DBMS > STSdb vs. SWC-DB vs. Trino vs. Vertica

System Properties Comparison STSdb vs. SWC-DB vs. Trino vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A high performance, scalable Wide Column DBMS

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Cloud 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 model

Key-value store

Wide column store

Relational DBMS

Column oriented

Secondary database models

Time Series DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

github.com/STSSoft/STSdb4

github.com/kashirin-alex/swc-db
www.swcdb.org

trino.io

www.vertica.com

Technical documentation

trino.io/broadcast
trino.io/docs/current

vertica.com/documentation

Developer

STS Soft SC

Alex Kashirin

Trino Software Foundation

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2011

2020

2012

2020 rebranded from PrestoSQL

2005

Current release

4.0.8, September 2015

0.5, April 2021

12.0.3, January 2023

License

Commercial or Open Source

Open Source

GPLv2, commercial license available

Open Source

GPL V3

Open Source

Apache Version 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

C++

Server operating systems

Windows

Linux

Linux
macOS

for devlopment

Linux

Data scheme

yes

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

depending on connected data-source

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

.NET Client API

Proprietary protocol
Thrift

JDBC
RESTful HTTP API
Trino CLI

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C#
Java

C++

Go
Java
JavaScript (Node.js)
Python
R
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes, depending on connected data-source

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

depending on connected data-source

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on connected data-source

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on connected data-source

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

depending on connected data-source

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

SQL standard access control

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

News

23 is a go, keeping pace with Java
17 September 2024

65: Performance boosts
12 September 2024

64: Control with Open Policy Agent OPA
22 August 2024

63: Querying with JS
1 August 2024

62: A lakehouse that simply works at Prezi
11 July 2024

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