DBMS > Apache IoTDB vs. EsgynDB vs. OpenTSDB vs. Stardog vs. Transwarp StellarDB

System Properties Comparison Apache IoTDB vs. EsgynDB vs. OpenTSDB vs. Stardog vs. Transwarp StellarDB

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

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

Scalable Time Series DBMS based on HBase

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

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.07
Rank	#371	Overall
	#39	Graph DBMS

Website

www.transwarp.cn/en/product/stellardb

Technical documentation

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

opentsdb.net/docs/build/html/index.html

docs.stardog.com

Developer

Apache Software Foundation

Esgyn

currently maintained by Yahoo and other contributors

Stardog-Union

Transwarp

Initial release

2018

2015

2011

2010

Current release

1.1.0, April 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

LGPL

commercial

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

commercial

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

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

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

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

yes

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

SQL-like query language

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC

HTTP API
Telnet API

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

OpenCypher

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC/ADO.Net

Erlang
Go
Java
Python
R
Ruby

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Java Stored Procedures

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

based on HBase

none

horizontal partitioning

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

Multi-source replication between multi datacenters

selectable replication factor

based on HBase

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

based on HBase

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

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

fine grained access rights according to SQL-standard

Access rights for users and roles

yes

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More resources

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