I have an index users with user type. Mapping is dynamic. For now user hs a following structure:
"user": {
"nested": {
"one":51
},
"other": {
"two":"hi!"
},
"three":false
}
I need to find all users which has other.two field with value hi or has three field with value false. E.g. user which has other.two must have hi value in it or three field with value false. How to select this from elastic search?
I have tried:
GET /users/user/_search
{"query": {"match": {"other.two": "hi"}}}
Returns a user for me, but
GET /users/user/_search
{"query": {"match": {"other.two": "hi", "three":false}}}
Returns me a SearchPhaseExecutionException.
How combine several fields and values for searching?
Use a Bool filter or Bool Query
As #rvheddeg suggested above, here is the query that works for me:
{
"query": {
"bool": {
"should": [
{ "match": { "other.two": "hi" }},
{ "match": { "three": false }}
],
"minimum_should_match": 1
}
}
}
Related
I have two resolver.
The one is Company resolve that return the company details like id, name and list of documents ids, like this example:
{
"data": {
"companyOne": {
"name": "twitter",
"documents": [
"5c6c0213f0fa854bd7d4a38c",
"5c6c02948e0001a16529a1a1",
"5c6c02ee7e76c12075850119",
"5c6ef2ddd16e19889ffaffd0",
"5c72fb723ebf7b2881679ced",
"5c753d1c2e080fa4a2f86c87",
...
]
}
}
}
And the another resolver gets me all the details of documents like this example:
{
"data": {
"documentsMany": [{
"name": "doc1",
"_id": 5c6c0213f0fa854bd7d4a38c,
}, {
"name": "doc2",
"_id": 5c6c02948e0001a16529a1a1,
},
...
]
}
}
How to match every data.companyOne.documents[id] to data.documentsMany[..]._id? in the query level? is it possible to do this graphql?
The expect results should be when I run the companyOne query (without change the code - just in the query level) it's should return with documents as object instead of array of string ids.
maybe something like?
query {
companyOne {
name,
documents on documentsMany where _id is ___???
}
}
I need to translate the following SQL query to ES query:
SELECT *
FROM SKILL
WHERE SKILL.name LIKE 'text' and SKILL.type = 'hard'
I have tried the following using "elasticsearch" library for python3:
query = self.__es.search(index="skills",
body={"from" : skip, "size" : limit,
"query":
{"query_string":
{"query": 'text'}
})
and this worked well. But now, I don't know how to check that the field 'type' is equal to 'hard'.
How can I do that?
Thank you.
You have to use a bool query and in the "must" part put two queries, the full text one and a term one:
{
"query": {
"bool": [{
"match": {
"name": "this is a test"
}
}, {
"term": {
"type": "hard"
}
}]
}
}
Before this you have to store the type property as a keyword field.
I think it's best if I describe my intent and try to break it down to code.
I want users to have the ability of complex queries should they choose to that query_string offers. For example 'AND' and 'OR' and '~', etc.
I want to have fuzziness in effect, which has made me do things I feel dirty about like "#{query}~" to the sent to ES, in other words I am specifying fuzzy query on the user's behalf because we offer transliteration which could be difficult to get the exact spelling.
At times, users search a number of words that are suppose to be in a phrase. query_string searches them individually and not as a phrase. For example 'he who will' should bring me the top match to be when those three words are in that order, then give me whatever later.
Current query:
{
"indices_boost": {},
"aggregations": {
"by_ayah_key": {
"terms": {
"field": "ayah.ayah_key",
"size": 6236,
"order": {
"average_score": "desc"
}
},
"aggregations": {
"match": {
"top_hits": {
"highlight": {
"fields": {
"text": {
"type": "fvh",
"matched_fields": [
"text.root",
"text.stem_clean",
"text.lemma_clean",
"text.stemmed",
"text"
],
"number_of_fragments": 0
}
},
"tags_schema": "styled"
},
"sort": [
{
"_score": {
"order": "desc"
}
}
],
"_source": {
"include": [
"text",
"resource.*",
"language.*"
]
},
"size": 5
}
},
"average_score": {
"avg": {
"script": "_score"
}
}
}
}
},
"from": 0,
"size": 0,
"_source": [
"text",
"resource.*",
"language.*"
],
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "inna alatheena",
"fuzziness": 1,
"fields": [
"text^1.6",
"text.stemmed"
],
"minimum_should_match": "85%"
}
}
],
"should": [
{
"match": {
"text": {
"query": "inna alatheena",
"type": "phrase"
}
}
}
]
}
}
}
Note: alatheena searched without the ~ will not return anything although I have allatheena in the indices. So I must do a fuzzy search.
