Index data from Azure Cosmos DB for NoSQL for queries in Azure AI Search

In this article, learn how to configure an indexer that imports content from Azure Cosmos DB for NoSQL and makes it searchable in Azure AI Search.

This article supplements Create an indexer with information that's specific to Cosmos DB. It uses the REST APIs to demonstrate a three-part workflow common to all indexers: create a data source, create an index, create an indexer. Data extraction occurs when you submit the Create Indexer request.

Because terminology can be confusing, it's worth noting that Azure Cosmos DB indexing and Azure AI Search indexing are different operations. Indexing in Azure AI Search creates and loads a search index on your search service.

Prerequisites

• An Azure Cosmos DB account, database, container and items. Use the same region for both Azure AI Search and Azure Cosmos DB for lower latency and to avoid bandwidth charges.

• An automatic indexing policy on the Azure Cosmos DB collection, set to Consistent. This is the default configuration. Lazy indexing isn't recommended and can result in missing data.

• Read permissions. A "full access" connection string includes a key that grants access to the content, but if you're using Azure RBAC (Microsoft Entra ID), make sure the search service managed identity is assigned both Cosmos DB Account Reader Role and Cosmos DB Built-in Data Reader Role.

• A REST client to create the data source, index, and indexer.

Define the data source

The data source definition specifies the data to index, credentials, and policies for identifying changes in the data. A data source is an independent resource that can be used by multiple indexers.

1. Create or update a data source to set its definition:

   POST https://[service name].search.windows.net/datasources?api-version=2023-11-01
   Content-Type: application/json
   api-key: [Search service admin key]
   {
       "name": "[my-cosmosdb-ds]",
       "type": "cosmosdb",
       "credentials": {
         "connectionString": "AccountEndpoint=https://[cosmos-account-name].documents.azure.com;AccountKey=[cosmos-account-key];Database=[cosmos-database-name]"
       },
       "container": {
         "name": "[my-cosmos-db-collection]",
         "query": null
       },
       "dataChangeDetectionPolicy": {
         "@odata.type": "#Microsoft.Azure.Search.HighWaterMarkChangeDetectionPolicy",
       "  highWaterMarkColumnName": "_ts"
       },
       "dataDeletionDetectionPolicy": null,
       "encryptionKey": null,
       "identity": null
   }
   

2. Set "type" to "cosmosdb" (required). If you're using an older Search API version 2017-11-11, the syntax for "type" is "documentdb". Otherwise, for 2019-05-06 and later, use "cosmosdb".

3. Set "credentials" to a connection string. The next section describes the supported formats.

4. Set "container" to the collection. The "name" property is required and it specifies the ID of the database collection to be indexed. The "query" property is optional. Use it to flatten an arbitrary JSON document into a flat schema that Azure AI Search can index.

5. Set "dataChangeDetectionPolicy" if data is volatile and you want the indexer to pick up just the new and updated items on subsequent runs.

6. Set "dataDeletionDetectionPolicy" if you want to remove search documents from a search index when the source item is deleted.

Supported credentials and connection strings

Indexers can connect to a collection using the following connections.

Avoid port numbers in the endpoint URL. If you include the port number, the connection will fail.

Using queries to shape indexed data

In the "query" property under "container", you can specify a SQL query to flatten nested properties or arrays, project JSON properties, and filter the data to be indexed.

Example document:

    {
        "userId": 10001,
        "contact": {
            "firstName": "andy",
            "lastName": "hoh"
        },
        "company": "microsoft",
        "tags": ["azure", "cosmosdb", "search"]
    }

Filter query:

SELECT * FROM c WHERE c.company = "microsoft" and c._ts >= @HighWaterMark ORDER BY c._ts

Flattening query:

SELECT c.id, c.userId, c.contact.firstName, c.contact.lastName, c.company, c._ts FROM c WHERE c._ts >= @HighWaterMark ORDER BY c._ts

Projection query:

SELECT VALUE { "id":c.id, "Name":c.contact.firstName, "Company":c.company, "_ts":c._ts } FROM c WHERE c._ts >= @HighWaterMark ORDER BY c._ts

Array flattening query:

SELECT c.id, c.userId, tag, c._ts FROM c JOIN tag IN c.tags WHERE c._ts >= @HighWaterMark ORDER BY c._ts

Unsupported queries (DISTINCT and GROUP BY)

Queries using the DISTINCT keyword or GROUP BY clause aren't supported. Azure AI Search relies on SQL query pagination to fully enumerate the results of the query. Neither the DISTINCT keyword or GROUP BY clauses are compatible with the continuation tokens used to paginate results.

