DBMS > Apache IoTDB vs. Apache Jena - TDB vs. EsgynDB vs. Google Cloud Datastore vs. mSQL

System Properties Comparison Apache IoTDB vs. Apache Jena - TDB vs. EsgynDB vs. Google Cloud Datastore vs. mSQL

Editorial information provided by DB-Engines

Name

Description

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 RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	3.75
Rank	#84	Overall
	#3	RDF stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Website

iotdb.apache.org

jena.apache.org/documentation/tdb/index.html

www.esgyn.cn

cloud.google.com/datastore

hughestech.com.au/products/msql

Technical documentation

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

jena.apache.org/documentation/tdb/index.html

cloud.google.com/datastore/docs

Developer

Apache Software Foundation

originally developed by HP Labs

Esgyn

Google

Hughes Technologies

Initial release

2018

2000

2015

2008

1994

Current release

1.1.0, April 2023

4.9.0, July 2023

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License, Version 2.0

commercial

free licenses can be provided

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

C++, Java

Server operating systems

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

RDF Schemas

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes

SQL-like query language (GQL)

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

JDBC
Native API

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

ADO.NET
JDBC
ODBC

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

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java

All languages supporting JDBC/ODBC/ADO.Net

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

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes

Java Stored Procedures

using Google App Engine

Triggers

yes

via event handler

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

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

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Multi-source replication between multi datacenters

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

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.

none

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

TDB Transactions

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

yes

Access control via Jena Security

fine grained access rights according to SQL-standard

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

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