Any thoughts?
I see that you're doing ES indexing of Qur'anic verses, +1 ...
Much of your problem domain, if I understood it correctly, can be solved simply by storing lots of transliteration variants (and permutations of their combining) in a separate field on your Aayah documents.
First off, you should make a char filter that replaces all double letters with single letters [aa] => [a], [ll] => [l]
Maybe also make a separate field containing all of [a, e, i] (because of their "vocative"/transcribal ambiguity) replaced with € or something similar, and do the same while querying in order to get as many matches as possible...
Also, TH in "allatheena" (which as a footnote may really be Dhaal, Thaa, Zhaa, Taa+Haa, Taa+Hhaa, Ttaa+Hhaa transcribed ...) should be replaced by something, or both the Dhaal AND the Thaa should be transcribed multiple times.
Then, because it's Qur'anic script, all Alefs without diacritics, Hamza, Madda, etc should be treated as Alef (or Hamzat) ul-Wasl, and that should also be considered when indexing / searching, because of Waqf / Wasl in reading arabic. (consider all the Wasl`s in the first Aayah of Surat Al-Alaq for example)
Dunno if this is answering your question in any way, but I hope it's of some assistance in implementing your application nontheless.
You should use Dis Max Query to achieve that.
A query that generates the union of documents produced by its
subqueries, and that scores each document with the maximum score for
that document as produced by any subquery, plus a tie breaking
increment for any additional matching subqueries.
This is useful when searching for a word in multiple fields with
different boost factors (so that the fields cannot be combined
equivalently into a single search field). We want the primary score to
be the one associated with the highest boost.
Quick example how to use it:
POST /_search
{
"query": {
"dis_max": {
"tie_breaker": 0.7,
"boost": 1.2,
"queries": [
{
"match": {
"text": {
"query": "inna alatheena",
"type": "phrase",
"boost": 5
}
}
},
{
"match": {
"text": {
"query": "inna alatheena",
"type": "phrase",
"fuzziness": "AUTO",
"boost": 3
}
}
},
{
"query_string": {
"default_field": "text",
"query": "inna alatheena"
}
}
]
}
}
}
It will run all of your queries, and the one, which scored highest compared to others, will be taken. So just define your rules using it. You should achieve what you wanted.
I'm trying to implement a match search in Elasticsearch, and I noticed that the behavior is different depending if I use _all or if a enter a specific string value as the field name of my query.
To give some context, I've created an index with the following settings:
{
"settings": {
"analysis": {
"analyzer": {
"default": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"standard",
"lowercase",
"stop",
"kstem",
"word_delimiter"
]
}
}
}
}
}
If I create a document like:
{
"name": "Hello.World"
}
And I execute a search using _all like:
curl -d '{"query": { "match" : { "_all" : "hello" } }}' http://localhost:9200/myindex/mytype/_search
It will correctly match the document (since I'm using the stop filter to split the words at the dot), but if I execute this query instead:
curl -d '{"query": { "match" : { "name" : "hello" } }}' http://localhost:9200/myindex/mytype/_search
Nothing is being returned instead. How is this possible?
Issue a GET for /myindex/mytype/_mapping and see if your index is configured the way you think it is. Meaning, see if that "name" field is not_analyzed, for example.
Even more, run the following query to see how name field is actually indexed:
{
"query": {
"match": {
"name": "hello"
}
},
"fielddata_fields": ["name"]
}
You should see something like this in the result:
"fields": {
"name": [
"hello",
"world"
]
}
If you don't, then you know something's wrong with your mapping for the name field.
How do I search for all unique values of a given field with Elasticsearch?
