DBMS > Atos Standard Common Repository vs. DolphinDB vs. Graph Engine vs. H2 vs. Hypertable

System Properties Comparison Atos Standard Common Repository vs. DolphinDB vs. Graph Engine vs. H2 vs. Hypertable

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

An open source BigTable implementation based on distributed file systems such as Hadoop

Primary database model

Document store
Key-value store

Time Series DBMS

Graph DBMS
Key-value store

Relational DBMS

Wide column store

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.dolphindb.com

www.graphengine.io

www.h2database.com

Technical documentation

docs.dolphindb.cn/en/help200/index.html

www.graphengine.io/docs/manual

www.h2database.com/html/main.html

Developer

Atos Convergence Creators

DolphinDB, Inc

Microsoft

Thomas Mueller

Hypertable Inc.

Initial release

2016

2018

2010

2005

2009

Current release

1703

v2.00.4, January 2022

2.2.220, July 2023

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

free community version available

Open Source

MIT License

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

GNU version 3. Commercial license available

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++

.NET and C

Java

C++

Server operating systems

Linux

Linux
Windows

.NET

All OS with a Java VM

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

optional

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

only exact value or prefix value scans

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

LDAP

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

RESTful HTTP API

JDBC
ODBC

C++ API
Thrift

Supported programming languages

All languages with LDAP bindings

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

C#
C++
F#
Visual Basic

Java

C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

selectable replication factor on file system level

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 depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

LDAP bind authentication

Administrators, Users, Groups

fine grained access rights according to SQL-standard

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