I am using Elasticsearch and would like to combine 2 sets of query results into one query if possible.
I am using 3 fields for this.
First query is that I want just 5 results that have the field "featured" to be the value "1" but also fuzzy match the term "seo" in fields "title" and "description".
Then I want the remainder of results that just fuzzy match the term "seo" in fields "title" and "description" with the featured to be "0".
I am unsure if the limit 5 can be used. Any ideas anyone. If you need more information please let me know.
Thanks in advance.
Consider merging this two queries into one bool query with "should" clause.
Related
I'm trying to improve search on my service but get stuck on complex queries.
I need to match some documents by terms but return only documents that contains all of provided terms in any order and contains only this terms.
So for example, lets take movie titles:
"Jurassic Park"
"Lost World: Jurassic Park"
"Jurassic Park III"
When I type "Park Jurassic" I want only first document to be returned because it contains both words and nothing more.
This is silly example of complex problem but I've simplified it.
I tried with terms queries, match etc but I don't know how to check if entire field was matched.
So in short it must match all tokens in any order.
Field is mapped as text and also as keyword.
You tested the terms set query?
Returns documents that contain a minimum number of exact terms in a
provided field.
The terms_set query is the same as the terms query, except you can
define the number of matching terms required to return a document.
I'm trying to find out how to properly write my query in order to do a LIKE query with ElasticSearch.
Let's say I have a record of firstname and I want to find every one where there is ma in it.
So I've tried multiple things but none are working. Here is a list :
{"match": {"text": ".*ma.*"}}
{"match": {"text": "*ma*"}}
{"match":{"text"{"query":"ma","fuzziness":"AUTO","prefix_length":1}}}
Do you have an idea of how to do that or where am I missing something?
You might look into using the N-Gram tokenizer to split your documents' tokens up into their substrings.
This will allow you to search against the index with the "partial" matches you're describing.
Bear in mind that this will affect how your documents are tokenized for search so, if you are using other types of analysis for other parts of your application, you may want to create additional fields for your N-Gram tokenized values (or even create a separate index for them).
As a rule of thumb, always try to optimize your index for the queries you want to perform, rather than trying to solve your search problems at query time.
What is the difference between the Query Context and the Filter Context in the Elastic Search in Query DSL.
My Understanding is Query Context- How well the document matches the query parameters.
Ex:
{ "match": { "title": "Search" }}
If I am searching for the documents with title 'Search' then if I contains two documents
i)title:"Search"
ii)title:"Search 123"
Then first document is a perfect match and document two is a semi-match. Then the first document is given in the first place and the second document given the second place. Is my understanding correct?
Filter Context:
Ex:
{ "term": { "status": "published" }}
If I am searching for the documents with status 'published' then if I contains two documents
i)status:"published"
ii)status:"published 123"
Then the first document is perfect so it is returned and the second match is not a perfect match so it is not returned. Is my understanding correct?
Basically in Query context, the elastic search scans all the documents and tries to find out how well the documents match the query, means the score will will be calculated for each documents. Where as in filter context,it will just checks whether the documents matches the query or not i.e, only yes or no will be returned. The filter queries does not contribute to the score of the document.
Next coming to the difference between the match and term queries , if you mapped a field to keyword then that field will be not analysed and its inverted index contains the whole term as it is, i.e is if status is mapped to keyword then if you insert "published 123" in status field , then its inverted index contains ["published 123"] and if status is mapped to text then while inserting data to status filed it is analysed for ex: if you insert "published 123" then its inverted index will be ["published","123"].
So whenever you use term query for keyword fields the query string will not be analysed and it tries to find exact term in the inverted index and if you use match query it analyses the query string and it returns all the doc's that contain the one of the analysed string of query in it's inverted index
Your understanding about the difference between term and match queries is correct at the most basic level but like Jettro commented in the filter query you mentioned both the documents will be selected. When doing a term query it really depends what kind of analyzer you are using and how that affects the terms that are stored in inverted index that lucene uses.
To quote an example from the Elasticsearch: Th Definitive Guide "if you were to index ["Foo","Bar"] into an exact value not_analyzed field, or Foo Bar into an analyzed field with the whitespace analyzer, both would result in having the two terms Foo and Bar in the inverted index."
Now under the hood the term query will search all the terms in the inverted index for your query term and even if one of them matches it will be returned as a result.
So in the first case there is only "published" in the inverted index but in the second case too there are both terms "published" and "123", so both documents will be returned as matches.
It also is important to remember that the term query looks in the inverted index for the exact term only; it won’t match any variants like "Published" or "publisheD" with "published".
I want to create an index in elasticsearch that has a field of weighted keywords list, so when I search by term in this keywords - it will give better scores to those documents that has this key with higher weight?
For instance:
Doc1
"id" : "111"
"keywords" : "house"(20), "dog"(2)
Doc2
"id" : "222"
"keywords" : "house"(3), "dog"(40)
I want when searching "dog" to get doc2 with higher score.
How would you build the mapping and the query?
Note that it's different than searching with regular boost, as the boost per each term is different per document.
What about Elasticsearch payloads? See DrTech's answer with the delimited payload token filter to a separate unrelated question which might help you out. But, what you are describing seems to very much lend itself to the use of payloads and using script scoring to access these payloads and influence the scoring. Take note of the performance cost he mentions.
I'm trying to choose a database/search engine to return a list of results which shows any results the user has a relationship with first, then others after. Similar to the way Facebook works where you search a business name and one's you have liked appear first then others after?
I've seen this question which is similar to what I need but I believe it only show's results for that user: How can ElasticSearch be used to implement social search?
Is this possible with either ElasticSearch, Neo4j or anything else?
Elasticsearch can certainly do this.
Results are returned from Elasticsearch based on the score, which basically means the better the match the bigger the score.
You could use the "bool" query to specify your query as a "must" and then the user match as a "should". Optionally you might want to add a "boost" to the should query so it scores highest if matched.
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html