Create an indexer for data in Azure Cosmos DB for NoSQL

  • 4 minutes

An Azure Cognitive Search instance is comprised of a few core components:

  • Indexes that contain JSON documents that are searchable
  • Indexers to crawl data from various data sources and insert them into indexes
  • Data Sources that connect Azure Cognitive Search to various data platforms

Diagram of an Azure Cognitive Services account where an indexer indexes data from a data source and stores the result in an index

In the case of Azure Cosmos DB for NoSQL, you can configure a container as a data source, create a query and a frequency that the indexer will use to crawl data, and create a target index where the resulting searchable JSON documents are stored.

Connecting a data source

The first step is to create a data source. The data source points to somewhere where data is stored. With Azure Cosmos DB for NoSQL, the data source is a reference to an existing account with the following parameters configured:

Parameter Value
Connection string Connection string for Azure Cosmos DB account
Database Name of target database
Collection Name of target container
Query A SQL query to select items to be indexed

Customizing the index

Once the data source is configured, an index should be created that would be the target of the indexing operation. The index contains, at a minimum, a name and a key. The key refers to a unique identifier field for each JSON document in the index.

Each field in the index should be configured to enable or disable features when searching. These optional features allow extra search functionality on specific fields when it makes sense. For each field, you must configure whether the field is:

Feature Description
Retrievable Configures the field to be projected in search result sets
Filterable Accepts OData-style filtering on the field
Sortable Enables sorting using the field
Facetable Allows field to be dynamically aggregated and grouped
Searchable Allows search queries to match terms in the field

Configuring the indexer

The final step is to configure the indexer’s name and schedule. The schedule determines how often the indexer will run to pull data from the data source and populate the index with JSON documents.