I have recently started learning elasticsearch and I am getting a difference in the search results of my query. The mapping of the index named "products" is provided below(I am pasting the response from my Kibana console tool) :
{
"products" : {
"mappings" : {
"properties" : {
"in_stock" : {
"type" : "long"
},
"name" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"price" : {
"type" : "long"
},
"tags" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
}
}
The data in the index is as follows(I am pasting the response from my Kibana console tool):
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 16,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "products",
"_type" : "_doc",
"_id" : "202",
"_score" : 1.0,
"_source" : {
"name" : "Vegetable Chopper",
"price" : 10,
"in_stock" : 250,
"tags" : [
"kitchen appliances",
"vegetable slicer",
"chopper"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "203",
"_score" : 1.0,
"_source" : {
"name" : "Dish Washer",
"price" : 90,
"in_stock" : 60,
"tags" : [
"kitchen appliances",
"electrical",
"electric washer"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "205",
"_score" : 1.0,
"_source" : {
"name" : "Microwave Oven",
"price" : 100,
"in_stock" : 50,
"tags" : [
"kitchen appliances",
"electricals",
"oven",
"oven toaster",
"microwave"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "206",
"_score" : 1.0,
"_source" : {
"name" : "Mixer Grinder",
"price" : 55,
"in_stock" : 130,
"tags" : [
"kitchen appliances",
"electricals",
"mixer",
"grinder",
"juicer",
"food processor"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "207",
"_score" : 1.0,
"_source" : {
"name" : "Fruit Juicer",
"price" : 40,
"in_stock" : 100,
"tags" : [
"kitchen appliances",
"electicals",
"juicer",
"mixer",
"electric juicer",
"juice maker"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "208",
"_score" : 1.0,
"_source" : {
"name" : "Knife Set",
"price" : 15,
"in_stock" : 250,
"tags" : [
"kitchen knife",
"steel knives",
"cutlery"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "209",
"_score" : 1.0,
"_source" : {
"name" : "Rice Maker",
"price" : 85,
"in_stock" : 60,
"tags" : [
"kitchen appliances",
"electricals",
"electric rice cooker",
"electric pressure cooker"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "210",
"_score" : 1.0,
"_source" : {
"name" : "Induction Cooktop",
"price" : 30,
"in_stock" : 150,
"tags" : [
"kitchen appliances",
"electricals",
"hot plate heater",
"electric hot place",
"induction cooker",
"induction stove"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "211",
"_score" : 1.0,
"_source" : {
"name" : "Coffee Maker",
"price" : 50,
"in_stock" : 100,
"tags" : [
"kitchen appliances",
"electricals"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "212",
"_score" : 1.0,
"_source" : {
"name" : "Wine Glasses Set",
"price" : 50,
"in_stock" : 70,
"tags" : [
"kitchen and dining",
"glassware",
"stemware"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "213",
"_score" : 1.0,
"_source" : {
"name" : "Dinner Set",
"price" : 100,
"in_stock" : 40,
"tags" : [
"kitchen and dining",
"crockery",
"full dinner set"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "214",
"_score" : 1.0,
"_source" : {
"name" : "Whiskey Glasses Set",
"price" : 60,
"in_stock" : 50,
"tags" : [
"kitchen and dining",
"glassware",
"whiskey glasses",
"old fashioned glass",
"rocks glass",
"short tumbler"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "215",
"_score" : 1.0,
"_source" : {
"name" : "Mug And Saucer Set",
"price" : 35,
"in_stock" : 60,
"tags" : [
"kitchen and dining",
"mug set",
"mugs and saucer",
"crockery set"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "201",
"_score" : 1.0,
"_source" : {
"name" : "Milk Frother",
"price" : 25,
"in_stock" : 15,
"tags" : [
"kitchen appliances",
"electricals",
"milk"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "200",
"_score" : 1.0,
"_source" : {
"name" : "Espresso Maker",
"price" : 180,
"in_stock" : 5,
"tags" : [
"kitchen appliances",
"electrical",
"coffee maker"
]
}
},
{
"_index" : "products",
"_type" : "_doc",
"_id" : "204",
"_score" : 1.0,
"_source" : {
"name" : "Pressure Fryer",
"price" : 120,
"in_stock" : 50,
"tags" : [
"air fryer",
"kitchen appliances",
"electric fryer",
"fryer",
"health fryer"
]
}
}
]
}
}
Upon querying the data using the query below I am only matching six records: Query - 1
GET /products/_search
{
"query": {"terms" : {"tags": ["kitchen appliances","electricals"]}}
}
The document id's matched are (201,205,206,209,210,211)
When I executed the below query then I am matching 11 records: Query-2
GET /products/_search
{
"query": {"terms" : {"tags.keyword": ["kitchen appliances","electricals"]}}
}
The document id's that matched for the second query are : (200,201,202,203,204,205,206,207,209,210,211)
Can someone explain what is the difference between the two queries and why Query-1 is a subset of Query-2 even though both queries are being executed on the same field ?
It is better to use the match query if you have a text type field.
term query doesn't perform any analysis on the term. It returns the documents that contain exact term matching documents.
terms query works on exact terms. It returns those documents that have 1 or more exact terms.
QUERY 1:
{
"query": {
"terms": {
"tags": [
"kitchen appliances",
"electricals"
]
}
}
}
Search Result is
"hits": [
{
"_index": "67155973",
"_type": "_doc",
"_id": "3",
"_score": 1.0,
"_source": {
"name": "Microwave Oven",
"price": 100,
"in_stock": 50,
"tags": [
"kitchen appliances",
"electricals",
"oven",
"oven toaster",
"microwave"
]
}
},
{
"_index": "67155973",
"_type": "_doc",
"_id": "4",
"_score": 1.0,
"_source": {
"name": "Mixer Grinder",
"price": 55,
"in_stock": 130,
"tags": [
"kitchen appliances",
"electricals",
"mixer",
"grinder",
"juicer",
"food processor"
]
}
},
{
"_index": "67155973",
"_type": "_doc",
"_id": "7",
"_score": 1.0,
"_source": {
"name": "Rice Maker",
"price": 85,
"in_stock": 60,
"tags": [
"kitchen appliances",
"electricals",
"electric rice cooker",
"electric pressure cooker"
]
}
},
{
"_index": "67155973",
"_type": "_doc",
"_id": "8",
"_score": 1.0,
"_source": {
"name": "Induction Cooktop",
"price": 30,
"in_stock": 150,
"tags": [
"kitchen appliances",
"electricals",
"hot plate heater",
"electric hot place",
"induction cooker",
"induction stove"
]
}
},
{
"_index": "67155973",
"_type": "_doc",
"_id": "9",
"_score": 1.0,
"_source": {
"name": "Coffee Maker",
"price": 50,
"in_stock": 100,
"tags": [
"kitchen appliances",
"electricals"
]
}
},
{
"_index": "67155973",
"_type": "_doc",
"_id": "14",
"_score": 1.0,
"_source": {
"name": "Milk Frother",
"price": 25,
"in_stock": 15,
"tags": [
"kitchen appliances",
"electricals",
"milk"
]
}
}
]
As mentioned in the documentation
The term query does not analyze the search term. The term query only searches for the exact term you provide. This means the term query may return poor or no results when searching text fields.
QUERY 2:
{
"query": {
"terms": {
"tags.keyword": [
"kitchen appliances",
"electricals"
]
}
}
}
In the above query, you are using tags.keyword field which uses the keyword analyzer instead of the standard analyzer. Here the query searches for the exact terms i.e "kitchen appliances" OR "electricals", and therefore returns 11 documents.