Search Entities

Full-text search across all entity types in the metadata catalog

GET /v1/search/query

GET /v1/search/query

Python

from metadata.sdk import configure, get_client

configure(
    host="https://your-company.open-metadata.org/api",
    jwt_token="your-jwt-token"
)
client = get_client()

# Search for tables matching "customer"
response = client.get("/search/query", params={
    "q": "customer",
    "index": "table_search_index",
    "from": 0,
    "size": 10
})

for hit in response["hits"]["hits"]:
    entity = hit["_source"]
    print(f"{entity['entityType']}: {entity['fullyQualifiedName']} (score: {hit['_score']})")

Response

{
  "took": 8,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "failed": 0,
    "skipped": 0
  },
  "hits": {
    "total": {
      "value": 53,
      "relation": "eq"
    },
    "hits": [
      {
        "_index": "table_search_index",
        "_id": "455e3d9d-dbbf-455e-b3be-7191daa825f3",
        "_score": 2.3,
        "_source": {
          "id": "455e3d9d-dbbf-455e-b3be-7191daa825f3",
          "name": "dim_address",
          "fullyQualifiedName": "sample_data.ecommerce_db.shopify.dim_address",
          "entityType": "table",
          "serviceType": "BigQuery",
          "description": "Address dimension table",
          "owners": [],
          "tags": [],
          "columns": [
            {
              "name": "address_id",
              "dataType": "NUMERIC",
              "fullyQualifiedName": "sample_data.ecommerce_db.shopify.dim_address.address_id"
            }
          ]
        }
      }
    ]
  }
}

Search Entities

Full-text search across all entity types in the metadata catalog. Uses Elasticsearch query syntax under the hood.

Query Parameters

q · string · required

Search query string. Supports Elasticsearch query syntax including wildcards, boolean operators, and phrase matching.

index · string · default: "all"

Search index to query. Options: all, table_search_index, topic_search_index, dashboard_search_index, pipeline_search_index, mlmodel_search_index, container_search_index, search_entity_search_index, glossary_term_search_index, tag_search_index, user_search_index, team_search_index, domain_search_index, data_product_search_index.

from · integer · default: "0"

Starting offset for pagination.

size · integer · default: "10"

Number of results to return.

deleted · boolean · default: "false"

Include soft-deleted entities in the results.

sortField · string

Field to sort by (e.g., name, updatedAt, totalVotes).

sortOrder · string

Sort order: asc or desc.

trackTotalHits · boolean · default: "true"

Track exact total hit count. Set to false for faster queries when exact count is not needed.

getHierarchy · boolean · default: "false"

Include entity hierarchy in results.

fetchSource · boolean · default: "true"

Include the full source document in results. Set to false to return only metadata.

includeSourceFields · string

Comma-separated list of source fields to include. Use this to limit the response payload.

queryFilter · string

JSON query filter using Elasticsearch DSL. Applied before scoring.

postFilter · string

JSON post-filter using Elasticsearch DSL. Applied after scoring without affecting aggregations.

GET /v1/search/query

Python

from metadata.sdk import configure, get_client

configure(
    host="https://your-company.open-metadata.org/api",
    jwt_token="your-jwt-token"
)
client = get_client()

# Search for tables matching "customer"
response = client.get("/search/query", params={
    "q": "customer",
    "index": "table_search_index",
    "from": 0,
    "size": 10
})

for hit in response["hits"]["hits"]:
    entity = hit["_source"]
    print(f"{entity['entityType']}: {entity['fullyQualifiedName']} (score: {hit['_score']})")

Response

{
  "took": 8,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "failed": 0,
    "skipped": 0
  },
  "hits": {
    "total": {
      "value": 53,
      "relation": "eq"
    },
    "hits": [
      {
        "_index": "table_search_index",
        "_id": "455e3d9d-dbbf-455e-b3be-7191daa825f3",
        "_score": 2.3,
        "_source": {
          "id": "455e3d9d-dbbf-455e-b3be-7191daa825f3",
          "name": "dim_address",
          "fullyQualifiedName": "sample_data.ecommerce_db.shopify.dim_address",
          "entityType": "table",
          "serviceType": "BigQuery",
          "description": "Address dimension table",
          "owners": [],
          "tags": [],
          "columns": [
            {
              "name": "address_id",
              "dataType": "NUMERIC",
              "fullyQualifiedName": "sample_data.ecommerce_db.shopify.dim_address.address_id"
            }
          ]
        }
      }
    ]
  }
}

---

Returns

Returns an Elasticsearch-style response containing matched entities ranked by relevance score.

Response

took · integer

Time in milliseconds the search took to execute.

timed_out · boolean

Whether the search timed out.

hits · object

Search results container.

Show properties

total · object

Total hit count information.

Show properties

value · integer

Total number of matching entities.

relation · string

Relation to the actual count. eq means exact, gte means the count is a lower bound.

hits · array

Array of matching entity results.

Show properties

_index · string

Search index the result was found in (e.g., table_search_index).

_id · string

Unique identifier of the entity (UUID format).

_score · number

Relevance score for the match. Higher scores indicate better matches.

_source · object

The full entity object. Contents vary by entity type but always include id, name, fullyQualifiedName, and entityType.

---

Error Handling

Code	Error Type	Description
400	BAD_REQUEST	Invalid query syntax or parameters
401	UNAUTHORIZED	Invalid or missing authentication token
403	FORBIDDEN	User lacks permission to search entities