DBMS > Google Cloud Datastore vs. RDF4J vs. SwayDB vs. TDengine vs. Transbase

System Properties Comparison Google Cloud Datastore vs. RDF4J vs. SwayDB vs. TDengine vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

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

Time Series DBMS and big data platform

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

Primary database model

Secondary database models

Relational DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

cloud.google.com/datastore

rdf4j.org

swaydb.simer.au

github.com/taosdata/TDengine
tdengine.com

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

Technical documentation

cloud.google.com/datastore/docs

rdf4j.org/documentation

docs.tdengine.com

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

Developer

Google

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Simer Plaha

TDEngine, previously Taos Data

Transaction Software GmbH

Initial release

2008

2004

2018

2019

1987

Current release

3.0, August 2022

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GNU Affero GPL V3.0

Open Source

AGPL V3, also commercial editions available

commercial

free development license

Cloud-based only

Only available as a cloud service

yes

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

hosted

Linux
OS X
Unix
Windows

Linux
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

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

SQL-like query language (GQL)

Standard SQL with extensions for time-series applications

yes

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

Java
Kotlin
Scala

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

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

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

yes, via alarm monitoring

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Isolation support depends on the API used

Atomic execution of operations

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

Streamlining Time-Series Data Management with TDengine’s PostgreSQL Connector
12 June 2024

Enhancing IoT and Industrial Data Management with TDengine’s MySQL Connector
12 June 2024

Comprehensive Comparison Between TDengine and MongoDB
6 June 2024

Comprehensive Comparison Between TDengine and TimescaleDB
5 June 2024

Mastering Memory Leak Detection in TDengine
31 May 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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