- Elasticsearch Guide: other versions:
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- Tutorial: Getting started with security
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- Definitions
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- Elasticsearch version 7.6.2
- Elasticsearch version 7.6.1
- Elasticsearch version 7.6.0
- Elasticsearch version 7.5.2
- Elasticsearch version 7.5.1
- Elasticsearch version 7.5.0
- Elasticsearch version 7.4.2
- Elasticsearch version 7.4.1
- Elasticsearch version 7.4.0
- Elasticsearch version 7.3.2
- Elasticsearch version 7.3.1
- Elasticsearch version 7.3.0
- Elasticsearch version 7.2.1
- Elasticsearch version 7.2.0
- Elasticsearch version 7.1.1
- Elasticsearch version 7.1.0
- Elasticsearch version 7.0.0
- Elasticsearch version 7.0.0-rc2
- Elasticsearch version 7.0.0-rc1
- Elasticsearch version 7.0.0-beta1
- Elasticsearch version 7.0.0-alpha2
- Elasticsearch version 7.0.0-alpha1
Example: Enrich your data based on geolocation
editExample: Enrich your data based on geolocation
editgeo_match
enrich policies match enrich data to incoming
documents based on a geographic location, using a
geo_shape
query.
The following example creates a geo_match
enrich policy that adds postal
codes to incoming documents based on a set of coordinates. It then adds the
geo_match
enrich policy to a processor in an ingest pipeline.
Use the create index API to create a source index
containing at least one geo_shape
field.
PUT /postal_codes { "mappings": { "properties": { "location": { "type": "geo_shape" }, "postal_code": { "type": "keyword" } } } }
Use the index API to index enrich data to this source index.
PUT /postal_codes/_doc/1?refresh=wait_for { "location": { "type": "envelope", "coordinates": [[13.0, 53.0], [14.0, 52.0]] }, "postal_code": "96598" }
Use the put enrich policy API to create an enrich
policy with the geo_match
policy type. This policy must include:
- One or more source indices
-
A
match_field
, thegeo_shape
field from the source indices used to match incoming documents - Enrich fields from the source indices you’d like to append to incoming documents
PUT /_enrich/policy/postal_policy { "geo_match": { "indices": "postal_codes", "match_field": "location", "enrich_fields": ["location","postal_code"] } }
Use the execute enrich policy API to create an enrich index for the policy.
POST /_enrich/policy/postal_policy/_execute
Use the put pipeline API to create an ingest pipeline. In the pipeline, add an enrich processor that includes:
- Your enrich policy.
-
The
field
of incoming documents used to match the geo_shape of documents from the enrich index. -
The
target_field
used to store appended enrich data for incoming documents. This field contains thematch_field
andenrich_fields
specified in your enrich policy. -
The
shape_relation
, which indicates how the processor matches geo_shapes in incoming documents to geo_shapes in documents from the enrich index. See Spatial Relations for valid options and more information.
PUT /_ingest/pipeline/postal_lookup { "description": "Enrich postal codes", "processors": [ { "enrich": { "policy_name": "postal_policy", "field": "geo_location", "target_field": "geo_data", "shape_relation": "INTERSECTS" } } ] }
Use the ingest pipeline to index a document. The incoming document should
include the field
specified in your enrich processor.
PUT /users/_doc/0?pipeline=postal_lookup { "first_name": "Mardy", "last_name": "Brown", "geo_location": "POINT (13.5 52.5)" }
To verify the enrich processor matched and appended the appropriate field data, use the get API to view the indexed document.
GET /users/_doc/0
The API returns the following response:
{ "found": true, "_index": "users", "_type": "_doc", "_id": "0", "_version": 1, "_seq_no": 55, "_primary_term": 1, "_source": { "geo_data": { "location": { "type": "envelope", "coordinates": [[13.0, 53.0], [14.0, 52.0]] }, "postal_code": "96598" }, "first_name": "Mardy", "last_name": "Brown", "geo_location": "POINT (13.5 52.5)" } }
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