Elasticsearch correct aggregations for faceted search - search

Question

I want to offer a faceted search for the clothing products on some platform. As I already have Elasticsearch-based search functionality (simple query, only the name of the product), it would be nice to also implement the faceted search with ES.
This should be done with aggregations as facets are deprecated and one can also have nested aggregations.
However I cannot wrap my head around the millions of aggregations and which ones are the right for me - there is terms, filter, filters, nested, children and so on. And all of them seem suitable.
What I want to achieve may sound pretty basic: I have different facets (brand, condition, color) each having different values. For some facets (brand) the user can select only one value. For others (color) the user is allowed to select up to 3 (as some clothes have more than one color).
I started with multi-field terms facet. Now the next natural step would be to convert this to a terms aggregation (reasons above) but multi-fields are not supported in terms aggregations.
{
"query" : {
"match_all" : { }
},
"facets" : {
"groupByBrandAndCondition" : {
"terms" : {
"fields" : ["brand", "condition"],
"size" : 10
}
}
}
}
I am somehow missing some easy but crucial point here on how to proceed with having parallel multi level bucketing. Speaking in UI terms the user should be able to select something like:
Brands (10)
A (7)
B (3) [X]
Colors (5)
Blue (3) [X]
Red (2) [X]
Read: select A (7), Blue (3) and Red (2)

Answer 1

I created basic mapping like this
POST your_index/your_type/_mapping
{
"your_type": {
"properties": {
"product": {
"type": "string"
},
"brand": {
"type": "string"
},
"color": {
"type": "string"
}
}
}
}
I inserted some documents like this
PUT your_index/your_type/111
{
"product" : "jeans" ,"brand" : "lee", "color" : "blue"
}
PUT your_index/your_type/1111
{
"product" : "shoes" ,"brand" : "levi", "color" : "black"
}
And so on
Simple aggregation query like this
GET your_index/_search
{
"size": 0,
"aggs": {
"prod_agg": {
"terms": {
"field": "product"
},
"aggs": {
"brand_agg": {
"terms": {
"field": "brand"
},
"aggs": {
"color_agg": {
"terms": {
"field": "color"
}
}
}
}
}
}
}
}
will retrun
"aggregations": {
"prod_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "shoes",
"doc_count": 4,
"brand_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "nike",
"doc_count": 3,
"color_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "blue",
"doc_count": 2
},
{
"key": "black",
"doc_count": 1
}
]
}
},
{
"key": "levi",
"doc_count": 1,
"color_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "black",
"doc_count": 1
}
]
}
}
]
}
},
{
"key": "jeans",
"doc_count": 3,
"brand_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "lee",
"doc_count": 2,
"color_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "black",
"doc_count": 1
},
{
"key": "blue",
"doc_count": 1
}
]
}
},
{
"key": "levi",
"doc_count": 1,
"color_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "black",
"doc_count": 1
}
]
}
}
]
}
}
]
}
}
This could be used to populate UI search criteria.
Then If user wants to search for shoes, you could query
GET your_index/_search
{
"size": 0,
"query": {
"match": {
"product": "shoes"
}
},
"aggs": {
"brand_agg": {
"terms": {
"field": "brand"
},
"aggs": {
"color_agg": {
"terms": {
"field": "color"
}
}
}
}
}
}
which will give you
"aggregations": {
"brand_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "nike",
"doc_count": 3,
"color_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "blue",
"doc_count": 2
},
{
"key": "black",
"doc_count": 1
}
]
}
},
{
"key": "levi",
"doc_count": 1,
"color_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "black",
"doc_count": 1
}
]
}
}
]
}
}
or you could have them as separate buckets with query like
GET your_index/_search
{
"size": 0,
"query": {
"match": {
"product": "shoes"
}
},
"aggs": {
"brand_agg": {
"terms": {
"field": "brand"
}
},
"color_agg" : {
"terms": {
"field": "color"
}
}
}
}
which will give you
"aggregations": {
"color_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "black",
"doc_count": 2
},
{
"key": "blue",
"doc_count": 2
}
]
},
"brand_agg": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "nike",
"doc_count": 3
},
{
"key": "levi",
"doc_count": 1
}
]
}
}
Use doc_count value to tell users how many options they have.
Does this satisfy your requirements?