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This article compares technology choices for search data stores in Azure. A search data store is used to create and store specialized indexes for performing searches on free-form text. The text that is indexed may reside in a separate data store, such as blob storage. An application submits a query to the search data store, and the result is a list of matching documents. For more information about this scenario, see Processing free-form text for search.
In Azure, all of the following data stores will meet the core requirements for search against free-form text data by providing a search index:
For search scenarios, begin choosing the appropriate search data store for your needs by answering these questions:
Do you want a managed service rather than managing your own servers?
Can you specify your index schema at design time? If not, choose an option that supports updateable schemas.
Do you need an index only for full-text search, or do you also need rapid aggregation of numeric data and other analytics? If you need functionality beyond full-text search, consider options that support additional analytics.
Do you need a search index for Log Analytics, with support for log collection, aggregation, and visualizations on indexed data? If so, consider Elasticsearch, which is part of a Log Analytics stack.
Do you need to index data in common document formats such as PDF, Word, PowerPoint, and Excel? If yes, choose an option that provides document indexers.
Does your database have specific security needs? If yes, consider the security features listed below.
The following tables summarize the key differences in capabilities.
Capability | Cognitive Search | Elasticsearch | SQL Database |
---|---|---|---|
Is managed service | Yes | No | Yes |
REST API | Yes | Yes | No |
Programmability | .NET, Java, Python, JavaScript | Java | T-SQL |
Document indexers for common file types (PDF, DOCX, TXT, and so on) | Yes | No | No |
Capability | Cognitive Search | Elasticsearch | SQL Database |
---|---|---|---|
Updateable schema | Yes | Yes | Yes |
Supports scale out | Yes | Yes | No |
Capability | Cognitive Search | Elasticsearch | SQL Database |
---|---|---|---|
Supports analytics beyond full text search | No | Yes | Yes |
Part of a Log Analytics stack | No | Yes (ELK) | No |
Supports semantic search | Yes (find similar documents only) | Yes | Yes |
Capability | Cognitive Search | Elasticsearch | SQL Database |
---|---|---|---|
Row-level security | Partial (requires application query to filter by group ID) | Partial (requires application query to filter by group ID) | Yes |
Transparent data encryption | No | No | Yes |
Restrict access to specific IP addresses | Yes | Yes | Yes |
Restrict access to allow virtual network access only | Yes | Yes | Yes |
Active Directory authentication (integrated authentication) | No | No | Yes |
This article is maintained by Microsoft. It was originally written by the following contributors.
Principal author:
Begivenhed
17. mar., 21 - 21. mar., 10
Deltag i meetup-serien for at bygge skalerbare AI-løsninger baseret på brugscases fra den virkelige verden sammen med andre udviklere og eksperter.
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