Analyzers in elasticsearch

Let me give you a short answer.

An analyzer is used at index Time and at search Time.
It’s used to create an index of terms.

To index a phrase, it could be useful to break it in words.
Here comes the analyzer.

It applies tokenizers and token filters.
A tokenizer could be a Whitespace tokenizer. It split a phrase in tokens at each space.
A lowercase tokenizer will split a phrase at each non-letter and lowercase all letters.

A token filter is used to filter or convert some tokens. For example, a ASCII folding filter will convert characters like ê, é, è to e.

An analyzer is a mix of all of that.

You should read Analysis guide and look at the right all different options you have.

By default, Elasticsearch applies the standard analyzer. It will remove all common english words (and many other filters)

You can also use the Analyze Api to understand how it works. Very useful.

In Lucene, analyzer is a combination of tokenizer (splitter) + stemmer + stopword filter

In ElasticSearch, analyzer is a combination of

  1. Character filter: “tidy up” a string before it is tokenized e.g. remove HTML tags
  2. Tokenizer: It’s used to break up the string into individual terms or tokens. Must have 1 only.
  3. Token filter: change, add or remove tokens. Stemmer is an example of token filter. It’s used to get the base of the word e.g. happy and happiness both have the same base is happi.

See Snowball demo here

This is a sample setting:

        "index" : {
            "analysis" : {
                "analyzer" : {
                    "analyzerWithSnowball" : {
                        "tokenizer" : "standard",
                        "filter" : ["standard", "lowercase", "englishSnowball"]
                "filter" : {
                    "englishSnowball" : {
                        "type" : "snowball",
                        "language" : "english"


  1. Comparison of Lucene Analyzers

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