Azure Cosmos DB (Gremlin API) — Technology Profile & Architecture

Core Information

#core_information

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

Overview & Role

Azure Cosmos DB's Gremlin API is a fully managed graph database service that allows developers to use the Apache TinkerPop Gremlin graph traversal language to query and traverse highly connected datasets. It provides multi-model capabilities, global distribution, and elastic scalability with guaranteed low latency and high availability.

Domain Classification & Tags

Database

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Thousands to hundreds of thousands of operations/sec (depending on provisioned RUs and query complexity)

Payload Size: Typically 1-100 KB (for graph traversals, results can be larger depending on graph density and query depth)

Processing Latency: Typically 5-20 ms (for point reads/simple traversals, can be higher for complex traversals or cross-region reads)

Write Performance Profile

Throughput: Thousands to tens of thousands of operations/sec (depending on provisioned RUs and item size)

Payload Size: Typically 1-10 KB (for individual vertices or edges)

Processing Latency: Typically 5-20 ms (for single item writes, can be higher for bulk operations or cross-region writes)

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 Cosmos DB (Gremlin API).

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.