CODEWITHSUNDEEP
elasticsearchelasticsearch-2.0amazon-elasticsearch
Roy Solberg
Roy Solberg

Reputation: 19723

Set up Elasticsearch suggesters that can return suggestions from different data types

We're in the process of setting up Amazon Elasticsearch Service (running Elasticsearch version 2.3).

We have different types of data (that I'm currently thinking of as different document types within the same index).

We have a generic search in an app where we want an inline autocomplete function, that is, a completion suggester returning hits from all different data (document) types. How can that be set up?

When querying suggesters you have to specify an index, so that's why I wanted to keep all the data in the same index. According to the documentation, the completion suggester considers all documents in the index.

Setting up the completion suggester for the first document type was pretty straight forward and is working great. However, as far as I can see you to specify a suggest field when querying. That would be all good hadn't it been for the error message we get when setting up the mapping for the second document type:

Type: illegal_argument_exception Reason: "[suggest] is defined as an object in mapping [name_of_document_type] but this name is already used for a field in other types"

Writing this question I see that it's possible to specify more than one suggester in a single suggest query. Maybe that is what we have to solve it? (I.e. get X results from Y suggesters where we compare the scores to get the 1 suggestion we want to present to the user.)

Upvotes: 3

Views: 262

Answers (1)

Ant P
Ant P

Reputation: 25231

One of the core principles of good data design for Elasticsearch (as with many data stores) is to optimise your data storage for ease of reading. Usually, this means embracing duplication.

With this in mind, I'd suggest having a separate autocomplete index with a mapping that's designed specifically for the suggester queries.

Whenever you insert or write one of your other documents, map it to your autocomplete type and add or update it in your autocomplete index at the same time (or, depending on how up-to-date it needs to be, create an offline process to update your autocomplete index e.g. every day).

Then, when you do your suggest query, you can just use your autocomplete index and not worry about dealing with different types of documents with different fields.

Upvotes: 1

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