I'm very new to elastic search, how do I write a query which search for a keyword (ie. test keyword) in all fields in the document, and one more keyword which search in a specific field.
this can be done using query_string but we can't do search in nested fields with nested field specified, So i'm using LUQUM to convert lucene query to Elasticsearch DSL.
Below is the sample usecase:
I have a mapping:
"mappings": {
"properties": {
"grocery_name":{
"type": "text"
},
"items": {
"type": "nested",
"properties": {
"name": {
"type": "text"
},
"stock": {
"type": "integer"
},
"category": {
"type": "text"
}
}
}
}
}
}
and the data looks like below
{
"grocery_name": "Elastic Eats",
"items": [
{
"name": "Red banana",
"stock": "12",
"category": "fruit"
},
{
"name": "Cavendish banana",
"stock": "10",
"category": "fruit"
},
{
"name": "peach",
"stock": "10",
"category": "fruit"
},
{
"name": "carrot",
"stock": "9",
"category": "vegetable"
},
{
"name": "broccoli",
"stock": "5",
"category": "vegetable"
}
]
}
How can I query to get all items where the item name matches banana from grocery_name: Elastic Eats ?
tried with * and _all, it didn't work.
example query:
{
"query": {
"bool": {
"must": [
{
"match_phrase": {
"grocery_name": {
"query": "Elastic Eats"
}
}
},
{
"match": {
"*": {
"query": "banana",
"zero_terms_query": "all"
}
}
}
]
}
}
}
I'm sure I'm missing something obvious, but I have read the manual and I'm getting no joy at all.
UPDATE:
enabling include_in_parent for nested object works for below query, but it will internally duplicates data which will definitely impact on memory.
{
"query": {
"bool": {
"must": [
{
"match_phrase": {
"grocery_name": {
"query": "Elastic Eats"
}
}
},
{
"multi_match": {
"query": "banana"
}
}
]
}
}
}
Is there any other way to do this?
You need to use a nested match query with inner_hits resulting in an inner nested query to automatically match the relevant nesting level, rather than root
Search Query
{
"query": {
"bool": {
"filter": [
{
"term": {
"grocery_name": "elastic"
}
},
{
"nested": {
"path": "items",
"query": {
"bool": {
"must": [
{
"match": {
"items.name": "banana"
}
}
]
}
},
"inner_hits": {}
}
}
]
}
}
}
Search Result:
"inner_hits": {
"items": {
"hits": {
"total": {
"value": 2,
"relation": "eq"
},
"max_score": 0.744874,
"hits": [
{
"_index": "stof_64273970",
"_type": "_doc",
"_id": "1",
"_nested": {
"field": "items",
"offset": 0
},
"_score": 0.744874,
"_source": {
"name": "Red banana",
"stock": "12",
"category": "fruit"
}
},
{
"_index": "stof_64273970",
"_type": "_doc",
"_id": "1",
"_nested": {
"field": "items",
"offset": 1
},
"_score": 0.744874,
"_source": {
"name": "Cavendish banana",
"stock": "10",
"category": "fruit"
}
}
]
}
Update 1:
On the basis of your comments, you can use multi match query, for your use case
If no fields are provided, the multi_match query defaults to the index.query.default_field index settings, which in turn defaults to *.
(*) extracts all fields in the mapping that are eligible to term queries and filters the metadata fields. All extracted fields are then combined to build a query.
Search Query:
{
"query": {
"bool": {
"filter": [
{
"term": {
"grocery_name": "elastic"
}
},
{
"nested": {
"path": "items",
"query": {
"bool": {
"must": [
{
"multi_match": {
"query": "banana" <-- note this
}
}
]
}
},
"inner_hits": {}
}
}
]
}
}
}
Update 2:
You need to use a combination of multiple bool queries like this:
{
"query": {
"bool": {
"must": [
{
"match_phrase": {
"grocery_name": {
"query": "Elastic Eats"
}
}
},
{
"bool": {
"should": [
{
"bool": {
"must": [
{
"multi_match": {
"query": "banana"
}
}
]
}
},
{
"bool": {
"must": [
{
"nested": {
"path": "items",
"query": {
"bool": {
"must": [
{
"multi_match": {
"query": "banana"
}
}
]
}
},
"inner_hits": {}
}
}
]
}
}
]
}
}
]
}
}
}