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DBMS > Apache Spark (SQL) vs. Trino

System Properties Comparison Apache Spark (SQL) vs. Trino

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonTrino  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingFast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL
Primary database modelRelational DBMSRelational DBMS
Secondary database modelsDocument store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score21.62
Rank#29  Overall
#18  Relational DBMS
Score5.34
Rank#60  Overall
#34  Relational DBMS
Websitespark.apache.org/­sqltrino.io
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmltrino.io/­broadcast
trino.io/­docs/­current
DeveloperApache Software FoundationTrino Software Foundation
Initial release20142012 info2020 rebranded from PrestoSQL
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScalaJava
Server operating systemsLinux
OS X
Windows
Linux
macOS infofor devlopment
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesnodepending on connected data-source
SQL infoSupport of SQLSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
ODBC
JDBC
RESTful HTTP API
Trino CLI
Supported programming languagesJava
Python
R
Scala
Go
Java
JavaScript (Node.js)
Python
R
Ruby
Server-side scripts infoStored proceduresnoyes, depending on connected data-source
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Coredepending on connected data-source
Replication methods infoMethods for redundantly storing data on multiple nodesnonedepending on connected data-source
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemdepending on connected data-source
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanodepending on connected data-source
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesdepending on connected data-source
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlnoSQL standard access control
More information provided by the system vendor
Apache Spark (SQL)Trino
News

73: Wrapping Trino packages with a bow
9 April 2025

Core Principles and Design Practices of OLAP Engines
27 March 2025

72: Keeping the lake clean
17 March 2025

Twenty four
3 March 2025

71: Fake it real good
27 February 2025

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More resources
Apache Spark (SQL)Trino
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