query
is not returning any results.
You're unsure why some fields are not being analyzed.
You're having problems with the built-in analyzers dealing with
hyphens.
That's how your post reads. If that's correct, I would suggest you post each question separately. You'll get better answers if the question is precise. It's overwhelming trying to answer your question in the current format.
Now, let me take a stab in the dark at some of your problems:
For your first problem, if you're getting into really complex queries in Lucene, ask yourself whether it makes sense to be doing these queries here, rather than in a proper database. For a more generic answer, I'd try isolating the problem by removing parts of the query until you get results back. Once you find out what part of the query is causing no results, we can debug that further.
For the second problem, check the document you're adding to Lucene. Lucene provides options to store data but not index it. Make sure you've got the right option specified when adding fields to the document.
For the third problem, if the built-in analyzers don't work out for you, breaking on hyphens, just build your own analyzer. I ran into a similar issue with the '#' symbol, and to solve the problem, I wrote a custom analyzer that dealt with it properly. You could do the same for hyphens.
You should use PerFieldAnalyzerWrapper. As the name suggests, you can use different analyzers for different field. In this case, you can use KeywordAnalyzer for city name and StandardAnalyzer for text.