DBMS > DolphinDB vs. GeoMesa vs. HugeGraph vs. Valentina Server

System Properties Comparison DolphinDB vs. GeoMesa vs. HugeGraph vs. Valentina Server

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Editorial information provided by DB-Engines

Name

Description

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.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

A fast-speed and highly-scalable Graph DBMS

Object-relational database and reports server

Primary database model

Secondary database models

Relational DBMS

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

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

www.dolphindb.com

www.geomesa.org

github.com/hugegraph
hugegraph.apache.org

www.valentina-db.net

Technical documentation

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

www.geomesa.org/documentation/stable/user/index.html

hugegraph.apache.org/docs

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

Developer

DolphinDB, Inc

CCRi and others

Baidu

Paradigma Software

Initial release

2018

2014

2018

1999

Current release

v2.00.4, January 2022

5.0.0, May 2024

0.9

5.7.5

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

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

C++

Scala

Java

Server operating systems

Linux
Windows

Linux
macOS
Unix

Linux
OS X
Windows

Data scheme

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

ODBC

Supported programming languages

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

Groovy
Java
Python

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

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

depending on storage layer

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on storage layer

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

yes

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

depending on storage layer

yes

User concepts

Access control

Administrators, Users, Groups

yes

depending on the DBMS used for storage

Users, roles and permissions

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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