DBMS > HugeGraph vs. ObjectBox vs. Raima Database Manager vs. Stardog vs. Vertica

System Properties Comparison HugeGraph vs. ObjectBox vs. Raima Database Manager vs. Stardog vs. Vertica

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

Extremely fast embedded database for small devices, IoT and Mobile

RDBMS with a small footprint, either embedded into an application or used as a database server

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Graph DBMS

Object oriented DBMS

Relational DBMS
Time Series DBMS

Graph DBMS
RDF store

Relational DBMS

Column oriented

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	0.46
Rank	#259	Overall
	#120	Relational DBMS
	#22	Time Series DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

Technical documentation

hugegraph.apache.org/docs

docs.objectbox.io

docs.raima.com

docs.stardog.com

vertica.com/documentation

Developer

Baidu

ObjectBox Limited

Raima Inc.

Stardog-Union

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2018

2017

1984

2010

2005

Current release

0.9

15, June 2021

7.3.0, May 2020

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache License 2.0

commercial

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

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Java

C++

Server operating systems

Linux
macOS
Unix

Android
iOS
Linux
macOS
Windows

Android
HP-UX
Integrity RTOS
iOS
Linux
OS X
QNX
Solaris
VxWorks
Windows

Linux
macOS
Windows

Linux

Data scheme

yes

schema-free and OWL/RDFS-schema support

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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.

Exporting/Importing of XML structures possible

Import/export of XML data possible

Secondary indexes

yes

also supports composite index and range index

yes

supports real-time indexing in full-text and geospatial

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

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

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

Proprietary native API

ADO.NET
JDBC
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Groovy
Java
Python

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
C
C++
Objective-C
PL/SQL

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

based on the ANSI/ISO SQL Persistent Stored Modules (PSM) specification

user defined functions and aggregates, HTTP Server extensions in Java

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

via event handlers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

none

horizontal partitioning

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

online/offline synchronization between client and server

Source-replica replication

Multi-source replication in HA-Cluster

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Users, roles and permissions

yes

Access rights for users and roles

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Raima Database Manager (RDM) is an embedded time-series database optimized to run...
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Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantages

Decreased time-to-market Industry-standard interfaces & tools for making your job...
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Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Typical use cases include: Internet of Things (IoT) Embedded In-Memory Database Time-Series...
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Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Infor, ABB, The Boeing Company, BAE Systems, Dow Jones, Dun & Bradstreet, Fujitsu,...
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Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Market metrics

Over 30 000 applications developers worldwide have field-tested RDM products More...
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Licensing and pricing models

Just as the technical aspects of Raima Database Manager are flexible, so are the...
» more

Cost-based models and subscription-based models are both available. One license is...
» more

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8 October 2020

Raima Joins BlackBerry QNX Channel Partner Program to Deliver Safety-Critical and Secure Software Solutions
1 October 2020

Raima Expands Their Reseller Network in Asia
19 May 2020

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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