from metadata.sdk import configure
from metadata.sdk.entities import Tables

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

table_id = "a1b2c3d4-e5f6-7890-abcd-ef1234567890"

# Add a table-level custom metric
Tables.add_custom_metric(table_id, {
    "name": "active_customer_ratio",
    "description": "Ratio of active customers to total customers",
    "expression": "SELECT CAST(SUM(CASE WHEN active = true THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) FROM {table}"
})

# Add a column-level custom metric
Tables.add_custom_metric(table_id, {
    "name": "email_domain_count",
    "description": "Number of distinct email domains",
    "columnName": "email",
    "expression": "SELECT COUNT(DISTINCT SPLIT_PART(email, '@', 2)) FROM {table}"
})

# Delete a custom metric
Tables.delete_custom_metric(table_id, "active_customer_ratio")

import static org.openmetadata.sdk.fluent.Tables.*;

String tableId = "a1b2c3d4-e5f6-7890-abcd-ef1234567890";

// Add a table-level custom metric
Tables.addCustomMetric(tableId, Map.of(
    "name", "active_customer_ratio",
    "description", "Ratio of active customers to total customers",
    "expression", "SELECT CAST(SUM(CASE WHEN active = true THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) FROM {table}"
));

// Add a column-level custom metric
Tables.addCustomMetric(tableId, Map.of(
    "name", "email_domain_count",
    "description", "Number of distinct email domains",
    "columnName", "email",
    "expression", "SELECT COUNT(DISTINCT SPLIT_PART(email, '@', 2)) FROM {table}"
));

// Delete a custom metric
Tables.deleteCustomMetric(tableId, "active_customer_ratio");

# Add a table-level custom metric
curl -X PUT "{base_url}/api/v1/tables/455e3d9d-dbbf-455e-b3be-7191daa825f3/customMetric" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "high_performer_ratio",
    "description": "Ratio of high-performing agents (score > 90) to total agents",
    "expression": "SELECT CAST(SUM(CASE WHEN performance_score > 90 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) FROM {table}"
  }'

# Add a column-level custom metric
curl -X PUT "{base_url}/api/v1/tables/455e3d9d-dbbf-455e-b3be-7191daa825f3/customMetric" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "avg_performance",
    "description": "Average performance score across all agents",
    "columnName": "performance_score",
    "expression": "SELECT AVG(performance_score) FROM {table}"
  }'

# Delete a custom metric
curl -X DELETE "{base_url}/api/v1/tables/455e3d9d-dbbf-455e-b3be-7191daa825f3/customMetric/high_performer_ratio" \
  -H "Authorization: Bearer {access_token}"

{
  "id": "455e3d9d-dbbf-455e-b3be-7191daa825f3",
  "name": "agent_performance_summary",
  "fullyQualifiedName": "sample_data.ecommerce_db.shopify.agent_performance_summary",
  "customMetrics": [
    {
      "name": "high_performer_ratio",
      "description": "Ratio of high-performing agents (score > 90) to total agents",
      "expression": "SELECT CAST(SUM(CASE WHEN performance_score > 90 THEN 1 ELSE 0 END) AS FLOAT) / COUNT(*) FROM {table}"
    }
  ]
}