17.01.2025 Problem:
The client encountered an issue in their Elasticsearch setup where search results did not return exact matches when the search phrase included special characters, such as “:” (colon). This problem persisted despite using a custom indexing configuration with the `index_word_delimiter_graph_filter`. The client needed a solution to preserve special characters for exact matches while maintaining efficient indexing and search performance.
Process:
Step 1 – Initial Analysis
The expert reviewed the client’s current configuration:
"filter": { "index_word_delimiter_graph_filter": { "type": "word_delimiter_graph", "preserve_original": true, "split_on_case_change": false, "catenate_all": true } }
It was observed that this configuration caused tokens with special characters like “:” to be split, preventing exact matches. The expert identified the root cause as the default behavior of the `word_delimiter_graph_filter` that splits tokens based on special characters.
Step 2 – Proposed Solutions
The expert provided the following approaches to address the issue:
- Approach 1 – Modify the `index_word_delimiter_graph_filter` Configuration: Disable splitting on special characters and numerics by setting `split_on_numerics` and `generate_word_parts` to false. This ensures tokens with special characters are preserved as-is.
- Approach 2 – Use a Keyword Field: For fields requiring exact matches, use the `keyword` data type to index values without tokenization. This allows exact search queries to work, even with special characters.
- Approach 3 – Escape Special Characters in Queries: Ensure search queries properly escape special characters using backslashes. For example, use `”field\\:value”` instead of `”field:value”` in queries.
- Approach 4 – Analyze Tokens with the `_analyze` API: Debug the custom analyzer using the `_analyze` API to understand how tokens are processed and confirm the impact of configuration changes.
- Approach 5 – Custom Tokenizer: Define a custom tokenizer that preserves special characters as part of tokens, ensuring these characters are not split during tokenization.
Solution:
The expert’s recommendations helped the client resolve the issue by modifying the filter configuration and exploring alternative solutions, such as using a keyword field or a custom tokenizer. The client implemented a solution that ensured exact matches, even with special characters like “:”.
Conclusion:
This case highlights the importance of understanding how Elasticsearch tokenizes and indexes text. By leveraging expert advice and tailored solutions, the client achieved their goal of accurate search results without sacrificing performance. This approach demonstrates the value of precise configuration and debugging tools, such as the `_analyze` API, in resolving search-related challenges.