I am trying to implement suggest feature - Suggest Usage | Elasticsearch .NET Client [8.4] | Elastic 1 for handling misspelled words in my search implementation.
My search query is executed across multiple indices but while trying to use the suggest functionality , i am running into failures due to unmappaed fields.
Suppose i have an index named People which has a field - "name". Another index named news which has a field named - "title". My query was executed across both indices at the same time and search query had rules defined for both name and title fields. But while using suggest, i only want to return suggestions for name field in person index as part of the same query. As a result of this my news index is returning a failure that no mapping found for field name.
Is there a work- around in the suggest functionality via which i can specify an index name for the field mentioned in suggest - Suggest Usage | Elasticsearch .NET Client [8.4] | Elastic 1 OR can i ignore unmapped fields and continue to return search results from the other index (news) without returning any suggestions for misspelled words for that index.
Related
I have a situation where I need to search from multiple indexes (products and users). Below is a sample query I am using to do that search
http://localhost:9200/_all/_search?q=*wood*
http://localhost:9200/users,products/_search?q=*wood*
With the above API request, it only returns search results for the product index. But if I search using the below API it returns search results for users index
http://localhost:9200/users/_search?q=*wood*
As you can see I am passing same value for "q" parameter. I need to search for both product and users index and check if there is the word "wood" in any attribute in both indexes. How can I achieve this
You can pass multiple index names instead of _all as it will search in other indices that you don't intent to by using the comma seprated index name like
http://localhost:9200/users,products/_search?q=*wood*
Although, _all should also fetch the result from users index which you get when you specify its name, you need to debug why its happening, maybe increase the size param to 1000 as by default Elasticsearch returns only 10 results and it seems in case of _all all the top results coming from products index only.
I am new to Elastic Search. I would like to know if the following steps are how typically people use ES to build a search engine.
Use Elastic Search to get a list of qualified documents/results based on a user's input.
Build and use a search ranking model to sort this list.
Use this sorted list as the output of the search engine to the user.
I would probably add a few steps
Think about your information model.
What kinds of documents are you indexing?
What are the important fields and what field types are they?
What fields should be shown in the search result?
All this becomes part of your mapping
Index documents
Are the underlying data changing or can you index it just once?
How are you detecting new docuemtns/deletes/updates?
This will be included in your connetors, that can be set up in multiple ways, for example using the Documents API
A bit of trial and error to sort out your ranking model
Depending on your use case, the default ranking may be enough.
have a look at the Search API to try out different ranking.
Use the search result list to present the results to the end user
I’m trying to tag my data according to a lookup table.
The lookup table has these fields:
• Key- represent the field name in the data I want to tag.
In the real data the field is a subfield of “Headers” field..
An example for the “Key” field:
“Server. (* is a wildcard)
• Value- represent the wanted value of the mentioned field above.
The value in the lookup table is only a part of a string in the real data value.
An example for the “Value” field:
“Avtech”.
• Vendor- the value I want to add to the real data if a combination of field- value is found in an document.
An example for combination in the real data:
“Headers.Server : Linux/2.x UPnP/1.0 Avtech/1.0”
A match with that document in the look up table will be:
Key= Server (with wildcard on both sides).
Value= Avtech(with wildcard on both sides)
Vendor= Avtech
So baisically I’ll need to add a field to that document with the value- “ Avtech”.
the subfields in “Headers” are dynamic fields that changes from document to document.
of a match is not found I’ll need to add to the tag field with value- “Unknown”.
I’ve tried to use the enrich processor , use the lookup table as the source data , the match field will be ”Value” and the enrich field will be “Vendor”.
In the enrich processor I didn’t know how to call to the field since it’s dynamic and I wanted to search if the value is anywhere in the “Headers” subfields.
Also, I don’t think that there will be a match between the “Value” in the lookup table and the value of the Headers subfield, since “Value” field in the lookup table is a substring with wildcards on both sides.
I can use some help to accomplish what I’m trying to do.. and how to search with wildcards inside an enrich processor.
or if you have other idea besides the enrich processor- such as parent- child and lookup terms mechanism.
Thanks!
Adi.
There are two ways to accomplish this:
Using the combination of Logstash & Elasticsearch
Using the only the Elastichsearch Ingest node
Constriant: You need to know the position of the Vendor term occuring in the Header field.
Approach 1
If so then you can use the GROK filter to extract the term. And based on the term found, do a lookup to get the corresponding value.
