Elasticsearch cross-index query with aggregations - elasticsearch

Question

I use: Elasticsearch 7.7 , Kibana 7.7
For example, lets take two indexes:
User index with simple mapping:
PUT /user_index
{
"mappings": {
"properties": {
"user_id": { "type": "text" },
"user_phone": { "type": "text" },
"name": { "type": "text" }
}
}
}
Check with simple mapping:
PUT /check_index
{
"mappings": {
"properties": {
"user_id": { "type": "text" },
"price": { "type": "integer" },
"goods_count": {"type": "integer"}
}
}
}
I want to build table visualization like that:
________________________________________________________________________
user_id | user_phone | average_price | sum_goods_count |
___________|_______________|_____________________|______________________
1 | 123 | 512 | 64 |
___________|_______________|_____________________|______________________
2 | 456 | 256 | 16 |
___________|_______________|_____________________|______________________
So my questions are:
Is it real?
Do I understand correctly that I need to query these two indexes, get a list of users, and then in a loop create shopping carts with checks?

Answer 1

First thing first, you should try to de-normalize data in ES as much as possible to get the best performance and capability offered by it, And I went through the samples provided by you and comments in the question and it seems it can be easily achieved in your use-case and shown in below example, by combining user and check index into single index.
Index mapping
{
"mappings": {
"properties": {
"user_id": {
"type": "text",
"fielddata": "true"
},
"price": {
"type": "integer"
},
"goods_count": {
"type": "integer"
}
}
}
}
Index Data:
With the index mapping defined above, index these three documents, where one document is having "user_id":"1" and 2 documents have "user_id":"2"
{
"user_id":"1",
"price":500,
"goods_count":100
}
{
"user_id":"2",
"price":500,
"goods_count":100
}
{
"user_id":"2",
"price":100,
"goods_count":200
}
Search Query:
Refer to ES official documentation on Terms Aggregation, Top Hits aggregation, Sum aggregation and Avg aggregation to get detailed explanation.
{
"size": 0,
"aggs": {
"user": {
"terms": {
"field": "user_id"
},
"aggs": {
"top_user_hits": {
"top_hits": {
"_source": {
"includes": [
"user_id"
]
}
}
},
"avg_price": {
"avg": {
"field": "price"
}
},
"goods_count": {
"sum": {
"field": "goods_count"
}
}
}
}
}
}
Search Result:
{
"took": 10,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 3,
"relation": "eq"
},
"max_score": null,
"hits": [
]
},
"aggregations": {
"user": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "2",
"doc_count": 2,
"top_user_hits": {
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 1.0,
"hits": [
{
"_index": "stof_63925596",
"_type": "_doc",
"_id": "2",
"_score": 1.0,
"_source": {
"user_id": "2"
}
},
{
"_index": "stof_63925596",
"_type": "_doc",
"_id": "3",
"_score": 1.0,
"_source": {
"user_id": "2"
}
}
]
}
},
"avg_price": {
"value": 300.0
},
"goods_count": {
"value": 300.0
}
},
{
"key": "1",
"doc_count": 1,
"top_user_hits": {
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 1.0,
"hits": [
{
"_index": "stof_63925596",
"_type": "_doc",
"_id": "1",
"_score": 1.0,
"_source": {
"user_id": "1"
}
}
]
}
},
"avg_price": {
"value": 500.0
},
"goods_count": {
"value": 100.0
}
}
]
}
}
}
As you can see in the search results above, for "user_id":"2" the average price is (500+100)/2 = 300 and sum of goods_count is 100+200 = 300.
Similarly for "user_id":"1" the average price is 500/1 = 500 and sum of goods_count is 100.