Inference bucket aggregation
A parent pipeline aggregation which loads a pre-trained model and performs inference on the collated result fields from the parent bucket aggregation.
To use the inference bucket aggregation, you need to have the same security privileges that are required for using the get trained models API.
Syntax
A inference aggregation looks like this in isolation:
{
"inference": {
"model_id": "a_model_for_inference",
"inference_config": {
"regression_config": {
"num_top_feature_importance_values": 2
}
},
"buckets_path": {
"avg_cost": "avg_agg",
"max_cost": "max_agg"
}
}
} | The unique identifier or alias for the trained model. | |
| The optional inference config which overrides the model’s default settings | |
| Map the value of |
Table 63. inference Parameters
| Parameter Name | Description | Required | Default Value |
|---|---|---|---|
| The ID or alias for the trained model. | Required | - |
| Contains the inference type and its options. There are two types: | Optional | - |
| Defines the paths to the input aggregations and maps the aggregation names to the field names expected by the model. See | Required | - |
Configuration options for inference models
The inference_config setting is optional and usually isn’t required as the pre-trained models come equipped with sensible defaults. In the context of aggregations some options can be overridden for each of the two types of model.
Configuration options for regression models
-
num_top_feature_importance_values - (Optional, integer) Specifies the maximum number of feature importance values per document. By default, it is zero and no feature importance calculation occurs.
Configuration options for classification models
-
num_top_classes - (Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
-
num_top_feature_importance_values - (Optional, integer) Specifies the maximum number of feature importance values per document. Defaults to 0 which means no feature importance calculation occurs.
-
prediction_field_type - (Optional, string) Specifies the type of the predicted field to write. Valid values are:
string,number,boolean. Whenbooleanis provided1.0is transformed totrueand0.0tofalse.
Example
The following snippet aggregates a web log by client_ip and extracts a number of features via metric and bucket sub-aggregations as input to the inference aggregation configured with a model trained to identify suspicious client IPs:
resp = client.search(
index="kibana_sample_data_logs",
size=0,
aggs={
"client_ip": {
"composite": {
"sources": [
{
"client_ip": {
"terms": {
"field": "clientip"
}
}
}
]
},
"aggs": {
"url_dc": {
"cardinality": {
"field": "url.keyword"
}
},
"bytes_sum": {
"sum": {
"field": "bytes"
}
},
"geo_src_dc": {
"cardinality": {
"field": "geo.src"
}
},
"geo_dest_dc": {
"cardinality": {
"field": "geo.dest"
}
},
"responses_total": {
"value_count": {
"field": "timestamp"
}
},
"success": {
"filter": {
"term": {
"response": "200"
}
}
},
"error404": {
"filter": {
"term": {
"response": "404"
}
}
},
"error503": {
"filter": {
"term": {
"response": "503"
}
}
},
"malicious_client_ip": {
"inference": {
"model_id": "malicious_clients_model",
"buckets_path": {
"response_count": "responses_total",
"url_dc": "url_dc",
"bytes_sum": "bytes_sum",
"geo_src_dc": "geo_src_dc",
"geo_dest_dc": "geo_dest_dc",
"success": "success._count",
"error404": "error404._count",
"error503": "error503._count"
}
}
}
}
}
},
)
print(resp) response = client.search(
index: 'kibana_sample_data_logs',
body: {
size: 0,
aggregations: {
client_ip: {
composite: {
sources: [
{
client_ip: {
terms: {
field: 'clientip'
}
}
}
]
},
aggregations: {
url_dc: {
cardinality: {
field: 'url.keyword'
}
},
bytes_sum: {
sum: {
field: 'bytes'
}
},
geo_src_dc: {
cardinality: {
field: 'geo.src'
}
},
geo_dest_dc: {
cardinality: {
field: 'geo.dest'
}
},
responses_total: {
value_count: {
field: 'timestamp'
}
},
success: {
filter: {
term: {
response: '200'
}
}
},
"error404": {
filter: {
term: {
response: '404'
}
}
},
"error503": {
filter: {
term: {
response: '503'
}
}
},
malicious_client_ip: {
inference: {
model_id: 'malicious_clients_model',
buckets_path: {
response_count: 'responses_total',
url_dc: 'url_dc',
bytes_sum: 'bytes_sum',
geo_src_dc: 'geo_src_dc',
geo_dest_dc: 'geo_dest_dc',
success: 'success._count',
"error404": 'error404._count',
"error503": 'error503._count'
}
}
}
}
}
}
}
)
puts response const response = await client.search({
index: "kibana_sample_data_logs",
size: 0,
aggs: {
client_ip: {
composite: {
sources: [
{
client_ip: {
terms: {
field: "clientip",
},
},
},
],
},
aggs: {
url_dc: {
cardinality: {
field: "url.keyword",
},
},
bytes_sum: {
sum: {
field: "bytes",
},
},
geo_src_dc: {
cardinality: {
field: "geo.src",
},
},
geo_dest_dc: {
cardinality: {
field: "geo.dest",
},
},
responses_total: {
value_count: {
field: "timestamp",
},
},
success: {
filter: {
term: {
response: "200",
},
},
},
error404: {
filter: {
term: {
response: "404",
},
},
},
error503: {
filter: {
term: {
response: "503",
},
},
},
malicious_client_ip: {
inference: {
model_id: "malicious_clients_model",
buckets_path: {
response_count: "responses_total",
url_dc: "url_dc",
bytes_sum: "bytes_sum",
geo_src_dc: "geo_src_dc",
geo_dest_dc: "geo_dest_dc",
success: "success._count",
error404: "error404._count",
error503: "error503._count",
},
},
},
},
},
},
});
console.log(response); GET kibana_sample_data_logs/_search
{
"size": 0,
"aggs": {
"client_ip": {
"composite": {
"sources": [
{
"client_ip": {
"terms": {
"field": "clientip"
}
}
}
]
},
"aggs": {
"url_dc": {
"cardinality": {
"field": "url.keyword"
}
},
"bytes_sum": {
"sum": {
"field": "bytes"
}
},
"geo_src_dc": {
"cardinality": {
"field": "geo.src"
}
},
"geo_dest_dc": {
"cardinality": {
"field": "geo.dest"
}
},
"responses_total": {
"value_count": {
"field": "timestamp"
}
},
"success": {
"filter": {
"term": {
"response": "200"
}
}
},
"error404": {
"filter": {
"term": {
"response": "404"
}
}
},
"error503": {
"filter": {
"term": {
"response": "503"
}
}
},
"malicious_client_ip": {
"inference": {
"model_id": "malicious_clients_model",
"buckets_path": {
"response_count": "responses_total",
"url_dc": "url_dc",
"bytes_sum": "bytes_sum",
"geo_src_dc": "geo_src_dc",
"geo_dest_dc": "geo_dest_dc",
"success": "success._count",
"error404": "error404._count",
"error503": "error503._count"
}
}
}
}
}
}
} | A composite bucket aggregation that aggregates the data by | |
| A series of metrics and bucket sub-aggregations. | |
| Inference bucket aggregation that specifies the trained model and maps the aggregation names to the model’s input fields. |
© 2023-2025 Elasticsearch
As of September 2024, Elasticsearch is available under a choice of three licenses: the Server Side Public License (SSPL), the Elastic License, or the AGPLv3 (OSI approved).
Elasticsearch and the Elasticsearch logo are trademarks of Elasticsearch B.V., registered in the U.S. and in other countries.
https://www.elastic.co/guide/en/elasticsearch/reference/8.17/search-aggregations-pipeline-inference-bucket-aggregation.html
