How does Elasticsearch accurately identify escaped or reserved characters?

As I have a string like 'message-ID: 1394.00 This is Henry.Lin', my goal is to utilize elasticsearch to locate all the words or phrases containing '.'. For this particular example, the words I am interested in are 1394.00 and Henry.Lin. However, when I attempt to index my document using the standard analyzer, it does not produce the desired results. It seems that the standard analyzer treats these characters differently. Subsequently, I switched to the ngram analyzer, but unfortunately, I still encountered issues. Any assistance with this matter would be greatly appreciated.

Answer №1

If you want to customize how dots are handled, consider using a character filter to replace them with "dot". Here's an example of how you can create a custom mapping for this:

"char_filter": {
"&_to_and": {
    "type":       "mapping",
    "mappings": [ ".=>dot"]
}}

For more information, refer to this documentation.

Now let's address why ngram may not be working as expected.

Are you using ngram as a tokenizer or token filter in combination with another analyzer? What are the min_gram and max_gram sizes you have set? Check out this example to understand the distinction better.

To delve deeper into how your data is indexed in Elasticsearch and troubleshoot matching issues with queries, consider utilizing the termvectors API.

Ultimately, I would advise caution when using ngrams to address this problem due to the potential drawbacks - 1) increasing index size significantly, and 2) deviating from their intended utility.

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