I have Elasticsearch documents for products with tags. Following is the structure of the documents:
{
"id": 'id-1',
"name: "prod-1",
"tags": [
{
"id": 'tag-id-1',
"name": 'tag-name-1'
},
{
"id": 'tag-id-2',
"name": 'tag-name-2'
}
]
}
What I want to do is:
Get all products with maximum overlapping tags (something like related questions in stackoverflow, assuming they have stored questions in Elasticsearch with tags). Output something like this:
{
"aggregations": {
"products": [
{
"key": "product-name-1",
"tags": [
{
"key": "tag-name-1",
}
{
"key": "tag-name-2"
}
]
},
{
"key": "product-name-2",
"tags": [
{
"key": "tag-name-2",
}
{
"key": "tag-name-3"
}
]
},
]
}
}
Get all tags grouped together with a tag maximum number of times.
{
"aggregations": {
"tags": [
{
"key": "tag-name-1",
"tags": [
{
"key": "tag-name-2",
}
{
"key": "tag-name-3"
}
]
},
{
"key": "product-name-2",
"tags": [
{
"key": "tag-name-1",
}
{
"key": "tag-name-5"
}
]
},
]
}
}
I am not very sure which type of aggregation methods to use. Any help will be useful.
Thanks.
Related
We use enterprise search indexes to store items that can be tagged by multiple tenants.
e.g
[
{
"id": 1,
"name": "document 1",
"tags": [
{ "company_id": 1, "tag_id": 1, "tag_name": "bla" },
{ "company_id": 2, "tag_id": 1, "tag_name": "bla" }
]
}
]
I'm looking to find a way to retrieve all documents with only the tags of company 1
This request:
{
"query": "",
"facets": {
"tags": {
"type": "value"
}
},
"sort": {
"created": "desc"
},
"page": {
"size": 20,
"current": 1
}
}
Is coming back with
...
"facets": {
"tags": [
{
"type": "value",
"data": [
{
"value": "{\"company_id\":1,\"tag_id\":1,\"tag_name\":\"bla\"}",
"count": 1
},
{
"value": "{\"company_id\":2,\"tag_id\":1,\"tag_name\":\"bla\"}",
"count": 1
}
]
}
],
}
...
Can I modify the request in a way such that I get no tags by "company_id" = 2 ?
I have a solution that involves modifying the results to strip the extra data after they are retrieved but I'm looking for a better solution.
Imagine two documents:
[
{
"_id": "abc",
"categories": {
"category-id-1": 1,
"category-id-2": 50
}
},
{
"_id": "def",
"categories": {
"category-id-1": 2
}
}
]
As you can see, each document can be associated with a number of categories, by setting a nested field into the categories field.
With this mapping, I should be able to request the documents from a defined category and to order them by the value set as value for this field.
My problem is that I now want to make an aggregation to count for each category the number of documents. That would give the following result for the dataset I provided:
{
"aggregations": {
"categories" : {
"buckets": [
{
"key": "category-id-1",
"doc_count": 2
},
{
"key": "category-id-2",
"doc_count": 1
}
]
}
}
}
I can't find anything in the documentation to solve this problem. I'm completely new to ElasticSearch so I may be doing something wrong either on my documentation research or on my mapping choice.
Is it possible to make this kind of aggregation with my mapping? I'm using ES 6.x
EDIT: Here is the mapping for the index:
{
"test1234": {
"mappings": {
"_doc": {
"properties": {
"categories": {
"properties": {
"category-id-1": {
"type": "long"
},
"category-id-2": {
"type": "long"
}
}
}
}
}
}
}
}
The most straightforward solution is to use a new field that contains all the distinct categories of a document.
If we call this field categories_list here could be a solution :
Change the mapping to
{
"test1234": {
"mappings": {
"_doc": {
"properties": {
"categories": {
"properties": {
"category-id-1": {
"type": "long"
},
"category-id-2": {
"type": "long"
}
}
},
"categories_list": {
"type": "keyword"
}
}
}
}
}
}
Then you need to modify your documents like this :
[
{
"_id": "abc",
"categories": {
"category-id-1": 1,
"category-id-2": 50
},
"categories_list": ["category-id-1", "category-id-2"]
},
{
"_id": "def",
"categories": {
"category-id-1": 2
},
"categories_list": ["category-id-1"]
}
]
then your aggregation request should be
{
"aggs": {
"categories": {
"terms": {
"field": "categories_list",
"size": 10
}
}
}
}
and will return
"aggregations": {
"categories": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "category-id-1",
"doc_count": 2
},
{
"key": "category-id-2",
"doc_count": 1
}
]
}
}
I'm attempting to find related tags to the one currently being viewed. Every document in our index is tagged. Each tag is formed of two parts - an ID and text name:
{
...
meta: {
...
tags: [
{
id: 123,
name: 'Biscuits'
},
{
id: 456,
name: 'Cakes'
},
{
id: 789,
name: 'Breads'
}
]
}
}
To fetch the related tags I am simply querying the documents and getting an aggregate of their tags:
{
"query": {
"bool": {
"must": [
{
"match": {
"item.meta.tags.id": "123"
}
},
{
...
