DBMS > HBase vs. IRONdb vs. ObjectBox vs. Riak TS vs. Teradata

System Properties Comparison HBase vs. IRONdb vs. ObjectBox vs. Riak TS vs. Teradata

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Extremely fast embedded database for small devices, IoT and Mobile

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

hbase.apache.org

www.circonus.com/solutions/time-series-database/

objectbox.io

www.teradata.com

Technical documentation

hbase.apache.org/book.html

docs.circonus.com/irondb/category/getting-started

docs.objectbox.io

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

docs.teradata.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Circonus LLC.

ObjectBox Limited

Open Source, formerly Basho Technologies

Teradata

Initial release

2008

2017

2015

1984

Current release

2.3.4, January 2021

V0.10.20, January 2018

3.0.0, September 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache License 2.0

Open Source

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 and C++

Erlang

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux

Android
iOS
Linux
macOS
Windows

Linux
OS X

hosted
Linux

Data scheme

schema-free, schema definition possible

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

text, numeric, histograms

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes, limited

yes

SQL 2016 + extensions

APIs and other access methods

Java API
RESTful HTTP API
Thrift

HTTP API

Proprietary native API

HTTP API
Native Erlang Interface

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

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++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes, in Lua

Erlang

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

online/offline synchronization between client and server

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

Single row ACID (across millions of columns)

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

The first On-Device Vector Database: ObjectBox 4.0
16 May 2024

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

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