DBMS > Stardog vs. TimescaleDB vs. Tkrzw vs. Trino

System Properties Comparison Stardog vs. TimescaleDB vs. Tkrzw vs. Trino

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Editorial information provided by DB-Engines

Name

Description

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

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

Primary database model

Secondary database models

Relational DBMS

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

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

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

Technical documentation

docs.stardog.com

docs.timescale.com

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

Developer

Stardog-Union

Timescale

Mikio Hirabayashi

Trino Software Foundation

Initial release

2010

2017

2020

2012

2020 rebranded from PrestoSQL

Current release

7.3.0, May 2020

2.15.0, May 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Java

Server operating systems

Linux
macOS
Windows

Linux
OS X
Windows

Linux
macOS

for devlopment

Data scheme

schema-free and OWL/RDFS-schema support

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

Import/export of XML data possible

yes

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

depending on connected data-source

SQL

Support of SQL

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C++
Java
Python
Ruby

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates, HTTP Server extensions in Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes, depending on connected data-source

Triggers

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, across time and space (hash partitioning) attributes

none

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in HA-Cluster

Source-replica replication with hot standby and reads on replicas

none

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in HA-Cluster

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

SQL standard access control

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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More resources

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