Examples of unsupported queries:

SELECT DISTINCT c.id, c.userId, c._ts FROM c WHERE c._ts >= @HighWaterMark ORDER BY c._ts

SELECT DISTINCT VALUE c.name FROM c ORDER BY c.name

SELECT TOP 4 COUNT(1) AS foodGroupCount, f.foodGroup FROM Food f GROUP BY f.foodGroup

Although Azure Cosmos DB has a workaround to support SQL query pagination with the DISTINCT keyword by using the ORDER BY clause, it isn't compatible with Azure AI Search. The query returns a single JSON value, whereas Azure AI Search expects a JSON object.

-- The following query returns a single JSON value and isn't supported by Azure AI Search
SELECT DISTINCT VALUE c.name FROM c ORDER BY c.name

Add search fields to an index

In a search index, add fields to accept the source JSON documents or the output of your custom query projection. Ensure that the search index schema is compatible with source data. For content in Azure Cosmos DB, your search index schema should correspond to the Azure Cosmos DB items in your data source.

1. Create or update an index to define search fields that store data:

   POST https://[service name].search.windows.net/indexes?api-version=2023-11-01
   Content-Type: application/json
   api-key: [Search service admin key]
   {
       "name": "mysearchindex",
       "fields": [{
           "name": "rid",
           "type": "Edm.String",
           "key": true,
           "searchable": false
       }, 
       {
           "name": "description",
           "type": "Edm.String",
           "filterable": false,
           "searchable": true,
           "sortable": false,
           "facetable": false,
           "suggestions": true
       }
     ]
   }
   

2. Create a document key field ("key": true). For partitioned collections, the default document key is the Azure Cosmos DB _rid property, which Azure AI Search automatically renames to rid because field names can’t start with an underscore character. Also, Azure Cosmos DB _rid values contain characters that are invalid in Azure AI Search keys. For this reason, the _rid values are Base64 encoded.

3. Create more fields for more searchable content. See Create an index for details.

Mapping data types

Configure and run the Azure Cosmos DB for NoSQL indexer

Once the index and data source have been created, you're ready to create the indexer. Indexer configuration specifies the inputs, parameters, and properties controlling run time behaviors.

1. Create or update an indexer by giving it a name and referencing the data source and target index:

   POST https://[service name].search.windows.net/indexers?api-version=2023-11-01
   Content-Type: application/json
   api-key: [search service admin key]
   {
       "name" : "[my-cosmosdb-indexer]",
       "dataSourceName" : "[my-cosmosdb-ds]",
       "targetIndexName" : "[my-search-index]",
       "disabled": null,
       "schedule": null,
       "parameters": {
           "batchSize": null,
           "maxFailedItems": 0,
           "maxFailedItemsPerBatch": 0,
           "base64EncodeKeys": false,
           "configuration": {}
           },
       "fieldMappings": [],
       "encryptionKey": null
   }
   

2. Specify field mappings if there are differences in field name or type, or if you need multiple versions of a source field in the search index.

3. See Create an indexer for more information about other properties.

An indexer runs automatically when it's created. You can prevent this by setting "disabled" to true. To control indexer execution, run an indexer on demand or put it on a schedule.

Check indexer status

To monitor the indexer status and execution history, send a Get Indexer Status request:

GET https://myservice.search.windows.net/indexers/myindexer/status?api-version=2023-11-01
  Content-Type: application/json  
  api-key: [admin key]

The response includes status and the number of items processed. It should look similar to the following example:

    {
        "status":"running",
        "lastResult": {
            "status":"success",
            "errorMessage":null,
            "startTime":"2022-02-21T00:23:24.957Z",
            "endTime":"2022-02-21T00:36:47.752Z",
            "errors":[],
            "itemsProcessed":1599501,
            "itemsFailed":0,
            "initialTrackingState":null,
            "finalTrackingState":null
        },
        "executionHistory":
        [
            {
                "status":"success",
                "errorMessage":null,
                "startTime":"2022-02-21T00:23:24.957Z",
                "endTime":"2022-02-21T00:36:47.752Z",
                "errors":[],
                "itemsProcessed":1599501,
                "itemsFailed":0,
                "initialTrackingState":null,
                "finalTrackingState":null
            },
            ... earlier history items
        ]
    }

Execution history contains up to 50 of the most recently completed executions, which are sorted in the reverse chronological order so that the latest execution comes first.

