I have an index with a few hundred thousand documents. Some of them have a rank_features field called my_field. I want to retrieve documents without that field.
I tried:
"query": {
"bool": {
"must_not": [
{"exists": {"field":"my_field"}}]
...
But I get the following error:
"error": {
"root_cause": [
{
"type": "query_shard_exception",
"reason": "failed to create query: [rank_features] fields do not support [exists] queries",
...
The index mapping is defined as follows:
"mappings": {
"dynamic": "strict",
"_routing": {
"required": true
},
"properties": {
"my_field": {
"properties": {
"my_subfield": {
"type": "rank_features"
}
}
...
"settings": {
"index": {
"routing": {
"allocation": {
"include": {
"_tier_preference": "data_content"
}
}
},
"mapping": {
"total_fields": {
"limit": "2000"
}
},
"refresh_interval": "1s",
"number_of_shards": "10",
"blocks": {
"write": "false"
},
Note that despite the mapping being strict, this field was added recently and older documents don't have it.
Tldr;
You are doing a exist query against a field that only support rank_feature queries
As per the documentation of the rank_features field.
rank_features fields do not support sorting or aggregating and may only be queried using rank_feature queries.
Related
Elastic version 7.17
Below I've pasted a simplified version of my mappings which represent a nested object structure. One top-level-object will have one or more second-level-object. A second-level-object will have one or more third-level-object. Fields field_a, field_b, and field_c on third-level-object are all related to each other so I'd like to copy them into a single field that can be partial matched against. I've done this on a lot of attributes at the top-level-object level, so I know it works.
{
"mappings": {
"_doc": { //one top level object
"dynamic": "false",
"properties": {
"second-level-objects": { //one or more second level objects
"type": "nested",
"dynamic": "false",
"properties": {
"third-level-objects": { //one or more third level objects
"type": "nested",
"dynamic": "false",
"properties": {
"my_copy_to_field": { //should have the values from field_a, field_b, and field_c
"type": "text",
"index": true
},
"field_a": {
"type": "keyword",
"index": false,
"copy_to": "my_copy_to_field"
},
"field_b": {
"type": "long",
"index": false,
"copy_to": "my_copy_to_field"
},
"field_c": {
"type": "keyword",
"index": false,
"copy_to": "my_copy_to_field"
},
"field_d": {
"type": "keyword",
"index": true
}
}
}
}
}
}
}
}
}
However, when I run a nested query against that my_copy_to_field I get no results because the field is never populated, even though I know my documents have data in the 3 fields with copy_to. If I perform a nested query against field_d which is not part of the copied info I get the expected results, so it seems like there's something going on with nested (or double-nested in my case) usage of copy_to that I'm overlooking. Here is my query which returns nothing:
GET /my_index/_search
{
"query": {
"nested": {
"inner_hits": {},
"path": "second-level-objects",
"query": {
"nested": {
"inner_hits": {},
"path": "second-level-objects.third-level-objects",
"query": {
"bool": {
"should": [
{"match": {"second-level-objects.third-level-objects.my_copy_to_field": "my search value"}}
]
}
}
}
}
}
}
}
I've tried adding include_in_root:true to the third-level-objects, but that didn't make any difference. If I could just get the field to populate with the copied data then I'm sure I can work through getting the query working. Is there something I'm missing about using copy_to with nested fields?
Additionally, when I view my data in Kibana -> Discover, I see second-level-objects as an available "Nested" field, but I don't see anything for third-level-objects, even though KQL recognizes it as a field. Is that symptomatic of an issue?
You must add complete path nested, like this:
"field_a": {
"type": "keyword",
"copy_to": "second-level-objects.third-level-objects.my_copy_to_field"
},
"field_b": {
"type": "long",
"copy_to": "second-level-objects.third-level-objects.my_copy_to_field"
},
"field_c": {
"type": "keyword",
"copy_to": "second-level-objects.third-level-objects.my_copy_to_field"
}
I'm trying to make range aggregation on the following data set:
{
"ProductType": 1,
"ProductDefinition": "fc588f8e-14f2-4871-891f-c73a4e3d17ca",
"ParentProduct": null,
"Sku": "074617",
"VariantSku": null,
"Name": "Paraboot Avoriaz/Jannu Marron Brut Marron Brown Hiking Boot Shoes",
"AllowOrdering": true,
"Rating": null,
"ThumbnailImageUrl": "/media/1106/074617.jpg",
"PrimaryImageUrl": "/media/1106/074617.jpg",
"Categories": [
"399d7b20-18cc-46c0-b63e-79eadb9390c7"
],
"RelatedProducts": [],
"Variants": [
"84a7ff9f-edf0-4aab-87f9-ba4efd44db74",
"e2eb2c50-6abc-4fbe-8fc8-89e6644b23ef",
"a7e16ccc-c14f-42f5-afb2-9b7d9aefbc5c"
],
"PriceGroups": [
"86182755-519f-4e05-96ef-5f93a59bbaec"
],
"DisplayName": "Paraboot Avoriaz/Jannu Marron Brut Marron Brown Hiking Boot Shoes",
"ShortDescription": "",
