Elastic Search: Bool Query in nested properties - elasticsearch

Question

Lets assume I have data structured like this:
{ "id": "120400871755634330808993320",
"name": "Metaalschroef binnenzeskant, DIN 912 RVS A4-80",
"description": "m16x70 cilinderschroef bzk a4-80 din912 klasse 80",
"fullDescription": "Metaalschroef met een binnenzeskant cilinderkop",
"synonyms": [],
"properties": [
{
"name": "draad",
"value": "16",
"sort": 99
},
{
"name": "lengte",
"value": "70",
"sort": 99
},
{
"name": "materiaal",
"value": "roestvaststaal",
"sort": 99
},
{
"name": "kwaliteit (materiaal)",
"value": "A4",
"sort": 99
},
{
"name": "DIN",
"value": "912",
"sort": 99
},
{
"name": "AISI",
"value": "316",
"sort": 99
},
{
"name": "draadsoort",
"value": "metrisch",
"sort": 99
},
{
"name": "Merk",
"value": "Elcee Holland",
"sort": 1
}
]
}
How do I write a boolean query where I select all documents that have a property with name "draad" and value "16" and a property with name "lengte" and value "70".
Right now I have this but it returns 0 results:
"query" : {
"nested" : {
"path" : "properties",
"query" : {
"bool" : {
"must" : [{
"bool" : {
"must" : [{
"term" : {
"properties.name" : "Merk"
}
}, {
"term" : {
"properties.value" : "Facom"
}
}
]
}
}, {
"bool" : {
"must" : [{
"term" : {
"properties.name" : "materiaal"
}
}, {
"term" : {
"properties.value" : "kunststof"
}
}
]
}
}
]
}
}
}
}
Replacing the highest level "must" with "should" returns too many results, which makes sense as it translates to an "or".

Answer 1

When using must, the engine is trying to search for nested documents with name:Merk and value:Facom. But also with name:materiaal and value:kunststof - which is impossible to happen in the same nested document at once.
When using should as you mentioned, it translate to or - which is indeed possible.
Problem is, you also getting the entire parent document with all it's nested documents.
In my own answer I'm showing the steps to create an index with nested documents (you should mark the field properties as nested type`).
After complete those steps, you'll be able to get results with the following query:
{
"_source": [
"id",
"name",
"description"
],
"query": {
"bool": {
"must": [
{
"nested": {
"path": "properties",
"query": {
"bool": {
"should": [
{
"bool": {
"must": [
{
"term": {
"properties.name": "Merk"
}
},
{
"term": {
"properties.value": "Facom"
}
}
]
}
},
{
"bool": {
"must": [
{
"term": {
"properties.name": "materiaal"
}
},
{
"term": {
"properties.value": "kunststof"
}
}
]
}
}
]
}
},
"inner_hits":{
"size": 10
}
}
}
]
}
}
}

Answer 2

I found a solution that is working very well!
My property object now looks like this:
{
"name": "breedte(mm)",
"value": "1000",
"unit": "mm",
"sort": 99,
"nameSlug": "breedte-mm",
"slug": "breedte-mm-1000"
},
I added a slug (containing a normalized string for key + value) and a nameslug which is a normalized string for the name.
My index is mapped like this:
"properties": {
"type": "nested",
"include_in_parent": true,
"properties": {
"name": {
"type": "keyword"
},
"nameSlug": {
"type": "keyword"
},
"slug": {
"type": "keyword"
},
"sort": {
"type": "long"
},
"unit": {
"type": "text",
"index": false
},
"value": {
"type": "keyword"
}
}
}
The "include_in_parent" is important here. It allows me to do the query below:
"query": {
"bool": {
"must": [
{
"terms": {
"properties.slug": [
"merk-orbis",
"merk-bahco"
]
}
},
{
"terms": {
"properties.slug": [
"materiaal-staal",
"materiaal-kunststof"
]
}
}
]
}
},
This queries searches for all documents where "merk" is "Orbis" or "Bahco" and where "materiaal" is "staal" or "kunststof".
My aggregations look like this:
"merk_query": {
"filter": {
"bool": {
"must": [
{
"terms": {
"properties.slug": [
"materiaal-staal",
"materiaal-kunststof"
]
}
}
]
}
},
"aggs": {
"merk_facets": {
"nested": {
"path": "properties"
},
"aggs": {
"merk_only": {
"filter": {
"term": {
"properties.nameSlug": {
"value": "merk"
}
}
},
"aggs": {
"facets": {
"terms": {
"field": "properties.name",
"size": 1
},
"aggs": {
"facetvalues": {
"terms": {
"field": "properties.value",
"size": 10
}
}
}
}
}
}
}
}
}
},
I run filteraggregate which filters all documents that match a facet (but not the current one I am bulding).
The result of this aggragate is something like this:
"merk_query": {
"doc_count": 7686,
"merk_facets": {
"doc_count": 68658,
"merk_only": {
"doc_count": 7659,
"facets": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Merk",
"doc_count": 7659,
"facetvalues": {
"doc_count_error_upper_bound": 10,
"sum_other_doc_count": 438,
"buckets": [
{
"key": "Orbis",
"doc_count": 6295
},
{
"key": "DX",
"doc_count": 344
},
{
"key": "AXA",
"doc_count": 176
},
{
"key": "Talen Tools",
"doc_count": 127
},
{
"key": "Nemef",
"doc_count": 73
},
{
"key": "bonfix",
"doc_count": 67
},
{
"key": "Bahco",
"doc_count": 64
},
{
"key": "Henderson",
"doc_count": 27
},
{
"key": "Maasland Groep",
"doc_count": 25
},
{
"key": "SYSTEC",
"doc_count": 23
}
]
}
}
]
}
}
}
}
},
And this is the end result in the browser: