Introduction
Recently, I’ve investigated autocomplete functionality of our system as there were a lot of complaints that it was returning irrelevant results. The approach we had taken was pretty naive: our backend wrapped query into wildcard symbols and executed it as query_string on fields __title, title and commonInfo.RealName. Index we’ve executed search upon contained entity with _title equal 3 foxes but autocomplete query 3 foxes suggested BRN-3 / QCK 3 / 19 foxes, AC d / 3 foxes, 3 BRW / 1 foxes. The exact match was nowhere in sight!
So I’ve chosen 3 foxes as my relevance baseline and turned my eye to specific Elasticsearch queries that facilitate autocomplete functionality.
Search as You Type
As the name implies, search as you type seemed a perfect fit for autocomplete functionality. To start off, I’ve changed the mapping of my __title field to search_as_you_type and performed bool_prefix query straight from the documentation.
{
"_source": [
"__title"
],
"from": 0,
"size": 3,
"query": {
"multi_match": {
"query": "3 foxe",
"type": "bool_prefix",
"fields": [
"__title",
"__title.2gram",
"__title.3gram"
]
}
}
}
Now this was better!
{
"took": 21,
"timed_out": false,
"_shards": {
"total": 33,
"successful": 33,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 272,
"relation": "eq"
},
"max_score": 4.5528774,
"hits": [
{
"_index": "data",
"_type": "_doc",
"_id": "cdf7f3aded8745d1827e9c92dea1e8b7",
"_score": 4.5528774,
"_source": {
"__title": "3 oxe/ 3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "1a42873cead94f18a31d0b102b4fbdcd",
"_score": 4.285463,
"_source": {
"__title": "3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "6f1588bbe1a440028af1de4337bf8fac",
"_score": 3.9906564,
"_source": {
"__title": "9 mfx/ 3 oxe/ 3 foxes"
}
}
]
}
}
But still, it was not good enough since the exact match was only second. Since out-of-the-box solutions didn’t help, I’ve decided to read on about search_as_you_type mapping and n-gram fields. After reading for a while, I’ve learned that n-grams are basically sequences of words extracted from the text mixed in a random order which allows searching words out of order in my autocomplete query. The downside of this is that the Elasticsearch cluster consumes extra memory to store n-grams which may affect the cluster state. And fancy search_as_you_type mapping just means that n-gram fields are created automatically.
Since typing out-of-order words wasn’t my use case, I’ve decided not to mess with it and improve my relevance query time instead of index-time.
Match Phrase Query
In order to boost exact match relevance, I’ve switched to match phrase prefix query.
{
"_source": [
"__title"
],
"from": 0,
"size": 3,
"query": {
"match_phrase_prefix": {
"__title": {
"query": "3 foxe"
}
}
}
}
Now that was what I was looking for.
{
"took": 10,
"timed_out": false,
"_shards": {
"total": 33,
"successful": 33,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 28,
"relation": "eq"
},
"max_score": 12.053555,
"hits": [
{
"_index": "data",
"_type": "_doc",
"_id": "1a42873cead94f18a31d0b102b4fbdcd",
"_score": 12.053555,
"_source": {
"__title": "3 foxes"
}
},
//omited for brevity
]
}
}
Can we wrap up at this point? Not so soon! As you may recall, our autocomplete functionality uses 3 fields, but we’ve examined only one of them. So how do we combine multiple fields? Since match_phrase_prefix doesn’t support multiple fields, the first guess was the plain old bool query.
{
"_source":[
"__title",
"title",
"commonInfo.RealNameShort"
],
"explain":false,
"from":0,
"size":3,
"query":{
"bool":{
"should":[
{
"match_phrase_prefix":{
"__title":{
"query":"3 foxe"
}
}
},
{
"match_phrase_prefix":{
"title":{
"query":"3 foxe"
}
}
},
{
"match_phrase_prefix":{
"commonInfo.RealNameShort":{
"query":"3 foxe"
}
}
}
]
}
}
}
And the result was,
{
"took": 13,
"timed_out": false,
"_shards": {
"total": 33,
"successful": 33,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 28,
"relation": "eq"
},
"max_score": 28.880083,
"hits": [
{
"_index": "data",
"_type": "_doc",
"_id": "15e4e503cc1d4284aeb34664cb61c5ae",
"_score": 28.880083,
"_source": {
"__title": "apt 3 foxes",
"commonInfo": {
"RealNameShort": "apt 3 foxes"
},
"title": "apartmetnt 3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "83b2653a851c4ca19d3df0410ab1c41f",
"_score": 26.242756,
"_source": {
"__title": "rest/ 3 foxes",
"commonInfo": {
"RealNameShort": "rest/ 3 foxes"
},
"title": "restaraunt/ 3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "1a42873cead94f18a31d0b102b4fbdcd",
"_score": 23.940828,
"_source": {
"__title": "3 foxes",
"title": "3 completely irrelevant to real name words",
"commonInfo": {
"RealNameShort": "3 foxes"
}
}
}
]
}
}
Huh? What happened? Let’s run the same query with explain": true to understand. Since the output is huge, I’ll focus only on important parts. In the topmost document, we’ll notice,
"value": 10.268458,
"description": "weight(__title:\"3 (foxe foxes)\" in 1156) [PerFieldSimilarity], result of:",
...
"value": 8.497357,
"description": "weight(title:\"3 foxes\" in 1156) [PerFieldSimilarity], result of:",
...
"value": 10.114267,
"description": "weight(commonInfo.RealNameShort:\"3 (foxes foxe)\" in 1156)
[PerFieldSimilarity], result of:",
And here’s the document we expect to be the topmost,
"value": 12.053555,
"description": "weight(__title:\"3 (foxe foxes)\" in 1180) [PerFieldSimilarity], result of:",
...
"value": 11.887274,
"description": "weight(commonInfo.RealNameShort:\"3 (foxes foxe)\" in 1180)
[PerFieldSimilarity], result of:",
...
"value": 0.0,
"description": "match on required clause, product of:",
So as we might expect, the document which contains 3 foxes in __title scores most by the field __title. But since apt 3 foxes contains somewhat relevant results in each field of interest it outweighs the desired document. If only we could somehow order documents by the most relevant match!
Disjunction Max Query
And indeed, we can try Disjunction max query just for that case. Let’s try the example right from the docs,
{
"_source":[
"__title",
"title",
"commonInfo.RealNameShort"
],
"explain":false,
"from":0,
"size":3,
"query": {
"dis_max": {
"queries": [
{
"match_phrase_prefix":{
"__title":{
"query":"3 foxe"
}
}
},
{
"match_phrase_prefix":{
"title":{
"query":"3 foxe"
}
}
},
{
"match_phrase_prefix":{
"commonInfo.RealNameShort":{
"query":"3 foxe"
}
}
}
],
"tie_breaker": 0.7
}
}
}
Still not good, but at least the scores are closer to each other.
{
"took": 13,
"timed_out": false,
"_shards": {
"total": 33,
"successful": 33,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 28,
"relation": "eq"
},
"max_score": 23.296595,
"hits": [
{
"_index": "data",
"_type": "_doc",
"_id": "15e4e503cc1d4284aeb34664cb61c5ae",
"_score": 23.296595,
"_source": {
"__title": "apt 3 foxes",
"commonInfo": {
"RealNameShort": "apt 3 foxes"
},
"title": "apartmetnt 3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "83b2653a851c4ca19d3df0410ab1c41f",
"_score": 21.053097,
"_source": {
"__title": "rest/ 3 foxes",
"commonInfo": {
"RealNameShort": "rest/ 3 foxes"
},
"title": "restaraunt/ 3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "1a42873cead94f18a31d0b102b4fbdcd",
"_score": 20.374645,
"_source": {
"__title": "3 foxes",
"title": "3 completely irrelevant to real name words",
"commonInfo": {
"RealNameShort": "3 foxes"
}
}
}
]
}
}
It doesn’t seem that obvious what the tie_breaker parameter does. Let’s tweak it to find out. At first, we’ll set it to 1.
{
"took": 13,
"timed_out": false,
"_shards": {
"total": 33,
"successful": 33,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 28,
"relation": "eq"
},
"max_score": 28.880083,
"hits": [
{
"_index": "data",
"_type": "_doc",
"_id": "15e4e503cc1d4284aeb34664cb61c5ae",
"_score": 28.880083,
"_source": {
"__title": "apt 3 foxes",
"commonInfo": {
"RealNameShort": "apt 3 foxes"
},
"title": "apartmetnt 3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "83b2653a851c4ca19d3df0410ab1c41f",
"_score": 26.242756,
"_source": {
"__title": "rest/ 3 foxes",
"commonInfo": {
"RealNameShort": "rest/ 3 foxes"
},
"title": "restaraunt/ 3 foxes"
}
},
{
"_index": "data",
"_type": "_doc",
"_id": "1a42873cead94f18a31d0b102b4fbdcd",
"_score": 23.940828,
"_source": {
"__title": "3 foxes",
"title": "3 completely irrelevant to real name words",
"commonInfo": {
"RealNameShort": "3 foxes"
}
}
}
]
}
}
So as we see increasing it leads us in the wrong direction. Let’s remove it altogether.
{
"took": 15,
"timed_out": false,
"_shards": {
"total": 33,
"successful": 33,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 28,
"relation": "eq"
},
"max_score": 12.053555,
"hits": [
{
"_index": "data",
"_type": "_doc",
"_id": "1a42873cead94f18a31d0b102b4fbdcd",
"_score": 12.053555,
"_source": {
"__title": "3 foxes",
"title": "3 completely irrelevant to real name words",
"commonInfo": {
"RealNameShort": "3 foxes"
}
}
},
//omitted for brevity
]
}
}
Success! This was exactly what we were looking for.
Conclusion
When implementing autocomplete functionality with Elasticsearch, don’t jump straight away to the naive query_string approach. Explore rich Elasticsearch query language first. Leveraging search_as_you_type mapping at index-time might not be a silver bullet as well as the main aim of it is to combat search queries with out-of-order words by creating n-gram fields for you. So it might be sufficient to resort solely to query-time improvements such as bool_prefix query type if you want to get more lenient results or match_phrase_prefix query type if you want your results to be more strict.
When combining autocomplete on multiple fields, you may use the dis_max query type. In such a case, increasing the tie_breaker parameter increases the degree by which all fields influence on resulting score.
And finally, when in doubt about why query results don’t match your expectations, you may resort to explaining ": true query parameter.