Min aggregation
editMin aggregation
editA single-value
metrics aggregation that keeps track and returns the minimum
value among numeric values extracted from the aggregated documents.
The min
and max
aggregation operate on the double
representation of
the data. As a consequence, the result may be approximate when running on longs
whose absolute value is greater than 2^53
.
Computing the min price value across all documents:
response = client.search( index: 'sales', size: 0, body: { aggregations: { min_price: { min: { field: 'price' } } } } ) puts response
POST /sales/_search?size=0 { "aggs": { "min_price": { "min": { "field": "price" } } } }
Response:
{ ... "aggregations": { "min_price": { "value": 10.0 } } }
As can be seen, the name of the aggregation (min_price
above) also serves as
the key by which the aggregation result can be retrieved from the returned
response.
Script
editIf you need to get the min
of something more complex than a single field,
run the aggregation on a runtime field.
response = client.search( index: 'sales', body: { size: 0, runtime_mappings: { 'price.adjusted' => { type: 'double', script: "\n double price = doc['price'].value;\n if (doc['promoted'].value) {\n price *= 0.8;\n }\n emit(price);\n " } }, aggregations: { min_price: { min: { field: 'price.adjusted' } } } } ) puts response
POST /sales/_search { "size": 0, "runtime_mappings": { "price.adjusted": { "type": "double", "script": """ double price = doc['price'].value; if (doc['promoted'].value) { price *= 0.8; } emit(price); """ } }, "aggs": { "min_price": { "min": { "field": "price.adjusted" } } } }
Missing value
editThe missing
parameter defines how documents that are missing a value should
be treated. By default they will be ignored but it is also possible to treat
them as if they had a value.
response = client.search( index: 'sales', body: { aggregations: { grade_min: { min: { field: 'grade', missing: 10 } } } } ) puts response
Histogram fields
editWhen min
is computed on histogram fields, the result of the aggregation is the minimum
of all elements in the values
array. Note, that the counts
array of the histogram is ignored.
For example, for the following index that stores pre-aggregated histograms with latency metrics for different networks:
response = client.indices.create( index: 'metrics_index', body: { mappings: { properties: { latency_histo: { type: 'histogram' } } } } ) puts response response = client.index( index: 'metrics_index', id: 1, refresh: true, body: { 'network.name' => 'net-1', latency_histo: { values: [ 0.1, 0.2, 0.3, 0.4, 0.5 ], counts: [ 3, 7, 23, 12, 6 ] } } ) puts response response = client.index( index: 'metrics_index', id: 2, refresh: true, body: { 'network.name' => 'net-2', latency_histo: { values: [ 0.1, 0.2, 0.3, 0.4, 0.5 ], counts: [ 8, 17, 8, 7, 6 ] } } ) puts response response = client.search( index: 'metrics_index', size: 0, filter_path: 'aggregations', body: { aggregations: { min_latency: { min: { field: 'latency_histo' } } } } ) puts response
PUT metrics_index { "mappings": { "properties": { "latency_histo": { "type": "histogram" } } } } PUT metrics_index/_doc/1?refresh { "network.name" : "net-1", "latency_histo" : { "values" : [0.1, 0.2, 0.3, 0.4, 0.5], "counts" : [3, 7, 23, 12, 6] } } PUT metrics_index/_doc/2?refresh { "network.name" : "net-2", "latency_histo" : { "values" : [0.1, 0.2, 0.3, 0.4, 0.5], "counts" : [8, 17, 8, 7, 6] } } POST /metrics_index/_search?size=0&filter_path=aggregations { "aggs" : { "min_latency" : { "min" : { "field" : "latency_histo" } } } }
The min
aggregation will return the minimum value of all histogram fields:
{ "aggregations": { "min_latency": { "value": 0.1 } } }