DBMS > DolphinDB vs. OrientDB vs. SwayDB vs. SWC-DB

System Properties Comparison DolphinDB vs. OrientDB vs. SwayDB vs. SWC-DB

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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.

Multi-model DBMS (Document, Graph, Key/Value)

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A high performance, scalable Wide Column DBMS

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store

Key-value store

Wide column store

Secondary database models

Relational DBMS

Time Series DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.dolphindb.com

orientdb.org

swaydb.simer.au

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

www.orientdb.com/docs/last/index.html

Developer

DolphinDB, Inc

OrientDB LTD; CallidusCloud; SAP

Simer Plaha

Alex Kashirin

Initial release

2018

2010

2018

2020

Current release

v2.00.4, January 2022

3.2.29, March 2024

0.5, April 2021

License

Commercial or Open Source

commercial

free community version available

Open Source

Apache version 2

Open Source

GNU Affero GPL V3.0

Open Source

GPL V3

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++

Java

Scala

C++

Server operating systems

Linux
Windows

All OS with a Java JDK (>= JDK 6)

Linux

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

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

SQL

Support of SQL

SQL-like query language

SQL-like query language, no joins

SQL-like query language

APIs and other access methods

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

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Kotlin
Scala

C++

Server-side scripts

Stored procedures

yes

Java, Javascript

Triggers

Hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

yes

ACID

Atomic execution of operations

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

Administrators, Users, Groups

Access rights for users and roles; record level security configurable

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