Spark Structured Streaming — Technology Profile & Architecture

Core Information

#core_information

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

Overview & Role

Spark Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Apache Spark SQL engine. It allows users to express continuous computations on unbounded streams of data using the same DataFrame/Dataset API used for batch processing. This unified API simplifies development by treating streaming data as a continuously appending table.

Domain Classification & Tags

Parallel Processing

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Varies greatly, from thousands to millions of records/sec depending on source, cluster size, and data complexity

Payload Size: Varies greatly, from bytes to MBs per record

Processing Latency: Typically sub-second to a few seconds for micro-batches; near real-time for continuous processing (tens to hundreds of ms)

Write Performance Profile

Throughput: Varies greatly, from thousands to millions of records/sec depending on sink, cluster size, and data complexity

Payload Size: Varies greatly, from bytes to MBs per record

Processing Latency: Typically sub-second to a few seconds for micro-batches; near real-time for continuous processing (tens to hundreds of ms)

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

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 Spark Structured Streaming.

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.