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Index Property: name

Supported index types

Use the name option to provide an optional name for the index.

All examples use the following sample document:

{
  "productId": "PROD133726",
  "sku": "SKU24224",
  "name": "Basic Printer",
  "manufacturer": "The Manufacturer",
  "tags": [ "printer", "basic", "electronics", "business" ],
  "barcodes": [ "542364671", "886330670", "437445606" ],
  "reviews": [
    {
      "review_date": ISODate('2024-01-19T21:37:10.585Z'),
      ...
    }
  ],
  "material": "Polycarbonate",
  "color": "Space Gray",
  "supplier": {
    "supplierId": "SUP4",
    "location": {
      "type": "Point",
      "coordinates": [ -71.0589, 42.3601 ]
    }
  },
  "productEmbedding": [
    -0.019320633663838058,
    0.019672111388113596
  ],
  "lastUpdated": ISODate('2025-10-20T21:37:10.585Z')
}

Single field

db.collection.createIndex(
  {
    "productId": 1
  },
  {
    "name": "single_field_index"
  }
)

Compound

db.collection.createIndex(
  {
    "productId": 1,
    "manufacturer": 1
  },
  {
    "name": "compound_index"
  }
)

Multi-key

db.collection.createIndex(
  {
    "tags": 1
  },
  {
    "name": "multikey_index"
  }
)

Text

db.collection.createIndex(
  {
    "name": "text"
  },
  {
    "name": "text_index"
  }
)

Geospatial

db.collection.createIndex(
  {
    "supplier.location": "2dsphere"
  },
  {
    "name": "geospatial_index"
  }
)

Vector

db.runCommand({
  "createIndexes": "collection", 
  "indexes": [{
    "key": {
      "productEmbedding": "vector"
    },
    "name": "hnsw_index",
    "vectorOptions": {
      "type": "hnsw",
      "dimensions": 2,
      "similarity": "euclidean",
      "m": 16,
      "efConstruction": 64
    }
  }] 
})