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Discover a smooth way to set up an `Elasticsearch` filter and tokenizer to handle queries with "and" and "&" by concatenating words effectively and improving search results.
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How to Normalize Queries in Elasticsearch by Concatenating Words with "and"

In the realm of search engines and database queries, having clear and well-structured searches can make a world of difference. If you're using Elasticsearch, you might have encountered a challenge with how it handles certain terms like "and" or "&". You may have queries like:

"henry&william book" → Should return "henrywilliam book"

"henry and william book" → Should also return "henrywilliam book"

However, when inputting such queries, Elasticsearch tends to break these phrases down, losing the concatenated aspect when you want them combined.

The Problem

You've likely noticed that your queries including "and" or "&" might end up being broken into separate tokens. In the earlier example, "henry & william" simply becomes ["henry", "william"]. While this may seem logical, it does not serve your intention of retaining the concatenated format.

Why This Happens

The issue arises because of the sequence in which Elasticsearch processes text. By the time the analyzer and filter run, the tokenizer has already divided the terms into separate tokens, effectively losing the manner in which they should be treated.

The Solution

To tackle this issue, we can utilize a combination of character filters and a custom analyzer that runs prior to the tokenizer. Below are the steps to configure Elasticsearch to meet your requirements smoothly:

Step 1: Configuration Setup

You’ll create two types of character filters:

Mapping Character Filter: This filter will replace occurrences of "and" with "&".

Pattern Replacement Character Filter: This will identify adjacent terms separated by "&" and concatenate them.

Here's a sample configuration to illustrate:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Testing the Analyzer

You can test this analyzer using the _analyze API. Here's how your results would look after submitting various inputs:

[[See Video to Reveal this Text or Code Snippet]]

Results:

Token: "henrywilliam book"

You can perform similar actions with other test cases:

"henry & william book" → Token: "henrywilliam book"

"henry and william book" → Token: "henrywilliam book"

"henry william book" → Token: "henry william book" (remains unchanged)

Conclusion

By implementing a combination of character filters and a custom analyzer, we can effectively normalize search queries in Elasticsearch. This not only improves the search output but also makes the system better at handling user queries that involve connectors like "and" and "&".

If you're grappling with similar issues, consider adopting this solution to enhance your Elasticsearch search functionalities. Happy searching!