[[shingles]] === Finding Associated Words As useful as phrase and proximity queries can be, they still have a downside. They are overly strict: all terms must be present for a phrase query to match, even when using `slop`.((("proximity matching", "finding associated words", range="startofrange", id="ix_proxmatchassoc"))) The flexibility in word ordering that you gain with `slop` also comes at a price, because you lose the association between word pairs. While you can identify documents in which `sue`, `alligator`, and `ate` occur close together, you can't tell whether _Sue ate_ or the _alligator ate_. When words are used in conjunction with each other, they express an idea that is bigger or more meaningful than each word in isolation. The two clauses _I'm not happy I'm working_ and _I'm happy I'm not working_ contain the sames words, in close proximity, but have quite different meanings. If, instead of indexing each word independently, we were to index pairs of words, then we could retain more of the context in which the words were used. For the sentence `Sue ate the alligator`, we would not only index each word (or _unigram_) as((("unigrams"))) a term ["sue", "ate", "the", "alligator"] but also each word _and its neighbor_ as single terms: ["sue ate", "ate the", "the alligator"] These word ((("bigrams")))pairs (or _bigrams_) are ((("shingles")))known as _shingles_. [TIP] ================================================== Shingles are not restricted to being pairs of words; you could index word triplets (_trigrams_) as ((("trigrams")))well: ["sue ate the", "ate the alligator"] Trigrams give you a higher degree of precision, but greatly increase the number of unique terms in the index. Bigrams are sufficient for most use cases. ================================================== Of course, shingles are useful only if the user enters the query in the same order as in the original document; a query for `sue alligator` would match the individual words but none of our shingles. Fortunately, users tend to express themselves using constructs similar to those that appear in the data they are searching. But this point is an important one: it is not enough to index just bigrams; we still need unigrams, but we can use matching bigrams as a signal to increase the relevance score. ==== Producing Shingles Shingles need to be created at index time as part of the analysis process.((("shingles", "producing at index time"))) We could index both unigrams and bigrams into a single field, but it is cleaner to keep unigrams and bigrams in separate fields that can be queried independently. The unigram field would form the basis of our search, with the bigram field being used to boost relevance. First, we need to create an analyzer that uses the `shingle` token filter: [source,js] -------------------------------------------------- DELETE /my_index PUT /my_index { "settings": { "number_of_shards": 1, <1> "analysis": { "filter": { "my_shingle_filter": { "type": "shingle", "min_shingle_size": 2, <2> "max_shingle_size": 2, <2> "output_unigrams": false <3> } }, "analyzer": { "my_shingle_analyzer": { "type": "custom", "tokenizer": "standard", "filter": [ "lowercase", "my_shingle_filter" <4> ] } } } } } -------------------------------------------------- // SENSE: 120_Proximity_Matching/35_Shingles.json <1> See <<relevance-is-broken>>. <2> The default min/max shingle size is `2` so we don't really need to set these. <3> The `shingle` token filter outputs unigrams by default, but we want to keep unigrams and bigrams separate. <4> The `my_shingle_analyzer` uses our custom `my_shingles_filter` token filter. First, let's test that our analyzer is working as expected with the `analyze` API: [source,js] -------------------------------------------------- GET /my_index/_analyze?analyzer=my_shingle_analyzer Sue ate the alligator -------------------------------------------------- Sure enough, we get back three terms: * `sue ate` * `ate the` * `the alligator` Now we can proceed to setting up a field to use the new analyzer. ==== Multifields We said that it is cleaner to index unigrams and bigrams separately, so we will create the `title` field ((("multifields")))as a multifield (see <<multi-fields>>): [source,js] -------------------------------------------------- PUT /my_index/_mapping/my_type { "my_type": { "properties": { "title": { "type": "string", "fields": { "shingles": { "type": "string", "analyzer": "my_shingle_analyzer" } } } } } } -------------------------------------------------- With this mapping, values from our JSON document in the field `title` will be indexed both as unigrams (`title`) and as bigrams (`title.shingles`), meaning that we can query these fields independently. And finally, we can index our example documents: [source,js] -------------------------------------------------- POST /my_index/my_type/_bulk { "index": { "_id": 1 }} { "title": "Sue ate the alligator" } { "index": { "_id": 2 }} { "title": "The alligator ate Sue" } { "index": { "_id": 3 }} { "title": "Sue never goes anywhere without her alligator skin purse" } -------------------------------------------------- ==== Searching for Shingles To understand the benefit ((("shingles", "searching for")))that the `shingles` field adds, let's first look at the results from a simple `match` query for ``The hungry alligator ate Sue'': [source,js] -------------------------------------------------- GET /my_index/my_type/_search { "query": { "match": { "title": "the hungry alligator ate sue" } } } -------------------------------------------------- This query returns all three documents, but note that documents 1 and 2 have the same relevance score because they contain the same words: [source,js] -------------------------------------------------- { "hits": [ { "_id": "1", "_score": 0.44273707, <1> "_source": { "title": "Sue ate the alligator" } }, { "_id": "2", "_score": 0.44273707, <1> "_source": { "title": "The alligator ate Sue" } }, { "_id": "3", <2> "_score": 0.046571054, "_source": { "title": "Sue never goes anywhere without her alligator skin purse" } } ] } -------------------------------------------------- <1> Both documents contain `the`, `alligator`, and `ate` and so have the same score. <2> We could have excluded document 3 by setting the `minimum_should_match` parameter. See <<match-precision>>. Now let's add the `shingles` field into the query. Remember that we want matches on the `shingles` field to act as a signal--to increase the relevance score--so we still need to include the query on the main `title` field: [source,js] -------------------------------------------------- GET /my_index/my_type/_search { "query": { "bool": { "must": { "match": { "title": "the hungry alligator ate sue" } }, "should": { "match": { "title.shingles": "the hungry alligator ate sue" } } } } } -------------------------------------------------- We still match all three documents, but document 2 has now been bumped into first place because it matched the shingled term `ate sue`. [source,js] -------------------------------------------------- { "hits": [ { "_id": "2", "_score": 0.4883322, "_source": { "title": "The alligator ate Sue" } }, { "_id": "1", "_score": 0.13422975, "_source": { "title": "Sue ate the alligator" } }, { "_id": "3", "_score": 0.014119488, "_source": { "title": "Sue never goes anywhere without her alligator skin purse" } } ] } -------------------------------------------------- Even though our query included the word `hungry`, which doesn't appear in any of our documents, we still managed to use word proximity to return the most relevant document first. ==== Performance Not only are shingles more flexible than phrase queries,((("shingles", "better performance than phrase queries"))) but they perform better as well. Instead of paying the price of a phrase query every time you search, queries for shingles are just as efficient as a simple `match` query. A small price is paid at index time, because more terms need to be indexed, which also means that fields with shingles use more disk space. However, most applications write once and read many times, so it makes sense to optimize for fast queries. This is a theme that you will encounter frequently in Elasticsearch: enables you to achieve a lot at search time, without requiring any up-front setup. Once you understand your requirements more clearly, you can achieve better results with better performance by modeling your data correctly at index time. ((("proximity matching", "finding associated words", range="endofrange", startref ="ix_proxmatchassoc")))