The first step to setting up index-time search-as-you-type is to define our analysis chain, which we discussed in [configuring-analyzers], but we will go over the steps again here.
The first step is to configure a custom edge_ngram
token filter, which we
will call the autocomplete_filter
:
{
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
}
}
}
This configuration says that, for any term that this token filter receives, it should produce an n-gram anchored to the start of the word of minimum length 1 and maximum length 20.
Then we need to use this token filter in a custom analyzer, which we will call
the autocomplete
analyzer:
{
"analyzer": {
"autocomplete": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter" (1)
]
}
}
}
-
Our custom edge-ngram token filter
This analyzer will tokenize a string into individual terms by using the
standard
tokenizer, lowercase each term, and then produce edge n-grams of each
term, thanks to our autocomplete_filter
.
The full request to create the index and instantiate the token filter and analyzer looks like this:
PUT /my_index
{
"settings": {
"number_of_shards": 1, (1)
"analysis": {
"filter": {
"autocomplete_filter": { (2)
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
}
},
"analyzer": {
"autocomplete": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter" (3)
]
}
}
}
}
}
-
First we define our custom token filter.
-
Then we use it in an analyzer.
You can test this new analyzer to make sure it is behaving correctly by using
the analyze
API:
GET /my_index/_analyze
{
"analyzer": "autocomplete",
"text": "quick brown"
}
The results show us that the analyzer is working correctly. It returns these terms:
-
q
-
qu
-
qui
-
quic
-
quick
-
b
-
br
-
bro
-
brow
-
brown
To use the analyzer, we need to apply it to a field, which we can do
with the update-mapping
API:
PUT /my_index/_mapping/my_type
{
"my_type": {
"properties": {
"name": {
"type": "string",
"analyzer": "autocomplete"
}
}
}
}
Now, we can index some test documents:
POST /my_index/my_type/_bulk
{ "index": { "_id": 1 }}
{ "name": "Brown foxes" }
{ "index": { "_id": 2 }}
{ "name": "Yellow furballs" }
If you test out a query for `brown fo'' by using a simple `match
query
GET /my_index/my_type/_search
{
"query": {
"match": {
"name": "brown fo"
}
}
}
you will see that both documents match, even though the Yellow furballs
doc contains neither brown
nor fo
:
{
"hits": [
{
"_id": "1",
"_score": 1.5753809,
"_source": {
"name": "Brown foxes"
}
},
{
"_id": "2",
"_score": 0.012520773,
"_source": {
"name": "Yellow furballs"
}
}
]
}
As always, the validate-query
API shines some light:
GET /my_index/my_type/_validate/query?explain
{
"query": {
"match": {
"name": "brown fo"
}
}
}
The explanation
shows us that the query is looking for edge n-grams of every
word in the query string:
name:b name:br name:bro name:brow name:brown name:f name:fo
The name:f
condition is satisfied by the second document because
furballs
has been indexed as f
, fu
, fur
, and so forth. In retrospect, this
is not surprising. The same autocomplete
analyzer is being applied both at
index time and at search time, which in most situations is the right thing to
do. This is one of the few occasions when it makes sense to break this rule.
We want to ensure that our inverted index contains edge n-grams of every word,
but we want to match only the full words that the user has entered (brown
and fo
). We can do this by using the autocomplete
analyzer at
index time and the standard
analyzer at search time. One way to change the
search analyzer is just to specify it in the query:
GET /my_index/my_type/_search
{
"query": {
"match": {
"name": {
"query": "brown fo",
"analyzer": "standard" (1)
}
}
}
}
-
This overrides the
analyzer
setting on thename
field.
Alternatively, we can specify the analyzer
and search_analyzer
in
the mapping for the name
field itself. Because we want to change only the
search_analyzer
, we can update the existing mapping without having to
reindex our data:
PUT /my_index/my_type/_mapping
{
"my_type": {
"properties": {
"name": {
"type": "string",
"analyzer": "autocomplete", (1)
"search_analyzer": "standard" (2)
}
}
}
}
-
Use the
autocomplete
analyzer at index time to produce edge n-grams of every term. -
Use the
standard
analyzer at search time to search only on the terms that the user has entered.
If we were to repeat the validate-query
request, it would now give us this
explanation:
name:brown name:fo
Repeating our query correctly returns just the Brown foxes
document.
Because most of the work has been done at index time, all this query needs to
do is to look up the two terms brown
and fo
, which is much more efficient
than the match_phrase_prefix
approach of having to find all terms beginning
with fo
.
Using edge n-grams for search-as-you-type is easy to set up, flexible, and fast. However, sometimes it is not fast enough. Latency matters, especially when you are trying to provide instant feedback. Sometimes the fastest way of searching is not to search at all.
The {ref}/search-suggesters-completion.html[completion suggester] in Elasticsearch takes a completely different approach. You feed it a list of all possible completions, and it builds them into a finite state transducer, an optimized data structure that resembles a big graph. To search for suggestions, Elasticsearch starts at the beginning of the graph and moves character by character along the matching path. Once it has run out of user input, it looks at all possible endings of the current path to produce a list of suggestions.
This data structure lives in memory and makes prefix lookups extremely fast,
much faster than any term-based query could be. It is an excellent match for
autocompletion of names and brands, whose words are usually organized in a
common order: Johnny Rotten'' rather than
Rotten Johnny.''
When word order is less predictable, edge n-grams can be a better solution than the completion suggester. This particular cat may be skinned in myriad ways.
The edge n-gram approach can also be used for structured data, such as the
postcodes example from earlier in this chapter. Of course,
the postcode
field would need to be analyzed
instead of not_analyzed
, but
you could use the keyword
tokenizer to treat the postcodes as if they were
not_analyzed
.
Tip
|
The |
This example uses the keyword
tokenizer to convert the postcode string into a token stream, so that we can use the edge n-gram token filter:
{
"analysis": {
"filter": {
"postcode_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 8
}
},
"analyzer": {
"postcode_index": { (1)
"tokenizer": "keyword",
"filter": [ "postcode_filter" ]
},
"postcode_search": { (2)
"tokenizer": "keyword"
}
}
}
}
-
The
postcode_index
analyzer would use thepostcode_filter
to turn postcodes into edge n-grams. -
The
postcode_search
analyzer would treat search terms as if they werenot_analyzed
.