Example: Enrich your data based on exact values
editExample: Enrich your data based on exact values
editmatch
enrich policies match enrich data to incoming
documents based on an exact value, such as a email address or ID, using a
term
query.
The following example creates a match
enrich policy that adds user name and
contact information to incoming documents based on an email address. It then
adds the match
enrich policy to a processor in an ingest pipeline.
Use the create index API or index API to create a source index.
The following index API request creates a source index and indexes a new document to that index.
resp = client.index( index="users", id="1", refresh="wait_for", document={ "email": "mardy.brown@asciidocsmith.com", "first_name": "Mardy", "last_name": "Brown", "city": "New Orleans", "county": "Orleans", "state": "LA", "zip": 70116, "web": "mardy.asciidocsmith.com" }, ) print(resp)
response = client.index( index: 'users', id: 1, refresh: 'wait_for', body: { email: 'mardy.brown@asciidocsmith.com', first_name: 'Mardy', last_name: 'Brown', city: 'New Orleans', county: 'Orleans', state: 'LA', zip: 70_116, web: 'mardy.asciidocsmith.com' } ) puts response
const response = await client.index({ index: "users", id: 1, refresh: "wait_for", document: { email: "mardy.brown@asciidocsmith.com", first_name: "Mardy", last_name: "Brown", city: "New Orleans", county: "Orleans", state: "LA", zip: 70116, web: "mardy.asciidocsmith.com", }, }); console.log(response);
PUT /users/_doc/1?refresh=wait_for { "email": "mardy.brown@asciidocsmith.com", "first_name": "Mardy", "last_name": "Brown", "city": "New Orleans", "county": "Orleans", "state": "LA", "zip": 70116, "web": "mardy.asciidocsmith.com" }
Use the create enrich policy API to create an enrich policy with the
match
policy type. This policy must include:
- One or more source indices
-
A
match_field
, the field from the source indices used to match incoming documents - Enrich fields from the source indices you’d like to append to incoming documents
resp = client.enrich.put_policy( name="users-policy", match={ "indices": "users", "match_field": "email", "enrich_fields": [ "first_name", "last_name", "city", "zip", "state" ] }, ) print(resp)
response = client.enrich.put_policy( name: 'users-policy', body: { match: { indices: 'users', match_field: 'email', enrich_fields: [ 'first_name', 'last_name', 'city', 'zip', 'state' ] } } ) puts response
const response = await client.enrich.putPolicy({ name: "users-policy", match: { indices: "users", match_field: "email", enrich_fields: ["first_name", "last_name", "city", "zip", "state"], }, }); console.log(response);
PUT /_enrich/policy/users-policy { "match": { "indices": "users", "match_field": "email", "enrich_fields": ["first_name", "last_name", "city", "zip", "state"] } }
Use the execute enrich policy API to create an enrich index for the policy.
POST /_enrich/policy/users-policy/_execute?wait_for_completion=false
Use the create or update 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 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.
resp = client.ingest.put_pipeline( id="user_lookup", processors=[ { "enrich": { "description": "Add 'user' data based on 'email'", "policy_name": "users-policy", "field": "email", "target_field": "user", "max_matches": "1" } } ], ) print(resp)
const response = await client.ingest.putPipeline({ id: "user_lookup", processors: [ { enrich: { description: "Add 'user' data based on 'email'", policy_name: "users-policy", field: "email", target_field: "user", max_matches: "1", }, }, ], }); console.log(response);
PUT /_ingest/pipeline/user_lookup { "processors" : [ { "enrich" : { "description": "Add 'user' data based on 'email'", "policy_name": "users-policy", "field" : "email", "target_field": "user", "max_matches": "1" } } ] }
Use the ingest pipeline to index a document. The incoming document should
include the field
specified in your enrich processor.
resp = client.index( index="my-index-000001", id="my_id", pipeline="user_lookup", document={ "email": "mardy.brown@asciidocsmith.com" }, ) print(resp)
const response = await client.index({ index: "my-index-000001", id: "my_id", pipeline: "user_lookup", document: { email: "mardy.brown@asciidocsmith.com", }, }); console.log(response);
PUT /my-index-000001/_doc/my_id?pipeline=user_lookup { "email": "mardy.brown@asciidocsmith.com" }
To verify the enrich processor matched and appended the appropriate field data, use the get API to view the indexed document.
resp = client.get( index="my-index-000001", id="my_id", ) print(resp)
response = client.get( index: 'my-index-000001', id: 'my_id' ) puts response
const response = await client.get({ index: "my-index-000001", id: "my_id", }); console.log(response);
GET /my-index-000001/_doc/my_id
The API returns the following response:
{ "found": true, "_index": "my-index-000001", "_id": "my_id", "_version": 1, "_seq_no": 55, "_primary_term": 1, "_source": { "user": { "email": "mardy.brown@asciidocsmith.com", "first_name": "Mardy", "last_name": "Brown", "zip": 70116, "city": "New Orleans", "state": "LA" }, "email": "mardy.brown@asciidocsmith.com" } }