Elasticsearch document aliases - elasticsearch

I have multiple mappings which come from the same datasource but have small differences, like the example below.
{
"type_A" : {
"properties" : {
"name" : {
"type" : "string"
}
"meta_A" : {
"type" : "string"
}
}
}
}
{
"type_B" : {
"properties" : {
"name" : {
"type" : "string"
}
"meta_B" : {
"type" : "string"
}
}
}
}
What I want to be able to is:
Directly query specific fields (like meta_A)
Directly query all documents from the datsource
Query all documents from a specific mapping
What I was looking into is the type filter, so preferably I could write a query like this:
{
"query": {
"filtered" : {
"filter" : {
"type" : { "value" : "unified_type" }
}
}
// other query clauses
}
}
So instead of typing "type_A","type_B" in an or clause in the type filter I would like to have this "unified_type", but without giving up the possibility to directly query "type_A".
How could I achive this?

I don't think that it's possible. However, you could use copy_to functionality, so you would have your fields as they are now and their values copied into unified name.
The copy_to parameter allows you to create custom _all fields. In
other words, the values of multiple fields can be copied into a group
field, which can then be queried as a single field. For instance, the
first_name and last_name fields can be copied to the full_name field
as follows:
So you'd be copying both "meta_A" and "meta_B" into some "unified_meta" field and query this one.

Related

Performance with nested data in a script field

I am wondering if there is a more performant way of performing a calculation on nested data in a script field or of organizing the data. In the code below, the data will contain values for 50 states and/or other regions. Each user is tied to an area, so the script above will search to see that the averageValue in their area is above a certain threshold and return a true/false value for each matching document.
Mapping
{
"mydata" : {
"properties" : {
...some fields,
"related" : {
"type" : "nested",
"properties" : {
"average_value" : {
"type" : "integer"
},
"state" : {
"type" : "string"
}
}
}
}
}
}
Script
"script_fields" : {
"inBudget" : {
"script" : {
"inline" : "_source.related.find { it.state == default_area && it.average_value >= min_amount } != null",
"params" : {
"min_amount" : 100,
"default_area" : "CA"
}
}
}
}
I have a working solution using the above method, but it slows my query down and I am curious if there is a better solution. I have been toying with the idea of using a inner object with a key, like: related_CA and having each states data in a separate object, however for flexibility I would rather not have to pre-define each region in a mapping (as I may not have them all ahead of time). I feel like I might be missing a simpler/better way and I am open to either reorganizing the data/mapping and/or changes to the script.

Aggregation on different document type

i'm trying to solve a problem here, I'm not even sure it can be done how I want it to be done.
Basically I need to query my index on the type Company. This type does have a field named Company_Name. Now I have another type which is Employee that does have a field named Company_Name as well, which basically "links him" to the Company.
What I need to do is to make an aggregation in my query of companies, that gets me the number of employees that are within the company (for each result).
Any way to do this ?
EDIT :
The mapping :
"company" : {
"properties" : {
"company_name" : {
"type" : "text"
}
}
},
"employee" : {
"properties" : {
"employee_name" : {
"type" : "text"
},
"company_name" : {
"type" : "text"
}
}
}
Not sure if I fully understood what you want but you mentioned:
Now I have another type which is Employee that does have a field named Company_Name
And then you said you want, number of employees that are withing the company.
So for me it looks like all you need is an aggregation on Employees doc type by company.
If you really need to query both indexes then you can apply a multi-index search and the aggregation to the result of the multi index search.
This or something close to this should work:
http://localhost:9200/$index/employee/_search?search_type=count
{
"aggs" : {
"company" : {
"terms" : {
"field" : "company_name"
}
}
}
}
One think you should notice is that if your field company_name is analyzed it will be tokenized and you can't aggregate on that. You should then add a raw field with company name to use for your aggregation.
Some useful references:
https://www.elastic.co/guide/en/elasticsearch/guide/current/relations.html
https://www.elastic.co/guide/en/elasticsearch/guide/current/_closing_thoughts.html and

How to get elasticsearch suggestions working on multiple fields from multiple types?

I have a requirement to have a search box globally on the website that user can type anything in, a bit like google. As the user is typing along, he should get suggestions. I have multiple types in an Index, I am using Completion suggester to get suggestions from one field like below:
GET /index/_suggest/
{
"person-suggest" : {
"text" : "m",
"completion" : {
"field" : "nameSuggest"
}
}
}
The requirement is such that when they type a person name which is stored in person type or type in a company name which is stored in company type...both suggestions should appear. Also, within the same type, it should be able to suggest based on multiple fields not just one like what I've got. And finally once the user selects a suggestion, do a search and show facets which are based on multiple types.
For multi-type suggestions on multiple fields, use-
curl -XPOST 'localhost:9200/indexName/type1,type2/_suggest' -d '{
"my-suggestion-1" : {
"text" : "some text",
"term" : {
"field" : "field1"
}
},
"my-suggestion-2" : {
"text" : "some text",
"term" : {
"field" : "field2"
}
}
}'

elasticsearch - how to query relations

I have the currenct structure:
localhost:9200/objects/content
{
id:1,
author:{
name:"john"
},
body:"abc"
}
localhost:9200/objects/reaction
{
content_id:1
message:'I like it'
}
How can I query to get all reactions of contents writed by "john"?
This means a query on reactions, checking the content specified by id, if author is "someone".
This Elasticsearch blog post describes how to manage relationships inside Elasticsearch.
You will need to set a parent mapping between your reactions and your content.
{
"reaction" : {
"_parent" : {
"type" : "content"
}
}
}
You will then index your reaction as a child of content id 1:
curl -XPOST localhost:9200/test/homes?parent=1 -d'
{
message:'I like it'
}
You can then use a Has Parent Query to retrieve all reactions to authors named john:
{
"has_parent" : {
"parent_type" : "content",
"query" : {
"term" : {
"author.name" : "john"
}
}
}
}
I would recommend you to add some redundancy to your model:
localhost:9200/objects/reaction
{
content_id:1
message:'I like it'
author_name:'john'
}
This will increase the index, of course. On the other hand the query to get all reactions of contents writed by "john" will be simple and fast.

Full-text schema in ElasticSearch

I'm (extremely) new to ElasticSearch so forgive my potentially ridiculous question. I currently use MySQL to perform full-text searches, and want to move this to ElasticSearch. Currently my table has a fulltext index spanning three columns:
title,description,tags
In ES, each document would therefore have title, description and tags fields, allowing me to do a fulltext search for a general phrase, or filter on a given tag.
I also want to add further searchable fields such as username (so I can retrieve posts by a given user). So, how do I specify that a fulltext search should match title OR description OR tags but not username?
From the OR filter example, I'd assume I'd have to use something like this:
{
"filtered" : {
"query" : {
"match_all" : {}
},
"filter" : {
"or" : [
{
"term" : { "title" : "foobar" }
},
{
"term" : { "description" : "foobar" }
},
{
"term" : { "tags" : "foobar" }
}
]
}
}
}
Coming at this new, it doesn't seem like this is very efficient. Is there a better way of doing this, or do I need to move the username field to a separate index?
This is fine.
I general I would suggest getting familiar with ElasticSearch mapping types and options.
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/mapping.html

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