Apache Spark SQL — Technology Profile & Architecture

Core Information

#core_information

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

Overview & Role

Apache Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can act as a distributed SQL query engine, allowing users to query data using SQL or the DataFrame API across various data sources.

Domain Classification & Tags

Analytics

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Hundreds of MB/s to several GB/s per node (for analytical queries), or thousands to tens of thousands of rows/sec (for transactional-like queries on small data)

Payload Size: Typically large datasets (GBs to TBs) for analytical queries; individual rows or small batches for point lookups

Processing Latency: Sub-second to minutes, depending on query complexity, data size, and cluster resources. Milliseconds for simple point lookups on indexed/partitioned data.

Write Performance Profile

Throughput: Hundreds of MB/s to several GB/s per node (for analytical writes), or thousands to tens of thousands of rows/sec (for transactional-like writes)

Payload Size: Typically large datasets (GBs to TBs) for analytical writes; individual rows or small batches for point inserts/updates

Processing Latency: Seconds to minutes for large-scale writes; milliseconds to seconds for small batch inserts/updates

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 Spark SQL.

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.