Analytics Evaluated
Apache Kudu logo

Apache Kudu

Apache Kudu is a free and open source columnar storage system for structured data that supports fast analytics on large datasets. It offers a unique combination of strong consistency and high-performance random access, making it suitable for workloads that require both real-time updates and analytical queries. Kudu integrates well with other Apache projects like Impala, Spark, and Flink.

Category / Domain Analytics
I/O Performance Empirical Profile

Core Information

#core_information

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

Overview & Role

Apache Kudu is a free and open source columnar storage system for structured data that supports fast analytics on large datasets. It offers a unique combination of strong consistency and high-performance random access, making it suitable for workloads that require both real-time updates and analytical queries. Kudu integrates well with other Apache projects like Impala, Spark, and Flink.

Domain Classification & Tags

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Hundreds of thousands to millions of rows/sec (e.g., 500,000 - 2,000,000 rows/sec for analytical scans)

Payload Size: Hundreds of bytes to tens of KB per row (optimized for columnar scans of many rows)

Processing Latency: Tens to hundreds of milliseconds for analytical queries (e.g., 50-500 ms for complex scans); sub-10ms for single-row point lookups

Write Performance Profile

Throughput: Hundreds of thousands to millions of rows/sec (e.g., 200,000 - 1,000,000 rows/sec for inserts/updates)

Payload Size: Hundreds of bytes to tens of KB per row

Processing Latency: Tens of milliseconds (e.g., 10-50 ms for inserts/updates, depending on replication factor and batching)

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 Apache Kudu.

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.