DBMS > Apache IoTDB vs. EsgynDB vs. Riak KV vs. Tibero vs. Valentina Server

System Properties Comparison Apache IoTDB vs. EsgynDB vs. Riak KV vs. Tibero vs. Valentina Server

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

Distributed, fault tolerant key-value store

A secure RDBMS, designed for easy portability from Oracle

Object-relational database and reports server

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

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	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

iotdb.apache.org

www.esgyn.cn

us.tmaxsoft.com/products/tibero

www.valentina-db.net

Technical documentation

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

www.tiot.jp/riak-docs/riak/kv/latest

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Apache Software Foundation

Esgyn

OpenSource, formerly Basho Technologies

TmaxSoft

Paradigma Software

Initial release

2018

2015

2009

2003

1999

Current release

1.1.0, April 2023

3.2.0, December 2022

6, April 2015

5.7.5

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

C and Assembler

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
OS X

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

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.

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

All languages supporting JDBC/ODBC/ADO.Net

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Java Stored Procedures

Erlang

Persistent Stored Procedure (PSM)

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

no "single point of failure"

horizontal partitioning

by range, hash, list or composite

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

Multi-source replication
Source-replica replication

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

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 capabilities

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

yes

planned for next version

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

yes, using Riak Security

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

fine grained access rights according to SQL-standard

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