[[sorting-collations]] === Sorting and Collations

So far in this chapter, we have looked at how to normalize tokens for the purposes of search.((("tokens", "normalizing", "for sorting and collation"))) The final use case to consider in this chapter is that of string sorting.((("sorting")))

In <>, we explained that Elasticsearch cannot sort on an analyzed string field, and demonstrated how to use multifields to index the same field once as an analyzed field for search, and once as a not_analyzed field for sorting.((("not_analyzed fields", "for string sorting")))((("analyzed fields", "for searh")))

The problem with sorting on an analyzed field is not that it uses an analyzer, but that the analyzer tokenizes the string value into multiple tokens, like a bag of words, and Elasticsearch doesn't know which token to use for sorting.

Relying on a not_analyzed field for sorting is inflexible: it allows us to sort on only the exact value of the original string. However, we can use analyzers to achieve other sort orders, as long as our chosen analyzer always emits only a single token for each string value.

[[case-insensitive-sorting]] ==== Case-Insensitive Sorting

Imagine that we have three user documents whose name fields contain Boffey,((("case insensitive sorting")))((("sorting", "case insensitive"))) BROWN, and bailey, respectively. First we will apply the technique described in <> of using a not_analyzed field for sorting:


PUT /my_index { "mappings": { "user": { "properties": { "name": { <1> "type": "string", "fields": { "raw": { <2> "type": "string", "index": "not_analyzed" } } } } } }


<1> The analyzed name field is used for search.

<2> The not_analyzed name.raw field is used for sorting.

We can index some documents and try sorting:


PUT /my_index/user/1 { "name": "Boffey" }

PUT /my_index/user/2 { "name": "BROWN" }

PUT /my_index/user/3 { "name": "bailey" }

GET /my_index/user/_search?sort=name.raw

The preceding search request would return the documents in this order: BROWN, Boffey, bailey. This is known as lexicographical order as ((("lexicographical order")))((("alphabetical order")))opposed to alphabetical order. Essentially, the bytes used to represent capital letters have a lower value than the bytes used to represent lowercase letters, and so the names are sorted with the lowest bytes first.

That may make sense to a computer, but doesn't make much sense to human beings who would reasonably expect these names to be sorted alphabetically, regardless of case. To achieve this, we need to index each name in a way that the byte ordering corresponds to the sort order that we want.

In other words, we need an analyzer that will emit a single lowercase token:


PUT /my_index { "settings": { "analysis": { "analyzer": { "case_insensitive_sort": { "tokenizer": "keyword", <1> "filter": [ "lowercase" ] <2> } } } }


<1> The keyword tokenizer emits the original input string as a single unchanged token.((("keyword tokenizer")))

<2> The lowercase token filter lowercases the token.

With((("lowercase token filter"))) the case_insentive_sort analyzer in place, we can now use it in our multifield:


PUT /my_index/_mapping/user { "properties": { "name": { "type": "string", "fields": { "lower_case_sort": { <1> "type": "string", "analyzer": "case_insensitive_sort" } } } } }

PUT /my_index/user/1 { "name": "Boffey" }

PUT /my_index/user/2 { "name": "BROWN" }

PUT /my_index/user/3 { "name": "bailey" }

GET /my_index/user/_search?sort=name.lower_case_sort

<1> The name.lower_case_sort field will provide us with case-insentive sorting.

The preceding search request returns our documents in the order that we expect: bailey, Boffey, BROWN.

But is this order correct? It appears to be correct as it matches our expectations, but our expectations have probably been influenced by the fact that this book is in English and all of the letters used in our example belong to the English alphabet.

What if we were to add the German name Böhm?

Now our names would be returned in this order: bailey, Boffey, BROWN, Böhm. The reason that böhm comes after BROWN is that these words are still being sorted by the values of the bytes used to represent them, and an r is stored as the byte 0x72, while ö is stored as 0xF6 and so is sorted last. The byte value of each character is an accident of history.

Clearly, the default sort order is meaningless for anything other than plain English. In fact, there is no ``right'' sort order. It all depends on the language you speak.

==== Differences Between Languages

Every language has its own sort order, and((("sorting", "differences between languages")))((("languages", "sort order, differences in"))) sometimes even multiple sort orders.((("Swedish, sort order")))((("German", "sort order")))((("English", "sort order"))) Here are a few examples of how our four names from the previous section would be sorted in different contexts:

  • English: bailey, boffey, böhm, brown

  • German: bailey, boffey, böhm, brown

  • German phonebook: bailey, böhm, boffey, brown

  • Swedish: bailey, boffey, brown, böhm


The reason that the German phonebook sort order places böhm before boffey is that ö and oe are considered synonyms when dealing with names and

places, so böhm is sorted as if it had been written as boehm.

[[uca]] ==== Unicode Collation Algorithm

Collation is the process of sorting text into a predefined order.((("collation")))((("Unicode Collation Algorithm (UCA)"))) The Unicode Collation Algorithm, or UCA (see http://www.unicode.org/reports/tr10/[_www.unicode.org/reports/tr10_]) defines a method of sorting strings into the order defined in a Collation Element Table (usually referred to just as a collation).

The UCA also defines the Default Unicode Collation Element Table, or DUCET, which defines the default sort order((("Default Unicode Collation Element Table (DUCET)"))) for all Unicode characters, regardless of language. As you have already seen, there is no single correct sort order, so DUCET is designed to annoy as few people as possible as seldom as possible, but it is far from being a panacea for all sorting woes.

Instead, language-specific collations((("languages", "collations"))) exist for pretty much every language under the sun. Most use DUCET as their starting point and add a few custom rules to deal with the peculiarities of each language.