Indexing new and changed documents

Once an indexer has fully populated a search index, you might want subsequent indexer runs to incrementally index just the new and changed documents in your database.

To enable incremental indexing, set the "dataChangeDetectionPolicy" property in your data source definition. This property tells the indexer which change tracking mechanism is used on your data.

For Azure Cosmos DB indexers, the only supported policy is the HighWaterMarkChangeDetectionPolicy using the _ts (timestamp) property provided by Azure Cosmos DB.

The following example shows a data source definition with a change detection policy:

"dataChangeDetectionPolicy": {
    "@odata.type": "#Microsoft.Azure.Search.HighWaterMarkChangeDetectionPolicy",
"  highWaterMarkColumnName": "_ts"
},

Incremental indexing and custom queries

If you're using a custom query to retrieve documents, make sure the query orders the results by the _ts column. This enables periodic check-pointing that Azure AI Search uses to provide incremental progress in the presence of failures.

In some cases, even if your query contains an ORDER BY [collection alias]._ts clause, Azure AI Search might not infer that the query is ordered by the _ts. You can tell Azure AI Search that results are ordered by setting the assumeOrderByHighWaterMarkColumn configuration property.

To specify this hint, create or update your indexer definition as follows:

{
    ... other indexer definition properties
    "parameters" : {
        "configuration" : { "assumeOrderByHighWaterMarkColumn" : true } }
} 

Indexing deleted documents

When rows are deleted from the collection, you normally want to delete those rows from the search index as well. The purpose of a data deletion detection policy is to efficiently identify deleted data items. Currently, the only supported policy is the Soft Delete policy (deletion is marked with a flag of some sort), which is specified in the data source definition as follows:

"dataDeletionDetectionPolicy"": {
    "@odata.type" : "#Microsoft.Azure.Search.SoftDeleteColumnDeletionDetectionPolicy",
    "softDeleteColumnName" : "the property that specifies whether a document was deleted",
    "softDeleteMarkerValue" : "the value that identifies a document as deleted"
}

If you're using a custom query, make sure that the property referenced by softDeleteColumnName is projected by the query.

The softDeleteColumnName must be a top-level field in the index. Using nested fields within complex data types as the softDeleteColumnName is not supported.

The following example creates a data source with a soft-deletion policy:

POST https://[service name].search.windows.net/datasources?api-version=2023-11-01
Content-Type: application/json
api-key: [Search service admin key]

{
    "name": "[my-cosmosdb-ds]",
    "type": "cosmosdb",
    "credentials": {
        "connectionString": "AccountEndpoint=https://[cosmos-account-name].documents.azure.com;AccountKey=[cosmos-account-key];Database=[cosmos-database-name]"
    },
    "container": { "name": "[my-cosmos-collection]" },
    "dataChangeDetectionPolicy": {
        "@odata.type": "#Microsoft.Azure.Search.HighWaterMarkChangeDetectionPolicy",
        "highWaterMarkColumnName": "_ts"
    },
    "dataDeletionDetectionPolicy": {
        "@odata.type": "#Microsoft.Azure.Search.SoftDeleteColumnDeletionDetectionPolicy",
        "softDeleteColumnName": "isDeleted",
        "softDeleteMarkerValue": "true"
    }
}

Use .NET

For data accessed through the SQL API protocol, you can use the .NET SDK to automate with indexers. We recommend that you review the previous REST API sections to learn concepts, workflow, and requirements. You can then refer to following .NET API reference documentation to implement a JSON indexer in managed code:

• azure.search.documents.indexes.models.searchindexerdatasourceconnection
• azure.search.documents.indexes.models.searchindexerdatasourcetype
• azure.search.documents.indexes.models.searchindex
• azure.search.documents.indexes.models.searchindexer

Next steps

You can now control how you run the indexer, monitor status, or schedule indexer execution. The following articles apply to indexers that pull content from Azure Cosmos DB:

• Set up an indexer connection to an Azure Cosmos DB database using a managed identity
• Index large data sets
• Indexer access to content protected by Azure network security features