Create an index in Azure Cognitive Search

In Azure Cognitive Search, query requests target the searchable text in a search index.

In this article, learn the steps for defining and publishing a search index. Creating an index establishes the physical data structure (folders and files) on your search service. Once the index definition exists, loading the index follows as a separate task.


  • Write permissions on the search service. Permission can be granted through an admin API key on the request. Alternatively, if you're using role-based access control, you can issue your request as a member of the Search Contributor role.

  • An external data source that provides the content to be indexed. You should refer to the data source to understand the schema requirements of your search index. Index creation is largely a schema definition exercise. Before creating one, you should have:

    • A clear idea of which source fields you want to make searchable, retrievable, filterable, facetable, and sortable in the search index (see the schema checklist for guidance).

    • A unique field in source data that can be used as the document key (or ID) in the index.

  • A stable index location. Moving an existing index to a different search service is not supported out-of-the-box. Revisit application requirements and make sure that your existing search service, its capacity and location, are sufficient for your needs.

  • Finally, all service tiers have index limits on the number of objects that you can create. For example, if you are experimenting on the Free tier, you can only have 3 indexes at any given time. Within the index itself, there are limits on the number of complex fields and collections.

Document keys

A search index has one required field: a document key. A document key is the unique identifier of a search document. In Azure Cognitive Search, it must be a string, and it must originate from unique values in the data source that's providing the content to be indexed. A search service does not generate key values, but in some scenarios (such as the Azure Table indexer) it will synthesize existing values to create a unique key for the documents being indexed.

During incremental indexing, where just new and updated content is indexed, incoming documents with new keys are added, while incoming documents with existing keys are either merged or overwritten, depending on whether index fields are null or populated.

Schema checklist

Use this checklist to assist the design decisions for your search index.

  1. Review naming conventions so that index and field names conform to the naming rules.

  2. Review supported data types. The data type will impact how the field is used. For example, numeric content is filterable but not full text searchable. The most common data type is Edm.String for searchable text, which is tokenized and queried using the full text search engine.

  3. Identify a document key. A document key is an index requirement. It's a single string field and it will be populated from a source data field that contains unique values. For example, if you're indexing from Blob Storage, the metadata storage path is often used as the document key because it uniquely identifies each blob in the container.

  4. Identify the fields in your data source that will contribute searchable content in the index. Searchable content includes short or long strings that are queried using the full text search engine. If the content is verbose (small phrases or bigger chunks), experiment with different analyzers to see how the text is tokenized.

    Field attribute assignments will determine both search behaviors and the physical representation of your index on the search service. Determining how fields should be specified is an iterative process for many customers. To speed up iterations, start with sample data so that you can drop and rebuild easily.

  5. Identify which source fields can be used as filters. Numeric content and short text fields, particularly those with repeating values, are good choices. When working with filters, remember:

    • Filterable fields can optionally be used in faceted navigation.

    • Filterable fields are returned in arbitrary order, so consider making them sortable as well.

  6. Determine whether you'll use the default analyzer ("analyzer": null) or a different analyzer. Analyzers are used to tokenize text fields during indexing and query execution. If strings are descriptive and semantically rich, or if you have translated strings, consider overriding the default with a language analyzer.


Full text search is conducted over terms that are tokenized during indexing. If your queries fail to return the results you expect, test for tokenization to verify the string actually exists. You can try different analyzers on strings to see how tokens are produced for various analyzers.

Create an index

When you're ready to create the index, use a search client that can send the request. You can use the Azure portal or REST APIs for early development and proof-of-concept testing.

During development, plan on frequent rebuilds. Because physical structures are created in the service, dropping and re-creating indexes is necessary for many modifications. You might consider working with a subset of your data to make rebuilds go faster.

Index design through the portal enforces requirements and schema rules for specific data types, such as disallowing full text search capabilities on numeric fields.

  1. Sign in to Azure portal.

  2. In the search service Overview page, choose either option for creating a search index:

    The wizard is an end-to-end workflow that creates an indexer, a data source, and a finished index. It also loads the data. If this is more than what you want, use Add index instead.

The following screenshot highlights where Add index and Import data appear on the command bar. After an index is created, you can find it again in the Indexes tab.

Add index command


After creating an index in the portal, you can copy the JSON representation and add it to your application code.

Set corsOptions for cross-origin queries

Index schemas include a section for setting corsOptions. Client-side JavaScript cannot call any APIs by default since the browser will prevent all cross-origin requests. To allow cross-origin queries to your index, enable CORS (Cross-Origin Resource Sharing) by setting the corsOptions attribute. For security reasons, only query APIs support CORS.

"corsOptions": {
  "allowedOrigins": [
  "maxAgeInSeconds": 300

The following properties can be set for CORS:

  • allowedOrigins (required): This is a list of origins that will be granted access to your index. This means that any JavaScript code served from those origins will be allowed to query your index (assuming it provides the correct api-key). Each origin is typically of the form protocol://<fully-qualified-domain-name>:<port> although <port> is often omitted. See Cross-origin resource sharing (Wikipedia) for more details.

    If you want to allow access to all origins, include * as a single item in the allowedOrigins array. This is not a recommended practice for production search services but it is often useful for development and debugging.

  • maxAgeInSeconds (optional): Browsers use this value to determine the duration (in seconds) to cache CORS preflight responses. This must be a non-negative integer. The larger this value is, the better performance will be, but the longer it will take for CORS policy changes to take effect. If it is not set, a default duration of 5 minutes will be used.

Allowed updates on existing indexes

Create Index creates the physical data structures (files and inverted indices) on your search service. Once the index is created, your ability to effect changes using Update Index is contingent upon whether your modifications invalidate those physical structures. Most field attributes can't be changed once the field is created in your index.

Alternatively, you can create an index alias that serves as a stable reference in your application code. Instead of updating your code, you can update an index alias to point to newer index versions.

To minimize churn in the design process, the following table describes which elements are fixed and flexible in the schema. Changing a fixed element requires an index rebuild, whereas flexible elements can be changed at any time without impacting the physical implementation.

Element Can be updated?
Name No
Key No
Field names and types No
Field attributes (searchable, filterable, facetable, sortable) No
Field attribute (retrievable) Yes
Analyzer You can add and modify custom analyzers in the index. Regarding analyzer assignments on string fields, you can only modify "searchAnalyzer". All other assignments and modifications require a rebuild.
Scoring profiles Yes
Suggesters No
cross-origin remote scripting (CORS) Yes
Encryption Yes

Next steps

Use the following links to become familiar with loading an index with data, or extending an index with a synonyms map.