PrestoSQL

PrestoSQL
Name	PrestoSQL
Developer	Multiple contributors
Initial release	2019
Programming language	Java
License	Apache License 2.0

PrestoSQL is a distributed SQL query engine designed for high-performance analytic queries against large data sets. It was developed as a fork and community-driven project with a focus on extensibility, connector architecture, and cloud-native deployments. The project targeted interoperability with a variety of storage systems and orchestration platforms while engaging contributors from organizations and open source communities.

History

PrestoSQL emerged from a community-driven divergence involving stakeholders from companies such as Facebook, Uber Technologies, Netflix, LinkedIn, and Airbnb; contributors and engineers who had previously participated in projects like Apache Hadoop, Apache Spark, Dremio, Cloudera, and Hadoop Distributed File System played roles in shaping its roadmap. The timeline included interactions with projects and events such as Apache Software Foundation, OpenOffice, Linux Foundation, and conferences like Strata Data Conference, KubeCon, AWS re:Invent, and Google Cloud Next where maintainers discussed governance, contributions, and integration patterns. Governance decisions were influenced by practices at institutions like University of California, Berkeley, Massachusetts Institute of Technology, Stanford University, and industry collaborations with Intel Corporation, IBM, Microsoft, Amazon Web Services, and Google. The fork and community rebrand occurred amid discussions that mirrored precedent disputes in other projects involving entities like MySQL AB, MariaDB Corporation, Node.js Foundation, and OpenStack Foundation.

Architecture

The engine implements a coordinator-worker architecture influenced by distributed systems research from groups at University of California, Berkeley, Carnegie Mellon University, ETH Zurich, and companies like Facebook and Google. Its runtime is written in Java (programming language) and relies on components and libraries from ecosystems represented by Apache Maven, Netty (software), gRPC, and Thrift. The design emphasizes pluggable connectors echoing patterns used by Apache Hive, Apache HBase, Apache Cassandra, and Apache Kafka integrations, and runtime behaviors comparable to projects such as Presto (disambiguation), Apache Drill, Trino, and ClickHouse. Storage and execution interaction models reference technologies like Amazon S3, Google Cloud Storage, Azure Blob Storage, Hadoop Distributed File System, and distributed metadata systems similar to Apache Hive Metastore, Apache Ranger, and Apache Atlas.

Query Processing and Optimizer

The query pipeline uses a SQL parser and analyzer with optimizer stages inspired by research from System R, Volcano (query optimizer), Starburst Data, and academic work published at conferences like SIGMOD, VLDB, ICDE, and EuroSys. The planner produces distributed pipelines that leverage vectorized and columnar processing strategies akin to innovations from Apache Arrow, Parquet (protocol), ORC (file format), and projects such as Impala, Snowflake (company), and Teradata. Cost-based and rule-based optimization features interface with statistics gathered from metadata services like Apache Hive Metastore and catalog systems used by Databricks and AWS Glue. Query execution borrows scheduling and resource isolation concepts discussed in Kubernetes, YARN, Mesos, and cluster management at companies such as Google, Facebook, and Netflix.

Connectors and Storage Integration

PrestoSQL supports a wide range of connectors enabling integration with systems like Apache Hive, Apache HBase, Apache Cassandra, MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, Amazon Redshift, Snowflake (company), Google BigQuery, Elasticsearch, MongoDB, Kafka (software platform), and object stores including Amazon S3, Google Cloud Storage, and Azure Blob Storage. Connector implementations reflect patterns and interoperability efforts seen in Apache Sqoop, Apache Flink, Apache Beam, and Talend. Metadata interoperability leverages standards and systems such as Apache Hive Metastore, AWS Glue, and catalog efforts similar to Data Catalog (Google Cloud), while schema evolution and serialization draw from Apache Avro, Apache Thrift, Protocol Buffers, and Apache Parquet.

Performance and Scalability

Performance characteristics align with optimizations common in analytic engines developed by Facebook, Google, Amazon Web Services, Netflix, and Uber Technologies: parallel execution, predicate pushdown, columnar reads, and vectorized processing influenced by Apache Arrow and Intel hardware optimizations. Scaling strategies mirror cluster provisioning patterns used in Kubernetes and in orchestration at providers like Amazon Web Services and Google Cloud Platform, and resource management choices echo scheduler research from Mesos and YARN. Benchmarking comparisons often reference workloads and tools from TPC (Transaction Processing Performance Council), TPC-DS, TPC-H, and community benchmarks established by BenchmarkSQL and corporate performance engineering teams at SAP and Oracle Corporation.

Security and Authentication

Security integrations support authentication methods and identity providers such as LDAP, Kerberos, OAuth 2.0, SAML, Active Directory, and cloud IAM systems like AWS Identity and Access Management and Google Cloud Identity. Authorization and auditing integrate with policy platforms and projects such as Apache Ranger, Apache Sentry, and standards propagated by OWASP and compliance frameworks used by organizations like PCI DSS, HIPAA, and GDPR-affected enterprises. Network-level protections and encryption rely on TLS practices promulgated by bodies like IETF and implementations used in OpenSSL and Let's Encrypt.

Adoption and Community

Adoption spans enterprises, cloud providers, and data teams at companies including Facebook, Uber Technologies, Netflix, LinkedIn, Airbnb, Apple Inc., Spotify, and Pinterest, as well as integration into managed services and platforms offered by Amazon Web Services, Google Cloud Platform, Microsoft Azure, and startups like Starburst Data. The community interacts via code hosting platforms exemplified by GitHub, collaboration forums resembling Stack Overflow, issue tracking influenced by practices at JIRA (software), and contribution governance models drawing lessons from Linux kernel development and the Apache Software Foundation. Conferences, meetups, and summits such as Strata Data Conference, KubeCon, DataEngConf, and corporate roadshows have been venues for architecture discussions, interoperability demos, and case studies from major industry adopters.

Category:SQL engines