}
]
}
},
"aggs": {
"baked_goods": {
"terms": {
"field": "item.meta.tags.id",
"min_doc_count": 2
}
}
}
}
This works perfectly, I am getting the results I want. However, I require both the tag ID and name to do anything useful. I have explored how to accomplish this, the solutions seem to be:
Combine the fields when indexing
A script to munge together the fields
A nested aggregation
Option one and two are are not available to me so I have been going with 3 but it's not responding in an expected manner. Given the following query (still searching for documents also tagged with 'Biscuits'):
{
...
"aggs": {
"baked_goods": {
"terms": {
"field": "item.meta.tags.id",
"min_doc_count": 2
},
"aggs": {
"name": {
"terms": {
"field": "item.meta.tags.name"
}
}
}
}
}
}
I will get this result:
{
...
"aggregations": {
"baked_goods": {
"buckets": [
{
"key": "456",
"doc_count": 11,
"name": {
"buckets": [
{
"key": "Biscuits",
"doc_count": 11
},
{
"key": "Cakes",
"doc_count": 11
}
]
}
}
]
}
}
}
The nested aggregation includes both the search term and the tag I'm after (returned in alphabetical order).
I have tried to mitigate this by adding an exclude to the nested aggregation but this slowed the query down far too much (around 100 times for 500000 docs). So far the fastest solution is to de-dupe the result manually.
What is the best way to get an aggregation of tags with both the tag ID and tag name in the response?
Thanks for making it this far!
By the looks of it, your tags is not nested.
For this aggregation to work, you need it nested so that there is an association between an id and a name. Without nested the list of ids is just an array and the list of names is another array:
"item": {
"properties": {
"meta": {
"properties": {
"tags": {
"type": "nested", <-- nested field
"include_in_parent": true, <-- to, also, keep the flat array-like structure
"properties": {
"id": {
"type": "integer"
},
"name": {
"type": "string"
}
}
}
}
}
}
}
Also, note that I've added to the mapping this line "include_in_parent": true which means that your nested tags will, also, behave like a "flat" array-like structure.
So, everything you had so far in your queries will still work without any changes to the queries.
But, for this particular query of yours, the aggregation needs to change to something like this:
{
"aggs": {
"baked_goods": {
"nested": {
"path": "item.meta.tags"
},
"aggs": {
"name": {
"terms": {
"field": "item.meta.tags.id"
},
"aggs": {
"name": {
"terms": {
"field": "item.meta.tags.name"
}
}
}
}
}
}
}
}
And the result is like this:
"aggregations": {
"baked_goods": {
"doc_count": 9,
"name": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 123,
"doc_count": 3,
"name": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "biscuits",
"doc_count": 3
}
]
}
},
{
"key": 456,
"doc_count": 2,
"name": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "cakes",
"doc_count": 2
}
]
}
},
.....
Is that a possible to define an aggregation function in elastic search?
E.g. for data:
author weekday status
me monday ok
me tuesday ok
me moday bad
I want to get an aggregation based on author and weekday, and as a value I want to get concatenation of status field:
agg1 agg2 value
me monday ok,bad
me tuesday ok
I know you can do count, but is that possible to define another function used for aggregation?
EDIT/ANSWER: Looks like there is no multirow aggregation support in ES, thus we had to use subaggregations on last field (see Akshay's example). If you need to have more complex aggregation function, then aggregate by id (note, you won't be able to use _id, so you'll have to duplicate it in other field) - that way you'll be able to do advanced aggregation on individual items in each bucket.
You can get get roughly what you want by using sub aggregations available in 1.0. Assuming the documents are structured as author, weekday and status, you could using the aggregation below:
{
"size": 0,
"aggs": {
"author": {
"terms": {
"field": "author"
},
"aggs": {
"days": {
"terms": {
"field": "weekday"
},
"aggs": {
"status": {
"terms": {
"field": "status"
}
}
}
}
}
}
}
}
Which gives you the following result:
{
...
"aggregations": {
"author": {
"buckets": [
{
"key": "me",
"doc_count": 3,
"days": {
"buckets": [
{
"key": "monday",
"doc_count": 2,
"status": {
"buckets": [
{
"key": "bad",
"doc_count": 1
},
{
"key": "ok",
"doc_count": 1
}
]
}
},
{
"key": "tuesday",
"doc_count": 1,
"status": {
"buckets": [
{
"key": "ok",
"doc_count": 1
}
]
}
}
]
}
}
]
}
}
}
I'm indexing an array of key value pairs. The key is always a UUID and the value is a user entered value. I've been crawling through the documentation but I can't figure out exactly how to query in this scenarioExample schema:
{
"id": 1,
"owner_id": 1,
"values": [
{ "key": "k3kfa23rewf", "value": "the red card" },
{ "key": "23a2dd23108", "value": "purple balloons" },
]
},
{
"id": 2,
"owner_id": 1,
"values": [
{ "key": "k3kfa23rewf", "value": "the blue card" },
{ "key": "23a2dd23108", "value": "purple balloons" },
]
}
I would like to query:
{ "term": { "owner_id": 1 },
{ "term": { "values.key": "23a2dd23108" }, "match": { "values.value": "purple" } },
{ "term": { "values.key": "k3kfa23rewf" }, "match": { "values.value": "blue" } }
So that the record with ID 2 is returned. Any suggestions?
I think that you need here to use nested documents.
That way, you will be able to create BoolQueries, with a Must clause with a TermQuery on owner_id and two must clauses with nested queries with Term and Match queries on values.key and values.value.
Does it help?