The UCA takes a string and a collation as inputs and outputs a binary sort key. Sorting a collection of strings according to the specified collation then becomes a simple comparison of their binary sort keys.

==== Unicode Sorting


The approach described in this section will probably change in ((("Unicode", "sorting")))((("sorting", "Unicode")))a future version of Elasticsearch. Check the <> documentation for the latest information.


The icu_collation token filter defaults((("icu_collation token filter"))) to using the DUCET collation for sorting. This is already an improvement over the default sort. To use it, all we need to do is to create an analyzer that uses the default icu_collation filter:


PUT /my_index { "settings": { "analysis": { "analyzer": { "ducet_sort": { "tokenizer": "keyword", "filter": [ "icu_collation" ] <1> } } } }


<1> Use the default DUCET collation.

Typically, the field that we want to sort on is also a field that we want to search on, so we use the same multifield approach as we used in <>:


PUT /my_index/_mapping/user { "properties": { "name": { "type": "string", "fields": { "sort": { "type": "string", "analyzer": "ducet_sort" } } } }


With this mapping, the name.sort field will contain a sort key that will be used only for sorting. ((("Default Unicode Collation Element Table (DUCET)")))((("Unicode Collation Algorithm (UCA)"))) We haven't specified a language, so it defaults to using the <>.

Now, we can reindex our example docs and test the sorting:


PUT /my_index/user/_bulk { "index": { "_id": 1 }} { "name": "Boffey" } { "index": { "_id": 2 }} { "name": "BROWN" } { "index": { "_id": 3 }} { "name": "bailey" } { "index": { "_id": 4 }} { "name": "Böhm" }

GET /my_index/user/_search?sort=name.sort


Note that the sort key returned with each document, which in earlier examples looked like brown and böhm, now looks like gobbledygook: ᖔ乏昫တ倈⠀\u0001. The reason is that the icu_collation filter emits keys

intended only for efficient sorting, not for any other purposes.

The preceding search returns our docs in this order: bailey, Boffey, Böhm, BROWN. This is already an improvement, as the sort order is now correct for English and German, but it is still incorrect for German phonebooks and Swedish. The next step is to customize our mapping for different languages.

==== Specifying a Language

The icu_collation filter can be ((("icu_collation token filter", "specifying a language")))((("languages", "collation table for a specific language, icu_collation filter using")))configured to use the collation table for a specific language, a country-specific version of a language, or some other subset such as German phonebooks. This can be done by creating a custom version of the token filter by ((("German", "collation table for, icu_collation filter using")))using the language, country, and variant parameters as follows:

English:: +


{ "language": "en" }

German:: +


{ "language": "de" }

Austrian German:: +


{ "language": "de", "country": "AT" }

German phonebooks:: +


{ "language": "en", "variant": "@collation=phonebook" }


You can read more about the locales supported by ICU at: http://bit.ly/1u9LEdp.


This example shows how to set up the German phonebook sort order:


PUT /my_index { "settings": { "number_of_shards": 1, "analysis": { "filter": { "german_phonebook": { <1> "type": "icu_collation", "language": "de", "country": "DE", "variant": "@collation=phonebook" } }, "analyzer": { "german_phonebook": { <2> "tokenizer": "keyword", "filter": [ "german_phonebook" ] } } } }, "mappings": { "user": { "properties": { "name": { "type": "string", "fields": { "sort": { <3> "type": "string", "analyzer": "german_phonebook" } } } } } }


<1> First we create a version of the icu_collation customized for the German phonebook collation.

<2> Then we wrap that up in a custom analyzer.

<3> And we apply it to our name.sort field.

Reindex the data and repeat the same search as we used previously:


PUT /my_index/user/_bulk { "index": { "_id": 1 }} { "name": "Boffey" } { "index": { "_id": 2 }} { "name": "BROWN" } { "index": { "_id": 3 }} { "name": "bailey" } { "index": { "_id": 4 }} { "name": "Böhm" }

GET /my_index/user/_search?sort=name.sort

This now returns our docs in this order: bailey, Böhm, Boffey, BROWN. In the German phonebook collation, Böhm is the equivalent of Boehm, which comes before Boffey.

===== Multiple sort orders

The same field can support multiple ((("sorting", "multiple sort orders supported by same field")))sort orders by using a multifield for each language:


PUT /my_index/_mapping/_user { "properties": { "name": { "type": "string", "fields": { "default": { "type": "string", "analyzer": "ducet" <1> }, "french": { "type": "string", "analyzer": "french" <1> }, "german": { "type": "string", "analyzer": "german_phonebook" <1> }, "swedish": { "type": "string", "analyzer": "swedish" <1> } } } }


<1> We would need to create the corresponding analyzers for each of these collations.

With this mapping in place, results can be ordered correctly for French, German, and Swedish users, just by sorting on the name.french, name.german, or name.swedish fields. Unsupported languages can fall back to using the name.default field, which uses the DUCET sort order.

==== Customizing Collations

The icu_collation token filter takes((("collation", "customizing collations")))((("icu_collation token filter", "customizing collations"))) many more options than just language, country, and variant, which can be used to tailor the sorting algorithm. Options are available that will do the following:

  • Ignore diacritics
  • Order uppercase first or last, or ignore case
  • Take punctuation and whitespace into account or ignore it
  • Sort numbers as strings or by their numeric value
  • Customize existing collations or define your own custom collations

Details of these options are beyond the scope of this book, but more information can be found in the https://github.com/elasticsearch/elasticsearch-analysis-icu[ICU plug-in documentation] and in the http://userguide.icu-project.org/collation/concepts[ICU project collation documentation].