Reference
https://www.elastic.co/guide/en/logstash/current/plugins-filters-grok.html
https://www.elastic.co/guide/en/logstash/current/plugins-filters-kv.html
https://www.elastic.co/guide/en/logstash/current/plugins-filters-jdbc_static.html
https://www.elastic.co/guide/en/logstash/current/plugins-filters-jdbc_streaming.html
Approach 2
Create an index consisting of KV pairs. In the ingest node, create a pipeline which consists of Grok processor and then Enrich it. The Grok would work the same way mentioned in the Approach 1. And you seem to have already got the Enrich part working.
Reference
https://www.elastic.co/guide/en/elasticsearch/reference/current/grok-processor.html
If you are able to isolate the sub field within the Header where the Term of interest is present then it would make things easier for you.
We have an elasticsearch that contains over half a billion documents that each have a url field that stores a URL.
The url field mapping currently has the settings:
{
index: not_analyzed
doc_values: true
...
}
We want our users to be able to search URLs, or portions of URLs without having to use wildcards.
For example, taking the URL with path: /part1/user#site/part2/part3.ext
They should be able to bring back a matching document by searching:
part3.ext
user#site
part1
part2/part3.ext
The way I see it, we have two options:
Implement an analysed version of this field (which can no longer have doc_values: true) and do match querying instead of wildcards. This would also require using a custom analyser to leverage the pattern tokeniser to make the extracted terms correct (the standard tokeniser would split user#site into user and site).
Go through our database and for each document create a new field that is a list of URL parts. This field could have doc_values: true still so would be stored off-heap, and we could do term querying on exact field values instead of wildcards.
My question is this:
Which is better for performance: having a list of variable lengths that has doc_values on, or having an analysed field? (ie: option 1 or option 2) OR is there an option 3 that would be even better yet?!
Thanks for your help!
Your question is about a field where you need doc_values but can not index with keyword-analyzer.
You did not mention why you need doc_values. But you did mention that you currently not search in this field.
So I guess that the name of the search-field do not have to be the same: you can copy the field value in an other field which is only for search ( "store": false ). For this new field you can use the pattern-analyzer or pattern-tokenizer for your use case.
It seems that no-one has actually performance tested the two options, so I did.
I took a sample of 10 million documents and created two new indices:
An index with an analysed field that was setup as suggested in the other answer.
An index with a string field that would store all permutations of URL segmentation.
I ran an enrichment process over the second index to populate the fields. The field values on the first index were created when I re-indexed the sample data from my main index.
Then I created a set of gatling tests to run against the indices and compared the gatling results and netdata (https://github.com/firehol/netdata) landscape for each.
The results were as follows:
Regarding the netadata landscape: The analysed field showed a spike - although only a small one - on all elastic nodes. The not_analysed list field tests didn't even register.
It is worth mentioning that enriching the list field with URL segmentation permutations bloated the index by about 80% in our case. So there's a trade off - you never need to do wildcard searches for exact sub-segment matching on URLs, but you'll need a lot more disk to do it.
Update
Don't do this. Go for doc_values. Doing anything with analyzed strings that have a massive number of possible terms will mean massive field data that will, eventually, never fit in the amount of memory you can allocate it.
I have a use case which is a bit similar to the ES example of dynamic_template where I want certain strings to be analyzed and certain not.
My document fields don't have such a convention and the decision is made based on an external schema. So currently my flow is:
I grab the inputs document from the DB
I grab the approrpiate schema (same database, currently using logstash for import)
I adjust the name in the document accordingly (using logstash's ruby mutator):
if not analyzed I don't change the name
if analyzed I change it to ORIGINALNAME_analyzed
This will handle the analyzed/not_analyzed problem thanks to dynamic_template I set but now the user doesn't know which fields are analyzed so there's no easy way for him to write queries because he doesn't know what's the name of the field.
I wanted to use field name aliases but apparently ES doesn't support them. Are there any other mechanisms I'm missing I could use here like field rename after indexation or something else?
For example this ancient thread mentions that field.sub.name can be queried as just name but I'm guessing this has changed when they disallowed . in the name some time ago since I cannot get it to work?
Let the user only create queries with the original name. I believe you have some code that converts this user query to Elasticsearch query. When converting to Elasticsearch query, instead of using the field name provided by the user alone use both the field names ORIGINALNAME as well as ORIGINALNAME_analyzed. If you are using a match query, convert it to multi_match. If you are using a term query, convert it to a bool should query. I guess you get where I am going with this.
Elasticsearch won't mind if a field does not exists. This can be a problem if there is already a field with _analyzed appended in its original name. But with some tricks that can be fixed too.