"LongDescription": "<ul><li>Paraboot Avoriaz Mountaineering Boots</li><li>Marron Brut Marron (Brown)</li><li>Full leather inners and uppers</li><li>Norwegien Welted Commando Sole</li><li>Hand made in France</li><li>Style number : 074617</li></ul><p>As featured on Pritchards.co.uk</p>",
"UnitPrices": {
"EUR 15 pct": 343.85
},
"Taxes": {
"EUR 15 pct": 51.5775
},
"PricesInclTax": {
"EUR 15 pct": 395.4275
},
"Slug": "paraboot-avoriazjannu-marron-brut-marron-brown-hiking-boot-shoes",
"VariantsProperties": [
{
"Key": "ShoeSize",
"Value": "8"
},
{
"Key": "ShoeSize",
"Value": "10"
},
{
"Key": "ShoeSize",
"Value": "6"
}
],
"Guid": "0d4f6899-c66a-4416-8f5d-26822c3b57ae",
"Id": 178,
"ShowOnHomepage": true
}
I'm aggregating on VariantsProperties which have the following mapping
"VariantsProperties": {
"type": "nested",
"properties": {
"Key": {
"type": "keyword"
},
"Value": {
"type": "keyword"
}
}
}
Terms aggregations are working fine with following code:
{
"aggs": {
"Nest": {
"nested": {
"path": "VariantsProperties"
},
"aggs": {
"fieldIds": {
"terms": {
"field": "VariantsProperties.Key"
},
"aggs": {
"values": {
"terms": {
"field": "VariantsProperties.Value"
}
}
}
}
}
}
}
}
However when I try to do a range aggregation to get shoes in size between 8 - 12 such as:
{
"aggs": {
"Nest": {
"nested": {
"path": "VariantsProperties"
},
"aggs": {
"fieldIds": {
"range": {
"field": "VariantsProperties.Value",
"ranges": [ { "from": 8, "to": 12 }]
}
}
}
}
}
}
I get the following error:
{
"error": {
"root_cause": [
{
"type": "illegal_argument_exception",
"reason": "Field [VariantsProperties.Value] of type [keyword] is not supported for aggregation [range]"
}
],
"type": "search_phase_execution_exception",
"reason": "all shards failed",
"phase": "query",
"grouped": true,
"failed_shards": [
{
"shard": 0,
"index": "product-avenueproductindexdefinition-24476f82-en-us",
"node": "ejgN4XecT1SUfgrhzP8uZg",
"reason": {
"type": "illegal_argument_exception",
"reason": "Field [VariantsProperties.Value] of type [keyword] is not supported for aggregation [range]"
}
}
],
"caused_by": {
"type": "illegal_argument_exception",
"reason": "Field [VariantsProperties.Value] of type [keyword] is not supported for aggregation [range]",
"caused_by": {
"type": "illegal_argument_exception",
"reason": "Field [VariantsProperties.Value] of type [keyword] is not supported for aggregation [range]"
}
}
},
"status": 400
}
Is there a way to "transform" the terms aggregation into a range aggregation, without the need of changing the schema? I know I could build the ranges myself by extracting the data from the terms aggregation and building the ranges out of it, however, I would prefer a solution within the elastic itself.
There are two ways to solve this:
Option A: Use a script instead of a field. This option will work without having to reindex your data, but depending on your volume of data, the performance might suffer.
POST test/_search
{
"aggs": {
"Nest": {
"nested": {
"path": "VariantsProperties"
},
"aggs": {
"fieldIds": {
"range": {
"script": "Integer.parseInt(doc['VariantsProperties.Value'].value)",
"ranges": [
{
"from": 8,
"to": 12
}
]
}
}
}
}
}
}
Option B: Add an integer sub-field in your mapping.
PUT my-index/_mapping
{
"properties": {
"VariantsProperties": {
"type": "nested",
"properties": {
"Key": {
"type": "keyword"
},
"Value": {
"type": "keyword",
"fields": {
"numeric": {
"type": "integer",
"ignore_malformed": true
}
}
}
}
}
}
}
Once your mapping is modified, you can run _update_by_query on your index in order to reindex the VariantsProperties.Value data
PUT my-index/_update_by_query
Finally, when this last command is done, you can run the range aggregation on the VariantsProperties.Value.numeric field.
Also note that this second but will be more performant on the long term.
How can I define mapping in Elasticsearch 7 to index a document with a field value from another index? For example, if I have a users index which has a mapping for name, email and account_number but the account_number value is actually in another index called accounts in field number.
I've tried something like this without much success (I only see "name", "email" and "account_id" in the results):
PUT users/_mapping
{
"properties": {
"name": {
"type": "text"
},
"email": {
"type": "text"
},
"account_id": {
"type": "integer"
},
"accounts": {
"properties": {
"number": {
"type": "text"
}
}
}
}
}
The accounts index has the following mapping:
{
"properties": {
"name": {
"type": "text"
},
"number": {
"type": "text"
}
}
}
As I understand it, you want to implement field joining as is usually done in relational databases. In elasticsearch, this is possible only if the documents are in the same index. (Link to doc). But it seems to me that in your case you need to work differently, I think your Account object needs to be nested for User.
PUT /users/_mapping
{
"mappings": {
"properties": {
"account": {
"type": "nested"
}
}
}
}
You can further search as if it were a separate document.
GET /users/_search
{
"query": {
"nested": {
"path": "account",
"query": {
"bool": {
"must": [
{ "match": { "account.number": 1 } }
]
}
}
}
}
}
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.