DBMS > AgensGraph vs. Apache IoTDB vs. SwayDB vs. Transbase

System Properties Comparison AgensGraph vs. Apache IoTDB vs. SwayDB vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Graph DBMS
Relational DBMS

Time Series DBMS

Key-value store

Relational DBMS

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

Score	0.16
Rank	#333	Overall
	#30	Graph DBMS
	#146	Relational DBMS

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

bitnine.net/agensgraph

iotdb.apache.org

swaydb.simer.au

www.transaction.de/en/products/transbase.html

Technical documentation

bitnine.net/documentation

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

www.transaction.de/en/products/transbase/features.html

Developer

Bitnine Global Inc.

Apache Software Foundation

Simer Plaha

Transaction Software GmbH

Initial release

2016

2018

1987

Current release

2.1, December 2018

1.1.0, April 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

Open Source

GNU Affero GPL V3.0

commercial

free development license

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

Scala

C and C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

depending on used data model

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

Cypher Query Language
JDBC

JDBC
Native API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
Java
JavaScript
Python

C
C#
C++
Go
Java
Python
Scala

Java
Kotlin
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

none

Source-replica replication

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

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of operations

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

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