Azure Cognitive Search — Technology Profile & Architecture

Core Information

#core_information

Name, description, vendor website, logo, technology type and tags.

Overview & Role

Azure Cognitive Search is an AI-powered cloud search service that allows developers to build rich search experiences over diverse content. It integrates with other Azure AI services to add capabilities like natural language processing, image recognition, and knowledge extraction to search indexes, enabling more intelligent and relevant search results.

Domain Classification & Tags

Search Engine

Performance Profile

#performance_profile

Typical read and write throughput, payload size and processing latency.

Read Performance Profile

Throughput: Hundreds to thousands of queries/sec (depending on tier, index size, query complexity, and replicas)

Payload Size: Varies greatly; query string (tens to hundreds of bytes), results (tens of KB to several MB per page)

Processing Latency: Typically 10-100 ms (for simple queries on well-indexed data, can be higher for complex queries or large result sets)

Write Performance Profile

Throughput: Hundreds to thousands of documents/sec (for small documents, depends on tier, index complexity, and batch size)

Payload Size: Typically 1-100 KB per document (can be larger, up to 16 MB per document)

Processing Latency: Typically 50-500 ms (for batch indexing, can be higher for large batches or complex indexing operations)

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

open_in_new Standalone Tab

Features

#features

Catalogued product capabilities and what each one does.

Empirical data for features is currently being compiled in the global catalog.

Typical Use Cases

#typical_use_cases

Scenarios the technology is commonly chosen for.

Empirical data for typical use cases is currently being compiled in the global catalog.

Known Customers

#known_customers

Publicly referenced organisations using the technology.

Empirical data for known customers is currently being compiled in the global catalog.

Known Integrations

#known_integrations

Other products and services it is documented to work with.

Empirical data for known integrations is currently being compiled in the global catalog.

Connectors

#connectors

Directional data connections to other technologies, with direction and maturity.

Empirical data for connectors is currently being compiled in the global catalog.

Reference Architectures

#reference_architectures

Published architectures where the technology is used or mentioned.

No published reference architecture blueprints currently link to Azure Cognitive Search.

Security Features

#security_features

Built-in security and access-control capabilities.

Empirical data for security features is currently being compiled in the global catalog.

Known Issues

#known_issues

Documented limitations, defects and operational pitfalls.

Empirical data for known issues is currently being compiled in the global catalog.

Guidelines

#guidelines

Recommended practices for adopting and operating the technology.

Empirical data for guidelines is currently being compiled in the global catalog.

Standards & Compliance

#standards_and_compliance

Standards, certifications and control requirements it maps to.

Empirical data for standards & compliance is currently being compiled in the global catalog.

Sources

#sources

Documentation and research references behind the recorded information.

Empirical data for sources is currently being compiled in the global catalog.

Expert Validation

#expert_validation

Whether domain experts reviewed and confirmed the content.

Empirical data for expert validation is currently being compiled in the global catalog.