Elasticsearch - Analysis

When a query is processed during a search operation, the content in any index is analyzed by the analysis module. This module consists of analyzer, tokenizer, tokenfilters and charfilters. If no analyzer is defined, then by default the built in analyzers, token, filters and tokenizers get registered with analysis module.
In the following example, we use a standard analyzer which is used when no other analyzer is specified. It will analyze the sentence based on the grammar and produce words used in the sentence.
POST _analyze
{
   "analyzer": "standard",
   "text": "Today's weather is beautiful"
}
On running the above code, we get the response as shown below −
{
   "tokens" : [
      {
         "token" : "today's",
         "start_offset" : 0,
         "end_offset" : 7,
         "type" : "",
         "position" : 0
      },
      {
         "token" : "weather",
         "start_offset" : 8,
         "end_offset" : 15,
         "type" : "",
         "position" : 1
      },
      {
         "token" : "is",
         "start_offset" : 16,
         "end_offset" : 18,
         "type" : "",
         "position" : 2
      },
      {
         "token" : "beautiful",
         "start_offset" : 19,
         "end_offset" : 28,
         "type" : "",
         "position" : 3
      }
   ]
}

We can configure the standard analyser with various parameters to get our custom requirements.
In the following example, we configure the standard analyzer to have a max_token_length of 5.
For this, we first create an index with the analyser having max_length_token parameter.
PUT index_4_analysis
{
   "settings": {
      "analysis": {
         "analyzer": {
            "my_english_analyzer": {
               "type": "standard",
               "max_token_length": 5,
               "stopwords": "_english_"
            }
         }
      }
   }
}
Next we apply the analyser with a text as shown below. Please note how the token is does not appear as it has two spaces in the beginning and two spaces at the end. For the word “is”, there is a space at the beginning of it and a space at the end of it. Taking all of them, it becomes 4 letters with spaces and that does not make it a word. There should be a nonspace character at least at the beginning or at the end, to make it a word to be counted.
POST index_4_analysis/_analyze
{
   "analyzer": "my_english_analyzer",
   "text": "Today's weather is beautiful"
}
On running the above code, we get the response as shown below −
{
   "tokens" : [
      {
         "token" : "today",
         "start_offset" : 0,
         "end_offset" : 5,
         "type" : "",
         "position" : 0
      },
      {
         "token" : "s",
         "start_offset" : 6,
         "end_offset" : 7,
         "type" : "",
         "position" : 1
      },
      {
         "token" : "weath",
         "start_offset" : 8,
         "end_offset" : 13,
         "type" : "",
         "position" : 2
      },
      {
         "token" : "er",
         "start_offset" : 13,
         "end_offset" : 15,
         "type" : "",
         "position" : 3
      },
      {
         "token" : "beaut",
         "start_offset" : 19,
         "end_offset" : 24,
         "type" : "",
         "position" : 5
      },
      {
         "token" : "iful",
         "start_offset" : 24,
         "end_offset" : 28,
         "type" : "",
         "position" : 6
      }
   ]
}

The list of various analyzers and their description are given in the table shown below −
Standard analyzer (standard)
stopwords and max_token_length setting can be set for this analyzer. By default, stopwords list is empty and max_token_length is 255.
Simple analyzer (simple)
This analyzer is composed of lowercase tokenizer.
Whitespace analyzer (whitespace)
This analyzer is composed of whitespace tokenizer.
Stop analyzer (stop)
stopwords and stopwords_path can be configured. By default stopwords initialized to English stop words and stopwords_path contains path to a text file with stop words.
Tokenizers are used for generating tokens from a text in Elasticsearch. Text can be broken down into tokens by taking whitespace or other punctuations into account. Elasticsearch has plenty of built-in tokenizers, which can be used in custom analyzer.
An example of tokenizer that breaks text into terms whenever it encounters a character which is not a letter, but it also lowercases all terms, is shown below −
POST _analyze
{
   "tokenizer": "lowercase",
   "text": "It Was a Beautiful Weather 5 Days ago."
}
On running the above code, we get the response as shown below −
{
   "tokens" : [
      {
         "token" : "it",
         "start_offset" : 0,
         "end_offset" : 2,
         "type" : "word",
         "position" : 0
      },
      {
         "token" : "was",
         "start_offset" : 3,
         "end_offset" : 6,
         "type" : "word",
         "position" : 1
      },
      {
         "token" : "a",
         "start_offset" : 7,
         "end_offset" : 8,
         "type" : "word",
         "position" : 2
      },
      {
         "token" : "beautiful",
         "start_offset" : 9,
         "end_offset" : 18,
         "type" : "word",
         "position" : 3
      },
      {
         "token" : "weather",
         "start_offset" : 19,
         "end_offset" : 26,
         "type" : "word",
         "position" : 4
      },
      {
         "token" : "days",
         "start_offset" : 29,
         "end_offset" : 33,
         "type" : "word",
         "position" : 5
      },
      {
         "token" : "ago",
         "start_offset" : 34,
         "end_offset" : 37,
         "type" : "word",
         "position" : 6
      }
   ]
}

A list of Tokenizers and their descriptions are shown here in the table given below −
Standard tokenizer (standard)
This is built on grammar based tokenizer and max_token_length can be
configured for this tokenizer.
Edge NGram tokenizer (edgeNGram)
Settings like min_gram, max_gram, token_chars can be set for this tokenizer.
Keyword tokenizer (keyword)
This generates entire input as an output and buffer_size can be set for this.
Letter tokenizer (letter)
This captures the whole word until a non-letter is encountered.

    14 Lectures 
     5 hours 

 Manuj Aggarwal

    20 Lectures 
     1 hours 

 Faizan Tayyab
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