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You have an existing Azure Cognitive Search service.
You have an Azure Blob storage account that contains millions of scanned documents stored as images and PDFs.
You need to make the scanned documents available to search as quickly as possible.
What should you do?
A. Split the data into multiple blob containers. Create a Cognitive Search service for each container. Within each indexer definition, schedule the same runtime execution pattern.
B. Split the data into multiple blob containers. Create an indexer for each container. Increase the search units. Within each indexer definition, schedule a sequential execution pattern.
C. Create a Cognitive Search service for each type of document.
D. Split the data into multiple virtual folders. Create an indexer for each folder. Increase the search units. Within each indexer definition, schedule the same runtime execution pattern.
Correct Answer: D
Explanation/Reference:
Incorrect Answers:
A: Need more search units to process the data in parallel.
B: Run them in parallel, not sequentially.
C: Need a blob indexer.
Note: A blob indexer is used for ingesting content from Azure Blob storage into a Cognitive Search index.
Index large datasets
Indexing blobs can be a time-consuming process. In cases where you have millions of blobs to index, you can speed up indexing by partitioning your data and using multiple indexers to process the data in parallel. Here’s how you can set this up:
Partition your data into multiple blob containers or virtual folders
Set up several data sources, one per container or folder.
Create a corresponding indexer for each data source. All of the indexers should point to the same target search index.
One search unit in your service can run one indexer at any given time. Creating multiple indexers as described above is only useful if they actually run in parallel.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-howto-indexing-azure-blob-storage
