Trino — Technology Profile & Architecture

Core Information

#core_information

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

Overview & Role

Trino (formerly PrestoSQL) is an open-source distributed SQL query engine designed for high-performance analytics against large datasets. It allows users to query data from various sources like HDFS, S3, relational databases, and more, without needing to move the data. Trino is known for its low-latency queries and ability to handle petabyte-scale data.

Domain Classification & Tags

Analytics Datalake

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Varies widely based on data source, query complexity, and cluster size. Can range from MB/s to multiple GB/s.

Payload Size: Queries typically process large datasets, often terabytes or petabytes, rather than individual 'payloads'. Result sets can range from KBs to GBs.

Processing Latency: Sub-second for simple queries on small datasets, seconds to minutes for complex analytical queries on large datasets.

Write Performance Profile

Throughput: Trino is primarily a read-oriented query engine. Write performance is dependent on the underlying data source connector (e.g., Hive, Iceberg, Delta Lake) and its capabilities. Not a primary strength of Trino itself.

Payload Size: N/A - Trino's write operations are typically inserts into tables, not individual payload writes.

Processing Latency: N/A - Write latency is dictated by the underlying data source. Trino itself adds minimal overhead.

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 Trino.

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.