Presto — Technology Profile & Architecture

Core Information

#core_information

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

Overview & Role

Presto is an open-source distributed SQL query engine designed for running interactive analytic queries against various data sources, including HDFS, Amazon S3, Alluxio, MySQL, PostgreSQL, Apache Kafka, and Apache Kudu. It enables users to query data where it lives, without needing to move it to a separate analytics system. Presto is known for its high performance and scalability, making it suitable for large-scale data analytics.

Domain Classification & Tags

Analytics Relational

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Varies greatly based on query complexity, data size, and cluster configuration. Can range from hundreds of MB/s to several GB/s for analytical queries.

Payload Size: Typically large, ranging from MBs to GBs per query result set.

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

Write Performance Profile

Throughput: Presto is primarily a read-optimized query engine and does not have native write capabilities for persistent storage. Writes are typically handled by the underlying data sources (e.g., Hive, Iceberg, Delta Lake).

Payload Size: N/A (as Presto is not a write engine)

Processing Latency: N/A (as Presto is not a write engine)

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 Presto.

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.