35_Shingles.asciidoc

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.

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 where 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…​'' and Happy I’m not…​'' 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 a term:

["sue", "ate", "the", "alligator"]

but also each word and its neighbour as single terms:

["sue ate", "ate the", "the alligator"]

These word pairs (or bigrams) are known as shingles.

Tip	Shingles are not restricted to being pairs of words; you could index word triplets (trigrams) as well: ["sue ate the", "ate the alligator"] Trigrams give you a higher degree of precision, but greatly increases the number of unique terms in the index. Bigrams are sufficient for most use cases.

Of course, shingles are only useful if the user enters their 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 similar constructs 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. We could index both unigrams and bigrams into a single field, but it is cleaner to keep unigrams and bigrams in separate fields which 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 which uses the shingle token filter:

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)
                    ]
                }
            }
        }
    }
}

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:

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.

Multi-fields

We said that it is cleaner to index unigrams and bigrams separately, so we will create the title field as a multi-field (see Multi-fields):

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:

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 that the shingles field adds, let’s first look at the results from a simple match query for ``The hungry alligator ate Sue'':

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:

{
  "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:

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".

{
  "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, 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.

There is a small cost that is payed 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: it makes it possible to achieve a lot with your existing data, without requiring any setup. But once you understand your requirements better it is worth putting in the extra effort to model your data at index time. A little bit of preparation will help you to achieve better results with better performance.