I have such a kind of query like select full_name from authors, so I can display the list to the users on a form.
You could make a terms facet on your 'full_name' field. But in order to do that properly you need to make sure you're not tokenizing it while indexing, otherwise every entry in the facet will be a different term that is part of the field content. You most likely need to configure it as 'not_analyzed' in your mapping. If you are also searching on it and you still want to tokenize it you can just index it in two different ways using multi field.
You also need to take into account that depending on the number of unique terms that are part of the full_name field, this operation can be expensive and require quite some memory.
For Elasticsearch 1.0 and later, you can leverage terms aggregation to do this,
query DSL:
{
"aggs": {
"NAME": {
"terms": {
"field": "",
"size": 10
}
}
}
}
A real example:
{
"aggs": {
"full_name": {
"terms": {
"field": "authors",
"size": 0
}
}
}
}
Then you can get all unique values of authors field.
size=0 means not limit the number of terms(this requires es to be 1.1.0 or later).
Response:
{
...
"aggregations" : {
"full_name" : {
"buckets" : [
{
"key" : "Ken",
"doc_count" : 10
},
{
"key" : "Jim Gray",
"doc_count" : 10
},
]
}
}
}
see Elasticsearch terms aggregations.
Intuition:
In SQL parlance:
Select distinct full_name from authors;
is equivalent to
Select full_name from authors group by full_name;
So, we can use the grouping/aggregate syntax in ElasticSearch to find distinct entries.
Assume the following is the structure stored in elastic search :
[{
"author": "Brian Kernighan"
},
{
"author": "Charles Dickens"
}]
What did not work: Plain aggregation
{
"aggs": {
"full_name": {
"terms": {
"field": "author"
}
}
}
}
I got the following error:
{
"error": {
"root_cause": [
{
"reason": "Fielddata is disabled on text fields by default...",
"type": "illegal_argument_exception"
}
]
}
}
What worked like a charm: Appending .keyword with the field
{
"aggs": {
"full_name": {
"terms": {
"field": "author.keyword"
}
}
}
}
And the sample output could be:
{
"aggregations": {
"full_name": {
"buckets": [
{
"doc_count": 372,
"key": "Charles Dickens"
},
{
"doc_count": 283,
"key": "Brian Kernighan"
}
],
"doc_count": 1000
}
}
}
Bonus tip:
Let us assume the field in question is nested as follows:
[{
"authors": [{
"details": [{
"name": "Brian Kernighan"
}]
}]
},
{
"authors": [{
"details": [{
"name": "Charles Dickens"
}]
}]
}
]
Now the correct query becomes:
{
"aggregations": {
"full_name": {
"aggregations": {
"author_details": {
"terms": {
"field": "authors.details.name"
}
}
},
"nested": {
"path": "authors.details"
}
}
},
"size": 0
}
Working for Elasticsearch 5.2.2
curl -XGET http://localhost:9200/articles/_search?pretty -d '
{
"aggs" : {
"whatever" : {
"terms" : { "field" : "yourfield", "size":10000 }
}
},
"size" : 0
}'
The "size":10000 means get (at most) 10000 unique values. Without this, if you have more than 10 unique values, only 10 values are returned.
The "size":0 means that in result, "hits" will contain no documents. By default, 10 documents are returned, which we don't need.
Reference: bucket terms aggregation
Also note, according to this page, facets have been replaced by aggregations in Elasticsearch 1.0, which are a superset of facets.
The existing answers did not work for me in Elasticsearch 5.X, for the following reasons:
I needed to tokenize my input while indexing.
"size": 0 failed to parse because "[size] must be greater than 0."
"Fielddata is disabled on text fields by default." This means by default you cannot search on the full_name field. However, an unanalyzed keyword field can be used for aggregations.
Solution 1: use the Scroll API. It works by keeping a search context and making multiple requests, each time returning subsequent batches of results. If you are using Python, the elasticsearch module has the scan() helper function to handle scrolling for you and return all results.
Solution 2: use the Search After API. It is similar to Scroll, but provides a live cursor instead of keeping a search context. Thus it is more efficient for real-time requests.