DBMS > AllegroGraph vs. Apache IoTDB vs. TimescaleDB vs. Trino

System Properties Comparison AllegroGraph vs. Apache IoTDB vs. TimescaleDB vs. Trino

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Editorial information provided by DB-Engines

Name

Description

High performance, persistent RDF store with additional support for Graph DBMS

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

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

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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	0.84
Rank	#204	Overall
	#35	Document stores
	#18	Graph DBMS
	#7	RDF stores
	#10	Vector DBMS

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.timescale.com

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

Developer

Franz Inc.

Apache Software Foundation

Timescale

Trino Software Foundation

Initial release

2004

2018

2017

2012

2020 rebranded from PrestoSQL

Current release

8.0, December 2023

1.1.0, April 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

Apache Version 2.0

Open Source

Apache 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

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Implementation language

Java

Server operating systems

Linux
OS X
Windows

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux
macOS

for devlopment

Data scheme

yes

RDF schemas

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.

bulk load of XML files possible

yes

Secondary indexes

yes

depending on connected data-source

SQL

Support of SQL

SPARQL is used as query language

SQL-like query language

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

RESTful HTTP API
SPARQL

JDBC
Native API

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

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

yes

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

Partitioning methods

Methods for storing different data on different nodes

with Federation

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

yes, across time and space (hash partitioning) attributes

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication with hot standby and reads on replicas

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

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

User concepts

Access control

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

fine grained access rights according to SQL-standard

SQL standard access control

More information provided by the system vendor

News

AllegroGraph Named a 2024 Trend Setting Product
16 September 2024

Gartner recognized Franz Inc. as a Key Neuro-Symbolic AI Provider in 2024 Hype Cycle for AI
10 September 2024

Next-Generation Cybersecurity: Integrating Knowledge Graphs and Neuro-Symbolic AI with STIX and TAXII
10 September 2024

Pruning Cycles in UMLS Metathesaurus: A Neuro Symbolic AI Approach
9 September 2024

Awesome AI Companies – Franz Inc.
17 July 2024

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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