Trim token filter

Trim token filter

Removes leading and trailing whitespace from each token in a stream. While this can change the length of a token, the trim filter does not change a token’s offsets.

The trim filter uses Lucene’s TrimFilter.

Many commonly used tokenizers, such as the standard or whitespace tokenizer, remove whitespace by default. When using these tokenizers, you don’t need to add a separate trim filter.

Example

To see how the trim filter works, you first need to produce a token containing whitespace.

The following analyze API request uses the keyword tokenizer to produce a token for " fox ".

  1. GET _analyze
  2. {
  3.   "tokenizer" : "keyword",
  4.   "text" : " fox "
  5. }

The API returns the following response. Note the " fox " token contains the original text’s whitespace. Note that despite changing the token’s length, the start_offset and end_offset remain the same.

  1. {
  2.   "tokens": [
  3.     {
  4.       "token": " fox ",
  5.       "start_offset": 0,
  6.       "end_offset": 5,
  7.       "type": "word",
  8.       "position": 0
  9.     }
  10.   ]
  11. }

To remove the whitespace, add the trim filter to the previous analyze API request.

  1. GET _analyze
  2. {
  3.   "tokenizer" : "keyword",
  4.   "filter" : ["trim"],
  5.   "text" : " fox "
  6. }

The API returns the following response. The returned fox token does not include any leading or trailing whitespace.

  1. {
  2.   "tokens": [
  3.     {
  4.       "token": "fox",
  5.       "start_offset": 0,
  6.       "end_offset": 5,
  7.       "type": "word",
  8.       "position": 0
  9.     }
  10.   ]
  11. }

Add to an analyzer

The following create index API request uses the trim filter to configure a new custom analyzer.

  1. PUT trim_example
  2. {
  3.   "settings": {
  4.     "analysis": {
  5.       "analyzer": {
  6.         "keyword_trim": {
  7.           "tokenizer": "keyword",
  8.           "filter": [ "trim" ]
  9.         }
  10.       }
  11.     }
  12.   }
  13. }