clarify indexer execution on a search service

Author: HeidiSteen

articles/search/search-indexer-overview.md

@@ -10,17 +10,21 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/05/2023
+ms.date: 12/18/2023
 ---
 
 # Indexers in Azure AI Search
 
-An *indexer* in Azure AI Search is a crawler that extracts searchable content from cloud data sources and populates a search index using field-to-field mappings between source data and a search index. This approach is sometimes referred to as a 'pull model' because the search service pulls data in without you having to write any code that adds data to an index. Indexers also drive the [AI enrichment](cognitive-search-concept-intro.md) capabilities of Azure AI Search, integrating external processing of content en route to an index.
+An *indexer* in Azure AI Search is a crawler that extracts textual data from cloud data sources and populates a search index using field-to-field mappings between source data and a search index. This approach is sometimes referred to as a 'pull model' because the search service pulls data in without you having to write any code that adds data to an index. 
 
-Indexers are cloud-only, with individual indexers for [supported data sources](#supported-data-sources). When configuring an indexer, you'll specify a data source (origin) and a search index (destination). Several sources, such as Azure Blob Storage, have more configuration properties specific to that content type.
+Indexers also drive [skillset execution and AI enrichment](cognitive-search-concept-intro.md), where you can configure skills to integrate extra processing of content en route to an index. A few examples are OCR over image files, text split skill for data chunking, text translation for multiple languages.
+
+Indexers target[supported data sources](#supported-data-sources). An indexer configuration specifies a data source (origin) and a search index (destination). Several sources, such as Azure Blob Storage, have more configuration properties specific to that content type.
 
 You can run indexers on demand or on a recurring data refresh schedule that runs as often as every five minutes. More frequent updates require a ['push model'](search-what-is-data-import.md) that simultaneously updates data in both Azure AI Search and your external data source.
 
+A search service runs one indexer job per search unit. If you need concurrent processing, make sure you have [sufficient replicas](/azure/search/search-capacity-planning#add-or-reduce-replicas-and-partitions). Indexers don't run in the background, so you might detect more query throttling than usual if the service is under pressure.
+
 ## Indexer scenarios and use cases
 
 You can use an indexer as the sole means for data ingestion, or in combination with other techniques. The following table summarizes the main scenarios.
@@ -30,7 +34,7 @@ You can use an indexer as the sole means for data ingestion, or in combination w
 | Single data source | This pattern is the simplest: one data source is the sole content provider for a search index. Most supported data sources provide some form of change detection so that subsequent indexer runs pick up the difference when content is added or updated in the source. |
 | Multiple data sources | An indexer specification can have only one data source, but the search index itself can accept content from multiple sources, where each indexer run brings new content from a different data provider. Each source can contribute its share of full documents, or populate selected fields in each document. For a closer look at this scenario, see [Tutorial: Index from multiple data sources](tutorial-multiple-data-sources.md). |
 | Multiple indexers | Multiple data sources are typically paired with multiple indexers if you need to vary run time parameters, the schedule, or field mappings. </br></br>[Cross-region scale out of Azure AI Search](search-reliability.md#data-sync) is another scenario. You might have copies of the same search index in different regions. To synchronize search index content, you could have multiple indexers pulling from the same data source, where each indexer targets a different search index in each region.</br></br>[Parallel indexing](search-howto-large-index.md#parallel-indexing) of very large data sets also requires a multi-indexer strategy, where each indexer targets a subset of the data. |
-| Content transformation | Indexers drive [AI enrichment](cognitive-search-concept-intro.md). Content transforms are defined in a [skillset](cognitive-search-working-with-skillsets.md) that you attach to the indexer.|
+| Content transformation | Indexers drive [skillset execution and AI enrichment](cognitive-search-concept-intro.md). Content transforms are defined in a [skillset](cognitive-search-working-with-skillsets.md) that you attach to the indexer. You can use skills to [incorporate data chunking and vectorization](vector-search-integrated-vectorization.md).|
 
  You should plan on creating one indexer for every target index and data source combination. You can have multiple indexers writing into the same index, and you can reuse the same data source for multiple indexers. However, an indexer can only consume one data source at a time, and can only write to a single index. As the following graphic illustrates, one data source provides input to one indexer, which then populates a single index:  
 
@@ -63,7 +67,7 @@ Azure Cosmos DB for Cassandra is not supported.
 
 Indexers accept flattened row sets, such as a table or view, or items in a container or folder. In most cases, it creates one search document per row, record, or item.
 
-Indexer connections to remote data sources can be made using standard Internet connections (public) or encrypted private connections when you use Azure virtual networks for client apps. You can also set up connections to authenticate using a managed identity. For more information about secure connections, see [Indexer access to content protected by Azure network security features](search-indexer-securing-resources.md) and [Connect to a data source using a managed identity](search-howto-managed-identities-data-sources.md).
+Indexer connections to remote data sources can be made using standard Internet connections (public) or encrypted private connections when you use a shared private link. You can also set up connections to authenticate using a managed identity. For more information about secure connections, see [Indexer access to content protected by Azure network security features](search-indexer-securing-resources.md) and [Connect to a data source using a managed identity](search-howto-managed-identities-data-sources.md).
 
 ## Stages of indexing