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DBMS > Apache Spark (SQL) vs. Trino vs. Vertica

System Properties Comparison Apache Spark (SQL) vs. Trino vs. Vertica

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonTrino  Xexclude from comparisonVertica infoOpenText™ Vertica™  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 PrestoSQLCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelRelational DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsDocument store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score20.40
Rank#29  Overall
#18  Relational DBMS
Score5.18
Rank#60  Overall
#34  Relational DBMS
Score9.85
Rank#42  Overall
#26  Relational DBMS
Websitespark.apache.org/­sqltrino.iowww.vertica.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmltrino.io/­broadcast
trino.io/­docs/­current
vertica.com/­documentation
DeveloperApache Software FoundationTrino Software FoundationOpenText infopreviously Micro Focus and Hewlett Packard
Initial release20142012 info2020 rebranded from PrestoSQL2005
Current release3.5.0 ( 2.13), September 202312.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaJavaC++
Server operating systemsLinux
OS X
Windows
Linux
macOS infofor devlopment
Linux
Data schemeyesyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyes
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.nonono
Secondary indexesnodepending on connected data-sourceNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL-like DML and DDL statementsyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
ODBC
JDBC
RESTful HTTP API
Trino CLI
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesJava
Python
R
Scala
Go
Java
JavaScript (Node.js)
Python
R
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoyes, depending on connected data-sourceyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnonoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Coredepending on connected data-sourcehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnonedepending on connected data-sourceMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemdepending on connected data-sourceImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanodepending on connected data-sourceACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesdepending on connected data-sourceyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlnoSQL standard access controlfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache Spark (SQL)TrinoVertica infoOpenText™ Vertica™
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)TrinoVertica infoOpenText™ Vertica™
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