So my problem is basically the same as described here, however it still remains unanswered on the group.
My mapping:
{
"abstract": {
"properties": {
"summary": {
"type": "string"
}
}
},
"authors": {
"type": "nested",
"properties": {
"first_name": {
"type": "string"
},
"last_name": {
"type": "string"
}
}
}
}
And I would like to perform a full-text search on both of these fields, probably unequally weighted. The query that comes to my mind, but unfortunately doesn't work, would be this:
{
"query": {
"bool": {
"should": [{
"multi_match": {
"query": "higgs boson",
"fields": ["abstract.summary^5", "author.last_name^2"]
}
}]
}
}
}
I don't get any results from the authors field, because of its nested mapping. I also can't get rid of the nested property - I use it for aggregations. Any elegant idea how to solve it?
The only solution that I managed to work out, which is not handy nor elegant but somehow works is such query:
"query": {
"bool": {
"should": [
{
"nested": {
"path": "authors",
"query": {
"multi_match": {
"query": "higgs",
"fields": ["last_name^2"]
}
}
}
},
{
"multi_match": {
"query": "higgs",
"fields": ["abstract.summary^5"]
}
}
]
}
}
I'm also not sure if the boosting will work as expected, providing it's set in different queries. Any suggestions appreciated.
Changing your mapping to the following one which uses include_in_root: true will allow you to use the query you original wrote:
{
"abstract": {
"properties": {
"summary": {
"type": "string"
}
}
},
"authors": {
"type": "nested",
"include_in_root": true,
"properties": {
"first_name": {
"type": "string"
},
"last_name": {
"type": "string"
}
}
}
}
You may want to index inner objects both as nested fields and as flattened object fields. This can be achieved by setting include_in_parent to true. - Link
Note: include_in_root may be deprecated in future versions of elasticsearch in favor of copy_to.
Related
I am trying to implement a search-as-you-type query inside an array.
This is the structure of the documents:
{
"guid": "6f954d53-df57-47e3-ae9e-cb445bd566d3",
"labels":
[
{
"name": "London",
"lang": "en"
},
{
"name": "Llundain",
"lang": "cy"
},
{
"name": "Lunnainn",
"lang": "gd"
}
]
}
and up to now this is what I came with:
{
"query": {
"multi_match": {
"fields": ["labels.name"],
"query": name,
"type": "phrase_prefix"
}
}
which works exactly as requested.
The problem is that I would like to search also by language.
What I tried is:
{
"query": {
"bool": {
"must": [
{
"multi_match": {
"fields": ["labels.name"],
"query": "london",
"type": "phrase_prefix"
}
},
{
"term": {
"labels.lang": "gd"
}
}
]
}
}
}
but these queries act on separate values of the array.
So, for example, I would like to search only Welsh language (cy). That means that my query that contains the city name should match only values that have "cy" on the "lang" tag.
How do I write this kind of query?
Internally, ElasticSearch flattens nested JSON objects, so it can't correlate the lang and name of a specific element in the labels array. If you want this kind of correlation, you'll need to index your documents differently.
The usual way to do this is to use the nested data type with a matching nested query.
The query would end up looking something like this:
{
"query": {
"nested": {
"path": "labels",
"query": {
"bool": {
"must": [
{
"multi_match": {
"fields": ["labels.name"],
"query": "london",
"type": "phrase_prefix"
}
},
{
"term": {
"labels.lang": "gd"
}
}
]
}
}
}
}
}
But note that you'll need to also specify nested mappings for your labels, e.g.:
"properties": {
"labels": {
"type": "nested",
"properties": {
"name": {
"type": "text"
/* you might want to add other mapping-related configuration here */
},
"lang": {
"type": "keyword"
}
}
}
}
Other ways to do this include:
Indexing each label as a separate document, repeating the guid field
Using parent/child documents
You should use Nested datatype in mapping instead of Object datatype. For detail explanation refer this:
https://www.elastic.co/guide/en/elasticsearch/reference/current/nested.html
So, you should define mapping of your field something like this:
{
"properties": {
"labels": {
"type": "nested",
"properties": {
"name": {
"type": "text"
},
"lang": {
"type": "keyword"
}
}
}
}
}
After this you could query using Nested Query as:
{
"query": {
"nested": {
"path": "labels",
"query": {
"bool": {
"must": [
{
"multi_match": {
"fields": ["labels.name"],
"query": "london",
"type": "phrase_prefix"
}
},
{
"term": {
"labels.lang": "gd"
}
}
]
}
}
}
}
}
I have an index like this:
{
"rentals": {
"aliases": {},
"mappings": {
"rental": {
"properties": {
"address": {
"type": "text"
},
"availability": {
"type": "nested",
"properties": {
"chargeBasis": {
"type": "text"
},
"date": {
"type": "date"
},
"isAvailable": {
"type": "boolean"
},
"rate": {
"type": "double"
}
}
}
}
And this is my use case:
I need to search for all the "rentals" that have a given address.
This is easy and done
I need to get "availability" data for all those "rentals" searched; only for today's date.
This is the part where I'm stuck at, how do I query the nested documents of all the "rentals"?
You need to use the nested query:
Because nested objects are indexed as separate hidden documents, we can’t query them directly. Instead, we have to use the nested query to access them.
