DBMS > Apache IoTDB vs. EsgynDB vs. TerminusDB vs. Transwarp Hippo

System Properties Comparison Apache IoTDB vs. EsgynDB vs. TerminusDB vs. Transwarp Hippo

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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 Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Secondary database models

Document store
RDF store

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

iotdb.apache.org

www.esgyn.cn

terminusdb.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

Esgyn

DataChemist Ltd.

Initial release

2018

2015

2018

2023

Current release

1.1.0, April 2023

11.0.0, January 2023

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL V3

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

Prolog, Rust

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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 (WOQL)

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC/ADO.Net

JavaScript
Python

C++
Java
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Graph Partitioning

Sharding

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

Journaling Streams

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

Foreign keys

Referential integrity

yes

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 journaling

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

Role-based access control

Role based access control and fine grained access rights

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