Try something like:
{
"query": {
"nested": {
"path": "availability",
"query": {
"term": {
"availability.date": "2015-01-01"
}
}
}
}
}
I have a mapping with an inner object as follows:
{
"mappings": {
"_all": {
"enabled": false
},
"properties": {
"foo": {
"name": {
"type": "string",
"index": "not_analyzed"
},
"address": {
"type": "object",
"properties": {
"address": {
"type": "string"
},
"city": {
"type": "string",
"index": "not_analyzed"
}
}
}
}
}
}
}
When I try the following aggregation it does not return any data:
post data:*/foo/_search?search_type=count
{
"query": {
"match_all": {}
},
"aggs": {
"unique": {
"cardinality": {
"field": "address.city"
}
}
}
}
When I try to put field city or address.city, aggregation returns zero but if i put foo.address.city it is then when i get the correct respond by elasticsearch. This also affects kibana behavior
Any ideas why this is happening? I saw there is a mapping refactoring that might affects this. I use elasticsearch version 1.7.1
To add on this if, I use the relative path in a search query as follows it works normally:
"query": {
"filtered": {
"filter": {
"term": {
"address.city": "london"
}
}
}
}
Seems its this same issue.
This is seen when the type name and field name is same.
Here's the mapping:
PUT books-index
{
"mappings": {
"books": {
"properties": {
"tags": {
"type": "nested",
"fields": {
"name": {
"type": "string"
},
"weight": {
"type": "float"
}
}
}
}
}
}
}
Then doing a nested query using a field_value_factor fails with an error
GET books-index/books/_search
{
"query": {
"nested": {
"path": "tags",
"score_mode": "sum",
"query": {
"function_score": {
"query": {
"match": {
"tags.name": "world"
}
},
"field_value_factor": {
"field": "weight"
}
}
}
}
}
}
The error: "nested: ElasticsearchException[Unable to find a field mapper for field [weight]]"
Interestingly, if there's one book in the index with tags - there's no error and the query works well.
Why is this happening? how can I prevent the error when there are no books with tags in the index?
Any ideas?
Thank you!
P.S. There's also an issue on github for this: https://github.com/elastic/elasticsearch/issues/12871
it looks like your mapping is incorrect.
After PUTing the mapping you provided, try executing GET books-index/_mapping, It will show these results:
"books-index": {
"mappings": {
"books": {
"properties": {
"tags": {
"type": "nested"
}
}
}
}
}
It's missing name and weight! The problem with the mapping is that you used either you used fields instead of properties, or you forget to put in a second properties key.
I modified your mapping to reflect that you were looking for a nested name and type within tags, as it looks like that is what your query wants.
PUT books-index
{
"mappings": {
"books": {
"properties": {
"tags": {
"type": "nested",
"properties": { // <--- HERE!
"name": {
"type": "string"
},
"weight": {
"type": "float"
}
}
}
}
}
}
}
Need help with Elasticsearch. I try to get first exact match result then those documents that have one field matched using the following query but with no luck. Basically, trying to get top score hits first and then less accurate and only matched by one field in the total search result.
The mapping is as following:
{
"palsx1493": {
"mappings": {
"pals": {
"properties": {
"aboutme": {
"type": "string"
},
"dob": {
"type": "date",
"format": "date"
},
"fccode": {
"type": "string"
},
"fcname": {
"type": "string"
},
"learning": {
"type": "nested",
"properties": {
"skillslevel": {
"type": "string"
},
"skillsname": {
"type": "string"
}
}
},
"name": {
"type": "string"
},
"rating": {
"type": "string"
},
"teaching": {
"type": "nested",
"properties": {
"skillslevel": {
"type": "string"
},
"skillsname": {
"type": "string"
}
}
},
"trate": {
"type": "string"
},
"treg": {
"type": "string"
}
}
}
}
}
}
When Searching, I need the result to return the exact matched documents followed by lower score matched with the teaching skillname in that prioritized order. what happens now is that I get the exact matches correctly first and then I get the learning.skillname matched, and then teaching.skillname matched. I want these two last ones swapped having the teaching.skillname coming after the exact matched results.
Exact match:
1. fcname (is crom country name and can be either a specific name or just set to "Any Country".
2. dob: Date of birth is a range value - a range value is given as input
3. teaching: skillname
4. learning: skillname
This is what I have tried with no luck:
{
"query": {
"bool": {
"should": [
{ "match": { "fcname": "spain"}},
{ "range": {
"bod": {
"from": "1950-10-10",
"to": "1967-12-12"
}
}
},
{
"nested": {
"path": "learning",
"score_mode": "max",
"query": {
"bool": {
"must": [
{ "match": { "learning.skillname": learningSkillName}}
]
}
}
}
},
{
"nested": {
"path": "teaching",
"query": {
"bool": {
"must": [
{ "match": { "teaching.skillname": teachingSkillName}}
]
}
}
}
}
]
}
}
}
Please look into indices. The default is a full text search which does inverted indexing to store data. So it would store the string according to the analyzer.
Fo exact string match please use : index = 'not_analyzed'
eg.
"nick"{
"type": "string",
"index":"not_analyzed"
},
https://www.elastic.co/guide/en/elasticsearch/reference/current/mapping-core-types.html
I figured it out. Solution was to use function_score feature to override/ add score to a document with certain matched field. Replacing the nested part above with following gave me the correct result:
"nested": {
"path": "teaching",
"query": {
"function_score": {
"query": {
"bool": {
"must": [
{ "match": { "teaching.skillname": "xxx"}}
]
}
},
"functions": [
{
"script_score": {
"script": "_score + 2"
}
}],